CN112906963A - Double-layer pricing method for energy storage polymerization platform - Google Patents
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Abstract
The invention belongs to the technical field of power system economy, and particularly relates to a double-layer pricing method for an energy storage polymerization platform. The invention aims at a pricing mode between a distributed energy storage and aggregation platform and an aggregation platform and a power grid; the first layer is centralized and dispersed distributed energy storage by the aggregation platform and carries out pricing through a long-term contract, and the second layer is used for pricing different aggregators through real-time electricity prices by the power grid. According to the method, a power deviation prediction model of the wind storage isolated network system is established, power and voltage regulation is implemented under the condition of considering the power deviation, and the reliability and the practicability of self-starting of the wind storage isolated network system are greatly improved. The double-layer pricing method adopted by the invention can effectively and reliably carry out pricing of the energy storage polymerization platform and provide technical basis and practical method for double-layer pricing of the energy storage polymerization platform; the distributed energy storage pricing efficiency is effectively improved, the complex pricing of a power grid to a large number of energy storage devices is simplified, the implementation is easy, the commercial development is facilitated, and the distributed energy storage pricing method has a good commercial development prospect.
Description
Technical Field
The invention belongs to the technical field of power system economy, and particularly relates to a double-layer pricing method for an energy storage aggregation platform, in particular to a pricing method for distinguishing a distributed energy storage device from the aggregation platform, and distinguishing the aggregation platform from a power grid.
Background
Energy storage is an important link of a modern energy system and is one of key support technologies of an intelligent power grid. The large-scale development of renewable energy power generation and the high proportion of grid connection put forward higher requirements on the flexible regulation capacity of the power system, thereby promoting the development of the energy storage industry. The energy storage system can be widely applied to each link of transmission, distribution and use of the power system, can improve the safety, reliability and flexibility of the power grid, and is considered by all parties to have wide development opportunities and application prospects. In recent years, the number of energy storage projects at home and abroad is increased year by year, and the cost is continuously reduced. Only the energy storage projects planned, built and put into operation in China are nearly hundred. However, the development of energy storage in China still faces challenges, on one hand, the maturity of the energy storage technology is further improved, and on the other hand, a profit model is lacked. China has not yet made systematic support policies such as price standards, fiscal taxes and the like for energy storage development, and is in a further deepening link of the innovation of the power industry system. The research on the electric power market is particularly important. Particularly, the research on the auxiliary service of the power grid by the energy storage system is more necessary and urgent, and how to realize a fair, reasonable and practical auxiliary service payment model becomes a difficult problem in the current market.
The energy aggregation platform is an important operation component of the distributed energy storage service, and plays a role in connecting a power grid with a link and a bridge of the distributed energy storage device. Its commercial operating environment includes grid companies and distributed energy storage. Grid company: and the power grid company and the energy aggregation platform carry out electricity purchasing and selling business transaction. In the aspect of power selling of a power grid company, the time-of-use electricity price is adopted, and the electricity selling price is adjusted according to the system load. In the aspect of power purchase of a power grid company, the power purchase price of the power grid company is kept unchanged in one day according to the current distributed energy storage internet policy. Distributed energy storage device: the distributed energy storage device stores electric energy for storing in the time-of-use electricity price valley time period or load valley time period in order to enlarge the profit per se and carry out peak-and-frequency modulation work according to the time-of-use electricity price policy. And selling electric energy at the peak of power utilization, namely the peak of electricity price, so as to realize arbitrage. This approach, as the most basic profitability model for distributed energy storage.
The pricing methods of the stored energy are roughly as follows.
The first is an energy storage pricing mechanism based on a uniform cost method. The homogenized energy cost is the ratio of the annual cost of the energy storage system to the annual electric energy output of the energy storage system, and the unit is Yuan/kilowatt hour, the homogenized cost pricing method is used for ensuring the total recovery of the investment cost of the energy storage and guaranteeing the reasonable profit permitted by the project. In this way, the pricing of the discharge electricity price of the energy storage device is determined according to the mode of 'uniform energy cost + reasonable profit + tax'.
