Multi-target low-carbon scheduling method considering carbon capture equipment and peak regulation initiative for electric power system containing energy storage

Abstract

The invention relates to the field of deep peak regulation of an energy storage auxiliary thermal power generating unit, in particular to a multi-target low-carbon dispatching method for an energy storage-containing electric power system considering carbon capture equipment and peak regulation initiative. According to the method, the hierarchical model is provided to make full use of the peak regulation advantage of the pumped storage double regulation capacity, the peak regulation capacity and the carbon capture level of thermal power are exerted, and a pumped storage and thermal power distribution scheme is decided. The upper layer utilizes the characteristics of rapid throughput power capacity and large capacity of pumped storage to follow the fluctuation of wind power and load, considers the full consumption of wind power, optimizes the output of the pumped storage unit with the minimum net load fluctuation and maximum pumped storage calling income as targets, and reduces the peak clipping and valley filling pressure of the thermal power generating unit on the optimized load. And the lower layer is based on the peak regulation capacity optimized by the upper layer, the peak regulation initiative constraint is considered, the lowest total operation cost of the system and the minimum wind curtailment amount are taken as targets, alternate iteration solving is carried out, and a thermal power internal power distribution scheme meeting the peak regulation initiative constraint and considering the economical efficiency of the system and the wind power consumption level is determined.

Description

Technical Field

[0001] The present invention relates to the field of deep peak regulation of energy storage-assisted thermal power units, and in particular to a multi-objective low-carbon scheduling method for an energy storage-containing power system that takes into account carbon capture equipment and peak regulation initiative. Background Art

[0002] In recent years, installed wind power capacity has increased annually, and the proportion of renewable energy has significantly increased. By the end of 2024, China's cumulative installed power capacity will reach 3.35 billion kilowatts, of which wind power will account for 520 million kilowatts, an 18.0% year-on-year increase. However, the high volatility and randomness of wind power increase the system's peak-shaving burden, widening the difference between net load peaks and valleys. To alleviate the peak-shaving dilemma, various power grids have implemented deep peak-shaving of thermal power units. However, deep peak-shaving has brought about a series of problems, including reduced economic efficiency of thermal power operations, increased lifespan loss of thermal power units, and increased carbon emissions. Furthermore, energy storage, due to its rapid power throughput capabilities and the increasing maturity of large-scale energy storage technology, has become an important peak-shaving method. Energy storage-assisted deep peak-shaving of thermal power units has become a research hotspot, but the high cost of energy storage technology is a key factor hindering its development. Therefore, it is necessary to establish a multi-source complementary coordination mechanism to fully tap the peak-shaving capacity of the power system, while meeting load demand and increasing renewable energy consumption, to achieve low-carbon, economical, and efficient operation of the power system. Summary of the Invention

[0003] The purpose of the present invention is to provide a multi-objective low-carbon scheduling method for a power system with energy storage that takes into account carbon capture equipment and peak-shaving initiatives. Through a two-layer optimization configuration method for energy storage-assisted grid peak-shaving, the total system peak-shaving cost is minimized and the output of energy storage and thermal power units is optimized.

[0004] The present invention provides a multi-objective low-carbon dispatching method for a power system with energy storage that takes into account carbon capture equipment and peak-shaving initiative, comprising:

[0005] The upper-level model is established with the goal of minimizing net load fluctuation and maximizing pumped storage utilization benefits;

[0006] The lower-level model is established with the goal of minimizing the total system operating cost and the amount of wind curtailment.

[0007] The upper model and the lower model are both multi-objective models, and the Pareto solution set of the multi-objective model is obtained based on the ε-constraint method;

[0008] Normalizing the objective function values ​​in the Pareto solution set to obtain a standardized evaluation matrix;

[0009] According to the standardized evaluation matrix, the improved CRITIC method is used to determine the weight of each objective in the multi-objective model;

[0010] Determine the positive ideal solution and the negative ideal solution according to the standardized evaluation matrix, and calculate the geometric distance and comprehensive satisfaction of each group of Pareto solutions in the Pareto solution set and the positive ideal solution and the negative ideal solution;

[0011] The set of solutions with the highest comprehensive satisfaction in the Pareto solution set is determined as the optimal compromise solution to serve as the system scheduling solution.

