Regional real-time electricity price evaluation method and device and computer readable medium

Abstract

The embodiment of the application provides a regional real-time electricity price evaluation method and device and a computer readable medium, and relates to the technical field of power dispatch. The method comprises the following steps: constructing a real-time power generation cost function and a real-time power generation amount function of a power generation unit based on different power generation categories; obtaining historical power consumption load data, generating a power consumption demand characteristic curve based on the historical power consumption load data, and determining a power supply change amount at a target moment; constructing a target function according to the real-time power generation cost function, the real-time power generation amount function and the power supply change amount, taking the power consumption demand amount at the target moment as a constraint condition, and taking the maximum power generation profit at the target moment as a target; and obtaining a power generation change amount of each power generation unit by solving the target function. The application constructs a target function based on marginal cost caused by the change amount of power consumption demand in the current region for electricity price evaluation, and can realize power grid peak shaving with the maximum power generation profit as the target.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, specifically to a regional real-time electricity price assessment method, a regional real-time electricity price assessment device, and a computer-readable medium. Background Technology

[0002] Currently, in my country's electricity spot market, the next-day generation plan curve of power plants is usually determined by the market trading center based on the day-ahead spot market quotations of all power plants. However, existing electricity price forecasting and evaluation models often do not take into account the generation costs of various types of power plants, especially the dynamic cost changes of various generation technologies, i.e., the cost changes caused by load changes in the process of meeting grid peak shaving requirements. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, and computer-readable medium for evaluating regional real-time electricity prices, in order to solve the problem that the existing technology does not consider the cost changes caused by peak load when determining the next day's power generation plan, resulting in suboptimal power generation costs.

[0004] To achieve the above objectives, in a first aspect of the present invention, a method for assessing regional real-time electricity prices is provided, comprising:

[0005] Construct real-time generation cost function and real-time generation function for generator sets based on different generation categories;

[0006] Historical electricity load data is obtained, and an electricity demand characteristic curve is generated based on the historical electricity load data. The electricity demand characteristic curve is used to predict the electricity demand at each unit time.

[0007] The power supply change at the target time is determined based on the power demand characteristic curve, whereby the power supply change is the difference between the power demand at the target time and the power demand at the current time.

[0008] Based on the real-time power generation cost function, the real-time power generation function, and the power supply change, and with the constraint of meeting the electricity demand at the target time, an objective function is constructed with the goal of maximizing the power generation profit at the target time.

[0009] Find the optimal solution for the objective function to obtain the change in power generation of each generator set when the electricity demand at the target time is met and the power generation profit at the target time is maximized.

[0010] Optionally, a real-time generation cost function based on different generation types of generator sets is constructed, including:

[0011] Real-time power generation cost functions for different types of generator sets are constructed by summing the fixed and variable costs of generator sets of different power generation categories.

[0012] Optionally, a real-time power generation function based on different power generation categories of generator sets is constructed, including:

[0013] For each type of generator set, a real-time power generation quantum function is constructed based on the sum of the long-term power generation and the variable power generation of the generator set at the current moment; wherein, the long-term power generation is the average value of the predetermined total long-term power generation of the generator set in this type of generator set at each moment within a predetermined time period.

[0014] The real-time power generation function of generator sets of different power generation categories is constructed by summing the real-time power generation quantum functions of generator sets of all power generation categories.

[0015] Optionally, based on the real-time power generation cost function, the real-time power generation function, and the power supply change, and with the constraint of meeting the electricity demand at the target time, an objective function is constructed with the objective of maximizing power generation profit at the target time, including:

[0016] Using the power supply change as the dependent variable and the power supply change of generator sets of different power generation categories as the independent variable, a real-time power supply change function is constructed in which the power supply change is the sum of the power supply change of generator sets of different power generation categories; wherein, the power supply change is the difference between the change in power generation of the generator set at the target time and the change in power generation at the current time.

