Power grid electricity purchasing scheme optimization method in regional electricity market

Abstract

The invention discloses a power grid electricity purchasing scheme optimization method in a regional electricity market, and belongs to the technical field of electricity. Comprising the following steps: S100, monthly load data, wind power monthly predicted electric quantity, day-ahead market electricity price predicted value and dynamic outsourcing electricity price information of a regional electricity market and real-time quotation and performance parameters of each generator set in a province are extracted and integrated from a plurality of information sources; s101, designing a power purchase optimization objective function including power purchase cost, environmental benefit, power grid stability risk and market fluctuation risk; according to the invention, the price information, the load demand data and the state information of the power generation resources of the power market are collected in real time, so that the dynamic real-time monitoring of the power market is realized. The real-time monitoring capability enables a power grid enterprise to obtain the latest market information in time, thereby making a more accurate and reliable power purchase decision.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method for optimizing a power grid power purchase plan in a regional power market. Background Art

[0002] With the gradual opening up of the power market and the intensification of competition, power grid companies need to formulate power purchase plans more accurately to reduce power purchase costs and improve economic benefits. However, traditional power purchase plans are often formulated based on historical data and experience, lacking real-time and accuracy, and are difficult to adapt to the rapid changes in the power market.

[0003] In addition, factors such as price fluctuations in the electricity market, uncertainty in load demand, and the diversity of power generation resources have also brought great challenges to the power purchase decisions of power grid companies. Therefore, how to comprehensively consider various factors and formulate an economical and reliable power purchase plan has become an important issue that current power grid companies need to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing a power grid power purchase plan in a regional power market to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for optimizing a power grid power purchase plan in a regional power market comprises the following steps:

[0007] S100, extract and integrate the monthly load data of the regional power market, the monthly forecast of wind power, the forecast value of the day-ahead market electricity price, the dynamic outsourced electricity price information, and the real-time quotation and performance parameters of each generator set in the province from multiple information sources;

[0008] S101. Design an electricity purchase optimization objective function that includes electricity purchase cost, environmental benefits, grid stability risk, and market volatility risk:

[0009] OF=min{W C ×C―W EB ×EB+W GSR ×GSR+W MVR ×MVR}

[0010] C, EB, GSR, and MVR are respectively the electricity purchase cost, environmental benefit, grid stability risk, and market volatility risk. C , W EB , W GSR , W MVR They are the weights of electricity purchase cost, environmental benefit, grid stability risk and market volatility risk, which can be adjusted dynamically according to policy orientation, market environment and grid operation strategy.

[0011] S102, solving the power purchase optimization model through genetic algorithm to obtain the monthly configuration plan of thermal power, wind power and external power purchase of the provincial power grid;

[0012] S103, according to the result of step S102, combined with the actual demand and constraints of the power grid operation, specify the daily and time period power distribution plan;

[0013] S104. Design a dynamic adjustment mechanism based on real-time data to monitor the grid operation status and market changes in real time and adjust the power purchase plan in a timely manner.

[0014] As a further solution of the present invention: wherein, the step S100 further includes weather forecast data, power grid topology data and historical fault record information source, and the specific steps are as follows:

[0015] By collecting and integrating weather forecast data, including temperature, humidity, and wind speed, to predict renewable energy generation potential and load demand changes;

[0016] Utilize grid topology data to analyze the transmission capacity and stability of the grid and ensure that the power purchase plan does not exceed the grid's carrying capacity;

[0017] Separate historical fault records, identify potential problems and risk points in power grid operation, and provide additional constraints for the optimization model.

[0018] As a further solution of the present invention: wherein, the formula for calculating the electricity purchase cost in step S101 is as follows:

[0019]

[0020] Among them, P i is the price of electricity of the ith power source; Q i is the amount of electricity purchased by the i-th power source; F i is the fuel cost coefficient of the ith power source, which can be set to 0 for non-fossil energy, T i is the tax coefficient of the i-th power source.

[0021] As a further solution of the present invention: wherein, the environmental benefit calculation formula in step S101 is as follows:

[0022]

[0023] Among them, E i is the carbon emission coefficient of the i-th power source; C CO2i is the value of a unit of carbon emissions.

[0024] As a further solution of the present invention: wherein, the grid stability risk calculation formula in step S101 is as follows:

[0025]

[0026] Among them, S i is the influence coefficient of the ith power source on the grid stability.

[0027] As a further solution of the present invention: wherein, the market volatility risk calculation formula in step S101 is as follows:

[0028]

[0029] where σ i is the price volatility of the i-th power source.