The second is an energy storage pricing mechanism based on an auxiliary service contribution method. The energy storage device has strong advantages in providing the electric auxiliary service by virtue of flexible and quick response capability and adjustment capability, so that the contribution of the energy storage device participating in the auxiliary service market is calculated when the energy storage device is priced and researched. When the contribution of the auxiliary service is calculated, two types of auxiliary service products, namely frequency modulation service and rotary standby service, are provided according to the energy storage equipment.
The third is an energy storage profit mechanism under the electric power marketization trading mechanism. Under the electric power spot market mode, according to the timesharing price level of each period of time based on the supply and demand relation release in electric power market, the energy storage is equipped the operation business company and can confirm own charge-discharge strategy according to the price level of electricity in different periods of time.
The existing three pricing modes have the problem that the initial investment price of the existing energy storage project is too high, the discharge electricity price of the energy storage determined according to the cost pricing method is far higher than the electricity price of the coal-fired unit, and a certain subsidy mechanism needs to be established. Research indicates that an energy storage electricity price subsidy mode based on an auxiliary service contribution method can be adopted to mobilize the enthusiasm of energy storage facilities for participating in auxiliary service market trading and obtain corresponding benefits.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a double-layer pricing method for an energy storage polymerization platform, which is a reliable and effective flexible self-starting method for a wind storage isolated network system. The invention aims to adopt the self-starting method, consider the output characteristic of the active power of the wind turbine generator in the starting process, realize more effective and reliable self-starting of the wind storage isolated network system and provide technical basis and practical method for the self-starting of the wind storage isolated network system.
The technical scheme adopted by the invention for realizing the purpose is as follows:
a double-layer pricing method for an energy storage aggregation platform is a pricing mode between a distributed energy storage and aggregation platform and an aggregation platform and a power grid; the first layer is distributed energy storage which is centralized and dispersed by the aggregation platform and is priced through a long-term contract, and the second layer is priced through real-time electricity prices of different aggregators by the power grid;
the method comprises the following steps:
step 1, determining parameters of a distributed energy storage device; (ii) a
Step 3, storing the distributed energy storage at the lowest price of the power grid;
step 4, establishing a real-time power grid price model, predicting the power price trend, and pricing by a aggregator;
step 5, the aggregator purchases the regulation and control right of the distributed energy storage for a period of time to perform auxiliary service;
step 6, bidding with the power grid through the auxiliary peak shaving service aggregator;
step 7, bidding with the power grid through an auxiliary frequency modulation service aggregator;
and 8, feeding back the pricing of the first layer by the aggregation platform according to the auxiliary service of the power grid.
Further, the determining of the parameters of the distributed energy storage device refers to determining the capacity, the comprehensive efficiency, the compensation effect and the utilization rate of renewable energy resources of different distributed energy storages, whether the parameters are needed for pricing, such as whether the parameters can participate in peak shaving or frequency modulation.
Further, the determining, distinguishing and classifying the distributed energy storage values means classifying the distributed energy storage devices with different capacities in different regions and implementing power grid auxiliary services due to the fact that the distributed energy storage devices are various in types and are dispersed in geographic positions.
