CN114662762A - Energy storage power station regulation and control method under electric power spot market background - Google Patents
Energy storage power station regulation and control method under electric power spot market background Download PDFInfo
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Abstract
The invention discloses a method for formulating a regulation and control strategy of an energy storage power station in the background of a power spot market. The technical scheme adopted by the invention is as follows: the method comprises the steps of adopting day-ahead electricity price, real-time electricity price and frequency modulation auxiliary service market electricity price data of a spot-shipment electric energy market as input, and adjusting an energy storage bidding strategy by considering day-ahead and real-time electric energy market electricity price difference, so that the economy of an energy storage power station under multi-scene application is further improved, uncertainty of the energy storage power station running loss and the electricity price under the background of the electric power spot-shipment market is considered, and a two-stage optimization scheduling model of the energy storage power station participating in peak modulation service with the maximum income is established. The optimal energy storage power station dispatching strategy is formulated by considering the operation loss and the electricity price uncertainty of the energy storage power station, and the multi-scene application of the energy storage power station under the background of the electric power spot market is realized, so that a basis is provided for the regulation and control of the electrochemical energy storage power station, and the economy of the energy storage power station is improved.
Description
Technical Field
The invention belongs to the technical field of dispatching of energy storage power stations in power systems, and relates to a method for regulating and controlling an energy storage power station in a power spot market background.
Background
In order to further promote the construction of an electric power market system, eight areas are determined in China to serve as first electric power spot market reform test points. With the implementation of the power system reform matching policy, the application value of the power grid energy storage gradually gets the attention and approval of the market, and related policies further put forward the conversion from research and development demonstration to commercialization and scale development in the future. In recent years, the policy of leaving more provinces in China supports the energy storage as a main body of the power market to participate in power market transaction.
On the other hand, the energy storage has the characteristics of high charging and discharging speed, high adjusting precision, quick response and the like, and the cost of the energy storage technology is in a descending trend along with the maturity of the energy storage technology; the energy storage power station can participate in the market of spot electric energy and frequency modulation auxiliary service for arbitrage, and the multi-scene arbitrage of the energy storage power station is the future energy storage development trend.
Therefore, how to make a regulation and control strategy for the energy storage power station participating in the peak-shaving frequency-modulation service in the background of the electric power spot market, analyze the economic benefits of the energy storage power station participating in the spot electric energy market and the frequency-modulation auxiliary service market, and make a regulation and control strategy and evaluate the profit for the energy storage power station is a problem worthy of deep research.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects in the prior art and provide an energy storage power station regulation and control method under the background of the electric power spot market.
Therefore, the technical scheme adopted by the invention is as follows:
an energy storage power station regulation and control method under the background of the electric power spot market comprises the following steps:
s1: acquiring day-ahead electricity price, real-time electricity price and frequency modulation auxiliary service market electricity price data of a spot-shipment electric energy market in a production period;
s2: in the energy storage power station regulation strategy formulation model, an energy storage bidding strategy is adjusted by considering day-ahead and real-time electric energy market electricity price difference, a two-stage optimization scheduling model considering energy storage power station operation loss and electricity price uncertainty under the background of an electric power spot market is established by combining day-ahead market (DAM) and real-time market (RTM) clearing rules and taking the net profit of the energy storage power station as an objective function;
s3, generating a series of electricity price prediction scenes through a clustering method and Monte Carlo simulation expansion based on the data obtained in S1, and solving a two-stage optimization scheduling model under all the generated electricity price prediction scenes to obtain an energy storage power station scheduling strategy under the background of the power spot market;
s4: and scheduling the energy storage power station based on the energy storage power station scheduling strategy formulated in the S3.