[0017] Based on the real-time power generation cost function, the real-time power generation quantity function, the real-time power supply change quantity function, and the predetermined flexibility cost curve, an objective function is constructed with the constraint of meeting the electricity demand at the target time and the objective of maximizing the power generation profit at the target time; wherein, the flexibility cost curve includes at least the flexibility costs corresponding to different power supply changes of generator sets of different power generation categories.

[0018] Optionally, based on the real-time power generation cost function, the real-time power generation quantity function, the real-time available power supply change function, and the predetermined flexibility cost curve, and with the constraint of meeting the electricity demand at the target time, and with the objective of maximizing the power generation profit at the target time, an objective function is constructed, including:

[0019] Based on the real-time power generation cost function, the real-time power generation quantity function, the real-time available power supply change quantity function, and the predetermined flexibility cost curve, the unit power generation cost function at the current time and the unit power generation cost function at the target time are constructed respectively.

[0020] Based on the unit power generation cost function at the current time and the unit power generation cost function at the target time, and by calculating the difference between the unit power generation cost at the target time and the unit power generation cost at the current time, a real-time power generation cost change function is constructed to characterize the change in unit power generation cost at the target time.

[0021] Based on the real-time power generation function, and with the real-time power generation change as the dependent variable, a real-time power generation change function is constructed to characterize the difference between the change in power generation of all generator units at the target time and the change in power generation of all generator units at the current time.

[0022] A dynamic cost function for power generation is constructed by multiplying the real-time power generation cost change function and the real-time power generation change function, which represents the difference between the power generation profit at the target time and the power generation profit at the current time.

[0023] Based on the aforementioned dynamic cost function for power generation, and with the constraint of meeting the electricity demand at the target time, an objective function is constructed with the goal of maximizing the profit from power generation at the target time.

[0024] Optionally, a unit power generation cost function for the current moment is constructed based on the real-time power generation cost function, the real-time power supply change function, and a predetermined flexibility cost curve, including:

[0025] Based on the real-time power supply change function and the flexibility cost curve, the flexibility cost corresponding to the current power supply change of the generator set is taken as the variable cost of the current generator set. Based on the real-time power generation cost function and the real-time power generation function, the unit power generation cost function at the current moment is constructed based on the ratio of the real-time power generation cost of all generator sets to the long-term contract power generation of all generator sets at the current moment.

[0026] Optionally, a unit power generation cost function for the target time is constructed based on the real-time power generation cost function, the real-time power supply change function, and a predetermined flexibility cost curve, including:

[0027] Based on the real-time power supply change function and the flexibility cost curve, the flexibility cost corresponding to the current power supply change of the generator set is used as the variable cost of the current generator set. Based on the real-time power generation cost function and the real-time power generation function, a unit power generation cost function for the target time is constructed with the real-time power generation cost of all generator sets at the target time as the numerator and the sum of the long-term power generation and real-time power generation change of all generator sets as the denominator.

[0028] Optionally, the optimal solution to the objective function is obtained to determine the change in power generation of each generator unit when the electricity demand at the target time is met and the power generation profit at the target time is maximized, including:

[0029] Find the optimal solution for the objective function, and take the change in power generation of each generator set when the electricity demand at the target time is met and the power generation profit at the target time is maximized as the change in power generation of the corresponding generator set.

[0030] In a second aspect of the invention, a regional real-time electricity price assessment device is provided, comprising:

[0031] The data prediction module is configured to acquire historical electricity load data and generate an electricity demand characteristic curve based on the historical electricity load data. The electricity demand characteristic curve is used to predict the electricity demand at each unit of time.

[0032] The calculation module is configured as follows:

[0033] Construct real-time generation cost function and real-time generation function for generator sets based on different generation categories;

[0034] The power supply change at the target time is determined based on the power demand characteristic curve, whereby the power supply change is the difference between the power demand at the target time and the power demand at the current time.

[0035] Based on the real-time power generation cost function, the real-time power generation function, and the power supply change, with the constraint of meeting the electricity demand at the target time and the objective of maximizing the power generation profit at the target time, an objective function is constructed.

