Dynamic adjustment strategy of peak regulation auxiliary service market transaction subject's allocation upper limit

Abstract

This invention discloses a dynamic adjustment strategy for the allocation ceiling of market participants in peak-shaving ancillary services. Specifically, it involves: predicting the next day's renewable energy generation capacity and load curve within the power system; establishing an objective function with the goal of maximizing the overall benefit for all market participants, and setting constraints; solving the objective function using simulation software to obtain the dynamic allocation ceiling adjustment parameters for each market participant; dynamically adjusting the allocation ceiling based on the Pareto optimality principle, which facilitates a more rational distribution of profits among market participants. This adjustment strategy aims to encourage decentralized decisions by market participants to align with the market designer's expectations; ensuring reasonable profits for thermal power plants; guiding them to actively explore peak-shaving potential; freeing up power generation space for renewable energy; alleviating the peak-shaving dilemma of the power grid; and better adapting to future changes, guiding the market towards a healthy and rational development.

Description

Technical Field

[0001] This invention belongs to the technical field of improved peak shaving ancillary service market allocation methods, specifically involving a dynamic adjustment strategy for the allocation ceiling for market participants in peak shaving ancillary services. Background Technology

[0002] In recent years, the installed capacity and power generation of new energy sources have grown rapidly. The latest data from the National Energy Administration shows that by the end of 2020, wind power capacity in Northeast China reached 34,314,700 kilowatts, generating 72.776 billion kilowatt-hours, accounting for 13.41% of total power generation; photovoltaic power capacity reached 13,818,800 kilowatts, generating 18.767 billion kilowatt-hours, accounting for 3.46% of total power generation. The Northeast Energy Bureau has actively responded to the national call, continuously adapted to the new situation of power development in Northeast China, promoted the construction of a new power development pattern, and established an ancillary service market that conforms to the characteristics of Northeast China's power industry in the new era. Launched on January 1, 2017, it has achieved certain results, guiding thermal power companies to provide paid peak-shaving ancillary services through market mechanisms, which has played a positive role in alleviating the contradictions between wind, solar, thermal, and nuclear power. However, at the same time, the rapid development of new energy sources in Northeast China has also brought new peak-shaving challenges to the power grid.

[0003] Under the current market rules for peak-shaving ancillary services, thermal power plants' actual revenue from deep peak shaving is lower than expected, resulting in reduced profits and a stagnant, even reversing, enthusiasm for participating in peak shaving. This problem stems from two main factors: firstly, the mismatch between the growth rates of renewable energy capacity and the deep peak-shaving capacity of thermal power plants in Northeast China; and secondly, the current method for allocating paid peak-shaving costs in the "two detailed rules" fails to reasonably divide the responsibility for allocation, which is the primary cause of the shortfall in peak-shaving costs. Therefore, it is necessary to revise the Northeast China power ancillary services market rules based on actual needs, reasonably protect the interests of market participants, guide thermal power plants to actively explore peak-shaving potential, free up power generation space for renewable energy, improve the operational flexibility of the Northeast power system, and promote a green, low-carbon, and high-quality development of the Northeast's energy structure. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic adjustment strategy for the allocation limit of peak-shaving ancillary service market participants, which can reduce the shortfall in paid peak-shaving fees and enhance the enthusiasm of thermal power plants for peak-shaving.

[0005] The technical solution adopted in this invention is a dynamic adjustment strategy for the allocation ceiling of market participants in peak shaving ancillary services, which is implemented according to the following steps:

[0006] Step 1: Predict the power generation capacity of new energy sources and the load curve within the power system for the next day;

[0007] Step 2: Establish an objective function with the goal of maximizing the overall benefits for all market participants, and set constraints.

[0008] Step 3: Solve the objective function using simulation software to obtain the dynamic allocation upper limit adjustment parameters for each trading entity in the market.

[0009] The invention is further characterized by:

[0010] Step 1 involves predicting the new energy power generation and power system load forecast data for the next day based on the new energy power generation capacity within a certain area and time period.

[0011] In step 2, the objective function is established with the goal of maximizing the overall benefit for all market participants:

[0012]

[0013] In equation (1), R represents the total revenue of all thermal power plants, wind farms, photovoltaic power plants, and nuclear power plants participating in the peak-shaving ancillary services market at time t; D-i,t Let I be the on-grid electricity revenue of the i-th thermal power unit at time t; D-i,t C represents the peak-shaving compensation cost for the i-th thermal power unit at time t. i,t The cost of providing peak-shaving ancillary services to the i-th thermal power unit at time t; F D-i,t F W-j,t F S-u,t F N-v,t These represent the peak-shaving allocation amounts for the i / j / u / v-th thermal power units, wind power units, photovoltaic arrays, and nuclear power units at time t, respectively. These represent the upper limit of the peak-shaving ancillary service cost sharing at time t for the thermal power plant, wind farm, photovoltaic power station, and nuclear power plant where the i / j / u / v thermal power units, wind power units, photovoltaic arrays, and nuclear power units are located; The revenue from grid-connected electricity obtained by the j-th wind turbine unit when participating in the peak-shaving market at time t; The profit earned by the j-th wind turbine unit from participating in the cross-regional spot market at time t; These represent the electricity revenue generated by the u / v photovoltaic array and the nuclear power unit at time t when participating in the peak-shaving market.