Preferably, in the step S2, in the model for making the energy storage station regulation and control strategy, only the spot electric energy market is released in the day ahead market, the real-time spot electric energy and the frequency modulation auxiliary service market are released jointly, the day ahead market yield of the energy storage station is the time-sharing electricity price difference of the energy storage station participating in the electric energy market at different time intervals for charging and discharging, and the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
for the day-ahead electric energy market of energy storage power stationsThe yield, tau, is the total time period of the production simulation,
is time period [ i, i +1 ]]The electricity price of the node at the day before,
is a time period [ i, i +1 ]]Internal energy storage power stations discharge power in the day-ahead market,
is a time period [ i, i +1 ]]Charging power of the internal energy storage power station in the day-ahead market; cp,1For the charge-discharge equivalent loss cost of the energy storage power station, the cost is calculated by the following formula:
in the formula, CwFor equivalent capacity price of energy-storage power stations, EesConfiguring capacity, N, for energy-storing power stations1The discharge depth of the energy storage battery in the peak regulation mode is D1Equivalent number of cycles of r1The interest rate coefficient under the peak regulation mode;
the real-time market income of the energy storage power station is the frequency modulation capacity income and the frequency modulation mileage income when the energy storage power station participates in the electric energy market and the frequency modulation auxiliary service, and the calculation formula of the frequency modulation income of the energy storage power station in the real-time market is as follows:
in the formula:
is a time period [ i, i +1 ]]Real-time electricity prices within;
and correcting day-ahead electric energy market bids by using the energy storage power stations in the real-time market, and calculating electric energy market income in the real-time market according to the electric power spot market rules, wherein the electric energy market income calculation formula in the real-time market is as follows:
in the formula (I), the compound is shown in the specification,
for frequency modulation gain, k, in energy storage power stations in real-time marketsregIn order to score the performance of the energy storage frequency modulation,
is a time period [ i, i +1 ]]The capacity of the fm market in the live market,
respectively a frequency modulation capacity electricity price and a frequency modulation mileage electricity price,
is a time period [ i, i +1 ]]The discharge power of the internal energy storage power station in the real-time market,
is a time period [ i, i +1 ]]The charging power of the internal energy storage power station in the real-time market, sigma is the energy storage frequency modulation mileage ratio,
respectively, time periods [ i, i +1]The proportion of upward and downward frequency modulation is internally provided; beta is the average electric quantity which is adjusted up or down within a unit hour of the energy storage frequency modulation; cp,2The equivalent loss cost for the energy storage participating in the frequency modulation auxiliary service is calculated by the following formula:
in the formula, N2For the energy storage battery, the discharge depth is D in the frequency modulation mode2Equivalent number of cycles of r2The interest rate coefficient under the frequency modulation mode;
aiming at the day-ahead market and the real-time market, establishing a two-stage optimization scheduling model considering the operation loss of the energy storage power station and the electricity price uncertainty under the background of the electric power spot market by taking the maximum net income of the energy storage power station as an objective function respectively:
wherein R is the discount rate;
and the constraint conditions in the two-stage optimization scheduling mode comprise energy storage charge state constraint and energy storage power constraint.
Preferably, in S1, the data acquired in the production cycle includes the electricity price of the electric energy market node in the day-ahead market, the electricity price of the electric energy market node in the real-time market, the frequency modulation capacity price and the frequency modulation mileage price.
Preferably, the specific process of step S3 is as follows:
extracting the node electricity price of the electric energy market in the day-ahead market, the node electricity price of the electric energy market in the real-time market, the frequency modulation capacity price and the frequency modulation mileage price from the data obtained in S1, and then clustering the node electricity price of the electric energy market in the day-ahead market, the node electricity price of the electric energy market in the real-time market, the frequency modulation capacity price and the frequency modulation mileage price by adopting a clustering method; generating a series of electricity price prediction scenes covering the uncertainty fluctuation of the electricity price by Monte Carlo simulation according to preset prediction deviation aiming at each category of electricity price data obtained by clustering; and solving a two-stage optimization scheduling model based on all power price prediction scenes generated by simulation, so as to obtain an optimal energy storage power station scheduling strategy under the background of the electric power spot market.
Preferably, the clustering method adopts K-means clustering.