[0036] Find the optimal solution for the objective function to obtain the change in power generation of each generator set when the electricity demand at the target time is met and the power generation profit at the target time is maximized.

[0037] In a third aspect of the invention, a computer-readable medium is provided, the computer-readable medium storing a computer program that, when processed and executed, implements the above-described regional real-time electricity price assessment method.

[0038] This invention predicts the current electricity demand in a region, considers the marginal cost caused by changes in current electricity demand, uses the target electricity demand as a constraint, and constructs an objective function to evaluate regional electricity prices with the goal of maximizing power generation profit at the target time. By finding the optimal solution of the objective function, the power generation change of each generator unit when the power generation profit is maximized is obtained, thereby achieving grid peak regulation with the goal of maximizing power generation profit.

[0039] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 This is a flowchart of a regional real-time electricity price assessment method provided by a preferred embodiment of the present invention;

[0042] Figure 2 This is a real-time electricity price assessment logic diagram provided by a preferred embodiment of the present invention;

[0043] Figure 3 This is a schematic block diagram of a regional real-time electricity price assessment device provided by a preferred embodiment of the present invention. Detailed Implementation

[0044] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0045] like Figure 1 As shown, in a first aspect of this embodiment, a method for evaluating real-time regional electricity prices is provided, comprising: constructing a real-time generation cost function and a real-time generation function based on generator sets of different generation types; acquiring historical electricity load data, generating an electricity demand characteristic curve based on the historical electricity load data, the electricity demand characteristic curve being used to predict the electricity demand at each unit time; determining the power supply change at a target time based on the electricity demand characteristic curve, the power supply change being the difference between the electricity demand at the target time and the electricity demand at the current time; constructing an objective function based on the real-time generation cost function, the real-time generation function, and the power supply change, with the requirement of satisfying the electricity demand at the target time as a constraint, and with the goal of maximizing the generation profit at the target time; finding the optimal solution for the objective function to obtain the generation change of each generator set when the electricity demand at the target time is satisfied and the generation profit at the target time is maximized.

[0046] Thus, this invention predicts the current electricity demand in the region, considers the marginal cost caused by the change in current electricity demand, takes the electricity demand at the target time as a constraint, constructs an objective function to evaluate regional electricity prices with the goal of maximizing power generation profit at the target time, and obtains the change in power generation of each generator unit when the power generation profit is maximized by finding the optimal solution of the objective function, thereby achieving grid peak regulation with the goal of maximizing power generation profit.

[0047] Existing electricity spot market price forecasting and evaluation models typically do not consider the generation costs of various types of power plants, particularly the dynamic cost changes of different generation technologies. This means they fail to account for the cost changes caused by load variations during grid peak shaving, hindering power plants from optimizing their pricing and maximizing profits while meeting grid peak shaving requirements. This proposed method addresses this issue by establishing a database of all power plant equipment within a target area operating under the same grid rules. It includes typical power plant cost sub-models and electricity demand forecasting models. The method uses the real-time dynamic costs of various power plants within the area and their long-term power sales agreements as the pricing basis. It predicts real-time generation targets based on historical grid demand. Based on the dynamic marginal cost of generation for each power plant at that moment, it forecasts the system-wide marginal cost at 3-15 minute intervals to meet future electricity demand. Then, by combining the predicted marginal cost distribution with the long-term contract prices of each power plant, it forecasts the operating profits of each power plant, thereby enabling the evaluation of spot electricity prices for various power plants.