[0012] Preferably, the objective function for minimizing the net load fluctuation is:

[0013]

[0014] Where: P netload,t is the net load power at time t; P netload,ave is the average net load power; N pss is the number of pumped storage units; and are the power generation / pumping power of pumped storage unit k at time t; T is the total number of sampling points within the scheduling day;

[0015] The objective function to maximize the benefit of pumped storage is:

[0016]

[0017] Where: P netload,t is the net load power at time t; P netload,ave is the average net load power; N pss is the number of pumped storage units; is the electricity efficiency of the kth pumped storage unit at time t, is the start-up and shutdown loss cost of the kth pumped storage unit at time t;

[0018] The objective function for minimizing the total operating cost of the system is:

[0019]

[0020] Where: C i,t is the peak regulation cost of thermal power at time t; is the penalty cost for wind curtailment at time t; The operating cost of energy storage; is the cost of carbon trading;

[0021] The objective function for minimizing the amount of wind curtailment is:

[0022]

[0023] Where, P represents the predicted wind power at time t; t wind is the wind power grid connected at time t.

[0024] Preferably, the calculation formulas for the geometric distance and the comprehensive satisfaction are:

[0025]

[0026] in, represents the standardized objective function value x of the i-th group of Pareto solutions ij To the positive ideal solution The geometric distance, Represents the standardized objective function value x of the Pareto solution group ij To the negative ideal solution The geometric distance, r i It represents the comprehensive satisfaction of the Pareto solution of group i.

[0027] Preferably, when the multi-objective model is an upper-layer model, the Pareto solution set of the multi-objective model obtained based on the ε-constraint method includes:

[0028] According to the concept of ε-constraint method, the multi-objective model is expressed as:

[0029]

[0030] Where x is the variable to be optimized, representing the pumped-storage unit's pumped-discharge power; f1(x) and f2(x) are the two objective functions of the upper-level model, representing the net load volatility and the pumped-storage operation benefit, respectively; A(x) and B(x) are the equality and inequality constraints, representing the power equality constraint and the pumped-storage operation constraint, respectively.

[0031] Let f1(x) be the main objective function, f2(x) be the secondary objective function, transform it into an inequality constraint, and take f2(x) corresponding to x obtained by minf1(x) as the single objective as the lower limit Take f2(x) obtained when solving maxf2(x) as a single objective as the upper limit

[0032] Convert the multi-objective model into:

[0033]

[0034] Where, m represents the number of segments into which the value range of f2 is divided;

[0035] By updating ε, we can obtain the multi-objective Pareto solution set, which is expressed as:

[0036]

[0037] Where y ij is the jth objective function value corresponding to the i-th Pareto optimal solution.

[0038] Preferably, the standardized evaluation matrix X after the standardization process is expressed as:

[0039] X=(x ij ) N×2

[0040] Among them, for the efficiency goal of the bigger the better,

[0041]

[0042] For cost-oriented goals, the smaller the better.

[0043]

[0044] As a preference, the improved CRITIC method is used to determine the weights of each target in the multi-target model, and the weight N of target j is j Obtained by the following formula:

[0045]

[0046] Where C j Indicates the amount of information contained in target j.

[0047] Preferably, the calculation formula for the amount of information contained in the target j is:

[0048]

[0049] Where, σ j is the Gini coefficient of target j, σ j ∈[0,1],σ j The larger the value is, the greater the contrast intensity of the target is; j is the information entropy utility value of target j; η ij The correlation coefficient between target i and target j.

[0050] Preferably, the formulas for the positive ideal solution and the negative ideal solution are:

[0051]

[0052] Where max(X(:,1)) represents the maximum value in the first column after normalization obtained by the ε-constraint method. and denote the positive ideal solution and negative ideal solution of target j respectively.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] This invention is a multi-objective, low-carbon dispatch method for power systems with energy storage, taking into account carbon capture equipment and proactive peak-shaving. The upper-level model leverages the rapid power throughput and large capacity of pumped storage to track fluctuations in wind power and load, taking into account full wind power absorption. The model optimizes the output of pumped storage units with the goals of minimizing net load fluctuations and maximizing pumped storage utilization, thereby reducing the peak-shaving and valley-filling pressure on thermal power units for the optimized load. The lower-level model, based on the peak-shaving capacity optimized by the upper-level model, comprehensively considers the deep peak-shaving effect of thermal power units, the carbon capture efficiency of carbon capture, and the peak-shaving and valley-filling effect of energy storage. With the goals of minimizing total system operating costs and minimizing wind curtailment, it uses proactive peak-shaving constraints to ensure that all entities benefit from peak-shaving transactions. Through alternating iterations, the solution determines an internal power allocation scheme for the thermal power plant that balances system economics and wind power absorption, thereby maximizing the utilization of all peak-shaving resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is the wind-fire-storage peak-shaving hierarchical scheduling model of the present invention;