Further, the establishing of a real-time power grid price model, the prediction of the power price trend and the pricing of the aggregator mean that the trend of the store is predicted by modeling the real-time power price of the power grid, and a proper price is selected; the aggregation platform carries out pricing purchase in the form of purchasing the usage right of the distributed energy storage device for a period of time; the method comprises the following steps:
describing the price of electricity sold in the market;
the method uses a fuzzy membership degree mode to describe the electricity selling price of the market, and the formula is as follows:
in the formula: r is the price of electricity selling, E is the electricity selling amount of the power grid, EmaxSelling electricity when the electricity price of the power grid is highest;
determining a pricing objective function;
the aggregation platform is used for acquiring a function satisfied by benefits, and the formula is as follows:
in the above formula:
in order to sell the peak shaving service income,
in order to sell the revenue of the frequency modulation service,
cost for purchasing distributed energy storage; wherein L istIs electric energy in t time, CfFor peak shaving unit price, CpIs a frequency modulation unit price; rt' is a point of purchaseThe unit price of the distributed energy storage is,
to purchase distributed energy storage capacity;
step (3) limiting conditions of electricity price;
the electricity price constraint condition is as follows:
Cmin≤Rt≤Cmax
in the formula Cmin，CmaxThe lowest and highest real-time electricity price values are indicated;
step (4), real-time electricity price reference;
the real-time electricity price reference is as follows:
in the above formula: cavIs the real-time electricity price reference value.
Further, the aggregator and the power grid compete for price through the auxiliary peak regulation service, namely when the power grid needs the auxiliary peak regulation service, distributed energy storage signed with an aggregation platform participates in the power grid peak regulation service; the formula is as follows:
in the above formula: ctfServing a total for peak shaving, CfFor peak shaver unit price, TkPeak shaver duration, P, for distributed energy storage devicesjFor distributed energy storage devices to exert force, TtfIs the peak shaving coefficient;
the method comprises the following steps:
(1) determining peak shaving duration T of distributed energy storage devicek；
(2) Determining distributed energy storage device output Pj；
(3) Determination of Peak Regulation Unit price Cf；
(4) Determining a peak shaving coefficient Ttf。
Further, the auxiliary frequency modulation service aggregator is used for bidding with the power grid, namely when the power grid needs frequency modulation auxiliary service, the distributed energy storage signed with the aggregation platform participates in the power grid frequency modulation service;
frequency modulation service charge of CtpThe formula is as follows:
in the above formula: cpIs frequency-modulated monovalent, QpFor modulating frequency electric quantity, TtpIs the frequency modulation coefficient, DtpThe qualified rate of unit frequency modulation is taken as P, and the regulation ratio of an AGC unit is taken as P;
the method comprises the following steps:
(1) determining the frequency modulation unit price Cp；
(2) Determining the amount of frequency-modulated electrical quantity Qp；
(3) Determining unit frequency modulation qualification rate Dtp；
(4) Determining an AGC unit adjustment ratio P;
P＝Pmaxi/Pmini；
in the above formula: pmaxiAdjusting an upper limit for the registered AGC of the AGC unit i; pminiAnd adjusting the lower limit for the registered AGC of the AGC unit i.
Further, the frequency modulation method is divided into two types:
the first one is: the AGC unit is not put into;
the second method is as follows: and putting into an AGC unit.
Further, the aggregation platform feeds back to the first-layer pricing according to the power grid auxiliary service, the objective function C is larger than or equal to 0 in the step 4, and the auxiliary service of real-time transaction between the aggregation platform and the power grid meets sigmatCfLf≥Ctf∑tCpLf≥Ctp(ii) a And the maximum peak-shaving unit price C in the step 5 is satisfiedfAnd peak shaving coefficient TtfMinimum; step 6, adjusting ratio P of AGC unit and determining unit frequency modulation qualification rate DtpAnd minimizing, thereby obtaining an optimal solution.
Further, the grid anticipates the impact of the self-determined production on the following aggregation platform, and in the starkeberg model, the market demand function is set as:
①D＝D(p1+p2)＝a-b(p1+p2)
wherein p1 and p2 are respectively a power grid company and a convergence platform, and the cost functions of the two enterprises are assumed to be the same, namely C0p, consider first the optimal production p2 for which the aggregation platform seeks to maximize its profit given the planned production of the grid company, namely:
②maxp2[a-b(p1+p2)]-Cp2
the p2 of the optimal solution in the above optimization model is obviously the function p2 ═ g (p1) of p 1; a is an undetermined coefficient, b is an undetermined coefficient, and Cp2 is a cost function of p 2;
knowing the response of the aggregation platform to any given production, the optimal production model for the grid company is:
③maxp1[a-b(p1+p2)]-Cp1,s.t.p2＝g(p1)
in the above formula: the optimal solution is obtained under the condition that nash equilibrium s.t.p2 ═ g (p1) is achieved.
