Double-layer pricing method for energy storage aggregation platform

A platform and pricing technology, applied in the economic field of the power system, can solve the problem of high initial investment price of energy storage projects, and achieve the effect of improving reliability and practicability and improving pricing efficiency.

Pending Publication Date: 2021-06-04

STATE GRID LIAONING ELECTRIC POWER RES INST +2

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AI-Extracted Technical Summary

Problems solved by technology

[0008] The existing three pricing methods Because the current initial investment price of energy storage projects is too high, the discharge electricity price of energy stora...

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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 aggregation platform. The method aims at a distributed energy storage and aggregation platform and a pricing mode between the aggregation platform and a power grid; in the first layer, the aggregation platform concentrates and disperses distributed energy storage and prices through a long-term contract, and in the second layer, the power grid prices different aggregators through real-time electricity prices. According to the method, the 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 practicability of self-starting of the wind storage isolated network system are greatly improved. According to the double-layer pricing method, pricing of the energy storage aggregation platform is effectively and reliably carried out, and a technical basis and a practical method are provided for double-layer pricing of the energy storage aggregation platform; and the distributed energy storage pricing efficiency is effectively improved, complex pricing of a large number of energy storage devices by a power grid is simplified, implementation is easy, commercialized development is facilitated, and the method has good commercial development prospects.

Application Domain

Market predictionsForecasting +2

Technology Topic

Voltage regulationPower grid +6

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Examples

Experimental program(2)

Example Embodiment

[0097] Example 1

[0098] The present invention is a double-layer pricing method for the energy storage aggregation platform, such as figure 1 Distance figure 1 The overall flow chart of the double-layer pricing method of the reservoir aggregation platform of the present invention.

[0099] The present invention is specifically a double-layer pricing method for the energy storage aggregation platform refers to a pricing method for distributed energy storage and aggregation platforms, aggregation platforms and grids. The first layer is distributed by the aggregation platform and priced by a long-term contract, and the second layer is priced by the grid via the real-time electricity price.

[0100] A bilayer pricing method for the energy storage polymerization platform is a pricing method between distributed energy storage and operational platforms and operational platforms and grids.

[0101] A dual layer pricing method for the energy storage aggregation platform includes the following steps:

[0102] Step 1. Determine the distributed energy storage device parameters;

[0103] The determining distributed energy storage device parameter refers to determining the capacity, integrated efficiency, compensation effect, and renewable energy utilization of each different distributed energy storage, whether it is possible to participate in the peak, and whether it can participate in the parameters required for the pricing or the like. .

[0104] Step 2. Determine the distinguished distributed energy storage device and conduct classification;

[0105] The determination distinguishing a distributed energy storage device, and categorically refers to a distributed energy storage device for distributed storage energy, and a distributed energy storage device for different regions, different capacities, which requires classification due to a wide variety of distributed reservoirs. In order to implement grid auxiliary services.

[0106] Step 3. Distributed energy storage in the lowest price of the grid;

[0107] Step 4. Establish a real-time grid price model, predict the electricity price trend, and the aggregator is priced;

[0108] Specifically, the store's trend is predicted by modeling the price of electric grids, and selects the right price. The aggregation platform is priced by purchasing a distributed energy storage device for a period of time using rights.

[0109] Specifically, the following steps:

[0110] Step (1) Describe the market selling electricity price;

[0111] Use the fuzzy membership to describe the market selling electricity price, formula is:

[0112]

[0113] Where: R is for the price of electricity price, e is the power supply of the grid, E max Selling electricity at the highest price of grid.

[0114] Step (2) Determine the pricing target function;

[0115] The polymerization platform is a function that gains benefits, the formula is:

[0116]

[0117] In the above formula: For sales of peak service revenue, For sales of FM service, To purchase distributed energy storage costs. Among them, L tElectrical energy in T time, C f To peak unit price, C p For FM unit prices. R t 'For purchase distributed energy storage unit price, To purchase distributed storage capacity.

[0118] Step (3) Electricity price constraints;

[0119] Electricity price constraints refers to:

[0120] C min ≤R t ≤ C max

[0121] Central C min , C max Refers to the lowest value of real-time electricity price.

[0122] Step (4) Real-time electricity price baseline.

[0123] Real-time electricity price reference refers to:

[0124]

[0125] In the above formula: CAV is the reference value for real-time electricity price.

[0126] Step 5. The aggregator purchases the regulation rights for distributed storage for a period of time, and supports secondary;

[0127] Say time means that the aggregator is signed with the distributed energy storage device owner to determine how long the distributed energy storage is purchased.

[0128] Step 6. By the auxiliary peak service aggregator and the power grid;

[0129] The aided peak service aggregator is bidding with the grid, which means that the distributed energy storage involved in the grid peak service with the aggregation platform when the power grid needs to peak assist service. The formula is:

[0130]

[0131] In the above formula: c tf Total price, C f In order to peak unit price, T k Pattern for distributed energy storage devices, P j For distributed energy storage devices, T tf To the peak coefficient.

[0132] Specifically, the following steps:

[0133] (1) Determine the peak time of distributed energy storage device k;

[0134] (2) Determine the distributed energy storage device for force P i;

[0135] (3) Determine the monopoli unit price C f;

[0136] (4) Determine the peak coefficient T tf.

[0137] Step 7. By the auxiliary FM service aggregator and the power grid;

[0138] The aided FM service aggregator is bidding with the grid, which means that the grid is involved in the distributed energy storage involved in the grid FM service with the aggregation platform when the FM assist service is required.