[0048] In this embodiment, a real-time power generation cost function is constructed based on generator sets of different power generation categories. This includes: constructing a real-time power generation cost function for different categories of generator sets by summing the fixed costs and variable costs of generator sets of different power generation categories. It can be understood that the real-time fixed cost of power generation is the amortized value of the fixed costs of the power plant's generator sets, including equipment depreciation, financial expenses, etc.; the real-time variable cost of power generation includes real-time load, fuel costs, maintenance and repair costs incurred during operation, material costs, etc., and the real-time variable cost of power generation can be a predetermined value for the power plant. Taking power generation types including thermal power, hydropower, wind power, and photovoltaic power generation as an example, the real-time power generation load of each power plant is introduced, or historical load data is used to evaluate a historical period, calculating the power generation cost of each type of power plant at the current time t. It can be understood that time t in this embodiment can represent a time period, for example, each time period is 15 minutes, then time t represents a time period of 15 minutes. The real-time power generation cost function is as follows:

[0049] C(t)=C 固 (t)+C 变 (t);

[0050]

[0051]

[0052]

[0053]

[0054] Where C(t) is the real-time power generation cost, C固 (t) represents the real-time fixed cost of power generation, C 变 (t) represents the real-time variable cost of power generation, C H (t) represents the real-time power generation cost of thermal power, C S (t) represents the real-time generation cost of hydropower, C F (t) represents the real-time generation cost of wind power, C G C(t) represents the real-time cost of photovoltaic power generation, m, n, p, q represent the number of thermal power, hydropower, wind power and photovoltaic power generation units in the region, respectively, and C(t) represents the real-time cost of all power generation units in the region at the current time t.

[0055] This implementation method uses historical regional electricity demand data as a basis to predict future electricity demand based on acquired historical electricity load data. For example, using the historical annual average daily electricity load curve as a benchmark, a machine learning algorithm model is trained using historical electricity load data from the past month as a training set. The trained model then adjusts the benchmark curve to predict regional electricity demand over a future period. Predicting electricity demand using machine learning algorithms is an existing technology; for example, open-source algorithms such as Bayesian algorithms can be used. The benchmark curve is adjusted based on factors such as temperature and economic level. The specific training and prediction process of the machine learning algorithm is not detailed here. If the predicted electricity demand at time t is Q(t), then the change in power supply between the target time and the current time can be determined based on the adjusted benchmark curve. Where Q(t) = E(t) + S(t), Q(t) is the electricity demand, i.e. the required real-time power generation, E(t) is the long-term contract power generation, and S(t) is the variable power generation. It can be understood that the long-term contract power generation E(t) refers to the average value of the total power generation determined in the long-term power sales agreement at time t, and the variable power generation S(t) is the average value of the variable power generation at time t.

[0056] In this embodiment, the real-time power generation function of generator sets based on different power generation categories is constructed, including: for each power generation category of generator set: constructing a real-time power generation quantum function of the generator set of the current power generation category by summing the long-term power generation and the variable power generation of the generator set at the current moment; constructing a real-time power generation function of generator sets of different power generation categories by summing the real-time power generation quantum functions of generator sets of all power generation categories; the long-term power generation is the average value of the predetermined total long-term power generation of the generator set of that category at each moment within a predetermined time period.

[0057] The real-time power generation of each power generation system is obtained by averaging the long-term contract power generation and the variable power generation. Therefore, the real-time power generation quantum function of generator units of different power generation categories is:

[0058] Q H (t)=EH (t)+S H (t);

[0059] Q S (t)=E S (t)+S S (t);

[0060] Q F (t)=E F (t)+S F (t);

[0061] Q G (t)=E G (t)+S G (t);

[0062] The real-time power generation functions for different types of generator sets are as follows:

[0063] Q T (t)=Q H (t)+Q S (t)+Q F (t)+Q G (t)≥Q(t);

[0064] Among them, Q H (t) represents the real-time available power supply from thermal power plants, Q S (t) represents the real-time available power supply from hydropower, Q F (t) represents the real-time available power supply from wind power, Q G (t) represents the real-time available power of photovoltaic power, Q T (t) represents the total real-time electricity demand of the region, Q T The maximum value of Q(t) is determined by the grid capacity. To ensure that power generation meets demand, therefore, Q T (t) must be greater than or equal to Q(t).