Further, the auxiliary service comprises peak shaving and frequency modulation;
the peak shaver part comprises the following constraints:
a. the peak regulation time length;
s. peak shaver unit price;
c. peak shaving coefficient;
d. an energy storage loading force;
the frequency modulation part comprises the following four constraint conditions:
a. frequency-modulated electricity quantity and electricity price;
b. frequency modulation of electric quantity;
c. the qualified rate of frequency modulation;
and d, adjusting the ratio of the AGC unit.
The invention has the following beneficial effects and advantages:
the invention relates to a double-layer pricing method for an energy storage polymerization platform, in particular to a flexible self-starting method for a wind storage isolated network system.
By hierarchical consideration, the economic relationship between distributed energy storage and aggregation platforms is considered first. Due to the dispersity and the large capacity difference of the distributed energy storage devices and the inconvenience of real-time management, a long-term contract is selected in a pricing mode. And secondly, considering the economic relation between the aggregation platform and the power grid. The distributed energy storage is mainly used for providing real-time services, such as peak shaving and frequency modulation, for auxiliary services of the power grid. Due to the existence of the aggregation platform, the decentralized management is greatly reduced, and the power grid only needs to release the auxiliary service requirement for the aggregation platform.
The double-layer pricing method adopted by the invention can effectively and reliably carry out pricing of the energy storage polymerization platform, and provides technical basis and practical method for double-layer pricing of the energy storage polymerization platform.
The invention aims at pricing of the distributed energy storage aggregation platform and can effectively improve the pricing efficiency of the distributed energy storage. The traditional pricing method is too complex for distributed energy storage devices and is suitable for a small number of distributed energy storage devices. But the number of the distributed energy storage devices is increased obviously in recent years due to the vigorous development of the distributed energy storage devices. The method can effectively simplify the complex pricing of the power grid to a large number of energy storage devices.
The process of the invention also has the advantage of being easy to implement. Based on the original distributed energy storage pricing, layered pricing is added. And (4) carrying out two steps of pricing and homing on the complex. First, pricing of distributed energy storage by the aggregation platform. And secondly, pricing the aggregation platform for the power grid. Pricing in the second part is an improvement over the original and a constraint on the first level pricing.
The method of the invention also has the advantage of being convenient for commercial development. With the increase of distributed energy storage application, the double-layer pricing method has larger requirement and has better commercial development prospect.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a general flow chart of a two-tier pricing method of the energy storage aggregation platform of the present invention;
FIG. 2 illustrates the requirements of a grid company for providing ancillary services with an energy storage aggregation platform;
FIG. 3 is a diagram of four steps of energy storage aggregator pricing for distributed energy storage long-term contract;
fig. 4 is a schematic diagram of a typical distributed energy storage peak shaving.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
The solution of some embodiments of the invention is described below with reference to fig. 1-4.
Example 1
The invention relates to a double-layer pricing method for an energy storage polymerization platform, and as shown in fig. 1, fig. 1 is a general flow chart of the double-layer pricing method for the energy storage polymerization platform.
The invention particularly relates to a double-layer pricing method for an energy storage aggregation platform, which is a pricing mode between a distributed energy storage and aggregation platform and an aggregation platform and a power grid. The first layer is centralized and dispersed distributed energy storage by the aggregation platform and carries out pricing through a long-term contract, and the second layer is used for pricing different aggregators through real-time electricity prices by the power grid.
The invention relates to a double-layer pricing method for an energy storage aggregation platform, which is a pricing method for distributed energy storage and operation platforms and between the operation platforms and a power grid.