[0139] FM service cost is c tp The formula is:

[0140]

[0141] In the above formula: c p For FM unit prices, Q p For FM power, T tp For the FM coefficient, D tp For the unit FM pass rate, P is the adjustment ratio of the AGC unit;

[0142] Specifically, the following steps:

[0143] (1) Determine the frequency modulated unit price C p;

[0144] (2) Determine the frequency of frequency p;

[0145] (3) Determine the unit frequency modification rate D tp;

[0146] (4) Determine the AGC unit adjustment ratio P;

[0147] P = P maxi / P mini;

[0148] In the above formula: P maxi The upper limit of the registration AGC adjustment of the AGC unit I; P mini The registration of the AGC unit I adjusts the lower limit.

[0149] To ensure that the grid frequency is 50Hz, the FM method is divided into two types:

[0150] The first is: not invested in the AGC unit;

[0151] The second is: put into the AGC unit.

[0152] Step 8. The aggregation platform is fed back to the first layer pricing according to the grid assistance service.

[0153] The aggregation platform feeds back to the first layer pricing index according to the grid assistance service:

[0154] The target function C ≥ 0 in step 4 should be met, the auxiliary service of the aggregation platform and the grid real-time transaction should be met t C f L f ≥C tf Σ t C p L f ≥C tp And should meet the maximum to meet step 5 f And peak coefficient T tf Minimal; step 6 in the AGC unit adjustment ratio P and determined unit faster pass rate D tp Minimum. This is the best solution.

Example Embodiment

[0155] Example 2

[0156] The present invention is a double-layer pricing method for the energy storage aggregation platform, such as figure 1 Distance figure 1 The overall flow chart of the flexible self-starting method of the wind reservoir network system of the present invention. In line with the steps in Example 1, it is worth explanation. figure 1 The flow can be seen that the energy storage aggregation platform is involved in the network auxiliary service by bidding bidding. Since the grid company occupies dominates, there is a master-slave game. Distributed energy storage devices, many and dispersed, so different distributed energy storage devices mastered by different aggregations.

[0157] The grid company will expect the yield of our own decisions on the followed aggregation platform. It is to consider this effect, and the grid company decides to be a maximized yield of the aggregated platform. In the existing mathematical model Starkberberg model, the power grid company's decision no longer needs its own reaction function. Set the market demand function as:

[0158] 1D = d (p1 + p2) = a-b (p1 + p2)

[0159] The P1 and P2 are grid companies and aggregation platforms, respectively. Suppose the cost function of the two enterprises is the same, it is c = c0p. First, consider the optimal output of the polymerization platform to maximize the profit under the plan yield of the Given Grid, namely:

[0160] 2maxp2 [A-B (p1 + p2)] - CP2

[0161] The optimal solution in the above optimization model is obviously the function P2 = G (P1) of the P1; A is a tolerance coefficient, B is the cost function of the pending coefficient, CP2 is P2 cost function.

[0162] After knowing the reaction of the aggregation platform, the optimal yield model of the grid company is:

[0163] 3maxp1 [A-B (P1 + P2)] - CP1, S.T.P2 = G (P1)

[0164] In the above formula: The optimal solution is obtained under the condition of Nash-balance S.p2 = G (P1).

[0165] Such as figure 2 Distance figure 2 It is the requirements for the grid company to provide assistance with the energy storage aggregation platform, and the main body is divided into two parts: peak and frequency modulation.

[0166] The peak portion includes the following four constraints:

[0167] a. Peak time;

[0168] Split single price;

[0169] C. Peak factor;

[0170] d. Energy storage force.

[0171] The FM portion includes the following four constraints:

[0172] a. Frequency voltage supply;

[0173] b. Frequency amount;

[0174] c. FM pass rate;

[0175] D.AGC unit adjustment ratio.

[0176] Such as image 3 Distance image 3 It is the four steps of the storage aggregator to distributed long-term contract pricing.

[0177] Specifically, the following steps:

[0178] Step A. Modeling the electricity price market;

[0179] Step B. Determine the function;

[0180] Step C. Set constraint conditions;

[0181] Step D. Implement the electricity price.

[0182] Such as Figure 4 Distance Figure 4 It is a schematic diagram of a typical distributed energy storage peak. Among them, the part of the dashed line is the load peak. The following sections below are adjusted the load curve.

[0183] Those skilled in the art will appreciate that embodiments of the present application can be provided as a method, system, or computer program product. Accordingly, the present application may employ a full hardware embodiment, a full software embodiment, or in conjunction with embodiments of software and hardware. Moreover, the present application may employ a computer program product that includes a computer available storage medium (including but not limited to disk memory, CD-ROM, optical memory, etc.) implemented in one or more computers.

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Description & Claims & Application Information

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the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

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Classification and recommendation of technical efficacy words

Improve pricing efficiency
Improve availability and reliability

Automatic tray pricing system based on surface acoustic wave technology

InactiveCN106127465AImprove pricing efficiencySolve the charging queuing problemTransmission systemsCo-operative working arrangementsRadio frequencyWorking range

Owner:SHENZHEN INSTITUTE OF INFORMATION TECHNOLOGY

Information generation method and device

PendingCN113506127AImprove pricing efficiencyMarket predictionsAdvertisementsProcess engineeringData science

Owner:特赞(上海)信息科技有限公司

Double-layer pricing method for energy storage aggregation platform

A platform and pricing technology, applied in the economic field of the power system, can solve the problem of high initial investment price of energy storage projects, and achieve the effect of improving reliability and practicability and improving pricing efficiency.

AI-Extracted Technical Summary

Problems solved by technology

Abstract

Image

Examples

Example Embodiment

Example Embodiment

PUM

Description & Claims & Application Information

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