[0014] The electricity revenue generated by the i-th thermal power unit at time t is:

[0015]

[0016] in, Let ρ be the on-grid power output of the i-th thermal power unit at time t. e The on-grid electricity price for thermal power units;

[0017] The peak-shaving compensation cost for the i-th thermal power unit at time t is:

[0018]

[0019] in, The paid peak-shaving power provided by the i-th thermal power unit at time t in the δ-th tier; ρ δ The actual clearing price for the δth tier; k is a correction coefficient, k = 1 during the heating season and k = 0.5 during the non-heating season;

[0020] The cost of the i-th thermal power unit providing peak-shaving auxiliary services at time t is:

[0021]

[0022] Where, ρ r For coal prices; P represents the planned output value of the i-th thermal power unit at time t; D-i,t Let a be the actual output value of the i-th thermal power unit at time t; i b i Let be the consumption characteristic parameters of the i-th thermal power unit;

[0023] The revenue from grid-connected electricity generated by the J / U / V typhoon turbines, photovoltaic arrays, and nuclear power units participating in the peak-shaving market is as follows:

[0024]

[0025]

[0026]

[0027] in, These represent the paid peak-shaving electricity purchased at time t for the j / u / v wind turbine, photovoltaic array, and nuclear power unit, respectively.

[0028] The profit that the j-th wind turbine unit gains from participating in the inter-regional spot market at time t is:

[0029]

[0030] in, For the j-th wind turbine unit's electricity sold in the inter-regional spot market at time t; ρ c For inter-regional spot market electricity prices;

[0031] The upper limit for the sharing of peak-shaving ancillary service costs at time t for the thermal power plant, wind farm, photovoltaic power station, and nuclear power plant where the i / j / u / v thermal power units, wind power units, photovoltaic arrays, and nuclear power units are located is:

[0032]

[0033]

[0034]

[0035]

[0036] Among them, Q D-i,t Q W-j,t Q S-u,t Q N-v,t α1 represents the actual power generation of the i / j / u / v thermal power units, wind power units, photovoltaic arrays, and nuclear power units at time t, respectively; α2 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of thermal power plants with load factors higher than the paid peak-shaving benchmark; α3 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of ordinary wind power plants; α4 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of unsubsidized photovoltaic power plants and photovoltaic power plants with a price difference of less than 1 cent between the on-grid price and the environmental benchmark price of thermal power; α5 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of ordinary photovoltaic power plants; α6 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of nuclear power plants.

[0037] The peak-shaving allocation amount for the i-th thermal power unit at time t is:

[0038]

[0039] in, Let be the power generation of the i-th thermal power unit after correction at time t; The total power generation of all thermal power units in the province participating in the cost-sharing at time t is the corrected total power generation. The total power generation of all wind turbine units in the province participating in the cost-sharing scheme at time t is the corrected total power generation. The total power generation of all photovoltaic arrays participating in the cost-sharing scheme within the province at time t is the corrected total power generation. The corrected total power generation of all nuclear power units in the province participating in the cost-sharing at time t;

[0040] The corrected power generation of thermal power units, wind power units, photovoltaic arrays, and nuclear power units can be expressed as:

[0041]

[0042]

[0043]

[0044]

[0045] Where k / d / p / q / z are correction coefficients, Q D-i-θ,t Let θ represent the actual power generation of the i-th thermal power unit at time t;

[0046] The peak-shaving allocation amount for the j-th wind turbine at time t is:

[0047]

[0048] The peak-shaving cost of the u-th photovoltaic array at time t is:

[0049]

[0050] If the nuclear power plant where the v-th nuclear power unit is located has two or more units operating, the peak-shaving allocation amount for the v-th nuclear power unit at time t is:

[0051]

[0052] If only one unit is operating in the nuclear power plant where the v-th nuclear power unit is located, the peak-shaving allocation amount for the v-th nuclear power unit at time t is:

[0053]

[0054] in, This represents the rated power generation capacity of the nuclear power plant where the vth nuclear power unit is located.

[0055] The constraints in step 2 include: system power balance constraints, thermal power unit operation constraints, new energy unit operation constraints, and wind / solar curtailment rate constraints, specifically:

[0056] The system power balance constraint is:

[0057]

[0058] Among them, P D-i,t P represents the peak-shaving capacity offered by the i-th thermal power unit willing to participate in peak shaving at time t; W-j,t P S-u,t P N-v,t These represent the power output values ​​of the j / u / v wind turbine, photovoltaic array, and nuclear power unit at time t, respectively.