[0065] This implementation method optimizes power generation for grid peak shaving by considering cost changes caused by changes in electricity demand, thereby improving the economic efficiency of power plants. Based on the real-time generation cost function, real-time generation function, and power supply variation, and constrained by meeting the target electricity demand, an objective function is constructed to maximize generation profit at the target time. This includes: constructing a real-time power supply variation function with power supply variation as the dependent variable and the power supply variation of different types of generator sets as the independent variable; the power supply variation is the difference between the variable generation of the current generator set at the target time and the variable generation at the current time; and constructing an objective function based on the real-time generation cost function, real-time generation function, real-time power supply variation function, and a predetermined flexibility cost curve, constrained by meeting the target electricity demand, with the goal of maximizing generation profit at the target time. The flexibility cost curve at least includes the flexibility costs corresponding to different power supply variations of different types of generator sets.

[0066] During power plant generation, the increased cost per additional kWh of electricity generated by a generator unit dynamically changes with the cost per kilowatt-hour. Furthermore, the increased cost varies depending on the rate of change in power generation of different generator units. Therefore, this implementation method, in order to optimize the peak-shaving cost of the power plant, constructs a function for the change in available power supply based on the change in real-time available power supply between the target time and the current time: L T =L H (t)+L S (t)+L F (t)+L G (t), where L H (t) represents the real-time change in the available power supply from thermal power plants, L S (t) represents the real-time change in the available power supply from hydropower, L F (t) represents the real-time change in the available power supply from wind power, L G (t) represents the real-time change in the amount of electricity that can be supplied by the photovoltaic system, L T (t) represents the total real-time electricity demand in the region, i.e., the change in power supply. L T (t) actually represents the rate of change of power generation load, i.e., L T (t)=(Q(t+1)-Q(t)) / 。The rate of load change is directly related to flexibility costs. For different generator sets, L T The level of (t) is related to the initial investment of the unit; the higher the unit performance, the higher L. TThe higher the value of (t), the more stable the generator set cost becomes; conversely, the faster the load changes, the higher the cost. The relationship between the change in available power supply and flexibility costs for different generator sets can be pre-calibrated by the power plant through testing, generating a flexibility cost curve. Therefore, based on the rate of change in electricity demand at time t+1 compared to time t, this method can calculate the generation flexibility costs required by various power plants to meet system power changes during that time period. Generation flexibility costs are included in the generation variable costs.

[0067] In this embodiment, based on the real-time generation cost function, real-time generation quantity function, real-time available power supply change function, and a predetermined flexibility cost curve, and constrained by meeting the electricity demand at the target time, an objective function is constructed with the goal of maximizing generation profit at the target time. This includes: constructing a unit generation cost function for the current time and a unit generation cost function for the target time based on the real-time generation cost function, real-time generation quantity function, real-time available power supply change function, and predetermined flexibility cost curve; and based on the unit generation cost function for the current time and the unit generation cost function for the target time, and considering the unit generation cost at the target time and the unit generation cost at the current time... A real-time power generation cost change function is constructed by subtracting the electricity cost to represent the change in unit power generation cost at the target time. Based on the real-time power generation function, and with the real-time power generation change as the dependent variable, a real-time power generation change function is constructed to represent the difference between the change in power generation of all generating units at the target time and the change in power generation of all generating units at the current time. Based on the product of the real-time power generation cost change function and the real-time power generation change function, a dynamic power generation cost function is constructed to represent the difference between the power generation profit at the target time and the power generation profit at the current time. Based on the dynamic power generation cost function, and with the constraint of meeting the electricity demand at the target time, an objective function is constructed with the goal of maximizing the power generation profit at the target time.

[0068] The process of constructing the unit power generation cost function at the current moment includes: based on the real-time power supply change function and the flexibility cost curve, using the flexibility cost corresponding to the current power supply change of the generator set as the variable cost of the current generator set; and based on the real-time power generation cost function and the real-time power generation function, constructing the unit power generation cost function at the current moment based on the ratio of the real-time power generation cost of all generator sets to the long-term contract power generation of all generator sets.

