Operation optimization method of offshore wind power participating in the electricity spot market considering carbon-green certificate trading

Abstract

The present invention discloses an operation optimization method for offshore wind power participating in the electricity spot market considering carbon-green certificate trading. By constructing an offshore wind power carbon trading model and a green certificate trading model, an operation optimization model for offshore wind power participating in the electricity spot market considering carbon-green certificate trading is constructed, and an improved particle swarm algorithm is used to solve the economically optimal offshore wind power target power. The present invention aims to improve the economic efficiency of offshore wind power operation, studies the mechanism of offshore wind power participating in the electricity spot market, and considers the impact of carbon trading and green certificate trading, and constructs an operation optimization model for offshore wind power participating in the electricity spot market considering carbon trading and green certificate trading. The optimization is performed with the goal of optimal economic efficiency, which helps to improve the overall benefits of offshore wind power participating in the electricity spot market.

Description

Technical Field

[0001] The present invention relates to the technical field of power market transactions, and in particular to an operation optimization method for offshore wind power participating in a power spot market taking into account carbon-green certificate transactions. Background Art

[0002] At present, there is no very clear mechanism for how offshore wind power can participate in electricity spot market transactions. At the same time, with the development and improvement of carbon trading and green certificate trading mechanisms, considering carbon trading and green certificate trading mechanisms in electricity spot market transactions will also help to achieve carbon reduction and control. On this basis, this technology constructs an operation optimization method for offshore wind power participating in the electricity spot market taking into account carbon trading and green certificate trading mechanisms, which is conducive to exploring the operation mechanism of offshore wind power participating in the electricity spot market.

[0003] In the existing technology, the decision-making method for offshore wind power to participate in the electricity spot market is still imperfect, and how to reasonably achieve the maximum profit path for offshore wind power to participate in the electricity spot market is still unclear. In the research on large-scale new energy sites participating in the electricity spot market, most of the current studies only consider the impact of carbon trading or green certificate trading, and there are few cases where both are considered at the same time. The mechanism for offshore wind power to participate in carbon trading or green certificate trading in the electricity spot market is still imperfect. Therefore, it is necessary to design an operation optimization method for offshore wind power to participate in the electricity spot market that considers carbon-green certificate trading to solve the above problems. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an operation optimization method for offshore wind power participating in the electricity spot market taking into account carbon-green certificate trading. With the goal of improving the economic efficiency of offshore wind power operation, the mechanism of offshore wind power participating in the electricity spot market is studied. At the same time, the impact of carbon trading and green certificate trading is considered, and an operation optimization model for offshore wind power participating in the electricity spot market taking into account carbon trading and green certificate trading is constructed. The optimization is carried out with the goal of optimal economic efficiency, which helps to improve the overall benefits of offshore wind power participating in the electricity spot market.

[0005] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0006] The operation optimization method of offshore wind power participating in the electricity spot market considering carbon-green certificate trading includes:

[0007] S1, build an offshore wind power carbon trading model: introduce the carbon trading mechanism, and build an offshore wind power carbon trading model on this basis;

[0008] S2, construct an offshore wind power green certificate transaction model, and the green certificate transaction cost model is:

[0009]

[0010] In the formula, C GR is the transaction cost of green certificates, yuan; are the number of green certificates purchased and sold by the power system at time t; f GR is the transaction price of green certificates;

[0011] S3: Build a mechanism for offshore wind power to participate in the electricity spot market taking into account carbon-green certificate trading;

[0012] S4, construct an operation optimization model for offshore wind power participating in the electricity spot market considering carbon-green certificate trading;

[0013] S5, obtaining various parameters of the offshore wind farm, including the rated power of the offshore wind power, the output situation of the previous day and the local load situation data; inputting the obtained parameters into the operation optimization model of step S4, solving it through the improved particle swarm algorithm, and obtaining the offshore wind power target power when the economic efficiency is optimal.

[0014] Furthermore, in step S1, the introduction of a carbon trading mechanism can effectively limit the output of high-carbon emission energy, which is conducive to the role of clean energy and better utilization of offshore wind power for energy supply, which is conducive to improving the new energy consumption rate and thus achieving energy conservation and emission reduction.