[0030] As a further solution of the present invention: wherein, the genetic algorithm steps in step S102 are as follows:

[0031] The electricity purchase scheme is encoded as individuals in the genetic algorithm, and each individual consists of a series of genes corresponding to different electricity purchase strategies;

[0032] A set of initial solutions is randomly generated as a population, each of which is a possible electricity purchase plan;

[0033] Define a fitness function to evaluate the quality of each individual, which corresponds to the objective function of the electricity purchase optimization model;

[0034] According to the result of the fitness function, a part of excellent individuals are selected as parents to produce the next generation;

[0035] Perform crossover operation on the selected parent individuals to generate new offspring individuals, and perform mutation operation on the newly generated offspring individuals to increase the diversity of the population. Repeat the above selection, crossover and mutation process until the predetermined number of iterations is reached or a solution that meets the requirements is found;

[0036] The final optimal individual is decoded into a specific electricity purchasing plan.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention realizes real-time monitoring of the power market dynamics by collecting real-time price information, load demand data and status information of power generation resources in the power market. This real-time monitoring capability enables power grid companies to obtain the latest market information in a timely manner, thereby making more accurate and reliable power purchase decisions.

[0039] In addition, the present invention comprehensively considers factors such as price fluctuations in the electricity market, changes in load demand, and the diversity of power generation resources, and quantitatively analyzes these factors through an algorithm model, thereby finding the optimal electricity purchase combination and timing. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of the method for optimizing the electricity purchasing scheme in the present invention; DETAILED DESCRIPTION

[0041] See also Figure 1 , a method for optimizing a power grid power purchase plan in a regional power market, comprising the following steps:

[0042] S100, extract and integrate the monthly load data of the regional power market, the monthly forecast of wind power, the forecast value of the day-ahead market electricity price, the dynamic outsourced electricity price information, and the real-time quotation and performance parameters of each generator set in the province from multiple information sources;

[0043] Wherein, the step S100 also includes weather forecast data, power grid topology data and historical fault record information sources, and the specific steps are as follows:

[0044] By collecting and integrating weather forecast data, including temperature, humidity, and wind speed, to predict renewable energy generation potential and load demand changes;

[0045] Utilize grid topology data to analyze the transmission capacity and stability of the grid and ensure that the power purchase plan does not exceed the grid's carrying capacity;

[0046] Separate historical fault records, identify potential problems and risk points in power grid operation, and provide additional constraints for the optimization model.

[0047] S101. Design an electricity purchase optimization objective function that includes electricity purchase cost, environmental benefits, grid stability risk, and market volatility risk:

[0048] The formula for calculating the electricity purchase cost is as follows:

[0049]

[0050] Among them, P i is the price of electricity of the ith power source; Q i is the amount of electricity purchased by the i-th power source; F i is the fuel cost coefficient of the ith power source, which can be set to 0 for non-fossil energy, T i is the tax coefficient of the i-th power source.

[0051] The environmental benefit calculation formula is as follows:

[0052]

[0053] Among them, E i is the carbon emission coefficient of the i-th power source; C CO2i is the value of a unit of carbon emissions.

[0054] The formula for calculating grid stability risk is as follows:

[0055]

[0056] Among them, S i is the influence coefficient of the ith power source on the grid stability.

[0057] The market volatility risk calculation formula is as follows:

[0058]

[0059] where σ i is the price volatility of the i-th power source.

[0060] Combining the above factors, the objective function can be constructed as follows:

[0061] OF=min{W C ×C―W EB ×EB+W GSR ×GSR+W MVR ×MVR}

[0062] W C , W EB , W GSR , W MVR They are the weights of electricity purchase cost, environmental benefit, grid stability risk and market volatility risk, which can be adjusted dynamically according to policy orientation, market environment and grid operation strategy.

[0063] Specifically, assuming there are three types of power sources, thermal power (F1), wind power (W1) and purchased power (P1), their electricity prices, fuel cost coefficients, tax coefficients, carbon emission coefficients, grid stability impact coefficients and electricity price volatility are shown in the following table:

[0064]

[0065] Assume that the amount of electricity we need to purchase is Q F1 =100kWh, Q w1=200kWh,Q P1 =150kWh, and we set W C =1,W EB =0.5,W GSR =0.2,W MVR=0.3. Substituting these values ​​into the objective function, the total cost can be calculated. The specific calculation process is omitted here.

[0066] S102, solving the power purchase optimization model through genetic algorithm to obtain the monthly configuration plan of thermal power, wind power and external power purchase of the provincial power grid;

[0067] The specific genetic algorithm steps are as follows:

[0068] The electricity purchase scheme is encoded as individuals in the genetic algorithm, and each individual consists of a series of genes corresponding to different electricity purchase strategies;

[0069] A set of initial solutions is randomly generated as a population, each of which is a possible electricity purchase plan;

[0070] Define a fitness function to evaluate the quality of each individual, which corresponds to the objective function of the electricity purchase optimization model;

[0071] According to the result of the fitness function, a part of excellent individuals are selected as parents to produce the next generation;

[0072] Perform crossover operation on the selected parent individuals to generate new offspring individuals, and perform mutation operation on the newly generated offspring individuals to increase the diversity of the population. Repeat the above selection, crossover and mutation process until the predetermined number of iterations is reached or a solution that meets the requirements is found;

[0073] The final optimal individual is decoded into a specific electricity purchasing plan.

[0074] According to the above steps, assuming there is a simple electricity purchase optimization problem, the goal is to minimize the electricity purchase cost while considering environmental benefits and grid stability. A genetic algorithm can be used to solve this problem.

