Thermal power plant declaration model considering price limit based on beta distribution

Abstract

The invention provides a thermal power plant declaration model considering price limit based on beta distribution. The thermal power plant declaration model comprises the steps of obtaining node side data, thermal power generating unit side data and user side data; constructing a node price limit model based on the node side data; based on the node side data and the thermal power generating unit side data, constructing cost models of the thermal power generating unit in different demand scenes; based on the user side data, a node price limit model and the cost model, determining a quotation range of the thermal power generating unit; and adopting beta distribution, carrying out multiple times of random generation on the quotation range of the thermal power generating unit, and determining a formal quotation price. According to the method provided by the invention, by adopting a data generation mode based on beta distribution, different quotation tendencies of the thermal power plant can be simulated, the cost factor is comprehensively considered, and the price limit factor is incorporated into the model consideration range. By utilizing the model, the influence of decision variables on the declaration of the thermal power plant can be quantitatively evaluated.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a thermal power plant application model based on beta distribution and price limits. Background Technology

[0002] Currently, the design of electricity spot market mechanisms still lacks a standardized and scientific ex-ante evaluation system. To evaluate the effectiveness of decision-making mechanisms, it is necessary to simulate the entire market transaction chain and construct a complete simulation path encompassing "decision variables - declaration - clearing - outcome data." Existing research literature largely focuses on innovations in clearing algorithms, with relatively little research on declaration models, and the few existing models are mostly based on game theory.

[0003] Under the unified market-based bidding mechanism adopted in the day-ahead electricity spot market, market participants uniformly clear their lots according to the marginal clearing price, and the game behavior of non-marginal units cannot substantially affect the clearing outcome. Furthermore, game theory models suffer from high complexity and the possibility of non-convergence when simulating large-scale unit numbers. Summary of the Invention

[0004] To overcome the aforementioned technical deficiencies, this application provides a thermal power plant application model based on beta distribution and considering price limits. To achieve the above objective, this application implements it according to the following technical solution:

[0005] This application provides a thermal power plant application model based on beta distribution and considering price limits, including:

[0006] Acquire node-side data, thermal power unit-side data, and user-side data;

[0007] Based on the node-side data, a node price limit model is constructed;

[0008] Based on the node-side data and the thermal power unit-side data, a cost model for thermal power units under different demand scenarios is constructed.

[0009] Based on the user-side data, the node price limit model, and the cost model, the price range for thermal power units is determined;

[0010] Using a beta distribution, the price range for the thermal power units is randomly generated multiple times to determine the final price quote.

[0011] Optionally, the node-side data includes the landed coal price, the benchmark price of coal, the price fluctuation ratio, historical power generation data, the average market transaction price, and thermal power capacity subsidies; the thermal power unit-side data includes the cost parameters of thermal power units, the total installed capacity of thermal power units, the minimum technical output of thermal power units, and the start-up and shutdown costs of thermal power units; and the user-side data includes historical power trading data.

[0012] Optionally, the node price limit model is a price cap model for nodes in the system, and the step of constructing the node price limit model based on the node-side data includes:

[0013] Based on the aforementioned price fluctuation ratio and the benchmark price of coal, a price limit model for nodes in the system is constructed.

[0014] Optionally, the cost model of the thermal power unit under different demand scenarios includes the marginal cost model of the thermal power unit, the economic cost model of the thermal power unit, and the average cost model of the thermal power unit.

[0015] Optionally, constructing a cost model for a thermal power unit based on the node-side data and the thermal power unit-side data includes:

[0016] Based on the landed coal price and the cost parameters of the thermal power unit, a total cost model for the thermal power unit is determined.

[0017] Differentiate the total cost model of the thermal power unit to construct the marginal cost model of the thermal power unit;

[0018] The average cost model of the thermal power unit is calculated by averaging the total cost model of the thermal power unit to construct the average cost model of the thermal power unit.

[0019] An economic cost model for thermal power units is constructed based on the minimum technical output of the thermal power units, the start-up and shutdown costs of the thermal power units, and the thermal power capacity subsidies.

