High-energy-consumption load side resource aggregation pricing calculation method for source-load collaborative optimization clearing

Abstract

The invention relates to the technical field of electric energy management, in particular to a high-energy-consumption load side resource aggregation pricing calculation method for source-load collaborative optimization clearing. Comprising the following steps: proposing a cooperative operation model of renewable energy sources and industrial loads in a county power grid; and proposing an industrial load day-ahead dynamic time-sharing pricing model oriented to renewable energy consumption. The invention provides a renewable energy source and industrial load coordinated operation method, a local green electricity market clearing model is established, and green electricity transaction between renewable energy sources and industrial loads is realized. By constructing a clearing model of a local green electricity market, social welfare maximization can be realized through green electricity transaction between renewable energy sources and industrial loads; the designed dynamic green power pricing method can effectively maintain the balance of supply and demand of green power and guide the industrial load to optimize the green power demand.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy management, and in particular to a resource aggregation pricing calculation method for high energy consumption load side with source-load coordinated optimization and clearing. Background Art

[0002] With the global attention to environmental protection and sustainable development, the development and utilization of renewable energy has become the focus of the energy field. However, renewable energy (such as wind energy, solar energy, etc.) has the characteristics of intermittency, volatility and uncertainty, which brings many challenges to its effective utilization. Under the current energy pattern, the proportion of renewable energy in electricity supply continues to increase. For example, wind power generation depends on natural wind conditions, and the strength of wind power varies unpredictably, resulting in unstable power generation of wind power plants; solar power generation is subject to the duration and intensity of sunshine, and cloudy days, rainy days, and alternation of day and night will cause large fluctuations in power generation. This unstable energy supply has brought difficulties to the stable operation of the power system. How to smooth the output of renewable energy and improve its predictability has become a key issue to be solved urgently. At the same time, industrial load, as an important part of power demand, has great adjustable potential. In the industrial production process, the operating time and power of some equipment are not fixed and there is a certain degree of flexibility. For example, in some non-continuous production industrial processes, the start and stop time of the equipment can be adjusted within a certain range; the operating power of some equipment can also be optimized according to actual needs. However, this flexibility of industrial load has not yet been fully explored and effectively utilized.

[0003] As an effective means of demand-side management, the dynamic electricity price mechanism has broad application prospects in coordinating the operation of different distributed resources. Existing studies have explored dynamic electricity prices in many fields. In the field of electric vehicles, a dynamic pricing model is proposed to guide plug-in electric vehicle users to change charging time through price signals, shifting charging load from peak hours to off-peak hours, thereby alleviating the power supply pressure of the power grid during peak hours and improving the overall operation efficiency of the power grid. In terms of fast charging stations, the corresponding dynamic pricing method helps to coordinate the charging needs of many electric vehicles, avoid the impact of excessive charging peaks on the power grid, and also reduce the operating costs of charging stations and improve the utilization efficiency of charging facilities. In the field of microgrids, dynamic pricing methods for users within microgrid operators can effectively balance the power consumption of end users with the energy supply of microgrid operators, promote the optimal energy configuration within the microgrid, and improve the self-sufficiency and stability of the microgrid.

[0004] Nevertheless, there are still many aspects that need to be studied in depth in the coordinated operation of renewable energy and industrial loads and the dynamic electricity price of the green power market. On the one hand, how to establish a more accurate and effective collaborative operation model, fully consider the uncertainty of renewable energy and the flexibility of industrial loads, and achieve efficient coordination between the two is an important challenge currently faced. On the other hand, the formulation of dynamic electricity prices in the green power market needs to comprehensively consider multiple factors, such as the power generation cost of renewable energy, the adjustment cost of industrial loads, social welfare maximization, and market supply and demand balance. How to build a reasonable pricing model to ensure that price signals can not only guide industrial loads to reasonably adjust their electricity consumption behavior, but also ensure the full absorption of renewable energy and the interests of market participants is a complex and unresolved problem. It is based on this background that the present invention is committed to proposing an effective resource aggregation pricing calculation method for high-energy-consuming load side with source-load coordinated optimization and clearing, so as to fill the research gap in this field and promote the efficient utilization and sustainable development of renewable energy in the industrial field. In view of this, we propose a resource aggregation pricing calculation method for high-energy-consuming load side with source-load coordinated optimization and clearing. Summary of the invention

[0005] The purpose of the present invention is to provide a resource aggregation pricing calculation method for high-energy-consuming load side with source-load collaborative optimization and clearing, so as to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the purpose of the present invention is to provide a resource aggregation pricing calculation method for high energy consumption load side with source-load coordinated optimization and clearing, comprising the following steps:

[0007] S1. Propose a coordinated operation model of renewable energy and industrial loads in the county power grid; specifically including:

[0008] S1.1. First, build a coordinated operation framework for renewable energy and industrial loads;

[0009] S1.2, secondly, construct a wind power plant model, evaluate the cost of the wind power plant, and calculate the optimal green power supply strategy;