[0056] Figure 2 This is a flow chart for solving the model in the present invention;

[0057] Figure 3 is the wind power-load prediction curve in the present invention;

[0058] Figure 4 This is a diagram showing the peak-shaving effect of pumped storage in the present invention;

[0059] Figure 5 This is a diagram of the unit output optimization results for each scenario in the present invention. DETAILED DESCRIPTION

[0060] It should be noted that: the technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other. The term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects before and after are in an "or" relationship.

[0061] Example 1

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0063] The present invention proposes a multi-objective low-carbon dispatching method for a power system with energy storage that takes into account carbon capture equipment and peak regulation initiative, with the aim of achieving the pumped storage power generation power P t pss and the internal power of the thermal power plant P t G The rational allocation of wind power can fully utilize the peak-shaving capabilities of each peak-shaving entity under the premise of ensuring the benefits of pumped storage and the initiative of deep regulation of thermal power, improve the economic efficiency of system operation, promote wind power consumption and reduce carbon emissions. It includes the following contents:

[0064] 1) Joint system two-layer model

[0065] Since wind-thermal-storage combined peak regulation is a complex integer nonlinear programming problem, in order to simplify the computational complexity and improve the solution speed, Figure 1-Figure 2 Based on the consideration of the peak-shaving sequence of thermal power and pumped storage, this embodiment proposes a hierarchical model to fully utilize the peak-shaving advantage of the double regulation capacity of pumped storage, give full play to the peak-shaving capacity and carbon capture level of thermal power, and decide on the power allocation plan of pumped storage and thermal power.

[0066] The upper layer uses the pumped storage's ability to quickly handle power and its large capacity to follow the fluctuations of wind power and load, taking into account the full absorption of wind power, and optimizes the output of the pumped storage unit with the goal of minimizing net load fluctuations and maximizing pumped storage call benefits, thereby reducing the pressure of thermal power units on peak shaving and valley filling of the optimized load.

[0067] The lower layer is based on the peak-shaving capacity optimized by the upper layer, taking into account the peak-shaving initiative constraints, with the goal of minimizing the total system operating cost and the minimum wind curtailment, and alternately iteratively solves the problem to determine the internal power allocation plan of thermal power that meets the peak-shaving initiative constraints and takes into account the system's economy and wind power consumption level.

[0068] The double-layer model structure diagram of this embodiment is as follows Figure 1 shown.

[0069] 2) Optimize the model objective function

[0070] (a) Upper-level model objective function

[0071] The upper model optimizes the output of the pumped storage unit with the goal of minimizing net load fluctuation and maximizing the benefit of pumped storage. Its sub-objective functions are as follows:

[0072] (1) Minimum net load fluctuation

[0073] To fully utilize the peak-shaving capacity of pumped storage to smooth out intermittent renewable energy power fluctuations, effectively minimize the variance of conventional unit output disturbances, significantly reduce losses caused by frequent start-stop or wide-band regulation of thermal power, and effectively extend the service life of the units while improving the overall economic efficiency of the system, this embodiment establishes the following objective function:

[0074]

[0075] Where: P netload,t is the net load power at time t; P netload,ave is the average net load power; N pss is the number of pumped storage units; and are the power generation and pumping power of pumped storage unit k at time t; T is the total number of sampling points in the dispatching day; P t d and are the load and wind power forecast at time t respectively.

[0076] (2) Pumped storage maximizes efficiency

[0077] Pumped storage units have different power losses due to different water-power conversion coefficients under different working conditions. The power efficiency of the kth pumped storage unit at time t is for:

[0078]

[0079] Where: P price,t is the time-of-use electricity price of the power grid at time t; Δt is the scheduling time interval.