[0015] Furthermore, in step S2, the green certificate trading mechanism is an auxiliary policy tool for the renewable energy quota system. The renewable energy quota index refers to the requirement that the renewable energy generation of power generation enterprises must reach or account for a certain proportion of the total power generation. The green electricity produced by power generation enterprises can be quantified into green certificates in proportion. When the green certificates produced by power generation enterprises exceed the quota index, they can sell green certificates to obtain profits; otherwise, they need to purchase green certificates.

[0016] Furthermore, in step S3, offshore wind power participating in the traditional electricity spot trading market needs to report the quotation and power curve. After receiving the declaration information, the independent system operator of the electricity market clears in the order of the day-ahead market and the real-time market. This technology constructs a mechanism for offshore wind power to participate in the electricity spot market that considers carbon-green certificate trading. First of all, the quotation cost of offshore wind power for carbon trading and green certificate trading needs to be considered in the quotation of the offshore wind farm. The carbon trading base price and the green certificate trading price need to be added to the quotation of offshore wind power and reported. After reporting, clearing is carried out in the order of the day-ahead market and the real-time market. When the supply and demand of electricity on the power supply side and the user side are equal, the adjusted price, i.e., the clearing price, is obtained. On this basis, the profit model of the day-ahead market and the real-time market is finally obtained.

[0017] Preferably, in step S1, a carbon trading mechanism is introduced, and on this basis, a model for offshore wind power participating in carbon trading is constructed, including:

[0018] Modelling carbon quotas for offshore wind turbines:

[0019]

[0020]

[0021] In the formula, λ P is the carbon emission quota per unit of electricity generation, P t W is the power generation of the wind turbine; Q CO2 is the carbon emission offset by the uncertainty of offshore wind power generation, a is the carbon emission coefficient of the unit, K W is the wind power reserve factor, P t W Providing power for offshore wind turbines;

[0022] Calculating the carbon trading costs of offshore wind turbines:

[0023]

[0024] In the formula, is the carbon trading cost; ξ is the carbon trading base price; To obtain carbon trading income.

[0025] Preferably, in step S3, constructing a mechanism for offshore wind power to participate in the electricity spot market taking into account carbon-green certificate trading includes:

[0026] Calculate offshore wind power price quote:

[0027] R O =R 1 +ξ+f GR ;

[0028] In the formula, R O Considering the price of offshore wind power after carbon trading and green certificate trading, R 1 is the original quotation, ξ is the carbon trading base price; f GR The transaction cost price of green certificates;

[0029] Constructing the Day-Ahead Return Model:

[0030]

[0031] In the formula, E 1 is the revenue of offshore wind farms in the day-ahead market; R t is the clearing price; P t TB is the bid value of offshore wind power in the day-ahead market;

[0032] Build a real-time revenue model:

[0033]

[0034] In the formula, E 2 is the revenue of offshore wind farms in the real-time market; t W is the actual power generation of offshore wind power; + λ - are the positive and negative penalty coefficients respectively.

[0035] Preferably, in step S4, constructing an operation optimization model for offshore wind power participating in the electricity spot market considering carbon-green certificate trading includes:

[0036] Establish the objective function, include the carbon trading cost into the objective function, and establish an operation optimization model based on the carbon-green certificate trading mechanism; the objective function is:

[0037]

[0038] Among them, F is the total revenue of the system; C OM It is the system operation and maintenance cost. t is the clearing price of offshore wind power in period t; P t W is the output power of the wind turbine, K y is the wind power operation cost maintenance coefficient;

[0039] Set constraints, including green certificate quota constraints, carbon quota constraints and equipment constraints.

[0040] Preferably, green certificate quota constraints include:

[0041]

[0042] Where η is the renewable energy quota coefficient; k g =1 book / (kW*h) is the quantification coefficient, which quantifies the number of renewable energy quotas into the number of green certificates; P L t is the load of the system at time t; is the power generation of the jth wind turbine at time t.