[0010] S1.3, then construct an industrial load model that can be adjusted in different time periods, evaluate the adjustment cost, and solve the optimal power demand curve of the industrial load;

[0011] S1.4. Finally, the optimal clearing model of the local green electricity market is constructed: the local green electricity market operator is considered to be a non-profit organization responsible for clearing the green electricity market to maximize social welfare;

[0012] S2. Propose a day-ahead dynamic time-of-use pricing model for industrial loads for renewable energy consumption; specifically, it includes:

[0013] S2.1. First, set the supply and demand balance constraints of the green electricity market;

[0014] S2.2, green power suppliers and green power consumers are coupled through supply and demand balance constraints; Lagrange multipliers are introduced to decouple the coupled supply and demand balance constraints;

[0015] S2.3. Based on the duality theory, calculate the dynamic green electricity price for each period;

[0016] S2.4. Finally, the pricing is calculated through a distributed iterative dynamic pricing algorithm.

[0017] As a further improvement of the present technical solution, in the step S1.1, a coordinated operation framework of renewable energy and industrial loads is constructed, that is, a local green electricity market framework is established, under which wind power plants can sell green electricity to industrial loads; the local green electricity market operator is responsible for the operation of the local green electricity market and maintains the balance of green electricity supply and demand by setting dynamic green electricity prices; when the green electricity of a wind power plant is insufficient for some reason (such as the uncertainty of wind power), the power grid company can purchase the remaining green electricity of the wind power plant and ensure the supply of electricity to the industrial loads.

[0018] As a further improvement of the technical solution, in step S1.2, in the wind power plant model, without loss of generality, the cost function of the wind power plant is evaluated by the following quadratic function:

[0019]

[0020] Wherein, the subscript WT indicates the wind power plant, and i is the serial number of the wind power plant; is the operating cost of the wind farm; are the parameters of the cost function of wind power plant i; is the power of the wind power plant in time period t;

[0021] A wind power plant that supplies 1MWh of green electricity will receive a green certificate, which can be sold on the green certificate market to make a profit; the green certificate income can be expressed as:

[0022]

[0023] In the formula, the subscript GC represents the green certificate. is the green certificate income of wind power plant i; GC is the green certificate price; θ GC is the conversion factor of green certificates; T is the number of time periods;

[0024] Wind power plants will be based on dynamic green electricity prices Adjust and optimize its green power supply solution to maximize its operating profit, which can be expressed as the difference between revenue from selling green electricity and operating costs;

[0025] By solving the following optimization problem, the optimal green power supply strategy can be calculated:

[0026]

[0027] in, The goal is to maximize operating profit. is the operating profit of wind farm i, is the green electricity price of wind power plant i in period t, is the income of wind power plant i from selling green electricity in period t, is the maximum power supply of wind power plant i.

[0028] As a further improvement of the technical solution, in step S1.3, in the industrial load model, load adjustment will generate a certain adjustment cost, which can be evaluated by the following quadratic function:

[0029]

[0030] Wherein, the subscript IL represents the industrial load, and j is the industrial load number; is the load adjustment cost of industrial load j; is the cost factor for load adjustment; They are actual load power and preferred load power respectively;

[0031] Industrial load j based on dynamic green electricity price strategy Optimize its electricity demand curve The operating cost of industrial load j can be expressed as the sum of the electricity purchase cost and the load adjustment cost;

[0032] Optimal power demand curve for industrial load j It can be obtained by solving the following optimization problem:

[0033]

[0034] in, The goal is to minimize the operating cost. is the operating cost of industrial load j, is the electricity purchase cost of industrial load j, δ is the step size parameter, is the maximum load power.

[0035] As a further improvement of the technical solution, in the optimal liquidation model of the local green power market in step S1.4, from the perspective of maximizing social welfare, the liquidation model of the local green power market can be expressed as follows:

[0036]

[0037]

[0038] in, It means that the maximum difference between the total operating profit and the total operating cost is the target, and I and J are the number of wind power plants and industrial loads respectively.

[0039] As a further improvement of the technical solution, in step S2.1, the supply-demand balance constraint is:

[0040] The green electricity market should meet the supply and demand balance constraints in each period, namely:

[0041]

[0042] in, is the total power generation of all wind power plants, is the total load power of all industrial loads.

[0043] As a further improvement of the technical solution, in step S2.2, the green power supplier and the green power consumer are coupled through the supply and demand balance constraint, i.e., equation (10), and the Lagrange multiplier is introduced to decouple equation (10). Then, the Lagrange equations of the original problem equations (8) and (9) can be defined as follows:

[0044]

[0045] Among them, λ t ∈λ is the Lagrange multiplier;

[0046]

[0047] in:

[0048]

[0049] Where M(λ) is a function of the Lagrange multiplier λ, and is the objective function after the Lagrange multiplier is introduced; S i represents the maximum operating profit after the introduction of Lagrange multipliers, B j Represents the minimum operating cost after the introduction of Lagrange multipliers.