[0080] The pumped storage unit has physical losses during the frequent start-up and shutdown process. The start-up and shutdown loss cost of the kth pumped storage unit at time t is for:

[0081]

[0082] Where: and are the loss costs of a single start and stop of the pumped storage unit; are Boolean variables indicating whether the pumped storage unit k is in power generation and pumping conditions at time t, respectively, with 1 representing yes and 0 representing no.

[0083] The call benefit of pumped storage can be expressed as the electricity revenue of pumped storage Start-stop costs The difference between , the target can be expressed as:

[0084]

[0085] Where: P netload,t is the net load power at time t; P netload,ave is the average net load power; N pss is the number of pumped storage units; and are the power generation and pumping power of pumped storage unit k at time t; T is the total number of sampling points in the dispatching day; P t d and are the load and wind power forecast at time t respectively.

[0086] (b) Lower-level model objective function

[0087] If the sole objective is to optimize economic efficiency, the peak-shaving depth of the units will be minimized, resulting in a large amount of wind curtailment. If the sole objective is to minimize wind curtailment, the system's peak-shaving economic efficiency will be reduced. Therefore, the lower-level model in this embodiment aims to minimize total system operating costs and minimize wind curtailment.

[0088] (1) The lowest total system operating cost

[0089] The lower-level model optimizes the system’s total operating cost, which includes thermal power peak regulation costs, wind curtailment penalty costs, CO2 storage costs, energy storage operating costs, and carbon trading costs, as the objective function. It can be expressed as:

[0090]

[0091] Where: γ ES Represents the charging and discharging cost coefficient of energy storage, P t c ,P t d They represent the charging power and discharging power of the energy storage at time t respectively.

[0092] Where: C i,t is the peak regulation cost of thermal power at time t; is the penalty cost for wind curtailment at time t; The operating cost of energy storage; is the carbon trading cost, which is calculated using a stepped carbon trading model.

[0093] At different peak load regulation depth stages, the total operating cost of thermal power units can be expressed as:

[0094]

[0095] Where: represents the coal consumption cost of thermal power unit i at time t; is the power generation capacity of thermal power unit; a i , b i , c i is the coefficient of the consumption characteristic function of the i-th thermal power unit; represents the loss cost of thermal power unit i at time t, β is the actual operation loss coefficient of the thermal power plant; S i is the purchase cost of thermal power unit i; N fis the number of rotor cracking cycles of thermal power units, N f (P) = 0.005778P 3 -2.682P 2 +484.8P-8411; represents the oil cost of the unit at time t, is the oil input of thermal power unit i at time t; S oil The oil price for the season.

[0096]

[0097] Where: K qwind is the wind curtailment penalty coefficient; P represents the predicted wind power at time t; t wind is the wind power grid-connected power at time t;

[0098] The CO2 captured by the carbon capture equipment needs to be transported to a designated location for storage. The CO2 storage cost can be expressed as:

[0099]

[0100] Where: θ is the cost of storing unit CO2, E i,t,r is the total amount of CO2 captured by unit i at time t.

[0101] Energy storage operating costs It can be expressed as:

[0102]

[0103] (2) Minimum wind curtailment

[0104]

[0105] 3) Optimization model solution method

[0106] Taking into account that both the upper and lower models are multi-objective problems and there is no unique global optimal solution, this embodiment designs an improved ε-constraint-top-ideal solution ranking method (TOPSIS) to obtain the optimal compromise solution. First, the ε-constraint method is introduced and ε is updated to obtain the multi-objective Pareto solution set. Secondly, in order to obtain the optimal compromise solution of each objective function, the TOPSIS method is used to calculate the progress of each objective function to determine the satisfaction of each optimal solution in each objective function. Finally, this embodiment improves the CRITIC method by analyzing the data of the Pareto solution set and measuring the relative importance of each objective to determine the weight, and then weighting the satisfaction of each group of Pareto solutions to determine the comprehensive satisfaction of the group of solutions.

[0107] (a) ε-constraint method

[0108] According to the concept of ε-constraint method, the dual-objective model can be expressed as:

[0109]

[0110] Taking the above model as an example, x is the variable to be optimized, which represents the pumped discharge power of the pumped storage unit; f1(x) and f2(x) are two objective functions, which represent the net load volatility and the pumped storage call efficiency respectively; A(x) and B(x) are equality constraints and inequality constraints, respectively, representing the power equality constraint and pumped storage operation constraint.