[0059] Operating constraints for thermal power units include ramp rate constraints and unit output constraints;

[0060] The climbing rate constraint is:

[0061]

[0062] in, The maximum upward ramp rate and maximum downward ramp rate are the thermal power units within the system that are willing to participate in peak shaving; ΔT is the statistical period.

[0063] The unit output constraint is:

[0064]

[0065] in, These are the maximum and minimum technical output values ​​of thermal power units within the system that are willing to participate in peak shaving, respectively. The actual output value of the i-th thermal power unit at time t after the implementation of the peak-shaving ancillary services market;

[0066] The operating constraints for new energy generating units are:

[0067]

[0068]

[0069]

[0070] in, These represent the maximum power output of the j / u / v wind turbine, photovoltaic array, and nuclear power unit at time t, respectively.

[0071] The wind / solar curtailment rate constraint is:

[0072]

[0073]

[0074] Among them, l W l S These are the daily curtailment limits for wind and solar power, set according to the grid demand in different regions.

[0075] The beneficial effects of the present invention are:

[0076] This invention presents a dynamic adjustment strategy for the allocation ceiling of peak-shaving ancillary service market participants. Based on the Pareto optimality principle, dynamically adjusting the allocation ceiling promotes a more rational distribution of profits among market participants. This adjustment strategy aims to encourage decentralized decisions by market participants to align with the market designer's expectations. Furthermore, this dynamic adjustment strategy ensures reasonable profits for thermal power plants, guides them to actively explore peak-shaving potential, frees up power generation space for new energy sources, and alleviates the grid's peak-shaving constraints. It can better adapt to future changes and guide the market towards a healthy and rational development. Attached Figure Description

[0077] Figure 1This is a flowchart of the dynamic adjustment strategy for the allocation limit of the peak-shaving auxiliary service market participants according to the present invention;

[0078] Figure 2 These are the actual load curve, new energy prediction curve, and actual new energy output curve for a typical day in this embodiment of the invention.

[0079] Figure 3 These are the actual load curve, new energy prediction curve, and actual new energy output curve for a typical day 2 in this embodiment of the invention.

[0080] Figure 4 These are the actual load curve, new energy prediction curve, and actual new energy output curve for a typical day 3 in this embodiment of the invention.

[0081] Figure 5 These are the actual load curve, new energy prediction curve, and actual new energy output curve for a typical day 4 in this embodiment of the invention.

[0082] Figure 6 These are the adjustment parameters for the upper limit of peak-shaving ancillary service cost sharing in each typical local wind farm in the embodiments of the present invention;

[0083] Figure 7 This is a typical daily cost allocation diagram for wind power peak shaving ancillary services in an embodiment of the present invention;

[0084] Figure 8 This is a typical daily cost allocation diagram for wind power peak-shaving ancillary services in an embodiment of the present invention;

[0085] Figure 9 This is a typical daily cost allocation diagram for wind power peak-shaving ancillary services in an embodiment of the present invention;

[0086] Figure 10 This is a typical daily wind power peak-shaving ancillary service cost allocation diagram in an embodiment of the present invention;

[0087] Figure 11 This is a comparison chart of peak-shaving revenue from a typical daily thermal power plant in an embodiment of the present invention;

[0088] Figure 12 This is a comparison chart of peak-shaving revenue from two typical thermal power plants per day in an embodiment of the present invention;

[0089] Figure 13 This is a comparison chart of peak-shaving revenue from three typical thermal power plants in an embodiment of the present invention;

[0090] Figure 14 This is a comparison chart of peak-shaving revenue from four typical thermal power plants in an embodiment of the present invention. Detailed Implementation

[0091] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0092] This invention is a dynamic adjustment strategy for the allocation ceiling of participants in the peak-shaving ancillary services market, such as... Figure 1 As shown, please follow these steps:

[0093] Step 1: Based on the renewable energy power generation capacity within a certain area and time period, predict the renewable energy power generation capacity and power system load forecast data for the next day.

[0094] Step 2: The peak-shaving ancillary service market should not only achieve supply and demand balance at both ends of the power grid, but also pursue the maximization of overall social benefits, absorb renewable energy as much as possible, and guide the market towards healthy and benign development. The achievement of this goal is closely related to the distribution of benefits among market participants. Therefore, the model's objective is to maximize the comprehensive benefits of all market participants. Thus, this invention establishes an objective function with the goal of maximizing the comprehensive benefits of all market participants, and sets the following constraints:

[0095]

[0096] In equation (1), R represents the total revenue of all thermal power plants, wind farms, photovoltaic power plants, and nuclear power plants participating in the peak-shaving ancillary services market at time t; D-i,t Let I be the on-grid electricity revenue of the i-th thermal power unit at time t; D-i,t C represents the peak-shaving compensation cost for the i-th thermal power unit at time t. i,t The cost of providing peak-shaving ancillary services to the i-th thermal power unit at time t; F D-i,t F W-j,t F S-u,t F N-v,t These represent the peak-shaving allocation amounts for the i / j / u / v-th thermal power units, wind power units, photovoltaic arrays, and nuclear power units at time t, respectively. These represent the upper limit of the peak-shaving ancillary service cost sharing at time t for the thermal power plant, wind farm, photovoltaic power station, and nuclear power plant where the i / j / u / v thermal power units, wind power units, photovoltaic arrays, and nuclear power units are located; The revenue from grid-connected electricity obtained by the j-th wind turbine unit when participating in the peak-shaving market at time t; The profit earned by the j-th wind turbine unit from participating in the cross-regional spot market at time t; These represent the electricity revenue generated by the u / v photovoltaic array and the nuclear power unit at time t when participating in the peak-shaving market.