[0050] As a further improvement of the technical solution, in step S2.3, the dynamic green electricity price calculation formula is: Based on the duality theory, the dual problem of the original problem can be expressed as follows:

[0051]

[0052] It can be seen that if the green electricity price is set to: g =λ, then model (13) is equivalent to model (3) and (4), and model (14) is equivalent to model (6) and (7); then, the original problem (8) and (9) can be replaced by solving their dual problem (15); since the first part of (12) is decomposed into the sub-problem of the wind power plant, the second part is decomposed into the sub-problem of the industrial load.

[0053] As a further improvement of the technical solution, the dual problem can be solved iteratively by the gradient projection method; the dynamic green electricity price for each period can be calculated as follows:

[0054]

[0055] Where k is the index of the number of iterations; They represent the values ​​of the Lagrange multiplier at the kth and k+1th iterations in time period t respectively; δ is the step size parameter, which is used to control the update step size of the Lagrange multiplier at each iteration; is the function M(λ) with respect to λ t The partial derivative of is the optimal green power supply strategy of wind power plant i, which can be calculated by solving the local optimization model equation (13); is the optimal green power supply strategy for industrial load j, which can be calculated by solving the local optimization model (14); [·] + Represents an orthographic projection operation.

[0056] As a further improvement of the technical solution, in step S2.4, the specific steps of the distributed iterative dynamic pricing algorithm process are as follows:

[0057] S2.4.1. Initialize the maximum number of iterations k max , the convergence accuracy of the algorithm ε=10 -4 , initial Lagrange multiplier λ0;

[0058] S2.4.2. Each wind power plant i, according to the green electricity price λ issued by the local green electricity operator k , solve the local optimization model (13) and obtain the power supply strategy

[0059] S2.4.3, each industrial load j is based on the green electricity price λ issued by the local green power operator k , solve the local optimization model (14) and obtain the power supply strategy

[0060] S2.4.4, the local green power operator updates the dynamic green power prices according to equation (16); and then publishes them to each wind power plant and industrial load;

[0061] S2.4.5 If The algorithm ends; otherwise, return to step S2.4.2.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. In the resource aggregation pricing calculation method for high-energy-consuming load side of the source-load coordinated optimization clearing, a coordinated operation model of renewable energy and industrial load in the county power grid is first proposed, and then a day-ahead dynamic time-of-day pricing model for industrial load for renewable energy consumption is proposed; a coordinated operation method for renewable energy and industrial load is proposed, and a local green power market clearing model is established to realize green power trading between renewable energy and industrial load;

[0064] 2. In the resource aggregation pricing calculation method for high-energy-consuming load side with source-load collaborative optimization and clearing, by constructing a clearing model of the local green electricity market, social welfare can be maximized through green electricity transactions between renewable energy and industrial loads; by designing a dynamic green electricity pricing method for the local green electricity market, this method can effectively maintain the supply and demand balance of green electricity and guide industrial loads to optimize their green electricity demand; a distributed iterative algorithm process is also proposed to clear the local green electricity market, which effectively protects the privacy of each market participant. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is an exemplary method flow chart of the present invention;

[0066] Figure 2 This is a framework diagram of the coordinated operation of exemplary renewable energy and industrial loads in the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Example 1

[0069] like Figure 1-Figure 2 As shown, this embodiment provides a method for calculating resource aggregation pricing of high energy consumption load side for source-load coordinated optimization and clearing, including the following steps:

[0070] S1. Propose a coordinated operation model of renewable energy and industrial loads in the county power grid; specifically including:

[0071] S1.1. First, build a coordinated operation framework for renewable energy and industrial loads;

[0072] In this step, a coordinated operation framework for renewable energy and industrial loads is constructed, that is, a local green electricity market framework is established, under which wind power plants can sell green electricity to industrial loads; the local green electricity market operator is responsible for the operation of the local green electricity market and maintains the balance of green electricity supply and demand by setting dynamic green electricity prices; when the green electricity of a wind power plant is insufficient for some reason (such as the uncertainty of wind power), the power grid company can purchase the remaining green electricity of the wind power plant and ensure the supply of electricity to industrial loads.

[0073] S1.2, secondly, construct a wind power plant model, evaluate the cost of the wind power plant, and calculate the optimal green power supply strategy;

[0074] In this step, in the wind farm model, without loss of generality, the cost function of the wind farm is evaluated by the following quadratic function:

[0075]

[0076] Wherein, the subscript WT indicates the wind power plant, and i is the serial number of the wind power plant; is the operating cost of the wind farm; are the parameters of the cost function of wind power plant i, specifically, Respectively represent the quadratic term coefficient and the linear term coefficient of the cost function; is the power of the wind power plant in time period t; the cost function of this form of formula (1) can reasonably reflect the relationship between the cost and power generation of the wind power plant, and is widely used in research and practice in related fields.