[0069] Constructing the unit power generation cost function at the target time includes: based on the real-time power supply change function and flexibility cost curve, using the flexibility cost corresponding to the current power supply change of the generator set as the variable cost of the current generator set; based on the real-time power generation cost function and real-time power generation function, constructing the unit power generation cost function at the target time with the real-time power generation cost of all generator sets at the target time as the numerator and the sum of the long-term contract power generation and real-time power generation change of all generator sets as the denominator.

[0070] The unit power generation cost function at the current moment is:

[0071] The unit power generation cost function at the target time is:

[0072] The function for the change in real-time power generation cost is: ΔC T (t+1)=C 实 (t+1)-C Base (t);

[0073] The real-time power generation change function is: ΔS T (t+1)=S T (t+1)-S T (t);

[0074] in,

[0075]

[0076]

[0077] The dynamic cost function for power generation is: ΔF T = T (t+)- T (t)=ΔC T (+1)×ΔS T Let (t+1) represent the change in power generation cost between time t+1 and time t. Since the fixed cost of power generation is constant, it actually reflects the change in flexibility cost among the variable costs between time t+1 and time t, that is, the change in flexibility cost caused by the change in power generation. Therefore, the objective function is: F H (t) represents the real-time profit of thermal power, F S (t) represents the real-time profit of hydropower, F F (t) represents the real-time profit of wind power, F G (t) represents the real-time profit of photovoltaic power, F T( ) represents the total real-time power generation profit of the region, where i represents the number of generating units. The real-time profit of each generating unit is obtained from the pre-determined long-term contract profit allocation and profit change in the long-term power sales agreements of each power plant. For example, the real-time profit of each generating unit is obtained by subtracting the change in power generation cost of the current generating unit at time t from the allocated value of the long-term contract profit at time t, and so on. Based on the constructed objective function, the optimal solution is found, and the change in power generation of each generating unit is taken as the change in power generation of the corresponding generating unit when the electricity demand at the target time is met and the power generation profit at the target time is maximized. Specifically, based on the function constructed above, and based on the available power change function L... T All solutions {L} for the change in the power supply capacity of each generator unit can be obtained when the power generation changes. H (t), L S (t), L F (t), L G (t)}, and determine L in each solution based on the flexibility cost curve. H (t), L S (t), L F (t) and L G (t) corresponds to the flexibility cost for L H (t), L S (t), L F (t) and L G (t) The total flexibility cost of the current solution is obtained by summing the corresponding flexibility costs. All total flexibility costs are sorted from low to high, and then the solution with the lowest total flexibility cost while meeting electricity demand is selected. This selected solution is the optimal solution when the total profit of system generation is maximized. Thus, the variable power generation of each generator unit at time t+1 when the total profit of system generation is maximized is obtained, and the variable power generation of each generator unit is used as the basis for grid peak regulation. It is understandable that in {L H (t), L S (t), L F (t), L G In (t)}, L H (t) can represent the sum of real-time changes in the available power supply of all thermal power units in the region, or it can represent the set of all thermal power units, i.e., L. H (t) represents That is, the solution for the change in the power supply capacity of each generator set is

[0078] like Figure 2As shown, the logic for evaluating the real-time electricity price at time t+1 is as follows: First, real-time generation load data of the target area power system at time t is acquired as the initial condition for calculation. The generation cost of various types of generator units at time t is determined. Then, the generation cost distribution of the entire system at time t is determined according to the real-time generation cost function. Based on the historical data of regional electricity demand, the electricity demand is predicted by fitting the data using a machine learning algorithm. Based on the constructed dynamic generation cost function and electricity demand, the generation cost at time t+1 is predicted. Combined with the generation cost distribution of the entire system at time t, the marginal cost of the entire system at time t+1 is predicted. Based on the long-term contracts of each power plant, the profit of each power plant in the entire system is predicted according to the objective function. Then, the real-time electricity price of the power system is evaluated based on the predicted marginal cost of the entire system at time t+1 and the profit of each power plant in the entire system. For example, the ratio of the profit to the marginal cost of each type of power plant can be used as the evaluation basis for the spot electricity price of each type of power plant. Other methods can also be used to evaluate the spot electricity price of each type of power plant. This implementation method does not limit the evaluation method.