[0111] Let f1(x) be the main objective function, f2(x) be the secondary objective function, transform it into an inequality constraint, and take f2(x) corresponding to x obtained by minf1(x) as the single objective as the lower limit Take f2(x) obtained when solving maxf2(x) as a single objective as the upper limit The original formula can be transformed into:

[0112]

[0113] Where, N represents the number of segments into which the value range of f2 is divided.

[0114] After this transformation, the multi-objective Pareto solution set can be obtained by updating ε, which can be expressed as:

[0115]

[0116] Where y ij is the jth objective function value corresponding to the i-th Pareto optimal solution.

[0117] (b) Improved sorting method of approaching ideal solutions

[0118] First, a standardized decision matrix is ​​established. Since the types and dimensions of the selected evaluation targets are different, this embodiment first standardizes the target values ​​to obtain a standardized evaluation matrix X = (x ij ) m×2 , the standardization processing method is as follows:

[0119] 1) Profitability goals (the bigger the better)

[0120]

[0121] 2) Cost target (the smaller the better)

[0122]

[0123] Secondly, determine the weight of each target. The calculation formula of the traditional CRITIC method has problems such as the standard deviation has a dimension and the correlation coefficient may be negative, and the traditional CRITIC method cannot measure the discreteness of the distribution of the objective function value. In response to the problems of the traditional CRITIC method, this embodiment improves it by introducing the Gini coefficient and the information entropy utility value to measure the contrast intensity of the target and the discreteness of the distribution of the objective function value. Based on the Pareto solution set obtained by the ε-constraint method, the volatility and discreteness of the same target (vertical) and the conflict between different targets (horizontal) are mathematically quantified to determine the weight of each target. The weight ω of target j j It can be obtained by the following formula:

[0124]

[0125] Where C j represents the amount of information contained in target j; σ j is the Gini coefficient of target j, σ j ∈[0,1],σ j The larger the value is, the greater the contrast intensity of the target is; j is the information entropy utility value of target j; η ij The correlation coefficient between target i and target j. Again, determine the positive ideal solution and the negative ideal solution, the formula is as follows:

[0126]

[0127] Where max(X(:,1)) represents the maximum value in the first column after normalization obtained by the ε-constraint method.

[0128] Finally, the geometric distance and comprehensive satisfaction of each group of Pareto solutions and the positive ideal solution and negative ideal solution are calculated using the following formula:

[0129]

[0130] The group with the highest overall satisfaction [y i1 y i2 ] is the optimal compromise solution.

[0131] The overall solution process is as follows Figure 2 shown.

[0132] 4) Constraints

[0133] (a) Pumped storage units are subject to their own characteristics and need to meet the following operating constraints: storage capacity constraint, flow constraint, unit single operating condition constraint, and start-stop frequency constraint.

[0134]

[0135] Where: is the upper reservoir capacity of the pumped storage power station at time t; η p and η g are the water-to-electricity conversion coefficients of the unit under pumping and power generation conditions respectively; and are the maximum and minimum capacities of the upper reservoir, respectively; and are the upper reservoir capacities at the beginning and end of the dispatch period, respectively; k is the upper and lower limits of the power of the pumped storage unit under power generation and pumping conditions respectively; M is the maximum number of starts and stops of a single pumped storage unit.

[0136] (b) Power balance constraints

[0137]

[0138] Where: is the fixed energy consumption of unit i at time t; is the operating energy consumption of the unit at time t; i is the carbon emission per unit output of unit i; E i,t is the total amount of CO2 produced by unit i at time t; E i,t,r is the total amount of CO2 captured by unit i at time t; is the carbon capture efficiency of unit i; γ E The energy consumption required for the unit to capture unit CO2.

[0139] (c) Thermal power operation constraints

[0140] The model of this embodiment takes into account the uncertainty of wind power and load, and reserves the deviation between its day-ahead output and actual output. The thermal power units must meet the following requirements:

[0141]

[0142] Where: and are the maximum upward and downward output change limits of unit i respectively; T i,off and T i,on are the minimum continuous shutdown and running time of unit i respectively; α load and α w are the reserve coefficients considering the uncertainty of load and wind power, which are set to 0.05 and 0.1 respectively.