[0077] A wind power plant that supplies 1MWh of green electricity will receive a green certificate, which can be sold on the green certificate market to make a profit; the green certificate income can be expressed as:

[0078]

[0079] In the formula, the subscript GC represents the green certificate. is the green certificate income of wind power plant i; GC is the green certificate price; θ GC is the conversion coefficient of the green certificate; T is the number of time periods; this formula accurately calculates the green certificate income of the wind power plant by multiplying the green certificate price, the conversion coefficient and the generated power and summing them up. The logic is clear and reasonable.

[0080] Wind power plants will be based on dynamic green electricity prices Adjust and optimize its green power supply solution to maximize its operating profit, which can be expressed as the difference between revenue from selling green electricity and operating costs;

[0081] By solving the following optimization problem, the optimal green power supply strategy can be calculated:

[0082]

[0083] in, The goal is to maximize operating profit. is the operating profit of wind farm i, is the green electricity price of wind power plant i in period t, is the income of wind power plant i from selling green electricity in period t, is the maximum power supply of wind power plant i.

[0084] Constraint (4) limits the power generation of the wind power plant to a range of 0 to the maximum power supply. The rationality and feasibility of the calculation are ensured.

[0085] S1.3, then construct an industrial load model that can be adjusted in different time periods, evaluate the adjustment cost, and solve the optimal power demand curve of the industrial load;

[0086] In this step, in the industrial load model, the industrial load has a large adjustable capacity and is considered to be a good adjustable load resource; there is a certain proportion of flexible load in the industrial load, which can be adjusted in different time periods; however, load adjustment will incur a certain adjustment cost, which can be evaluated by the following quadratic function:

[0087]

[0088] Wherein, the subscript IL represents the industrial load, and j is the industrial load number; is the load adjustment cost of industrial load j; is the cost factor for load adjustment; are the actual load power and the preferred load power respectively; this formula can accurately reflect the relationship between load adjustment cost and power change, and conforms to the actual characteristics of industrial load adjustment cost;

[0089] Industrial load j based on dynamic green electricity price strategy Optimize its electricity demand curve The operating cost of industrial load j can be expressed as the sum of the electricity purchase cost and the load adjustment cost;

[0090] Optimal power demand curve for industrial load j It can be obtained by solving the following optimization problem:

[0091]

[0092] in, The goal is to minimize the operating cost. is the operating cost of industrial load j, is the electricity purchase cost of industrial load j, δ is the step size parameter, is the maximum load power.

[0093] The first inequality in constraint condition (7) limits the power adjustment range of industrial loads to (1-δ) to (1+δ) times the maximum load power. The second equation ensures that the total load power before and after adjustment is equal, ensuring the rationality and stability of the industrial load during the optimization process.

[0094] S1.4. Finally, the optimal clearing model of the local green electricity market is constructed: the local green electricity market operator is considered to be a non-profit organization responsible for clearing the green electricity market to maximize social welfare;

[0095] In this step, in the optimal liquidation model of the local green electricity market, from the perspective of maximizing social welfare, the liquidation model of the local green electricity market can be expressed as follows:

[0096]

[0097] in, It means that the maximum difference between the total operating profit and the total operating cost is the target, and I and J are the number of wind power plants and industrial loads respectively.

[0098] Among them, constraint formula (9) combines the limitation of wind power plant power generation (similar to formula (4)), the limitation of industrial load power adjustment range (similar to the first inequality in formula (7)), and the supply and demand balance constraint of the green power market (achieved by the total power generation of wind power plants being equal to the total load power of industrial loads, which is consistent with the following formula (10)), and comprehensively and reasonably describes the clearing model of the local green power market.

[0099] It is worth noting that the clearing model shown in equations (8) and (9) is a concave optimization problem, which can be calculated using a centralized optimization method. However, the centralized optimization method requires complete parameters of each wind farm and industrial load, which may infringe their privacy security. Therefore, the pricing method of step S2 is proposed.

[0100] S2. Propose a day-ahead dynamic time-of-use pricing model for industrial loads for renewable energy consumption; specifically, it includes:

[0101] S2.1. First, set the supply and demand balance constraints of the green electricity market;

[0102] In this step, the supply and demand balance constraint is:

[0103] The green electricity market should meet the supply and demand balance constraints in each period, namely:

[0104]

[0105] in, is the total power generation of all wind power plants, is the total load power of all industrial loads. This formula clarifies the supply and demand balance relationship that the green power market should meet in each period, that is, the total power generation of all wind power plants is equal to the total load power of all industrial loads. This is the basic constraint condition for the stable operation of the green power market. It is concise and in line with practical logic.

[0106] S2.2, green power suppliers and green power consumers are coupled through supply and demand balance constraints; Lagrange multipliers are introduced to decouple the coupled supply and demand balance constraints;

[0107] In this step, the green power supplier and the green power consumer are coupled through the supply and demand balance constraint, i.e., equation (10). The Lagrange multiplier is introduced to decouple equation (10). Then, the Lagrange equations of the original problem equations (8) and (9) can be defined as follows:

[0108]

[0109] Among them, λ t∈λ is the Lagrange multiplier; Formula (11) constructs a new function by multiplying and summing each term in the original objective function with the difference between the Lagrange multiplier and the supply and demand balance constraint, which provides a basis for the subsequent solution based on the duality theory.