The invention relates to a double-layer pricing method for an energy storage polymerization platform, which comprises the following steps of:
step 1, determining parameters of a distributed energy storage device;
the step of determining the parameters of the distributed energy storage devices refers to determining the capacity, the comprehensive efficiency, the compensation effect and the utilization rate of renewable energy sources of different distributed energy storages, whether the parameters can participate in peak shaving, whether the parameters can participate in frequency modulation and the like which are needed by pricing.
the step of determining and distinguishing the distributed energy storage devices and classifying the distributed energy storage devices means that the distributed energy storage devices in different regions and different capacities need to be classified due to various distributed energy storage types and dispersed geographic positions. In order to implement grid ancillary services.
Step 3, storing the distributed energy storage at the lowest price of the power grid;
step 4, establishing a real-time power grid price model, predicting the power price trend, and pricing by a aggregator;
particularly, the trend of a shop is predicted by modeling the real-time electricity charge price of a power grid, and a proper price is selected. The aggregated platform makes pricing purchases in the form of purchasing usage rights for the distributed energy storage over a period of time.
The method specifically comprises the following steps:
describing the price of electricity sold in the market;
the method uses a fuzzy membership degree mode to describe the electricity selling price of the market, and the formula is as follows:
in the formula: r is the price of electricity selling, E is the electricity selling amount of the power grid, EmaxThe electricity is sold when the electricity price of the power grid is highest.
Determining a pricing objective function;
the aggregation platform is used for acquiring a function satisfied by benefits, and the formula is as follows:
in the above formula:
in order to sell the peak shaving service income,
in order to sell the revenue of the frequency modulation service,
for the purchase of distributed energy storage costs. Wherein L istIs electric energy in t time, CfFor peak shaving unit price, CpIs a frequency modulation unit price. Rt' in order to purchase a unit price of distributed energy storage,
in order to purchase distributed energy storage capacity.
Step (3) limiting conditions of electricity price;
the electricity price constraint condition is as follows:
Cmin≤Rt≤Cmax
in the formula Cmin，CmaxThe lowest and highest real-time electricity price values are used.
And (4) carrying out real-time electricity price reference.
The real-time electricity price reference is as follows:
in the above formula: cav is a real-time electricity price reference value.
Step 5, the aggregator purchases the regulation and control right of the distributed energy storage for a period of time to perform auxiliary service;
the period of time is as follows: the aggregator contracts with the distributed energy storage owner to decide how long to purchase distributed energy storage usage rights.
Step 6, bidding with the power grid through the auxiliary peak shaving service aggregator;
the auxiliary peak regulation service aggregator is used for bidding with the power grid, and means that the power grid participates in the power grid peak regulation service through distributed energy storage signed with an aggregation platform when the power grid needs the auxiliary peak regulation service. The formula is as follows:
in the above formula: ctfServing a total for peak shaving, CfFor peak shaver unit price, TkPeak shaver duration, P, for distributed energy storage devicesjFor distributed energy storage devices to exert force, TtfIs the peak shaving coefficient.
The method specifically comprises the following steps:
(1) determining peak shaving duration T of distributed energy storage devicek；
(2) Determining distributed energy storage device output Pi；
(3) Determination of Peak Regulation Unit price Cf；
(4) Determining a peak shaving coefficient Ttf。
Step 7, bidding with the power grid through an auxiliary frequency modulation service aggregator;
the auxiliary frequency modulation service aggregator is used for bidding with the power grid, and the power grid is signed with the aggregation platform to participate in the power grid frequency modulation service when the frequency modulation auxiliary service is needed.