[0020] Optionally, determining the price range for thermal power units based on the user-side data, the nodal price limit model, and the cost model includes:

[0021] Based on the aforementioned historical electricity trading data, determine the net load within the competitive bidding space for thermal power generation;

[0022] Based on the net load under the aforementioned thermal power bidding space and the total installed capacity of the thermal power units, determine the current supply-demand ratio of the thermal power units;

[0023] Based on the current supply-demand ratio of the thermal power units, the nodal price limit model, and the cost model, the price range for thermal power units is determined.

[0024] Optionally, determining the price range for thermal power units based on the current supply-demand ratio of the thermal power units, the nodal price limit model, and the cost model includes:

[0025] Obtain the range of the supply-demand ratio for thermal power units;

[0026] Based on the current supply-demand ratio of the thermal power units, the range of the supply-demand ratio of the thermal power units, and the cost model, determine the pricing benchmark and the price fluctuation range of the thermal power units corresponding to the range of the supply-demand ratio of the thermal power units.

[0027] Based on the quoted benchmark, the node price limit model, and the quoted price fluctuation range of the thermal power unit, the quoted price range of the thermal power unit is determined.

[0028] Optionally, the step of using a beta distribution to randomly generate multiple price ranges for thermal power units to determine the final price includes:

[0029] Using a beta distribution, the price range for the thermal power unit is randomly generated multiple times to determine multiple preliminary price quotes.

[0030] The CVaR method is used to process the multiple preliminary price quotations to obtain the final price quotation.

[0031] This application has the following beneficial effects:

[0032] The method proposed in this application, by employing a beta-distributed data generation approach, can not only simulate different bidding tendencies of thermal power plants but also comprehensively consider cost factors and incorporate price caps into the model's scope. Using this model, the impact of decision variables on thermal power plant bids can be quantitatively assessed. Furthermore, combined with a clearing model, the entire electricity market trading process can be simulated. By setting decision variables and constructing an evaluation index system, it is possible to compare and evaluate current market decisions, providing policymakers with a scientific policy basis.

[0033] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. The application will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0034] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0035] Figure 1 This is a flowchart illustrating a thermal power plant application model based on beta distribution and considering price limits, provided in an embodiment of this application. Detailed Implementation

[0036] The embodiments of this application are described in detail below with reference to the accompanying drawings, but this application can be implemented in many different ways as defined and covered by the claims.

[0037] Therefore, in order to solve the above problems, such as Figure 1 As shown, this application proposes a thermal power plant application model based on beta distribution and considering price limits, including:

[0038] Step S101: Obtain node-side data, thermal power unit-side data, and user-side data;

[0039] To determine the final bid price, relevant calculation data is required. Generally, node-side data, thermal power unit-side data, and user-side data are needed. The following is a brief explanation of each type of data:

[0040] Node-side data primarily revolves around "nodes" in the power system (such as substations, line connection points, and regional gateways), reflecting the status of power transmission, voltage, and load balance at these nodes. It forms the basis for power network operation and transaction reporting. Typical data includes landed coal prices, benchmark coal prices, price fluctuation ratios, historical power generation data, average market transaction prices, and thermal power capacity subsidies.

[0041] Data from thermal power units focuses on the generating equipment of thermal power plants, reflecting the unit's generating capacity, cost, and operating status. It is the core basis for thermal power units to participate in market applications (such as power generation bidding and maintenance plans). It generally includes the unit's cost parameters, installed capacity, minimum technical output, unit start-up and shutdown costs, and unit start-up and shutdown status.

[0042] User-side data targets end-consumers of electricity (such as residents, industrial enterprises, and commercial users), reflecting their electricity demand, load characteristics, and electricity consumption behavior. It is crucial for load forecasting and demand response reporting. It typically consists of historical electricity trading data (user load over a 24-hour period).

[0043] Step S102: Based on the node-side data, construct a node price limit model;

[0044] The node price limit model in this application is a model for the upper and lower price limits of nodes in the system. It is built using the existing "benchmark price + upward and downward fluctuation" price limit mechanism. That is, the upper and lower price limits of nodes in the system are constructed based on the upward and downward fluctuation ratio of the price and the benchmark price of coal, as shown in the following formula:

[0045]

[0046] In the formula, price cap,n and price floor,n P represents the upper and lower limits of the price for node n, respectively. base,n R represents the benchmark price of coal at node n; up,n and R down,n These represent the price increase and decrease rates for node n, respectively.