[0110]

[0111] in:

[0112]

[0113] Where M(λ) is a function of the Lagrange multiplier λ, and is the objective function after the Lagrange multiplier is introduced; S i represents the maximum operating profit after the introduction of Lagrange multipliers, B j Represents the minimum operating cost after the introduction of Lagrange multipliers.

[0114] Formula (12) further decomposes the Lagrangian equation into two parts, which represent the maximum operating profit S after the introduction of the Lagrangian multiplier. i and minimum operating cost B j This decomposition method helps to transform the original problem into more manageable sub-problems, which is in line with the solution ideas of duality theory and has clear and reasonable logic.

[0115] Formula (13) is based on the constraint of power generation limit by maximizing the Lagrange multiplier λ t , green certificate-related income, and operating costs are used to calculate the maximum operating profit of the wind power plant after the introduction of the Lagrange multiplier, which is consistent with the overall dual theory solution framework and can accurately reflect the optimization objectives of the wind power plant under new conditions.

[0116] Formula (14) is obtained by minimizing the Lagrange multiplier λ under certain constraints. t , electricity purchase cost and load adjustment cost are used to calculate the minimum operating cost of industrial load after the introduction of Lagrange multipliers, which corresponds to the overall dual theory solution framework and can reasonably describe the optimization goal of industrial load under new circumstances.

[0117] S2.3. Based on the duality theory, calculate the dynamic green electricity price for each period;

[0118] In this step, the dynamic green electricity price calculation formula is: Based on the duality theory, the dual problem of the original problem can be expressed as follows:

[0119]

[0120] Based on the duality theory, this formula transforms the original problem into a dual problem by minimizing the Lagrange multiplier λ t>0 provides a new way to solve the dynamic green electricity price, which is in line with the basic principles and solution methods of duality theory.

[0121] It can be seen that if the green electricity price is set to: g =λ, then model formula (13) is equivalent to model formula (3) and formula (4), and model formula (14) is equivalent to model formula (6) and formula (7); then, the original problem formula (8) and formula (9) can be replaced by solving its dual problem formula (15); since the first part of formula (12) is decomposed into the sub-problem of wind power plant, and the second part is decomposed into the sub-problem of industrial load. Therefore, a distributed iterative method is designed in this technical solution to solve this problem.

[0122] Specifically, the dual problem can be solved iteratively by the gradient projection method; the dynamic green electricity price for each period can be calculated as follows:

[0123]

[0124] This formula solves the dual problem in an iterative manner through the gradient projection method to calculate the dynamic green electricity price for each period. Where k is the index of the number of iterations; They represent the values ​​of the Lagrange multiplier at the kth and k+1th iterations in the t period. The Lagrange multiplier in the duality theory plays the role of introducing constraints into the objective function. By iteratively updating its value, it gradually approaches the optimal solution and then determines the dynamic green electricity price. δ is the step size parameter, which is used to control the update step size of the Lagrange multiplier at each iteration. It affects the convergence speed and stability of the algorithm. A suitable δ value can make the algorithm converge to the optimal solution faster. is the function M(λ) with respect to λ t The partial derivative of M(λ) is the objective function after the Lagrange multiplier is introduced. t The partial derivative of can reflect the objective function value as λ t The rate of change of , thereby guiding the update direction of the Lagrange multiplier in the iterative process; is the optimal green power supply strategy of wind power plant i, which can be calculated by solving the local optimization model equation (13); is the optimal green power supply strategy for industrial load j, which can be calculated by solving the local optimization model (14); [·] + represents a positive projection operation, that is, if the value in the bracket is less than 0, it is set to 0; this step ensures that the Lagrange multiplier always remains non-negative, which conforms to its physical meaning and mathematical requirements in the optimization problem, and ensures the correctness and stability of the algorithm. The various parameters and calculation methods in formula (16) are in line with the principle of the gradient projection method, which can effectively update the dynamic green electricity price and gradually approach the optimal solution.

[0125] Formula (16) is based on the gradient projection method, according to the Lagrange multiplier of the current iteration Step size parameter δ, objective function M(λ) vs. λ t The partial derivatives of and the optimal power supply strategy for wind power plants and industrial loads are used to calculate the Lagrange multiplier for the next iteration By continuously iteratively updating the Lagrange multiplier, the objective function gradually approaches the optimal value, and ultimately the dynamic green electricity price for each time period is determined, thereby achieving optimal clearing of the green electricity market, guiding the coordinated operation of renewable energy and industrial loads, and achieving the goals of maximizing social welfare and maintaining a balance between green electricity supply and demand.