Frequency modulation service charge of CtpThe formula is as follows:
in the above formula: cpIs frequency-modulated monovalent, QpFor modulating frequency electric quantity, TtpIs the frequency modulation coefficient, DtpThe qualified rate of unit frequency modulation is taken as P, and the regulation ratio of an AGC unit is taken as P;
the method specifically comprises the following steps:
(1) determining the frequency modulation unit price Cp；
(2) Determining the amount of frequency-modulated electrical quantity Qp；
(3) Determining unit frequency modulation qualification rate Dtp；
(4) Determining an AGC unit adjustment ratio P;
P＝Pmaxi/Pmini；
in the above formula: pmaxiAdjusting an upper limit for the registered AGC of the AGC unit i; pminiAnd adjusting the lower limit for the registered AGC of the AGC unit i.
In order to ensure that the frequency of a power grid is 50Hz, frequency modulation modes are divided into two types:
the first one is: the AGC unit is not put into;
the second method is as follows: and putting into an AGC unit.
And 8, feeding back the pricing of the first layer by the aggregation platform according to the auxiliary service of the power grid.
The aggregation platform feeds back to the first-layer pricing finger according to the power grid auxiliary service:
the objective function C is more than or equal to 0 in the step 4, and the auxiliary service of the real-time transaction between the aggregation platform and the power grid meets sigmatCfLf≥Ctf∑tCpLf≥Ctp(ii) a And the maximum peak-shaving unit price C in the step 5 is satisfiedfAnd peak shaving coefficient TtfMinimum; step 6, adjusting ratio P of AGC unit and determining unit frequency modulation qualification rate DtpAnd minimum. Thereby obtaining an optimal solution.
Example 2
The invention relates to a double-layer pricing method for an energy storage polymerization platform, which is shown in figure 1, and figure 1 is a general flow chart of a flexible self-starting method of a wind storage isolated network system. Consistent with the steps in the embodiment 1, it is worth explaining that as can be seen from the flow in fig. 1, the energy storage aggregation platform participates in the auxiliary service of the power grid by bidding and bidding. The main and subordinate games exist in the bidding process because the power grid company occupies the dominant position. Distributed energy storage devices are large in number and distributed, so that different aggregators have different mastered distributed energy storage devices.
Grid companies will expect the impact of their own decided production on the following aggregation platform. It is in consideration of this effect that the grid company decides to maximize the production of profit constrained by the response function of the aggregation platform. In the existing mathematical model starkeberg model, the decision of the power grid company does not need a reaction function of the power grid company. The market demand function is set as:
①D＝D(p1+p2)＝a-b(p1+p2)
wherein p1 and p2 are the grid company and the convergence platform, respectively. Assuming that the cost functions of both enterprises are the same, C0p, consider first the optimal production p2 that the aggregation platform seeks to maximize its profit given the planned production of the grid company, namely:
②maxp2[a-b(p1+p2)]-Cp2
the p2 of the optimal solution in the above optimization model is obviously the function p2 ═ g (p1) of p 1; a is an undetermined coefficient, b is an undetermined coefficient, and Cp2 is a cost function of p 2.
Knowing the response of the aggregation platform to any given production, the optimal production model for the grid company is:
③maxp1[a-b(p1+p2)]-Cp1,s.t.p2＝g(p1)
in the above formula: the optimal solution is obtained under the condition that nash equilibrium s.t.p2 ═ g (p1) is achieved.
As shown in fig. 2, fig. 2 illustrates the requirement of the power grid company for providing the auxiliary service with the energy storage aggregation platform, and the main part is divided into two parts: peak shaving and frequency modulation.
The peak shaving part comprises the following four constraints:
a. the peak regulation time length;
s. peak shaver unit price;
c. peak shaving coefficient;
d. and (4) energy storage and loading force.
The frequency modulation part comprises the following four constraint conditions:
a. frequency-modulated electricity quantity and electricity price;
b. frequency modulation of electric quantity;
c. the qualified rate of frequency modulation;
and d, adjusting the ratio of the AGC unit.
As shown in fig. 3, fig. 3 is a diagram of four steps of pricing a distributed energy storage long-term contract by an energy storage aggregator.