The invention has the following beneficial effects: the method and the device can formulate and take the running loss of the energy storage power station and the uncertainty of the electricity price into consideration to formulate an economic optimal bidding strategy, and realize multi-scene application of the energy storage power station under the background of the electric power spot market, thereby providing a basis for the dispatching of the energy storage power station and improving the economy of the energy storage power station.
Drawings
FIG. 1 is a flow chart of a method for regulating and controlling an energy storage power station according to the present invention;
FIG. 2 is data of the price of fresh electricity produced by the Zhejiang electric power spot market in the application example of the present invention after trial operation on a certain day;
FIG. 3 is a schematic diagram of a day-ahead market regulation strategy of an energy storage power station and an electric quantity change situation of the energy storage power station in a peak-shaving frequency modulation mode of the energy storage power station in an application example of the invention;
FIG. 4 is a schematic diagram of a real-time market regulation strategy of an energy storage power station and an electric quantity change situation of the energy storage power station in a peak-shaving frequency modulation mode of the energy storage power station in an application example of the invention;
FIG. 5 is a schematic diagram illustrating revenue distribution of DAM participation of energy storage power stations on a typical day in an application example of the present invention;
FIG. 6 is a schematic diagram of the revenue distribution of the energy storage power station participating in RTM on a typical day in an application example of the present invention;
Detailed Description
The invention will be further elucidated and described with reference to the drawings and the detailed description.
In a preferred embodiment of the present invention, there is provided a method for regulating and controlling an energy storage power station in a power spot market context, the method comprising the steps of:
s1: and in the production period, acquiring day-ahead electricity price, real-time electricity price and frequency modulation auxiliary service market electricity price data of a spot-shipment electric energy market.
Note that in this step, the data employed may be electric power spot market historical discharge data, and specifically, the data acquired in the production cycle includes the electric energy market node electricity price in the day-ahead market, the electric energy market node electricity price and the frequency modulation capacity price in the real-time market, and the frequency modulation mileage price. The acquired data can be input into an energy storage power station regulation and control strategy formulation model for solving the model. The specific making process of the two-stage regulation and control strategy making model of the power station needing energy storage is described in S2.
S2: in the energy storage power station regulation strategy formulation model, an energy storage bidding strategy is adjusted by considering day-ahead and real-time electric energy market electricity price difference, a two-stage optimization scheduling model considering energy storage power station operation loss and electricity price uncertainty under the background of an electric power spot market is established by combining day-ahead market (DAM) and real-time market (RTM) clearing rules and taking the net profit of the energy storage power station as an objective function.
In the invention, the model for making the regulation and control strategy of the energy storage power station is specifically as follows:
the method is characterized in that only the spot electric energy market is cleared in the day-ahead market, the real-time spot electric energy and the frequency modulation auxiliary service market are jointly cleared, the day-ahead market income of the energy storage power station is the time-sharing electricity price difference of the energy storage power station participating in the electric energy market at different time intervals to carry out charging and discharging, and the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
in order to obtain the day-ahead electric energy market income of the energy storage power station, tau is the total time period of production simulation,
is a time period [ i, i +1 ]]The electricity price of the node at the day before,
is a time period [ i, i +1 ]]Internal energy storage power stations discharge power in the day-ahead market,
is a time period [ i, i +1 ]]Charging power of the internal energy storage power station in the day-ahead market; cp,1For the charge-discharge equivalent loss cost of the energy storage power station, the cost is calculated by the following formula:
in the formula, CwFor storing energyEquivalent capacity price of power station, EesConfiguring capacity, N, for energy-storing power stations1The discharge depth of the energy storage battery in the peak regulation mode is D1Equivalent number of cycles of r1The interest rate coefficient under the peak regulation mode;
the real-time market income of the energy storage power station is the frequency modulation capacity income and the frequency modulation mileage income when the energy storage power station participates in the electric energy market and the frequency modulation auxiliary service, and the calculation formula of the frequency modulation income of the energy storage power station in the real-time market is as follows:
in the formula:
is a time period [ i, i +1 ]]Real-time electricity prices within;