[0126] S2.4. Finally, the pricing is calculated through the distributed iterative dynamic pricing algorithm. The specific steps of the distributed iterative dynamic pricing algorithm process are as follows:

[0127] S2.4.1. Initialize the maximum number of iterations k max (It is an upper limit value pre-set by the algorithm to control the termination condition of the iterative process), the convergence accuracy of the algorithm ε = 10 -4 (It is a very small threshold used to measure whether the algorithm has converged to a sufficiently close optimal solution), the initial Lagrange multiplier λ0 (it is the initial guess value given to the Lagrange multiplier at the beginning of the iterative algorithm);

[0128] In the distributed iterative dynamic pricing algorithm, the iteration process will continuously update the dynamic green electricity price and other related parameters. As the number of iterations increases, the algorithm gradually approaches the optimal solution. However, if the number of iterations is too large, it may cause a waste of computing resources or fall into a local optimal solution and cannot jump out. Therefore, setting k max This ensures that the algorithm runs within a reasonable range of computing resource consumption. When the number of iterations reaches k max When , the iteration will be stopped regardless of whether the algorithm has converged to the global optimal solution, to prevent the algorithm from running indefinitely;

[0129] In the iterative process, the dynamic green power prices obtained from two consecutive iterations are compared (specifically, the changes in the Lagrange multiplier λ are compared). If the difference between the two iteration results (in terms of If the price change is very small, within the preset accuracy range, the algorithm is considered to have obtained a sufficiently good approximate optimal solution, and the iteration is stopped at this time. This method can avoid unnecessary iterative calculations while ensuring the quality of the solution, thus improving the efficiency of the algorithm.

[0130] Lagrange multipliers are used to decouple the supply and demand balance constraints in the entire algorithm, and their values ​​are continuously updated through iteration to find the optimal solution. The selection of the initial value λ0 will have a certain impact on the convergence speed and final result of the algorithm. A reasonable initial value can make the algorithm converge to the optimal solution faster. In this algorithm, although the document does not clearly give the specific value of λ0, in general, it can be determined based on the characteristics of the problem, experience, or some heuristic methods. For example, an initial value close to the expected optimal solution can be selected based on historical data or a preliminary analysis of the problem so that the algorithm can converge faster.

[0131] S2.4.2. Each wind power plant i, according to the green electricity price λ issued by the local green electricity operator k , solve the local optimization model (13) and obtain the power supply strategy

[0132] S2.4.3, each industrial load j is based on the green electricity price λ issued by the local green power operator k , solve the local optimization model (14) and obtain the power supply strategy

[0133] S2.4.4, the local green power operator updates the dynamic green power prices according to equation (16); and then publishes them to each wind power plant and industrial load;

[0134] S2.4.5 If The algorithm ends; otherwise, return to step S2.4.2.

[0135] in, To determine the accuracy of the algorithm iteration formula, according to the algorithm iteration logic, the basis for determining whether the iteration is terminated should be whether the change in the Lagrange multiplier (used to calculate the dynamic green electricity price) between two adjacent iterations is small enough, that is, the Lagrange multiplier λ of this iteration k+1 and the Lagrange multiplier λ of the previous iteration k Is the difference between them less than the convergence accuracy ε?

[0136] When using hour, The square of the Euclidean norm is calculated, which comprehensively considers the changes of the Lagrange multiplier in each dimension (if the Lagrange multiplier is in vector form, each component represents a different dimension), and can comprehensively measure the difference between two iterations;

[0137] When this difference is less than the set convergence accuracy ε, it means that the algorithm has converged to a stable solution. At this time, relevant parameters such as the dynamic green electricity price are close enough to the optimal value. Continuing iteration will have very limited improvement on the result, so the algorithm can be terminated and the final pricing result can be output. This ensures that the algorithm obtains a solution that meets the accuracy requirements with reasonable computing resource consumption, and achieves effective optimization of the collaborative operation model of renewable energy and industrial loads.

[0138] This technical solution aims to solve the problem of how to achieve coordinated operation of renewable energy and industrial loads, and proposes a day-ahead dynamic time-of-use pricing model for high-energy-consuming industry enterprises with coordinated optimization of source and load. It has the following features:

[0139] Beneficial effects:

[0140] 1) Maximizing social welfare

[0141] The local green electricity market clearing model proposed in this plan fully considers the costs and benefits of all parties by accurately constructing a green electricity trading mechanism between renewable energy (wind power plants) and industrial loads. In this model, factors such as the cost function of wind power plants, green certificate income, and adjustment costs of industrial loads are all taken into consideration, thereby achieving optimal allocation of resources.