[0043] Preferably, the carbon quota constraints include:

[0044]

[0045] In the formula, It is the maximum carbon emission quota for wind power.

[0046] Preferably, the device constraints include:

[0047] 0≤P W(t)≤P W,rate ;

[0048] Where P W,rate is the rated power of the offshore wind turbine at time t.

[0049] Preferably, in step S5, solving the problem by using the improved particle swarm algorithm includes optimizing the particle movement speed of the particle swarm algorithm by using Levy distribution, and the formula is as follows:

[0050]

[0051] Where α is the step size parameter, which is 0 to 1; L(β) is the Levy distribution with parameter β, and the specific form is L(β)~μ=t -μ (1<β<3); is the individual extreme position of particle i in generation t; is the individual motion speed of particle i in generation t; is the individual position of particle i in generation t;

[0052] The improved particle speed is:

[0053]

[0054] In the formula, ω new is the inertia weight; c 1 and c 2 is the acceleration coefficient; r 1 and r 2 is a random number uniformly distributed between 0 and 1; is the position of the extreme value of the population in generation t.

[0055] Furthermore, Levy distribution is a probability density distribution function with strong search capabilities. Therefore, improving the particle swarm algorithm based on Levy distribution is conducive to preventing the particle swarm algorithm from falling into a local optimal situation during optimization.

[0056] Preferably, a computer device comprises a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the above-mentioned operation optimization method of offshore wind power participating in the electricity spot market considering carbon-green certificate trading by executing the computer instructions.

[0057] Preferably, a computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the above-mentioned operation optimization method of offshore wind power participating in the electricity spot market considering carbon-green certificate trading.

[0058] The beneficial effects of the present invention are:

[0059] 1. The present invention constructs a model for offshore wind power to participate in carbon trading and green certificate trading, which is conducive to improving the trading mechanism for offshore wind power to participate in carbon trading and green certificate trading, and is conducive to exploring market decisions for new energy to participate in carbon trading and green certificate trading.

[0060] 2. The present invention also takes into account the impact of carbon trading and green certificate trading on the participation of offshore wind power in the electricity spot market, which is conducive to better leveraging the role of large-scale new energy sites in energy conservation and emission reduction. On this basis, it helps to maximize the benefits of offshore wind power participating in electricity spot market transactions and improve the electricity spot market trading mechanism for offshore wind power.

[0061] 3. The present invention aims to maximize the operating profit of offshore wind power, considers constraints such as equipment constraints, carbon trading constraints, and green certificate constraints, and constructs an operation optimization model for offshore wind power participating in the electricity spot market taking into account carbon-green certificate trading, which is beneficial to improving the operating economy of offshore wind power and better adapting to the trading mechanism of the electricity market.

[0062] 4. In the present invention, the traditional particle swarm algorithm is considered to have the defect of being easily trapped in the local optimum when performing optimization solutions. Therefore, the Levy distribution with strong search ability is adopted. Based on this ability, the particle swarm algorithm is improved, which is beneficial to improving the search speed and search ability of the particle swarm algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the process of the present invention;

[0064] Figure 2 This is a flow chart of the improved particle swarm algorithm used in the present invention. DETAILED DESCRIPTION

[0065] Embodiment 1:

[0066] like Figure 1 As shown in the figure, the operation optimization method of offshore wind power participating in the electricity spot market considering carbon-green certificate trading includes:

[0067] S1, build an offshore wind power carbon trading model: introduce the carbon trading mechanism, and build an offshore wind power carbon trading model on this basis;

[0068] S2, construct an offshore wind power green certificate transaction model, and the green certificate transaction cost model is:

[0069]

[0070] In the formula, C GR is the transaction cost of green certificates, yuan; are the number of green certificates purchased and sold by the power system at time t; f GR is the transaction price of green certificates;

[0071] S3: Build a mechanism for offshore wind power to participate in the electricity spot market taking into account carbon-green certificate trading;

[0072] S4, construct an operation optimization model for offshore wind power participating in the electricity spot market considering carbon-green certificate trading;

[0073] S5, obtaining various parameters of the offshore wind farm, including the rated power of the offshore wind power, the output situation of the previous day and the local load situation data; inputting the obtained parameters into the operation optimization model of step S4, solving it through the improved particle swarm algorithm, and obtaining the offshore wind power target power when the economic efficiency is optimal.