[0047] Step S103: Based on the node-side data and the thermal power unit-side data, construct a cost model for the thermal power unit under different demand scenarios;

[0048] The cost of thermal power units varies depending on the supply and demand scenario. Therefore, different costs need to be obtained based on different supply and demand scenarios to provide corresponding pricing benchmarks. The cost models for thermal power units under different demand scenarios generally include the marginal cost model, the economic cost model, and the average cost model.

[0049] First, based on the landed coal price and the cost parameters of the thermal power unit, determine the total cost model of the thermal power unit:

[0050]

[0051] In the formula, C i (x i Let x be the total cost model for thermal power unit i. i For the output (MW) of thermal power unit i, a i ,b i ,c i Let p be the cost parameter of thermal power unit i. coal This refers to the landed coal price at the node where the thermal power unit is located.

[0052] In a supply-demand equilibrium scenario, thermal power units will prioritize using their own marginal cost (MC) as the basis for pricing. Marginal cost is the first derivative of the total cost function; that is, by differentiating the total cost model of thermal power units, the marginal cost model of thermal power units can be constructed.

[0053]

[0054] In the formula, This is the marginal cost model for thermal power unit i.

[0055] In scenarios where supply exceeds demand, thermal power units will consider using their own economic cost (EC) as the basis for pricing. This maximizes the amount of electricity discharged to mitigate potential downtime risks. The enterprise's own economic cost refers to the total opportunity cost of all resources used in the production process. Based on the minimum technical output of the thermal power unit, the start-up and shutdown costs of the thermal power unit, and thermal power capacity subsidies, an economic cost model for the thermal power unit is constructed as follows:

[0056]

[0057] In the formula, For the economic cost model of thermal power unit i, x iminFor the minimum technical output of thermal power unit i, For the start-up and shutdown cost of thermal power unit i, Capacity subsidies for thermal power unit i.

[0058] The start-up and shutdown costs of the aforementioned thermal power units are related to the real-time status of the units, as shown in the following formula:

[0059]

[0060] In the formula, and These represent the start-up and shutdown actions of thermal power unit i during time period t (1 indicates action, 0 indicates no action); and These represent the start-up cost and shutdown cost of thermal power unit i during time period t, respectively.

[0061] In scenarios where supply falls short of demand, thermal power units will consider using their own average cost as the basis for pricing, thereby achieving long-term cost recovery and a reasonable return on investment. This can be achieved by averaging the total cost model of thermal power units, thus constructing an average cost model for thermal power units.

[0062]

[0063] In the formula, This is the average cost model for thermal power units.

[0064] Step S104: Based on the user-side data, the node price limit model, and the cost model, determine the price range for thermal power units;

[0065] First, based on historical electricity trading data, specifically user load at different times, determine the net load D within the competitive bidding space. S :

[0066] D S,i =D tal,i -P WT,i -P HY,i -P PV,i -P nu,i (7)

[0067]

[0068] In the formula: D tal,i D represents the user load at time i; s,i Let P be the net load of the thermal power unit at time i. WT,i For the historical power generation data of the wind turbine at time i, P HY,i Historical power generation data of hydropower units, P PV,i Historical power generation data of photovoltaic units, P nu,i This refers to historical power generation data for nuclear power units.

[0069] Then, based on the net load under the competitive bidding space and the total installed capacity of thermal power units, the current supply-demand ratio of thermal power units is determined, as shown in the following formula:

[0070]

[0071] In the formula, P total The total installed capacity of thermal power units is represented by , and DSR represents the current supply-demand ratio of thermal power units.

[0072] Then, based on the current supply and demand ratio of thermal power units, the nodal price limit model, and the cost model, the price range for the units is determined. The specific determination process is as follows:

[0073] Since the pricing benchmark is related to the specific supply and demand scenario, it is necessary to obtain the range of the supply-demand ratio for thermal power units. The upper and lower limits of this range are the supply-demand ratio (DSR) when demand exceeds supply. shortage Supply-demand ratio (DSR) with oversupply surplus .