and correcting the day-ahead electric energy market bid by using the energy storage power station in the real-time market, and calculating the electric energy market profit in the real-time market according to the electric power spot market rule, wherein the electric energy market profit in the real-time market is calculated by the following formula:
in the formula (I), the compound is shown in the specification,
for frequency modulation gain, k, in energy storage power stations in real-time marketsregIn order to score the performance of the energy storage frequency modulation,
is time period [ i, i +1 ]]The capacity of the fm market in the live market,
respectively a frequency modulation capacity electricity price and a frequency modulation mileage electricity price,
is a time period [ i, i +1 ]]The discharge power of the internal energy storage power station in the real-time market,
is a time period [ i, i +1 ]]The charging power of the internal energy storage power station in the real-time market, sigma is the energy storage frequency modulation mileage ratio,
respectively, time periods [ i, i +1]The proportion of upward and downward frequency modulation is internally provided; beta is the average electric quantity which is adjusted up or down in the unit hour of the energy storage frequency modulation; cp,2The equivalent loss cost for the energy storage participating in the frequency modulation auxiliary service is calculated by the following formula:
in the formula, N2The discharge depth of the energy storage battery is D in a frequency modulation mode2Equivalent number of cycles of r2The interest rate coefficient under the frequency modulation mode;
aiming at the day-ahead market and the real-time market, establishing a two-stage optimization scheduling model considering the operation loss of the energy storage power station and the electricity price uncertainty under the background of the electric power spot market by taking the maximum net income of the energy storage power station as an objective function respectively:
wherein R is the discount rate;
and the constraint conditions in the two-stage optimization scheduling mode comprise energy storage charge state constraint and energy storage power constraint.
Therefore, the objective function and the corresponding constraint conditions form a two-stage optimization model, the optimization model is solved, and the optimal solution of the model can be used as an energy storage power station scheduling strategy.
And S3, generating a series of electricity price prediction scenes through a clustering method and Monte Carlo simulation expansion based on the data acquired in S1, and solving a two-stage optimization scheduling model under all the generated electricity price prediction scenes to obtain an energy storage power station scheduling strategy under the background of the electric power spot market.
In the invention, the adopted clustering method can be K-means clustering. K-means clustering and Monte Carlo are adopted to simulate and quantify the uncertainty of the electricity price, and the specific process is as follows:
extracting the node electricity price of the electric energy market in the day-ahead market, the node electricity price of the electric energy market in the real-time market, the frequency modulation capacity price and the frequency modulation mileage price from the data obtained in S1, and then clustering the node electricity price of the electric energy market in the day-ahead market, the node electricity price of the electric energy market in the real-time market, the frequency modulation capacity price and the frequency modulation mileage price by adopting a clustering method; generating a series of electricity price prediction scenes covering the uncertainty fluctuation of the electricity price by Monte Carlo simulation according to preset prediction deviation aiming at each category of electricity price data obtained by clustering; and solving a two-stage optimization scheduling model based on all power price prediction scenes generated by simulation, so as to obtain an optimal energy storage power station scheduling strategy under the background of the electric power spot market.
In the subsequent embodiment of the invention, the Mean Absolute Percentage Error (MAPE) of the day-ahead electric energy market node is set to be 10%, the real-time electric energy node MAPE is set to be 20%, the frequency modulation auxiliary service market error value is equal to the day-ahead electric energy market node electric price error, n (such as 1000) electric price prediction scenes are respectively generated through Monte Carlo simulation and are used for bidding strategy formulation and probability revenue analysis of the energy storage power station, and the economic application scene of the energy storage power station and the economic feasibility of application under the electric power spot market background are analyzed based on the probability revenue analysis result. And each electricity price forecasting scene corresponds to a group of electricity prices of the electric energy market nodes in the day-ahead market, electricity prices of the electric energy market nodes in the real-time market, frequency modulation capacity prices and frequency modulation mileage prices. Due to the adoption of Monte Carlo simulation, all the power rate prediction scenes obtained by the method can better simulate uncertainty fluctuation of the power rate relative to the original data acquired in S1. And then all power price prediction scenes generated by the simulation are used as data, and the two-stage optimization scheduling model is solved, so that the optimal energy storage power station scheduling strategy meeting all power price prediction scenes to the maximum extent can be obtained, and therefore, a basis is provided for energy storage power station scheduling, and reference is provided for the commercial operation mode of the energy storage power station under the power spot goods background.