[0074] Furthermore, in step S1, the introduction of a carbon trading mechanism can effectively limit the output of high-carbon emission energy, which is conducive to the role of clean energy and better utilization of offshore wind power for energy supply, which is conducive to improving the new energy consumption rate and thus achieving energy conservation and emission reduction.

[0075] Furthermore, in step S2, the green certificate trading mechanism is an auxiliary policy tool for the renewable energy quota system. The renewable energy quota index refers to the requirement that the renewable energy generation of power generation enterprises must reach or account for a certain proportion of the total power generation. The green electricity produced by power generation enterprises can be quantified into green certificates in proportion. When the green certificates produced by power generation enterprises exceed the quota index, they can sell green certificates to obtain profits; otherwise, they need to purchase green certificates.

[0076] Furthermore, in step S3, offshore wind power participating in the traditional electricity spot trading market needs to report the quotation and power curve. After receiving the declaration information, the independent system operator of the electricity market clears in the order of the day-ahead market and the real-time market. This technology constructs a mechanism for offshore wind power to participate in the electricity spot market that considers carbon-green certificate trading. First of all, the quotation cost of offshore wind power for carbon trading and green certificate trading needs to be considered in the quotation of the offshore wind farm. The carbon trading base price and the green certificate trading price need to be added to the quotation of offshore wind power and reported. After reporting, clearing is carried out in the order of the day-ahead market and the real-time market. When the supply and demand of electricity on the power supply side and the user side are equal, the adjusted price, i.e., the clearing price, is obtained. On this basis, the profit model of the day-ahead market and the real-time market is finally obtained.

[0077] Preferably, in step S1, a carbon trading mechanism is introduced, and on this basis, a model for offshore wind power participating in carbon trading is constructed, including:

[0078] Modelling carbon quotas for offshore wind turbines:

[0079]

[0080]

[0081] In the formula, λ P is the carbon emission quota per unit of electricity generation, and the values ​​are 0.728t / (MW·h); P t W is the power generation of the wind turbine; Q CO2 is the carbon emission offset by the uncertainty of offshore wind power generation, a is the carbon emission coefficient of the unit, K W is the wind power reserve factor, P t W Providing power for offshore wind turbines;

[0082] Calculating the carbon trading costs of offshore wind turbines:

[0083]

[0084] In the formula, is the carbon trading cost; ξ is the carbon trading base price; To obtain carbon trading income.

[0085] Preferably, in step S3, constructing a mechanism for offshore wind power to participate in the electricity spot market taking into account carbon-green certificate trading includes:

[0086] Calculate offshore wind power price quote:

[0087] R O =R 1 +ξ+f GR ;

[0088] In the formula, R O Considering the price of offshore wind power after carbon trading and green certificate trading, R 1 is the original quotation, ξ is the carbon trading base price; f GR The transaction cost price of green certificates;

[0089] Constructing the Day-Ahead Return Model:

[0090]

[0091] In the formula, E 1 is the revenue of offshore wind farms in the day-ahead market; R t is the clearing price; P t TB is the bid value of offshore wind power in the day-ahead market;

[0092] Build a real-time revenue model:

[0093]

[0094] In the formula, E 2 is the revenue of offshore wind farms in the real-time market;t W is the actual power generation of offshore wind power; + λ - are the positive and negative penalty coefficients respectively.

[0095] Preferably, in step S4, constructing an operation optimization model for offshore wind power participating in the electricity spot market considering carbon-green certificate trading includes:

[0096] Establish the objective function, include the carbon trading cost into the objective function, and establish an operation optimization model based on the carbon-green certificate trading mechanism; the objective function is:

[0097]

[0098] Among them, F is the total revenue of the system; C OM It is the system operation and maintenance cost. t is the clearing price of offshore wind power in period t; P t W is the output power of the wind turbine, K y is the wind power operation cost maintenance coefficient;

[0099] Set constraints, including green certificate quota constraints, carbon quota constraints and equipment constraints.