[0097] The electricity revenue generated by the i-th thermal power unit at time t is:

[0098]

[0099] in, Let ρ be the on-grid power output of the i-th thermal power unit at time t. e The on-grid electricity price for thermal power units;

[0100] The peak-shaving compensation cost for the i-th thermal power unit at time t is:

[0101]

[0102] in, The paid peak-shaving power provided by the i-th thermal power unit at time t in the δ-th tier; ρδ The actual clearing price for the δth tier; k is a correction coefficient, k = 1 during the heating season and k = 0.5 during the non-heating season;

[0103] The cost of the i-th thermal power unit providing peak-shaving auxiliary services at time t is:

[0104]

[0105] Where, ρ r For coal prices; P represents the planned output value of the i-th thermal power unit at time t; D-i,t Let a be the actual output value of the i-th thermal power unit at time t; i b i Let be the consumption characteristic parameters of the i-th thermal power unit;

[0106] The revenue from grid-connected electricity generated by the J / U / V typhoon turbines, photovoltaic arrays, and nuclear power units participating in the peak-shaving market is as follows:

[0107]

[0108]

[0109]

[0110] in, These represent the paid peak-shaving electricity purchased at time t for the j / u / v wind turbine, photovoltaic array, and nuclear power unit, respectively.

[0111] The profit that the j-th wind turbine unit gains from participating in the inter-regional spot market at time t is:

[0112]

[0113] in, For the j-th wind turbine unit's electricity sold in the inter-regional spot market at time t; ρ c For inter-regional spot market electricity prices;

[0114] The upper limit for the sharing of peak-shaving ancillary service costs at time t for the thermal power plant, wind farm, photovoltaic power station, and nuclear power plant where the i / j / u / v thermal power units, wind power units, photovoltaic arrays, and nuclear power units are located is:

[0115]

[0116]

[0117]

[0118]

[0119] Among them, Q D-i,t Q W-j,t Q S-u,t Q N-v,t α1 represents the actual power generation of the i / j / u / v thermal power units, wind power units, photovoltaic arrays, and nuclear power units at time t, respectively; α2 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of thermal power plants with load factors higher than the paid peak-shaving benchmark; α3 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of ordinary wind power plants; α4 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of unsubsidized photovoltaic power plants and photovoltaic power plants with a price difference of less than 1 cent between the on-grid price and the environmental benchmark price of thermal power; α5 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of ordinary photovoltaic power plants; α6 represents the adjustment parameter for the upper limit of the peak-shaving ancillary service cost allocation of nuclear power plants.

[0120] The peak-shaving allocation amount for the i-th thermal power unit at time t is:

[0121]

[0122] in, Let be the power generation of the i-th thermal power unit after correction at time t; The total power generation of all thermal power units in the province participating in the cost-sharing at time t is the corrected total power generation. The total power generation of all wind turbine units in the province participating in the cost-sharing scheme at time t is the corrected total power generation. The total power generation of all photovoltaic arrays participating in the cost-sharing scheme within the province at time t is the corrected total power generation. The corrected total power generation of all nuclear power units in the province participating in the cost-sharing at time t;

[0123] The corrected power generation of thermal power units, wind power units, photovoltaic arrays, and nuclear power units can be expressed as:

[0124]

[0125]

[0126]

[0127]

[0128] Where k / d / p / q / z are correction coefficients, Q D-i-θ,t Let θ represent the actual power generation of the i-th thermal power unit at time t;

[0129] The peak-shaving allocation amount for the j-th wind turbine at time t is:

[0130]

[0131] The peak-shaving cost of the u-th photovoltaic array at time t is:

[0132]

[0133] If the nuclear power plant where the v-th nuclear power unit is located has two or more units operating, the peak-shaving allocation amount for the v-th nuclear power unit at time t is:

[0134]

[0135] If only one unit is operating in the nuclear power plant where the v-th nuclear power unit is located, the peak-shaving allocation amount for the v-th nuclear power unit at time t is:

[0136]

[0137] in, This represents the rated power generation capacity of the nuclear power plant where the vth nuclear power unit is located.

[0138] The constraints include: system power balance constraints, thermal power unit operation constraints, new energy unit operation constraints, and wind / solar curtailment rate constraints, specifically:

[0139] The system power balance constraint is:

[0140]

[0141] Among them, P D-i,t P represents the peak-shaving capacity offered by the i-th thermal power unit willing to participate in peak shaving at time t; W-j,t P S-u,t P N-v,t These represent the power output values ​​of the j / u / v wind turbine, photovoltaic array, and nuclear power unit at time t, respectively.