S4: and scheduling the energy storage power station based on the energy storage power station scheduling strategy formulated in the S3.
When the economic feasibility assessment is carried out, the economic feasibility of the energy storage power station under the background of the spot power market can be analyzed through the income probability distribution description of the energy storage power station. Specifically, in the step S3, energy storage power station revenue conclusions under different electricity price scenes can be obtained according to the energy storage power station revenue probability distribution conditions, and the characteristics of dynamic changes of the electric power spot market are better met.
In order to verify the effectiveness of the method, the Zhejiang electric power spot market trial operation data is adopted in subsequent application examples to realize the method, specific steps are not repeated, and technical effects and implementation details are mainly given.
Application example
In this case, the energy storage power station regulation and control method in the power spot market background shown in S1 to S4 of the present invention is written by MATLAB software, and details thereof are not described herein. The specific technical effect is mainly shown for case data, and the model in the method is solved by calling Gurobi.
And (3) operating environment:
AMD Ryzen 53400G CPU 3.70GHz, 16GB memory, Microsoft Windows 10X64
Gurobi 9.0.3
MATLAB 2020B
The implementation results are as follows:
the application example is based on the pilot run data of the Zhejiang electric power spot market in a month, and comprises day-ahead node electricity price, real-time node electricity price, frequency modulation auxiliary service capacity compensation electricity price and mileage compensation price data. In the embodiment, the construction scale of the energy storage power station is 100MW/200MWh, the upper limit and the lower limit of SOC are respectively 0.9 and 0.1, the interval time is 1h, and the discount rate is 0. The proportion of upward frequency modulation and downward frequency modulation is 25%, and the mileage ratio of energy storage frequency modulation is 10. And formulating a two-stage regulation and control strategy under the peak-shaving frequency modulation mode of the energy storage power station under the background of the spot market through a maximum profit optimization model, and taking the two-stage regulation and control strategy as a profit evaluation reference of the energy storage power station.
Fig. 1 is a flow chart of a method for making a regulation and control strategy of an energy storage power station, and the method comprises two stages of a day-ahead market (DAM) and a real-time market (RTM).
FIG. 2 shows the data of the price of fresh electricity produced by the electricity spot market in Zhejiang on a trial run at a certain day.
Fig. 3 reflects the day-ahead market regulation and control strategy of the energy storage power station and the electricity change condition of the energy storage power station in the peak-shaving frequency-modulation mode, and fig. 4 reflects the real-time market regulation and control strategy of the energy storage power station and the electricity change condition of the energy storage power station in the peak-shaving frequency-modulation mode. The compensation price of the frequency modulation auxiliary service is higher, the income proportion from the frequency modulation service is higher, and the charging and discharging power of the energy storage power station in the electric energy market can change due to the difference between the real-time market electricity price and the day-ahead market electricity price. In addition, because the frequency modulation service simultaneously has the frequency modulation upwards and the frequency modulation downwards, the electric quantity of the energy storage power station changes relatively little when the frequency modulation service is involved.
FIG. 5 reflects the income distribution condition of a typical day of participation of the energy storage power station in DAM, and the average value of profit of the energy storage power station in the market of the typical day through time-of-use electricity price peak valley is 15899 yuan; fig. 6 reflects the profit distribution of the energy storage power station participating in RTM on a typical day, which is 248425 yuan by the average value of peak shaving and frequency modulation auxiliary service profit in the real-time market.