[0079] like Figure 3 As shown, in a second aspect of the present invention, a regional real-time electricity price assessment device is provided, comprising:

[0080] The data prediction module is configured to acquire historical electricity load data and generate an electricity demand characteristic curve based on the historical electricity load data. The electricity demand characteristic curve is used to predict the electricity demand at each unit of time.

[0081] The calculation module is configured as follows:

[0082] Construct real-time generation cost function and real-time generation function for generator sets based on different generation categories;

[0083] The change in power supply at the target time is determined based on the electricity demand characteristic curve. The change in power supply is the difference between the electricity demand at the target time and the electricity demand at the current time.

[0084] Based on the real-time power generation cost function, the real-time power generation function, and the power supply change, and with the constraint of meeting the electricity demand at the target time, the objective function is constructed with the goal of maximizing the power generation profit at the target time.

[0085] Find the optimal solution for the objective function to obtain the change in power generation of each generator unit when the electricity demand at the target time is met and the power generation profit at the target time is maximized.

[0086] In a third aspect of the invention, a computer-readable medium is provided, which stores a computer program that, when processed and executed, implements the above-described regional real-time electricity price assessment method.

[0087] In summary, this implementation method is based on the dynamic cost identification of various types of power plants in the region, using the dynamic cost of power generation as the fundamental basis for electricity price evaluation and system cost optimization. Based on a multi-dimensional power generation cost model with comprehensive technical coverage, this implementation method proposes the concept of real-time marginal cost of power system peak shaving. It calculates the potential cost distribution of various types of power plants based on the current electricity demand and the rate of change in demand in the region, thereby providing a reference price for the whole network electricity spot trading.

[0088] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0089] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0090] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, and should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for assessing regional real-time electricity prices, characterized in that, include: Construct real-time generation cost function and real-time generation function for generator sets based on different generation categories; Historical electricity load data is obtained, and an electricity demand characteristic curve is generated based on the historical electricity load data. The electricity demand characteristic curve is used to predict the electricity demand at each unit time. The power supply change at the target time is determined based on the power demand characteristic curve, whereby the power supply change is the difference between the power demand at the target time and the power demand at the current time. Using the power supply change as the dependent variable and the power supply change of generator sets of different power generation categories as the independent variable, a real-time power supply change function is constructed in which the power supply change is the sum of the power supply change of generator sets of different power generation categories; wherein, the power supply change is the difference between the change in power generation of the generator set at the target time and the change in power generation at the current time. Based on the real-time power generation cost function, the real-time power generation quantity function, the real-time power supply change quantity function, and the predetermined flexibility cost curve, the unit power generation cost function at the current time and the unit power generation cost function at the target time are constructed respectively. The flexibility cost curve includes at least the flexibility cost corresponding to different power supply changes of generator sets of different power generation categories. Based on the unit power generation cost function at the current time and the unit power generation cost function at the target time, and by calculating the difference between the unit power generation cost at the target time and the unit power generation cost at the current time, a real-time power generation cost change function is constructed to characterize the change in unit power generation cost at the target time. Based on the real-time power generation function, and with the real-time power generation change as the dependent variable, a real-time power generation change function is constructed to characterize the difference between the change in power generation of all generator units at the target time and the change in power generation of all generator units at the current time. Based on the product of the real-time power generation cost change function and the real-time power generation change function, a dynamic power generation cost function is constructed to represent the difference between the power generation cost at the target time and the power generation cost at the current time. Based on the aforementioned dynamic cost function for power generation, with the constraint of meeting the electricity demand at the target time and the objective of maximizing the profit from power generation at the target time, an objective function is constructed. The optimal solution is obtained for the objective function to obtain the change in power generation of each generator unit when the electricity demand at the target time is met and the power generation profit at the target time is maximized. Based on the change in power generation of each generator unit at the target time, the marginal cost of the whole system at the target time is determined. Based on the long-term contracts of each power plant, the profit of each power plant in the whole system at the target time is predicted according to the objective function. Then, the real-time electricity price of the power system is evaluated based on the marginal cost of the whole system at the target time and the profit of each power plant in the whole system.