[0142] Operating constraints for thermal power units include ramp rate constraints and unit output constraints;

[0143] The climbing rate constraint is:

[0144]

[0145] in, The maximum upward ramp rate and maximum downward ramp rate are the thermal power units within the system that are willing to participate in peak shaving; ΔT is the statistical period.

[0146] The unit output constraint is:

[0147]

[0148] in, These are the maximum and minimum technical output values ​​of thermal power units within the system that are willing to participate in peak shaving, respectively. The actual output value of the i-th thermal power unit at time t after the implementation of the peak-shaving ancillary services market;

[0149] The operating constraints for new energy generating units are:

[0150]

[0151]

[0152]

[0153] in, These represent the maximum power output of the j / u / v wind turbine, photovoltaic array, and nuclear power unit at time t, respectively.

[0154] The wind / solar curtailment rate constraint is:

[0155]

[0156]

[0157] Among them, l W l S These are the daily curtailment limits for wind and solar power, set according to the grid demand in different regions.

[0158] Step 3: Solve the objective function using simulation software to obtain the dynamic allocation limit adjustment parameters for each market participant. These parameters are: α1 for thermal power plants with load factors higher than the paid peak-shaving benchmark; α2 for unsubsidized wind farms and wind farms where the difference between the on-grid price and the environmental benchmark price of thermal power is less than 1 cent; α3 for ordinary wind farms; α4 for unsubsidized photovoltaic power plants and photovoltaic power plants where the difference between the on-grid price and the environmental benchmark price of thermal power is less than 1 cent; α5 for ordinary photovoltaic power plants; and α6 for nuclear power plants.

[0159] The dynamic adjustment strategy for the upper limit in this invention analyzes the shortcomings of existing methods for allocating costs for paid peak-shaving ancillary services and formulates a dynamic adjustment strategy for the upper limit of the trading entity based on Pareto equilibrium. A mathematical model is established with the goal of maximizing the comprehensive benefits of all market participants. Market-based mechanisms are used to redistribute the interests among market participants, tapping into the peak-shaving capacity of thermal power plants and realizing the absorption of new energy. A problem-oriented approach, considering practicality and specificity, is adopted to construct a market evaluation index system for peak-shaving ancillary services.

[0160] Based on the availability of the required data and the ability of the selected indicators to highlight key aspects, an indicator system is constructed from four aspects: market supply and demand, market behavior, market environmental protection, and market risk. The indicators selected for market supply and demand are the monthly supply-demand ratio of new energy and the supply-demand ratio during the heating season; the indicators selected for market behavior are the retention ratio; the indicators selected for market environmental protection are the clean energy consumption and CO2 / SO2 emission reduction; and the indicators selected for market risk are the price volatility.

[0161] Considering that March-April and September-October, months with abundant new energy resources (increased peak-shaving demand), and November-December, the heating season months (reduced peak-shaving resources), are key months that may lead to tight supply and demand for peak-shaving, an analysis of the supply-demand ratio for these key months is conducted. The supply-demand ratios for months with abundant new energy resources and the heating season are as follows:

[0162]

[0163]

[0164] Among them, Γ x The monthly supply-demand ratio is high due to abundant new energy resources; The electricity supply of peak-shaving ancillary service provider i in month x, where x is 3, 4, 9, or 10. For the electricity demand of buyer i in month x, which is a paid peak-shaving ancillary service; y This refers to the supply-demand ratio during the heating season. The electricity supplied by supplier i for paid peak-shaving ancillary services in month y; The electricity demand of buyer i for paid peak-shaving ancillary services in month y, where y is taken as 11 and 12.

[0165] The capacity retention ratio, the ratio of available ancillary service supply held to the total supply of available ancillary services, describes the supplier's capacity strategy. A higher retention ratio reflects a greater ability to manipulate the supply of ancillary services. Generally, when supply is tight, holding capacity is a strategy employed to attempt to exert market power, raise prices, and obtain higher profits. This requires close monitoring and appropriate regulatory measures to maintain market order and ensure prices reflect the supply-demand relationship reasonably. The retention ratio is:

[0166]

[0167] in, Theoretically, the maximum production capacity for peak shaving ancillary services; P dec,i The average daily actual number of declarations can be taken.

[0168] The growth rate of clean energy consumption is:

[0169]

[0170] Where, γ inc The growth rate of electricity consumption for clean energy; and These represent the amount of clean energy generated during statistical period t and statistical period t-1, respectively.

[0171] CO2 / SO2 emission reduction is:

[0172]

[0173]

[0174] in, CO2 emission reduction; To reduce SO2 emissions; β represents the peak-shaving capacity reduced by thermal power units per unit time; i denoted as coal consumption rate; c as carbon emission coefficient; γ as conversion rate of sulfur in coal to sulfur dioxide; s as total sulfur content in coal; and η as desulfurization efficiency. For simplicity, this paper assumes that no desulfurization device is used, i.e., η = 0.