[0075] The specific steps are as follows:

[0076] Coding: Encode the power purchase plan into a binary string, where each bit represents the amount of power purchased from a power source (for example, the first few bits represent thermal power, the middle few bits represent wind power, and the last few bits represent external power).

[0077] Initial population: Randomly generate a set of binary strings as the initial population.

[0078] Fitness function: Define a fitness function that calculates the fitness value of each individual based on factors such as electricity purchase cost, environmental benefits, and grid stability.

[0079] Selection: Use strategies such as roulette wheel selection to select excellent individuals as parents.

[0080] Crossover: Perform single-point crossover or multi-point crossover operations on the selected parent individuals.

[0081] Mutation: Perform mutation operations such as bit flipping on the newly generated offspring individuals.

[0082] Iteration: Repeat the above process until a satisfactory solution is found or the predetermined number of iterations is reached.

[0083] Decoding: The final optimal binary string is decoded into a specific power purchase plan, including the allocation of thermal power, wind power, and purchased power.

[0084] S103, according to the result of step S102, combined with the actual demand and constraints of the power grid operation, specify the daily and time period power distribution plan;

[0085] S104. Design a dynamic adjustment mechanism based on real-time data to monitor the grid operation status and market changes in real time and adjust the power purchase plan in a timely manner.

[0086] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing a power grid purchasing plan in a regional power market, characterized in that: The steps include: S100, extract and integrate the monthly load data of the regional power market, the monthly forecast of wind power, the forecast value of the day-ahead market electricity price, the dynamic outsourced electricity price information, and the real-time quotation and performance parameters of each generator set in the province from multiple information sources; S101. Design an electricity purchase optimization objective function that includes electricity purchase cost, environmental benefits, grid stability risk, and market volatility risk: OF=min{W C ×C-W EB ×EB+W GSR ×GSR+W MVR ×MVR} C, EB, GSR, and MVR are respectively the electricity purchase cost, environmental benefit, grid stability risk, and market volatility risk. C , W EB , W GSR , W MVR They are the weights of electricity purchase cost, environmental benefit, grid stability risk and market volatility risk, which are adjusted dynamically according to policy orientation, market environment and grid operation strategy; S102, solving the power purchase optimization model through genetic algorithm to obtain the monthly configuration plan of thermal power, wind power and external power purchase of the provincial power grid; S103, according to the result of step S102, combined with the actual demand and constraints of the power grid operation, specify the daily and time period power distribution plan; S104. Design a dynamic adjustment mechanism based on real-time data to monitor the grid operation status and market changes in real time and adjust the power purchase plan in a timely manner.

2. The method for optimizing power grid purchasing scheme in regional power market according to claim 1, characterized in that: The step S100 also includes weather forecast data, power grid topology data and historical fault record information sources, and the specific steps are as follows: By collecting and integrating weather forecast data, including temperature, humidity, and wind speed, to predict renewable energy generation potential and load demand changes; Utilize grid topology data to analyze the transmission capacity and stability of the grid and ensure that the power purchase plan does not exceed the grid's carrying capacity; Separate historical fault records, identify potential problems and risk points in power grid operation, and provide additional constraints for the optimization model.

3. The method for optimizing power grid purchasing scheme in regional power market according to claim 1, characterized in that: The formula for calculating the electricity purchase cost in step S101 is as follows: Among them, P i is the price of electricity of the ith power source; Q i is the amount of electricity purchased by the i-th power source; F i is the fuel cost coefficient of the ith power source, which is set to 0 for non-fossil energy, T i is the tax coefficient of the i-th power source.

4. The method for optimizing power grid purchasing scheme in regional power market according to claim 3 is characterized in that: The environmental benefit calculation formula in step S101 is as follows: Among them, E i is the carbon emission coefficient of the i-th power source; C CO2i is the value of a unit of carbon emissions.

5. The method for optimizing power grid purchasing scheme in regional power market according to claim 4 is characterized in that: The grid stability risk calculation formula in step S101 is as follows: Among them, S i is the influence coefficient of the ith power source on the grid stability.

6. The method for optimizing power grid purchasing scheme in regional power market according to claim 5, characterized in that: The market volatility risk calculation formula in step S101 is as follows: where σ i is the price volatility of the i-th power source.

7. The method for optimizing power grid purchasing scheme in regional power market according to claim 1, characterized in that: The genetic algorithm steps in step S102 are as follows: The electricity purchase scheme is encoded as individuals in the genetic algorithm, and each individual consists of a series of genes corresponding to different electricity purchase strategies; A set of initial solutions is randomly generated as a population, each of which is a possible electricity purchase plan; Define a fitness function to evaluate the quality of each individual, which corresponds to the objective function of the electricity purchase optimization model; According to the result of the fitness function, a part of excellent individuals are selected as parents to produce the next generation; Perform crossover operation on the selected parent individuals to generate new offspring individuals, and perform mutation operation on the newly generated offspring individuals to increase the diversity of the population. Repeat the above selection, crossover and mutation process until the predetermined number of iterations is reached or a solution that meets the requirements is found; The final optimal individual is decoded into a specific electricity purchasing plan.

Images