The method specifically comprises the following steps:
step A, modeling an electricity price market;
b, determining a function;
step C, determining constraint conditions;
and D, implementing the electricity price standard.
As shown in fig. 4, fig. 4 is a schematic diagram of a typical distributed energy storage peak shaving. The part above the dotted line represents a load peak. The part below the dashed line is the adjusted load curve.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A double-layer pricing method for an energy storage polymerization platform is characterized by comprising the following steps: a pricing mode between the aggregation platform and the power grid is set for the distributed energy storage and aggregation platform; the first layer is distributed energy storage which is centralized and dispersed by the aggregation platform and is priced through a long-term contract, and the second layer is priced through real-time electricity prices of different aggregators by the power grid; the method comprises the following steps:
step 1, determining parameters of a distributed energy storage device; (ii) a
Step 2, determining and distinguishing distributed energy storage values, and classifying; (ii) a
Step 3, storing the distributed energy storage at the lowest price of the power grid;
step 4, establishing a real-time power grid price model, predicting the power price trend, and pricing by a aggregator;
step 5, the aggregator purchases the regulation and control right of the distributed energy storage for a period of time to perform auxiliary service;
step 6, bidding with the power grid through the auxiliary peak shaving service aggregator;
step 7, bidding with the power grid through an auxiliary frequency modulation service aggregator;
and 8, feeding back the pricing of the first layer by the aggregation platform according to the auxiliary service of the power grid.
2. The method of claim 1, wherein the method comprises: the step of determining the parameters of the distributed energy storage devices refers to determining the capacity, the comprehensive efficiency, the compensation effect and the utilization rate of renewable energy sources of different distributed energy storages, whether the parameters can participate in peak shaving, whether the parameters can participate in frequency modulation and the like which are needed by pricing.
3. The method of claim 1, wherein the method comprises: the step of determining and distinguishing the distributed energy storage values and classifying the distributed energy storage values refers to the step of classifying the distributed energy storage devices with different capacities in different regions to implement power grid auxiliary services due to the fact that the distributed energy storage devices are various in types and are dispersed in geographic positions.
4. The method of claim 1, wherein the method comprises: the establishing of the real-time power grid price model, the prediction of the power price trend and the pricing of the aggregator mean that the trend of a shop is predicted by modeling the real-time power price of the power grid, and a proper price is selected; the aggregation platform carries out pricing purchase in the form of purchasing the usage right of the distributed energy storage device for a period of time; the method comprises the following steps:
describing the price of electricity sold in the market;
the method uses a fuzzy membership degree mode to describe the electricity selling price of the market, and the formula is as follows:
in the formula: r is the price of electricity selling, E is the electricity selling amount of the power grid, EmaxSelling electricity when the electricity price of the power grid is highest;
determining a pricing objective function;
the aggregation platform is used for acquiring a function satisfied by benefits, and the formula is as follows:
in the above formula:
in order to sell the peak shaving service income,
in order to sell the revenue of the frequency modulation service,
cost for purchasing distributed energy storage; wherein L istIs electric energy in t time, CfFor peak shaving unit price, CpIs a frequency modulation unit price; rt' in order to purchase a unit price of distributed energy storage,
to purchase distributed energy storage capacity;
step (3) limiting conditions of electricity price;
the electricity price constraint condition is as follows:
Cmin≤Rt≤Cmax
in the formula Cmin，CmaxThe lowest and highest real-time electricity price values are indicated;
step (4), real-time electricity price reference;
the real-time electricity price reference is as follows:
in the above formula: cavIs the real-time electricity price reference value.