[0175] Price volatility is the ratio of the difference between the highest and lowest clearing prices to the average price. In other words, it represents price volatility and thus market risk through the maximum range of price fluctuations. Price volatility is:

[0176]

[0177] in, The highest marginal price in the ancillary services market can be used as the actual price; in practice, the highest clearing price in the ancillary services market can be taken. To determine the minimum marginal electricity price, in practice, the minimum clearing price in the ancillary services market can be used; ρ cl This is the daily average clearing price.

[0178] Example

[0179] Using the actual load curve, renewable energy forecast curve, and renewable energy actual output curve of four typical days in a certain area as an example... Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown, according to Figure 2 , Figure 3 , Figure 4 , Figure 5 The maximum load values ​​are 23273MW, 24230MW, 26720MW, and 27295MW, respectively, and the minimum load values ​​are 19308MW, 19223MW, 23096MW, and 22643MW, respectively. The maximum output of new energy sources are 3676.6MW, 3557.9MW, 4749.7MW, and 5431.4MW, respectively, and the minimum output of new energy sources are 1513.6MW, 1703.1MW, 1142.5MW, and 511.1MW, respectively. A total of 72 thermal power units were operating on the selected typical day, and their specific parameters are shown in Table 1.

[0180] Table 1

[0181]

[0182]

[0183] To simplify calculations, a multi-objective optimization model was established with the goal of maximizing the combined revenue of wind and thermal power. Simulation software was used to solve the model, yielding adjustment parameters for the upper limit of peak-shaving ancillary service cost allocation for four typical daytime ordinary wind farms, as follows: Figure 6 As shown, from Figure 6 As can be seen, the adjustment parameters for each typical day dynamically change with different time periods, improving the flexibility of the allocation ceiling compared to the current coefficient of 0.6. Furthermore, most time periods within a day show improvements after optimization compared to the current strategy, indirectly revealing the drawback of the current strategy's inability to guarantee thermal power revenue under the constraint of the allocation ceiling. Secondly, based on the obtained adjustment parameters for the allocation ceiling of peak-shaving ancillary service fees for ordinary wind farms on four typical days, the dynamic allocation ceiling is calculated and compared with the current allocation ceiling and the actual amount to be allocated to wind power. Figure 7 , Figure 8 , Figure 9 , Figure 10 As shown, from Figure 7 , Figure 8 , Figure 9 , Figure 10 As can be seen, during most periods, the upper limit for the allocation of wind power peak-shaving ancillary service fees under the current strategy is lower than the actual amount that wind power should pay. This results in a significant peak-shaving fee gap in the deep peak-shaving transactions under the current strategy, reducing the incentive for thermal power to participate in peak shaving. Under the dynamic strategy, however, the upper limit for wind power allocation better matches the actual amount that wind power should pay. After considering the combined benefits of thermal and wind power, the upper limit coefficient for wind power allocation is optimized to change dynamically. The solution shows that the upper limit generally increases when the wind power output prediction deviation is large, and generally decreases when the wind power prediction deviation is small. Therefore, the prediction deviation of wind power output significantly affects the rationality of the dynamic allocation upper limit.

[0184] Figure 11 , Figure 12 , Figure 13 , Figure 14 A comparison of the revenue of four typical Rissho thermal power plants, by Figure 11 , Figure 12 , Figure 13 , Figure 14 It can be seen that the shaded area represents the additional peak-shaving revenue that thermal power would gain under the dynamic allocation limit compared to the current strategy, i.e., the potential revenue of thermal power. The potential revenue for thermal power within a day is RMB 189,600, RMB 310,500, RMB 459,700, and RMB 190,200, respectively. Among these, Figure 13 Thermal power has the greatest potential benefit because the cost of wind power peak shaving ancillary services has almost reached its upper limit on the day, leaving thermal power with a large potential profit margin. After dynamically adjusting the upper limit of the cost allocation, the due peak shaving revenue of thermal power is reasonably guaranteed.

[0185] Table 2 shows the wind power revenue analysis results under the two allocation strategies for each typical day, including cases with and without wind curtailment. It can be seen that, considering wind curtailment, wind power gains an additional 165,000 yuan, 96,000 yuan, 439,000 yuan, and 998,000 yuan per day, respectively; while without considering wind curtailment, the additional 171,000 yuan, 90,000 yuan, 449,000 yuan, and 1,009,000 yuan per day, respectively. Typical days 3 and 4 yield more profit than typical days 1 and 2, because the selected months are respectively high-wind months and low-wind months. This means that as wind power output increases, peak-shaving demand rises significantly, and the dynamic strategy ensures that wind power can obtain more profit.

[0186] Table 2

[0187]

[0188] Table 3 shows the market supply and demand and behavioral indicators for each typical day under the current and dynamic strategies.

[0189] Table 3

[0190]

[0191] Table 4 shows the market environmental and risk indicators for each typical day under the current and dynamic strategies.