According to the results of the embodiment, the energy storage power station has higher frequency modulation market compensation price, so the energy storage power station mainly participates in frequency modulation auxiliary service arbitrage and supplements the electric quantity of the energy storage power station through the electric energy market. In addition, due to the fact that the day-ahead market node electricity price and the real-time market node electricity price are different, modification of day-ahead electric energy market bidding is beneficial to the energy storage power station to obtain higher expected income in the real-time market, and therefore the requirement that the energy storage power station participates in various application scenes under the background of electric power spot goods is met, and the economical efficiency of the energy storage power station is improved.
Therefore, the energy storage power station regulation and control method can specify the energy storage power station scheduling strategy meeting the power spot background, further effectively schedule the energy storage power station, provide basis for the scheduling of the energy storage power station, and improve the economy of the energy storage power station.
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. On the contrary, the invention is intended to cover any alternatives, equivalents, and alternatives that may be included within the scope of the invention as defined by the appended claims. Furthermore, in order to provide a better understanding of the present invention to the public, certain specific details of the invention are set forth in the following description. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
Claims (5)
1. An energy storage power station regulation and control method under the background of the electric power spot market is characterized by comprising the following steps:
s1: acquiring day-ahead electricity price, real-time electricity price and frequency modulation auxiliary service market electricity price data of a spot-shipment electric energy market in a production period;
s2: in the energy storage power station regulation strategy formulation model, an energy storage bidding strategy is adjusted by considering day-ahead and real-time electric energy market electricity price difference, a two-stage optimization scheduling model considering energy storage power station operation loss and electricity price uncertainty under the background of an electric power spot market is established by combining day-ahead market (DAM) and real-time market (RTM) clearing rules and taking the net profit of the energy storage power station as an objective function;
s3, generating a series of electricity price prediction scenes through a clustering method and Monte Carlo simulation expansion based on the data obtained in S1, and solving a two-stage optimization scheduling model under all the generated electricity price prediction scenes to obtain an energy storage power station scheduling strategy under the background of the power spot market;
s4: and scheduling the energy storage power station based on the energy storage power station scheduling strategy formulated in the S3.
2. The method as claimed in claim 1, wherein in the step S2, in the model for the regulation and control strategy of the energy storage station, only the current market is released from the current energy market, the real-time current energy is released from the frequency modulation auxiliary service market, the current market income of the energy storage station is the time-sharing price difference of the energy storage station participating in the energy market at different time intervals for charging and discharging, and the calculation formula is:
in the formula (I), the compound is shown in the specification,
in order to obtain the day-ahead electric energy market income of the energy storage power station, tau is the total time period of production simulation,
is a time period [ i, i +1 ]]The electricity price of the node at the day before,
is time period [ i, i +1 ]]The internal energy storage power station discharges power in the day-ahead market,
is a time period [ i, i +1 ]]Charging power of the internal energy storage power station in the day-ahead market; cp,1For the charge-discharge equivalent loss cost of the energy storage power station, the cost is calculated by the following formula:
in the formula, CwFor equivalent capacity price of energy-storage power stations, EesConfiguring capacity, N, for energy-storing power stations1The discharge depth of the energy storage battery in the peak regulation mode is D1Equivalent number of cycles of (c), r1The interest rate coefficient under the peak regulation mode;
the real-time market income of the energy storage power station is the frequency modulation capacity income and the frequency modulation mileage income when the energy storage power station participates in the electric energy market and the frequency modulation auxiliary service, and the calculation formula of the frequency modulation income of the energy storage power station in the real-time market is as follows:
in the formula:
is a time period [ i, i +1 ]]Real-time electricity prices within;