[0143] (d) CCC equipment and wind power constraints

[0144] CCS equipment has ramp rate constraints and maximum operating energy consumption constraints.

[0145]

[0146] Where: Indicates the energy consumption of CCS equipment under maximum operating conditions; and They represent the upper and lower limits of the ramp rate of carbon capture unit i respectively.

[0147] (e) Energy storage-related constraints

[0148]

[0149] Where: E s is the rated capacity of energy storage; and They are the maximum charging power and the maximum discharging power respectively; is the state of charge of the energy storage system at time t; and are the upper and lower limits of the state of charge of the energy storage system respectively; and are the SOC states at the beginning and end of energy storage respectively; η c and η d are the charging and discharging efficiencies of energy storage at time t respectively; and Boolean variables representing whether the energy storage is in the charging and discharging states, respectively, with 1 for yes and 0 for no.

[0150] (f) Peak load shaving initiative constraints

[0151] In order to increase the enthusiasm of thermal power units for deep peak regulation, the "Northeast Electric Power Auxiliary Service Market Operation Rules" clearly state that during the non-heating period, the deep peak regulation compensation price for thermal power units adopts a "ladder-type" quotation, and compensation is given to units participating in deep peak regulation according to the peak regulation depth. The peak regulation compensation can be expressed as:

[0152]

[0153] Where: is the compensation obtained by thermal power unit i participating in deep peak regulation at time t; δ g,peak Compensation for unit electricity consumption of deep peak regulation; is the grid-connected power of thermal power unit i, It is the deep peak regulation space of thermal power unit i at time t.

[0154] In the deep peak load regulation auxiliary service, the compensation costs borne by the thermal power units and wind power units in the system that do not participate in deep peak load regulation are shared according to the proportion of grid-connected electricity and the proportion of total wind power generation on that day. The peak load regulation compensation sharing model is:

[0155] The compensation costs borne by thermal power units that only participate in conventional peak regulation are:

[0156]

[0157] The compensation costs borne by wind turbines are:

[0158]

[0159] Where: The amount of electricity consumed by thermal power unit i in conventional peak regulation; is the grid-connected power of wind turbine i; N G and N wind are the number of thermal power units and the number of wind power units respectively.

[0160] This example describes the peak-shaving willingness of each peak-shaving entity by establishing a profit difference model before and after peak-shaving:

[0161] (1) Constraints on the initiative of thermal power plants to participate in deep peak regulation It can be expressed as:

[0162]

[0163] Where: δ g represents the on-grid electricity price of thermal power, They respectively represent the benefits before and after thermal power participates in deep peak regulation.

[0164] (2) Active constraints on wind power plants participating in deep peak regulation It can be expressed as:

[0165]

[0166] Where: δ wind represents the on-grid electricity price of wind power, They respectively represent the benefits before and after thermal power participates in deep peak regulation.

[0167] The configuration adopted is: pumped storage with an installed capacity of 150MW and thermal power units with a total installed capacity of 3200MW. The parameters of the pumped storage and thermal power units are shown in Tables 1 and 2.

[0168] Table 1 Pumped storage unit operating parameters

[0169]

[0170] Table 2 Composition and parameters of thermal power units

[0171]

[0172] Since wind power output is random, this embodiment clusters wind power output scenarios based on the fuzzy C-means clustering algorithm, sets the number of clusters to 5 to reduce wind power output scenarios, and performs expected target calculations on each scenario, and performs simulation analysis on the scenario with the highest probability. The local power grid wind power data and load power data are as follows: Figure 3shown.

[0173] The peak regulation effect of pumped storage obtained by the optimization method of this embodiment is as follows: Figure 4 shown.

[0174] Through optimization of the upper-level model, the net load peak-to-valley difference was reduced from 941.5 MWh without the pumped storage plant to 781.5 MW with the pumped storage plant. This reduced the net load peak-to-valley difference and variance by 16.99% and 23.66%, respectively. This significantly improved the degree of fluctuation, reducing the peak-shaving pressure on thermal power units and the losses caused by frequent output fluctuations.

[0175] In order to conduct a comparative analysis of the lower-layer scheduling strategies of this embodiment, five different scenarios are set to verify the effectiveness of the lower-layer model.

[0176] Scenario 1: Thermal power units can only perform conventional peak load regulation.