[0192] Table 4

[0193]

[0194] It can be seen that the key monthly supply-demand ratio, a key market supply-demand indicator, is less than 1 on all typical days under both the current and dynamic strategies. This indicates that the peak-shaving ancillary service market is experiencing a supply shortage, with thermal power possessing strong market power and the ability to influence market prices. The peak-shaving potential has not been fully realized. However, under the dynamic strategy, the key monthly supply-demand ratio on each typical day increased by 0.048%, 0.071%, 0.052%, and 0.047%, respectively, indicating that the optimized allocation strategy can improve market competition and reduce the possibility of market power. The capacity retention ratio, a market behavior indicator on each typical day, decreased by 0.024%, 0.004%, 0.015%, and 0.018% under the dynamic strategy compared to the current strategy. This, to some extent, alleviates the control of peak-shaving supply by thermal power plants and reduces the possibility of them attempting to disrupt market order and seek high profits through limited supply and price increases. The three environmental indicators in the market reflect the energy conservation and emission reduction effects of the ancillary services market under the improved allocation strategy. It can be seen that compared to the current mechanism, the amount of clean energy consumed on each typical day increased by 0.179%, 0.246%, 0.403%, and 0.111%, respectively. Furthermore, under the dynamic strategy, CO2 and SO2 emissions per unit of electricity generation have also decreased to some extent. The peak-shaving ancillary services market is showing a positive development trend, which is conducive to its healthy and sustainable development. The market risk indicator, price volatility, reflects the volatility of marginal electricity prices. It can be seen that the degree of price volatility on each typical day is within a reasonable range, which can mitigate market risks and maintain transaction stability.

[0195] Through the above methods, this invention provides a dynamic adjustment strategy for the upper limit of cost sharing for market participants in peak shaving ancillary services. Based on Pareto equilibrium, it dynamically adjusts the upper limit of cost sharing for paid peak shaving, thus forming a dynamic peak shaving ancillary service market cost sharing strategy and implementation model. The strategy is optimized and solved using simulation software, and five representative evaluation indicators are selected to evaluate the market. Compared with the traditional cost sharing strategy for paid peak shaving ancillary services, it has better performance, higher application value, and social benefits of energy conservation and emission reduction.

Claims

1. A method for dynamically adjusting the upper limit of cost sharing for market participants in peak-shaving ancillary services, characterized in that: The specific steps are as follows: Step 1: Predict the power generation capacity of new energy sources and the load curve within the power system for the next day; The specific process is as follows: based on the renewable energy power generation capacity in a certain area and within a certain time period, predict the renewable energy power generation capacity and power system load forecast data for the next day; Step 2: Establish an objective function with the goal of maximizing the overall benefits for all market participants, and set constraints. The objective function established in step 2, with the goal of maximizing the overall benefit for all market participants, is as follows: (1) In equation (1), , , , For all thermal power plants, wind farms, photovoltaic power plants, and nuclear power plants participating in the peak-shaving ancillary services market, t The overall benefits at any given moment; For the first i Taiwan thermal power units t Battery consumption revenue during online activities; For the first i Taiwan thermal power units t Peak shaving compensation costs at all times; For the first i Taiwan thermal power units t The cost of providing peak-shaving ancillary services at all times; , , , The first i / j / u / v Taiwan's thermal power units, wind power units, photovoltaic arrays, and nuclear power units are in t Peak shaving sharing amount at any given time; , , , The first i / j / u / v Taiwan's thermal power units, wind power units, photovoltaic arrays, and nuclear power units are located in thermal power plants, wind farms, photovoltaic power stations, and nuclear power plants. t The upper limit for sharing the cost of peak-shaving ancillary services at any given time; For the first j Typhoon turbine units t Revenue from electricity generated through participation in the peak-shaving market; For the first j Typhoon turbine units t The profits gained from participating in cross-regional spot markets; , The first u / v Taiwan's photovoltaic arrays and nuclear power units t Revenue from electricity generated through participation in the peak-shaving market; No. i / j / u / v Taiwan's thermal power units, wind power units, photovoltaic arrays, and nuclear power units are located in thermal power plants, wind farms, photovoltaic power stations, and nuclear power plants. t The maximum cost-sharing limit for peak-shaving ancillary services is: （9） （10） （11） （12） in, , , , The first i / j / u / v Taiwan's thermal power units, wind power units, photovoltaic arrays, and nuclear power units are in t Actual power generation at any given moment; This indicates the adjustment parameter for the upper limit of the cost sharing for peak-shaving ancillary services of thermal power plants whose load factor is higher than the paid peak-shaving benchmark. The parameters for adjusting the upper limit of peak-shaving ancillary service cost sharing for unsubsidized wind farms and wind farms where the price difference between the grid-connected electricity price and the environmental benchmark price for thermal power is less than 1 cent are indicated. This indicates the adjustment parameter for the upper limit of cost sharing for peak-shaving ancillary services in ordinary wind farms. The parameters for adjusting the upper limit of peak-shaving ancillary service cost sharing for unsubsidized photovoltaic power plants and photovoltaic power plants whose feed-in tariff is less than 1 cent compared to the environmental benchmark tariff for thermal power are indicated. This indicates the parameter for adjusting the upper limit of the cost sharing for peak-shaving ancillary services of ordinary photovoltaic power plants. This indicates the adjustment parameters for the upper limit of cost sharing for peak-shaving ancillary services at nuclear power plants; Step 3: Solve the objective function using simulation software, and dynamically adjust the allocation upper limit according to the Pareto optimality principle to obtain the dynamic allocation upper limit adjustment parameters for each trading entity in the market.