[0142] From the perspective of mathematical models, by optimizing the objective function (such as the maximization of the difference between the total operating profit of all wind power plants and the total operating cost of all industrial loads in formula (8)), and combining a series of reasonable constraints (such as the constraints on the power generation of wind power plants, the power adjustment range of industrial loads, and the balance of supply and demand in the green power market in formula (9)), it is ensured that the total social welfare can be maximized during the market transaction process; this means that while meeting the basic operating requirements of all parties, the comprehensive benefits obtained by the entire society from the coordinated operation of renewable energy and industrial loads are optimized;

[0143] 2) Maintain the balance between green power supply and demand and optimize industrial load power consumption behavior

[0144] The dynamic green electricity pricing method proposed in this scheme is designed closely around the principle of supply and demand balance. By introducing Lagrange multipliers to decouple the supply and demand balance constraints (as shown in equations (10)-(12)), and calculating the dynamic green electricity price based on the duality theory (as shown in equations (15)-(16)), the green electricity price can be adjusted in real time according to the market supply and demand situation.

[0145] For industrial loads, this dynamic pricing mechanism provides them with a clear price signal. Based on the dynamic green electricity price strategy, industrial loads optimize their electricity demand curves (as shown in equations (6)-(7)). In the process of minimizing their own operating costs, they actively adjust their electricity consumption behaviors and reasonably distribute electricity demand in different time periods, thereby achieving effective matching with renewable energy supply, ensuring that the green electricity market can maintain a balance between supply and demand in each time period, and improving the stability and reliability of the power system.

[0146] 3) Protect the privacy of market participants

[0147] The innovative distributed iterative algorithm process is a highlight of the present invention; in this algorithm, each wind power plant and industrial load only needs to perform local optimization calculations based on local information (such as its own cost function, power generation limit or load power demand, etc.) and public information (such as green electricity price) released by local green power operators (as shown in equations (13)-(14));

[0148] Wind power plants solve local optimization models to obtain the optimal power supply strategy, and industrial loads solve corresponding models to obtain the optimal power consumption strategy, and then feed these strategies back to operators, who then update and publish electricity prices based on the aggregated information. During the entire process, each participant does not need to disclose all of its internal parameters to other parties, and can achieve market clearing by only exchanging limited key information (such as power supply or power consumption strategies, electricity prices, etc.). This approach effectively avoids the leakage of sensitive information, protects the privacy and security of each market participant, and enhances the enthusiasm and trust of market participants in participating in collaborative operations.

[0149] Those skilled in the art will appreciate that the process of implementing all or part of the steps of the above embodiments may be completed by hardware, or may be completed by instructing related hardware through a program.

[0150] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A resource aggregation pricing calculation method for high energy consumption load side with source-load coordinated optimization and clearing, characterized in that: The steps include: S1. Propose a coordinated operation model of renewable energy and industrial loads in the county power grid; specifically including: S1.

1. First, build a coordinated operation framework for renewable energy and industrial loads; S1.2, secondly, construct a wind power plant model, evaluate the cost of the wind power plant, and calculate the optimal green power supply strategy; S1.3, then construct an industrial load model that can be adjusted in different time periods, evaluate the adjustment cost, and solve the optimal power demand curve of the industrial load; S1.

4. Finally, the optimal clearing model of the local green power market is constructed: the local green power market operator is responsible for clearing the green power market to maximize social welfare; S2. Propose a day-ahead dynamic time-of-use pricing model for industrial loads for renewable energy consumption; specifically, it includes: S2.

1. First, set the supply and demand balance constraints of the green electricity market; S2.2, green power suppliers and green power consumers are coupled through supply and demand balance constraints; Lagrange multipliers are introduced to decouple the coupled supply and demand balance constraints; S2.

3. Based on the duality theory, calculate the dynamic green electricity price for each period; S2.

4. Finally, pricing calculation is performed through a distributed iterative dynamic pricing algorithm.

2. The resource aggregation pricing calculation method for high energy consumption load side of source-load collaborative optimization clearing according to claim 1 is characterized by: In the step S1.1, a coordinated operation framework of renewable energy and industrial loads is constructed, i.e., a local green electricity market framework is established, under which wind power plants can sell green electricity to industrial loads; the local green electricity market operator is responsible for the operation of the local green electricity market and maintains the balance of green electricity supply and demand by setting dynamic green electricity prices; when the green electricity of a wind power plant is insufficient for some reason, the power grid company can purchase the remaining green electricity of the wind power plant and ensure the supply of electricity to the industrial loads.

3. The resource aggregation pricing calculation method for high energy consumption load side of source-load collaborative optimization clearing according to claim 2 is characterized by: In step S1.2, in the wind power plant model, without loss of generality, the cost function of the wind power plant is evaluated by the following quadratic function: Wherein, the subscript WT indicates the wind power plant, and i is the serial number of the wind power plant; is the operating cost of the wind farm; are the parameters of the cost function of wind power plant i; is the power of the wind power plant in time period t; A wind power plant that supplies 1MWh of green electricity will receive a green certificate, which can be sold on the green certificate market to make a profit; the green certificate income can be expressed as: In the formula, the subscript GC represents the green certificate. is the green certificate income of wind power plant i; GC is the green certificate price; θ GC is the conversion factor of green certificates; T is the number of time periods; Wind power plants will be based on dynamic green electricity prices Adjust and optimize its green power supply solution to maximize its operating profit, which can be expressed as the difference between revenue from selling green electricity and operating costs; By solving the following optimization problem, the optimal green power supply strategy can be calculated: Among them, max: The goal is to maximize operating profit. is the operating profit of wind farm i, is the green electricity price of wind power plant i in period t, is the income of wind power plant i from selling green electricity in period t, is the maximum power supply of wind power plant i.