5. The method of claim 1, wherein the method comprises: the aggregator and the power grid compete for price through the auxiliary peak regulation service, namely the power grid participates in the peak regulation service of the power grid through distributed energy storage signed with an aggregation platform when the peak regulation auxiliary service is needed; the formula is as follows:
in the above formula: ctfServing a total for peak shaving, CfFor peak shaver unit price, TkPeak shaver duration, P, for distributed energy storage devicesjFor distributed energy storage devices to exert force, TtfIs the peak shaving coefficient;
the method comprises the following steps:
(1) determining peak shaving duration T of distributed energy storage devicek；
(2) Determining distributed energy storage device output Pj；
(3) Determination of Peak Regulation Unit price Cf；
(4) Determining a peak shaving coefficient Ttf。
6. The method of claim 1, wherein the method comprises: the auxiliary frequency modulation service aggregator is used for bidding with the power grid, namely when the power grid needs frequency modulation auxiliary service, the distributed energy storage signed with the aggregation platform participates in the power grid frequency modulation service;
frequency modulation service charge of CtpThe formula is as follows:
in the above formula: cpIs frequency-modulated monovalent, QpFor frequency-modulated electric quantity，TtpIs the frequency modulation coefficient, DtpThe qualified rate of unit frequency modulation is taken as P, and the regulation ratio of an AGC unit is taken as P;
the method comprises the following steps:
(1) determining the frequency modulation unit price Cp；
(2) Determining the amount of frequency-modulated electrical quantity Qp；
(3) Determining unit frequency modulation qualification rate Dtp；
(4) Determining an AGC unit adjustment ratio P;
P＝Pmaxi/Pmini；
in the above formula: pmaxiAdjusting an upper limit for the registered AGC of the AGC unit i; pminiAnd adjusting the lower limit for the registered AGC of the AGC unit i.
7. The method of claim 1, wherein the method comprises: the frequency modulation mode is divided into two types:
the first one is: the AGC unit is not put into;
the second method is as follows: and putting into an AGC unit.
8. The method of claim 1, wherein the method comprises: the aggregation platform feeds back to the first-layer pricing according to the power grid auxiliary service, the objective function C in the step 4 is larger than or equal to 0, and the auxiliary service of real-time transaction between the aggregation platform and the power grid meets sigmatCfLf≥Ctf∑tCpLf≥Ctp(ii) a And the maximum peak-shaving unit price C in the step 5 is satisfiedfAnd peak shaving coefficient TtfMinimum; step 6, adjusting ratio P of AGC unit and determining unit frequency modulation qualification rate DtpAnd minimizing, thereby obtaining an optimal solution.
9. The method of claim 1, wherein the method comprises: the power grid expects the influence of the output determined by the power grid on the following aggregation platform, and in the Starkelberg model, a market demand function is set as follows:
①D＝D(p1+p2)＝a-b(p1+p2)
wherein p1 and p2 are respectively a power grid company and a convergence platform, and the cost functions of the two enterprises are assumed to be the same, namely C0p, consider first the optimal production p2 for which the aggregation platform seeks to maximize its profit given the planned production of the grid company, namely:
②maxp2[a-b(p1+p2)]-Cp2
the p2 of the optimal solution in the above optimization model is obviously the function p2 ═ g (p1) of p 1; a is an undetermined coefficient, b is an undetermined coefficient, and Cp2 is a cost function of p 2;
knowing the response of the aggregation platform to any given production, the optimal production model for the grid company is:
③maxp1[a-b(p1+p2)]-Cp1，s.t.p2＝g(p1)
in the above formula: the optimal solution is obtained under the condition that nash equilibrium s.t.p2 ═ g (p1) is achieved.
10. The method of claim 1, wherein the method comprises: the auxiliary service comprises peak shaving and frequency modulation;
the peak shaver part comprises the following constraints:
a. the peak regulation time length;
s. peak shaver unit price;
c. peak shaving coefficient;
d. an energy storage loading force;
the frequency modulation part comprises the following four constraint conditions:
a. frequency-modulated electricity quantity and electricity price;
b. frequency modulation of electric quantity;
c. the qualified rate of frequency modulation;
and d, adjusting the ratio of the AGC unit.
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