2. The method for dynamically adjusting the upper limit of cost sharing for market participants in peak-shaving ancillary services as described in claim 1, characterized in that, No. i Taiwan thermal power units t The electricity consumption revenue from internet use at any given time is: （2） in, For the first i Taiwan thermal power units t Battery consumption during constant internet use The on-grid electricity price for thermal power units; No. i Taiwan thermal power units t The peak shaving compensation cost at any given time is: （3） in, For the first i Taiwan thermal power units t Time of the first The paid peak-shaving electricity provided by the government; For the first Actual clearing electricity price; k This is a correction factor, used during the heating season. During non-heating season ; No. i Taiwan thermal power units t The cost of providing peak-shaving ancillary services at all times is: （4） in, For coal prices; For the first i Taiwan thermal power units t Planned output value at any given moment; For the first i Taiwan thermal power units t The actual output value at any given moment; , For the first i Consumption characteristic parameters of a thermal power unit; No. j / u / v The revenue from grid-connected electricity generated by typhoon turbines, photovoltaic arrays, and nuclear power units participating in the peak-shaving market is as follows: （5） （6） （7） in, , , The first j / u / v Typhoon turbines, photovoltaic arrays, and nuclear power units are t Paid peak-shaving electricity purchased at any time; No. j Typhoon turbine units t The profits gained from participating in the cross-regional spot market are as follows: （8） in, For the first j Typhoon turbine units t Constantly participate in the sale of electricity in the cross-regional spot market; For inter-regional spot market electricity prices; No. i Taiwan thermal power units t The peak-shaving allocation amount for each moment is: （13） in, For the first i Taiwan thermal power units t Power generation after time correction; for t The total power generation of all thermal power units participating in the cost-sharing within the province / region at any given time; for t The total power generation of all wind turbine units participating in the cost-sharing within the province at any given time; for t The total power generation of all photovoltaic arrays participating in the cost-sharing within the province at any given moment; for t The total power generation of all nuclear power units in the province that participated in the cost-sharing at that time; The corrected power generation of thermal power units, wind power units, photovoltaic arrays, and nuclear power units can be expressed as: （14） （15） （16） （17） in, k / d / p / q / z For correction factor, For the first i Taiwan thermal power units t Time of the first The actual power generation of the unit; No. j Typhoon turbine units t The peak-shaving allocation amount for each moment is: （18） No. u Taiwan solar array t The peak-shaving allocation amount for each moment is: （19） If the first v When a nuclear power plant with two or more units is operating, the first... v Taiwanese nuclear power units t The peak-shaving allocation amount for each moment is: （20） If the first v When only one unit is running at the nuclear power plant where the Taiwanese nuclear power unit is located, the first v Taiwanese nuclear power units t The peak-shaving allocation amount for each moment is: （21） in, For the first v The rated power generation capacity of the nuclear power plant where the Taiwanese nuclear power unit is located.

3. The method for dynamically adjusting the upper limit of cost sharing for market participants in peak shaving ancillary services as described in claim 1, characterized in that, The constraints in step 2 include: system power balance constraints, thermal power unit operation constraints, new energy unit operation constraints, and wind / solar curtailment rate constraints, specifically: The system power balance constraint is: （22） in, For those willing to participate in peak shaving i Taiwan thermal power units t Peak-shaving capacity available at any time; , , The first j / u / v Typhoon turbines, photovoltaic arrays, and nuclear power units are t Output value at any given moment; Operating constraints for thermal power units include ramp rate constraints and unit output constraints; The climbing rate constraint is: （23） in, , The maximum upward ramp rate and the maximum downward ramp rate of thermal power units within the system that are willing to participate in peak shaving. For statistical periods; The unit output constraint is: （24） in, , These are the maximum and minimum technical output values ​​of thermal power units within the system that are willing to participate in peak shaving, respectively. For the first time after the implementation of the peak shaving ancillary services market i Taiwan thermal power units t The actual output value at any given moment; The operating constraints for new energy generating units are: （25） （26） （27） in, , , The first j / u / v Typhoon turbines, photovoltaic arrays, and nuclear power units are t Maximum output capacity at any given moment; The wind / solar curtailment rate constraint is: （28） （29） in, , These are the daily curtailment limits for wind and solar power, set according to the grid demand in different regions.

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