[0074] Based on the current supply-demand ratio of thermal power units, the range of the supply-demand ratio of thermal power units, and the cost model, determine the pricing benchmark P corresponding to the range of the supply-demand ratio of thermal power units. i h The margin for the price fluctuation range of thermal power units is shown in the following formula:

[0075]

[0076] In the formula, M base The basic floating range can be set by the user, and ε is the supply and demand sensitivity coefficient of the unit.

[0077] According to the above formula (10), when the current supply-demand ratio of thermal power units is less than the supply-demand ratio of insufficient supply, the quotation benchmark is the thermal power unit economic cost model. When the current supply-demand ratio of thermal power units is greater than or equal to the supply-demand ratio of insufficient supply and less than or equal to the supply-demand ratio of oversupply, the quotation benchmark is the thermal power unit marginal cost model. When the current supply-demand ratio of thermal power units is greater than the supply-demand ratio of oversupply, the quotation benchmark is the thermal power unit average cost model.

[0078] Furthermore, according to formula (11), it can be seen that when the current supply and demand ratio of thermal power units is in different ranges, the calculation method of the fluctuation range of thermal power unit price is also different. Since the specific calculation method of formula (11) is obvious, no further textual explanation is needed here.

[0079] Combining the above-mentioned node price limit model and pricing benchmark, as well as the price fluctuation range for thermal power units, the price range for thermal power units is determined, namely:

[0080] P cap,i =min(price) cap,n P i h *(1+margin)) (12)

[0081] P floor,i =max(price) floor,n ,P i h *(1-margin))

[0082] In the formula, P cap,i P represents the upper limit of the price quoted for thermal power unit i. floor,i This is the lower limit of the price quote for thermal power unit i.

[0083] Step S105: Using a beta distribution, the price range for the thermal power unit is randomly generated multiple times to determine the final price quote.

[0084] Once the price range for the generating unit is determined, a beta distribution can be used to generate a preliminary price quote, as follows:

[0085]

[0086] In the formula, P i The quotation for thermal power unit i is in 5 segments;

[0087] By selecting the Beta distribution parameters, the bidding tendencies of generator units of different sizes can be simulated. For example, α=5, β=2 makes the generated prices more concentrated at the low end, simulating the conservative tendency of actual generator units when bidding; α=2, β=5, on the contrary, yields aggressive bidding. betarnd(·) is a Matlab function. betarnd(α,β,5,1) will generate a random number column vector of length 5 that satisfies the Beta(α,β) distribution, and the size of the random numbers must be greater than 0 and less than 1.

[0088] Because of the random number generation, extreme cases may occur when the number of generating units is small, leading to deviations in the final bid results. Therefore, a method is needed to ensure that the model output results have a certain degree of convergence. Thus, a beta distribution is used to randomly generate multiple bid ranges for thermal power units, thereby obtaining multiple preliminary bid prices.

[0089] Then, the CvaR method is used to process the multiple preliminary bid prices, select the effective bid combinations, and take the average value to obtain the formal bid price P. i k To avoid the impact of random, extreme bids, the process for determining the official bid price is as follows:

[0090] CVaR is a risk metric used to assess expected losses in the worst-case scenario, and can also be used to evaluate power plant pricing strategies. The average market transaction price is obtained using historical nodal measurement data.

[0091] use Determine the clearing quantity for each simulation result, i.e., less than The electricity volume in the quoted segment is cleared. The formula is expressed as follows:

[0092]

[0093] x i,k x represents the cleared electricity volume of thermal power unit i in the k-th simulated bid. i,j,k This is the price p of segment j in this round of bidding for the thermal power unit. i,j,k The corresponding amount of electricity; ||[·] is an indicator function, which is 1 when the condition inside the parentheses is met, and 0 otherwise.

[0094] The total cleared electricity volume x of the k-th simulated bid is obtained by summing the cleared electricity volumes of all generating units. k The formula is expressed as follows:

[0095]

[0096] In this invention, CVaR is defined as the clearing charge under the worst-case 5% scenario:

[0097] CVaR 95 =Quantile 0.05 (x k (16)

[0098]

[0099] This formula can be understood as filtering out the 5% of simulated bids with the lowest cleared power and obtaining the average of the remaining simulated bids. By using CVaR to filter and average the results, the final output thermal power unit application curve can obtain the maximum number of units started, simulating the result of long-term market game and increasing the convergence of the model output results.