2. The regional real-time electricity price assessment method according to claim 1, characterized in that, Construct real-time generation cost functions based on different generation types of generator sets, including: Based on the summation of fixed and variable costs for generator sets of different power generation categories, a real-time power generation cost function for different categories of generator sets is constructed.

3. The regional real-time electricity price assessment method according to claim 2, characterized in that, Construct real-time power generation functions based on different power generation categories of generator sets, including: For each type of generator set, a real-time power generation quantum function is constructed based on the sum of the long-term power generation and the variable power generation of the generator set at the current moment; wherein, the long-term power generation is the average value of the predetermined total long-term power generation of the generator set in the power generation category at each moment within a predetermined time. Based on the summation of the real-time power generation quantum functions of generator sets for all power generation categories, a real-time power generation function for generator sets of different power generation categories is constructed.

4. The regional real-time electricity price assessment method according to claim 1, characterized in that, Based on the real-time power generation cost function, the real-time power generation function, the real-time available power supply change function, and the predetermined flexibility cost curve, a unit power generation cost function is constructed for the current moment, including: Based on the real-time power supply change function and the flexibility cost curve, the flexibility cost corresponding to the current power supply change of the generator set is taken as the variable cost of the current generator set. Based on the real-time power generation cost function and the real-time power generation function, and based on the ratio of the real-time power generation cost of all generator sets to the long-term contract power generation of all generator sets at the current moment, the unit power generation cost function at the current moment is constructed.

5. The regional real-time electricity price assessment method according to claim 1, characterized in that, Based on the real-time power generation cost function, the real-time power generation function, the real-time available power supply change function, and the predetermined flexibility cost curve, a unit power generation cost function for the target time is constructed, including: Based on the real-time power supply change function and the flexibility cost curve, the flexibility cost corresponding to the current power supply change of the generator set is used as the variable cost of the current generator set. Based on the real-time power generation cost function and the real-time power generation function, a unit power generation cost function for the target time is constructed with the real-time power generation cost of all generator sets at the target time as the numerator and the sum of the long-term power generation and real-time power generation change of all generator sets as the denominator.

6. The regional real-time electricity price assessment method according to claim 1, characterized in that, Find the optimal solution for the objective function to obtain the change in power generation of each generator unit when the electricity demand at the target time is met and the power generation profit at the target time is maximized, including: Find the optimal solution for the objective function, and take the change in power generation of each generator set when the electricity demand at the target time is met and the power generation profit at the target time is maximized as the change in power generation of the corresponding generator set.

7. A regional real-time electricity price assessment device, employing the regional real-time electricity price assessment method as described in any one of claims 1-6, characterized in that, The device includes: The data prediction module is configured to acquire historical electricity load data and generate an electricity demand characteristic curve based on the historical electricity load data. The electricity demand characteristic curve is used to predict the electricity demand at each unit of time. The calculation module is configured as follows: Construct real-time generation cost function and real-time generation function for generator sets based on different generation categories; The power supply change at the target time is determined based on the power demand characteristic curve, whereby the power supply change is the difference between the power demand at the target time and the power demand at the current time. Based on the real-time power generation cost function, the real-time power generation function, and the power supply change, with the constraint of meeting the electricity demand at the target time and the objective of maximizing the power generation profit at the target time, an objective function is constructed. Find the optimal solution for the objective function to obtain the change in power generation of each generator set when the electricity demand at the target time is met and the power generation profit at the target time is maximized.

8. A computer-readable medium storing a computer program, characterized in that, When the computer program is processed and executed, it implements the regional real-time electricity price assessment method according to any one of claims 1 to 6.

Images