[0100] Preferably, green certificate quota constraints include:

[0101]

[0102] Where η is the renewable energy quota coefficient; k g =1 book / (kW*h) is the quantification coefficient, which quantifies the number of renewable energy quotas into the number of green certificates; is the load of the system at time t; is the power generation of the jth wind turbine at time t.

[0103] Preferably, the carbon quota constraints include:

[0104]

[0105] In the formula, It is the maximum carbon emission quota for wind power.

[0106] Preferably, the device constraints include:

[0107] 0≤P W (t)≤P W,rate ;

[0108] Where P W,rate is the rated power of the offshore wind turbine at time t.

[0109] like Figure 2 As shown, preferably, in step S5, solving by using an improved particle swarm algorithm includes:

[0110] a. Determine the size of the initial population, the number of iterations, and the fitness function;

[0111] b. Initialize the position and speed of the population;

[0112] c. Calculate the particle fitness value;

[0113] d. Update individual limits and group limits;

[0114] e. Determine whether the maximum number of iterations has been reached. If so, terminate the operation. If not, use Levy distribution to improve the speed and position, return to step c to calculate the particle fitness value, and repeat the cycle until the maximum number of iterations is reached.

[0115] Among them, the optimization of particle movement speed of particle swarm algorithm using Levy distribution includes:

[0116]

[0117] Where α is the step size parameter, which is 0 to 1; L(β) is the Levy distribution with parameter β, and the specific form is L(β)~μ=t -μ (1<β<3); is the individual extreme position of particle i in generation t; is the individual motion speed of particle i in generation t; is the individual position of particle i in generation t;

[0118] The improved particle speed is:

[0119]

[0120] In the formula, ω new is the inertia weight; c 1 and c 2 is the acceleration coefficient; r 1 and r 2 is a random number uniformly distributed between 0 and 1; is the position of the extreme value of the population in generation t.

[0121] Furthermore, Levy distribution is a probability density distribution function with strong search capabilities. Therefore, improving the particle swarm algorithm based on Levy distribution is conducive to preventing the particle swarm algorithm from falling into a local optimal situation during optimization.

[0122] Preferably, a computer device comprises a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the above-mentioned operation optimization method of offshore wind power participating in the electricity spot market considering carbon-green certificate trading by executing the computer instructions.

[0123] Preferably, a computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the above-mentioned operation optimization method of offshore wind power participating in the electricity spot market considering carbon-green certificate trading.

[0124] Embodiment 2:

[0125] This technology takes an offshore wind farm in a coastal area as an example, collects equipment information, energy price information, local load conditions and other data of the offshore wind farm station, and feeds the data information into the constructed model, and establishes two scenarios to illustrate the effectiveness of the constructed model in this technical content. As shown in Table 1 below, scenario 1 is the operation optimization of offshore wind power participating in the electricity spot market without considering carbon-green certificate trading, and scenario 2 is the operation optimization of offshore wind power participating in the electricity spot market considering carbon-green certificate trading.

[0126] Total revenue (yuan) New energy consumption rate Scenario 1 1683108.44 87.33％ Scenario 2 1786593.21 95.32％

[0127] Table 1: Revenue and new energy consumption rate under two scenarios

[0128] As can be seen from Table 1, in the scenario of considering carbon and green certificate trading, the total revenue is increased by about 6.15% compared with the scenario without considering carbon and green certificate trading, and the new energy consumption rate is increased by 7.99%. Therefore, it is shown that the operation optimization model of offshore wind power participating in the power spot market considering carbon-green certificate trading is more effective in improving economic performance and new energy consumption.