[0100] In summary, the method proposed in this application, by employing a beta-distributed data generation approach, not only simulates the different bidding tendencies of thermal power plants but also comprehensively considers cost factors and incorporates price caps into the model's scope. Using this model, the impact of decision variables on thermal power plant bids can be quantitatively assessed. Furthermore, combined with a clearing model, the entire electricity market trading process can be simulated. By setting decision variables and constructing an evaluation index system, it is possible to compare and evaluate current market decisions, providing policymakers with a scientific policy basis.

[0101] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A thermal power plant application model based on beta distribution and considering price limits, characterized in that, include: Acquire node-side data, thermal power unit-side data, and user-side data; Based on the node-side data, a node price limit model is constructed; Based on the node-side data and the thermal power unit-side data, a cost model for thermal power units under different demand scenarios is constructed. Based on the user-side data, the node price limit model, and the cost model, the price range for thermal power units is determined; Using a beta distribution, the price range for the thermal power units is randomly generated multiple times to determine the final price quote.

2. The model according to claim 1, characterized in that, The node-side data includes the landed coal price, the benchmark price of coal, the price fluctuation ratio, historical power generation data, the average market transaction price, and thermal power capacity subsidies. The thermal power unit-side data includes the cost parameters of thermal power units, the total installed capacity of thermal power units, the minimum technical output of thermal power units, and the start-up and shutdown costs of thermal power units. The user-side data includes historical power trading data.

3. The model according to claim 2, characterized in that, The node price limit model is a model for the upper and lower price limits of nodes in the system. The construction of the node price limit model based on the node-side data includes: Based on the aforementioned price fluctuation ratio and the benchmark price of coal, a price limit model for nodes in the system is constructed.

4. The model according to claim 2, characterized in that, The cost models for thermal power units under different demand scenarios include the marginal cost model, the economic cost model, and the average cost model.

5. The model according to claim 4, characterized in that, The step of constructing a cost model for a thermal power unit based on the node-side data and the thermal power unit-side data includes: Based on the landed coal price and the cost parameters of the thermal power unit, a total cost model for the thermal power unit is determined. Differentiate the total cost model of the thermal power unit to construct the marginal cost model of the thermal power unit; The average cost model of the thermal power unit is calculated by averaging the total cost model of the thermal power unit to construct the average cost model of the thermal power unit. An economic cost model for thermal power units is constructed based on the minimum technical output of the thermal power units, the start-up and shutdown costs of the thermal power units, and the thermal power capacity subsidies.

6. The model according to claim 5, characterized in that, The process of determining the price range for thermal power units based on the user-side data, the node price limit model, and the cost model includes: Based on the aforementioned historical electricity trading data, determine the net load within the competitive bidding space for thermal power generation; Based on the net load under the aforementioned thermal power bidding space and the total installed capacity of the thermal power units, determine the current supply-demand ratio of the thermal power units; Based on the current supply-demand ratio of the thermal power units, the nodal price limit model, and the cost model, the price range for thermal power units is determined.

7. The model according to claim 6, characterized in that, The process of determining the price range for thermal power units based on the current supply-demand ratio, the nodal price limit model, and the cost model includes: Obtain the range of the supply-demand ratio for thermal power units; Based on the current supply-demand ratio of the thermal power units, the range of the supply-demand ratio of the thermal power units, and the cost model, determine the pricing benchmark and the price fluctuation range of the thermal power units corresponding to the range of the supply-demand ratio of the thermal power units. Based on the quoted benchmark, the node price limit model, and the quoted price fluctuation range of the thermal power unit, the quoted price range of the thermal power unit is determined.

8. The model according to claim 7, characterized in that, The method of using a beta distribution to randomly generate multiple price ranges for thermal power units to determine the final price includes: Using a beta distribution, the price range for the thermal power unit is randomly generated multiple times to determine multiple preliminary price quotes. The CVaR method is used to process the multiple preliminary price quotations to obtain the final price quotation.

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