4. The resource aggregation pricing calculation method for high energy consumption load side of source-load collaborative optimization clearing according to claim 3 is characterized by: In step S1.3, in the industrial load model, load adjustment will incur a certain adjustment cost, which can be evaluated by the following quadratic function: Wherein, the subscript IL represents the industrial load, and j is the industrial load number; is the load adjustment cost of industrial load j; is the cost factor for load adjustment; They are actual load power and preferred load power respectively; Industrial load j based on dynamic green electricity price strategy Optimize its electricity demand curve The operating cost of industrial load j can be expressed as the sum of the electricity purchase cost and the load adjustment cost; Optimal power demand curve for industrial load j It can be obtained by solving the following optimization problem: in, The goal is to minimize the operating cost. is the operating cost of industrial load j, is the electricity purchase cost of industrial load j, δ is the step size parameter, is the maximum load power.

5. The resource aggregation pricing calculation method for high energy consumption load side of source-load coordinated optimization clearing according to claim 4 is characterized by: In step S1.4, in the optimal liquidation model of the local green power market, from the perspective of maximizing social welfare, the liquidation model of the local green power market can be expressed as follows: in, It means that the maximum difference between the total operating profit and the total operating cost is the target, and I and J are the number of wind power plants and industrial loads respectively.

6. The resource aggregation pricing calculation method for high energy consumption load side in source-load coordinated optimization clearing according to claim 5 is characterized by: In step S2.1, the supply-demand balance constraint is: The green electricity market should meet the supply and demand balance constraints in each period, namely: in, is the total power generation of all wind power plants, is the total load power of all industrial loads.

7. The resource aggregation pricing calculation method for high energy consumption load side in source-load coordinated optimization clearing according to claim 6 is characterized by: In step S2.2, the green power supplier and the green power consumer are coupled through the supply and demand balance constraint, i.e., equation (10). The Lagrange multiplier is introduced to decouple equation (10). Then, the Lagrange equations of the original problem equations (8) and (9) can be defined as follows: Among them, λ t ∈λ is the Lagrange multiplier; in: Where M(λ) is a function of the Lagrange multiplier λ, and is the objective function after the Lagrange multiplier is introduced; S i represents the maximum operating profit after the introduction of Lagrange multipliers, B j Represents the minimum operating cost after the introduction of Lagrange multipliers.

8. The resource aggregation pricing calculation method for high energy consumption load side in source-load coordinated optimization clearing according to claim 7 is characterized by: In step S2.3, the dynamic green electricity price calculation formula is: Based on the duality theory, the dual problem of the original problem can be expressed as follows: It can be seen that if the green electricity price is set to: g =λ, then model (13) is equivalent to model (3) and (4), and model (14) is equivalent to model (6) and (7); then, the original problem (8) and (9) can be replaced by solving their dual problem (15); since the first part of (12) is decomposed into the sub-problem of the wind power plant, the second part is decomposed into the sub-problem of the industrial load.

9. The resource aggregation pricing calculation method for high energy consumption load side in source-load coordinated optimization clearing according to claim 8 is characterized by: The dual problem can be solved iteratively by the gradient projection method; the dynamic green electricity price for each period can be calculated as follows: Where k is the index of the number of iterations; denote the values ​​of the Lagrange multiplier at the kth and k+1th iterations in time period t respectively; δ is the step size parameter; is the function M(λ) with respect to λ t The partial derivative of is the optimal green power supply strategy of wind power plant i, which can be calculated by solving the local optimization model equation (13); is the optimal green power supply strategy for industrial load j, which can be calculated by solving the local optimization model (14); + Represents an orthographic projection operation.

10. The resource aggregation pricing calculation method for high energy consumption load side in source-load coordinated optimization clearing according to claim 9 is characterized by: In step S2.4, the specific steps of the distributed iterative dynamic pricing algorithm process are as follows: S2.4.

1. Initialize the maximum number of iterations k max , the convergence accuracy of the algorithm ε=10 -4 , initial Lagrange multiplier λ0; S2.4.

2. Each wind power plant i, according to the green electricity price λ issued by the local green electricity operator k , solve the local optimization model (13) and obtain the power supply strategy S2.4.3, each industrial load j is based on the green electricity price λ issued by the local green power operator k , solve the local optimization model (14) and obtain the power supply strategy S2.4.4, the local green power operator updates the dynamic green power prices according to equation (16); and then publishes them to each wind power plant and industrial load; S2.4.5 If The algorithm ends; otherwise, return to step S2.4.2.

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