and correcting day-ahead electric energy market bids by using the energy storage power stations in the real-time market, and calculating electric energy market income in the real-time market according to the electric power spot market rules, wherein the electric energy market income calculation formula in the real-time market is as follows:
in the formula (I), the compound is shown in the specification,
for frequency modulation gain, k, in energy storage power stations in real-time marketsregIn order to score the performance of the energy storage frequency modulation,
is time period [ i, i +1 ]]The capacity of the fm market in the live market,
respectively a frequency modulation capacity electricity price and a frequency modulation mileage electricity price,
is a time period [ i, i +1 ]]The discharge power of the internal energy storage power station in the real-time market,
is a time period [ i, i +1 ]]The charging power of the internal energy storage power station in the real-time market, sigma is the energy storage frequency modulation mileage ratio,
respectively, time periods [ i, i +1]The proportion of upward and downward frequency modulation is internally provided; beta is the average electric quantity which is adjusted up or down within a unit hour of the energy storage frequency modulation; cp,2The equivalent loss cost for the energy storage participating in the frequency modulation auxiliary service is calculated by the following formula:
in the formula, N2For the energy storage battery, the discharge depth is D in the frequency modulation mode2Equivalent number of cycles of (c), r2The interest rate coefficient under the frequency modulation mode;
aiming at the day-ahead market and the real-time market, establishing a two-stage optimization scheduling model considering the operation loss of the energy storage power station and the electricity price uncertainty under the background of the electric power spot market by taking the maximum net income of the energy storage power station as an objective function respectively:
wherein R is the discount rate;
and the constraint conditions in the two-stage optimization scheduling mode comprise energy storage charge state constraint and energy storage power constraint.
3. The method as claimed in claim 1, wherein the data obtained in the production cycle in S1 includes the node electricity prices of the electric energy market in the day-ahead market, the node electricity prices of the electric energy market in the real-time market, the frequency modulation capacity price, and the frequency modulation mileage price.
4. The method for regulating and controlling the energy storage power station in the context of the electric power spot market according to claim 2, wherein the specific process of the step S3 is as follows:
extracting the node electricity price of the electric energy market in the day-ahead market, the node electricity price of the electric energy market in the real-time market, the frequency modulation capacity price and the frequency modulation mileage price from the data obtained in S1, and then clustering the node electricity price of the electric energy market in the day-ahead market, the node electricity price of the electric energy market in the real-time market, the frequency modulation capacity price and the frequency modulation mileage price by adopting a clustering method; generating a series of electricity price prediction scenes covering electricity price uncertainty fluctuation by Monte Carlo simulation according to preset prediction deviation aiming at each category of electricity price data obtained by clustering; and solving a two-stage optimization scheduling model based on all power price prediction scenes generated by simulation, so as to obtain an optimal energy storage power station scheduling strategy under the background of the electric power spot market.
5. The method for regulating and controlling the energy storage power station in the context of the electric power spot market according to claim 1, characterized in that the clustering method adopts K-means clustering.
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| CN115619441A (en) * | 2022-12-20 | 2023-01-17 | 合肥华思***有限公司 | Reporting method and system for energy storage power station to participate in day-ahead power transaction |
| CN115659595A (en) * | 2022-09-26 | 2023-01-31 | 中国华能集团清洁能源技术研究院有限公司 | Energy storage control method and device of new energy station based on artificial intelligence |
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| CN115659595A (en) * | 2022-09-26 | 2023-01-31 | 中国华能集团清洁能源技术研究院有限公司 | Energy storage control method and device of new energy station based on artificial intelligence |
| CN115659595B (en) * | 2022-09-26 | 2024-02-06 | 中国华能集团清洁能源技术研究院有限公司 | Energy storage control method and device for new energy station based on artificial intelligence |
| CN115619441A (en) * | 2022-12-20 | 2023-01-17 | 合肥华思***有限公司 | Reporting method and system for energy storage power station to participate in day-ahead power transaction |
| CN117057634A (en) * | 2023-10-13 | 2023-11-14 | 国网湖北省电力有限公司经济技术研究院 | Low-carbon operation optimization method and system for participation of energy storage power station in electric power spot market |
| CN117057634B (en) * | 2023-10-13 | 2024-01-02 | 国网湖北省电力有限公司经济技术研究院 | Low-carbon operation optimization method and system for participation of energy storage power station in electric power spot market |
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