Claims

1. An operation optimization method for offshore wind power participating in the electricity spot market considering carbon-green certificate trading, characterized in that: include: S1, build an offshore wind power carbon trading model: introduce the carbon trading mechanism, and build an offshore wind power carbon trading model on this basis, including: Modelling carbon quotas for offshore wind turbines: ； ； In the formula, The carbon emission quota per unit of electricity generated, is the power generation of the wind turbine; is the carbon emissions offset by the uncertainty of offshore wind power generation, is the carbon emission coefficient of the unit, is the wind power reserve factor, Providing power for offshore wind turbines; Calculating the carbon trading costs of offshore wind turbines: ； In the formula, For carbon trading costs; is the carbon trading base price; when the carbon trading cost is less than 0, it is the carbon trading income; S2, construct an offshore wind power green certificate transaction model, and the green certificate transaction cost model is: ； In the formula, is the transaction cost of green certificates, yuan; , are the number of green certificates purchased and sold by the power system at time t, respectively; is the transaction price of green certificates; S3: Constructing a mechanism for offshore wind power to participate in the electricity spot market that takes into account carbon-green certificate trading, including: Calculate offshore wind power price quote: ； In the formula, Consider the price of offshore wind power after carbon trading and green certificate trading. For the original quotation, It is the base price for carbon trading; The transaction cost price of green certificates; Constructing the Day-Ahead Return Model: ； In the formula, The revenue of offshore wind farms in the day-ahead market; is the clearing price; is the bid value of offshore wind power in the day-ahead market; Build a real-time revenue model: ； In the formula, for the revenue of offshore wind farms in real-time markets; is the actual power generation of offshore wind power; are the positive and negative penalty coefficients respectively; S4, build an operation optimization model for offshore wind power participating in the electricity spot market considering carbon-green certificate trading, including: Establish the objective function, include the carbon trading cost into the objective function, and establish an operation optimization model based on the carbon-green certificate trading mechanism; the objective function is: ； in, is the total revenue of the system; System operation and maintenance costs; is the wind power operation cost maintenance coefficient; Set constraints, including green certificate quota constraints, carbon quota constraints and equipment constraints; S5, obtaining various parameters of the offshore wind farm, including the rated power of the offshore wind power, the output situation of the previous day and the local load situation data; inputting the obtained parameters into the operation optimization model of step S4, solving it through the improved particle swarm algorithm, and obtaining the offshore wind power target power when the economic efficiency is optimal.

2. The operation optimization method for offshore wind power participating in the electricity spot market considering carbon-green certificate trading according to claim 1 is characterized in that: Green certificate quota constraints include: ； In the formula, is the renewable energy quota factor; =1 book / (kW*h) is the quantification coefficient, which quantifies the number of renewable energy quotas into the number of green certificates; is the load of the system at time t; is the power generation of the jth wind turbine at time t.

3. The operation optimization method for offshore wind power participating in the electricity spot market considering carbon-green certificate trading according to claim 1 is characterized in that: Carbon quota constraints include: ； In the formula, It is the maximum carbon emission quota for wind power.

4. The operation optimization method for offshore wind power participating in the electricity spot market considering carbon-green certificate trading according to claim 1 is characterized in that: Device constraints include: ； In the formula, is the rated power of the offshore wind turbine at time t.

5. The operation optimization method for offshore wind power participating in the electricity spot market considering carbon-green certificate trading according to claim 1 is characterized in that: In step S5, solving the problem by using the improved particle swarm algorithm includes optimizing the particle movement speed of the particle swarm algorithm by using the Levy distribution, and the formula is as follows: ； In the formula, is the step size parameter, the value is 0~1; is a Levy distribution with parameter β, and its specific form is ; is the individual extreme position of particle i in generation t; is the individual motion speed of particle i in generation t; is the individual position of particle i in generation t; The improved particle speed is: ； In the formula, is the inertia weight; and is the acceleration factor; and is a random number uniformly distributed between 0 and 1; is the position of the extreme value of the population in generation t.

6. A computer device, characterized in that: It includes a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the operation optimization method of offshore wind power participating in the electricity spot market considering carbon-green certificate trading as described in any one of claims 1 to 5 by executing the computer instructions.

7. A computer-readable storage medium, characterized in that: Computer instructions are stored on a computer-readable storage medium, and the computer instructions are used to enable a computer to execute the operation optimization method for offshore wind power participating in the electricity spot market considering carbon-green certificate trading as described in any one of claims 1 to 5.

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