[0177] Scenario 2: Thermal power units can achieve deep peak regulation without installing carbon capture equipment or energy storage.

[0178] Scenario 3: Thermal power units can achieve deep peak regulation without installing carbon capture equipment, including energy storage.

[0179] Scenario 4: Thermal power units can be deeply peaked and equipped with carbon capture equipment, but without energy storage.

[0180] Scenario 5: Thermal power units can implement deep peak regulation and be equipped with carbon capture equipment, including energy storage.

[0181] The optimization results of each scene are as follows Figure 5 The economic comparison of each scenario is shown in Table 3.

[0182] Table 3 Comparison of economic efficiency of various scenarios

[0183]

[0184]

[0185] Scenario 5 adds carbon capture and energy storage to thermal power units performing deep peaking. During low-load periods, the combined effects of energy storage and carbon capture significantly reduce the peaking pressure on the thermal power units, allowing the units to operate in a conventional peaking mode. This reduces peaking costs while allowing the actual grid power to remain below the oil-fired peaking power (Pa). This increases wind power access, reducing the wind curtailment penalty by 59.4% compared to Scenario 2. During peak load periods, the energy storage system discharges, freeing up more power for the carbon capture equipment, compensating for the insufficient carbon capture capacity. This reduces carbon trading costs by 10,300 yuan compared to Scenario 4, lowering the overall system operating cost.

[0186] In order to verify the low-carbon advantage of the constructed optimization model in the deep peak-shaving scenario, a comparative analysis of the wind curtailment and carbon emissions under each scenario is performed, as shown in Table 4.

[0187] Table 4 Wind power consumption rate and carbon emissions in each scenario

[0188]

[0189] Scenario 5 has the highest wind power grid connection and the highest wind power absorption rate during low-load periods. Compared with Scenario 2, the addition of carbon capture and energy storage equipment in Scenario 5 not only increases the power generation of thermal power units while meeting the active peak-shaving constraint, achieving economic optimization of system operating costs, but also deepens the peak-shaving depth of thermal power units, reduces the grid connection power of thermal power, and provides more space for wind power grid connection. The amount of wind curtailment is reduced by 1.41%, validating the advantages of the established model in promoting renewable energy absorption while ensuring economic efficiency.

[0190] Compared with Scenario 2, Scenario 4 adds carbon capture equipment to the thermal power units, which can capture the carbon emissions of the thermal power units, reduce the system's carbon emissions, and deepen the peak-shaving depth of the thermal power units, accommodating more wind power. Compared with Scenario 4, Scenario 5 considers the coordinated peak-shaving of energy storage and carbon capture units, and reduces the amount of wind curtailment by 52.95% compared with the peak-shaving of carbon capture units alone. The addition of energy storage compensates for the insufficient carbon capture level of thermal power during peak hours, and carbon emissions are reduced by 162.22 tons, proving the superiority of combined peak-shaving of carbon capture and energy storage.

[0191] The above analysis shows that energy storage-assisted deep peak regulation of thermal power units has certain advantages in terms of system peak regulation effect and system peak regulation economy.

[0192] In order to demonstrate the advantages of the model of this embodiment in improving the peak-shaving initiative of each peak-shaving entity, this embodiment compares and analyzes the impact of deep peak-shaving on the benefits of both wind power and thermal power, as shown in Table 6.

[0193] Table 6 Peak load benefits in various scenarios

[0194]

[0195] For the same installed capacity, scenarios 1 through 5 show decreasing wind curtailment rates and total system operating costs, increasing wind power revenue, and increasing thermal power revenue, respectively, scenario 2, scenario 3, scenario 1, scenario 4, and scenario 5. The model proposed in this example significantly increases the enthusiasm of power generation companies to participate in peak load regulation while ensuring system economics.

[0196] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0197] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0198] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0199] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0200] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A multi-objective low-carbon dispatch method for a power system with energy storage that considers carbon capture equipment and peak-shaving initiative, characterized by: include: The upper-level model is established with the goal of minimizing net load fluctuation and maximizing pumped storage utilization benefits; The lower-level model is established with the goal of minimizing the total system operating cost and the amount of wind curtailment. The upper model and the lower model are both multi-objective models, and the Pareto solution set of the multi-objective model is obtained based on the ε-constraint method; Normalizing the objective function values ​​in the Pareto solution set to obtain a standardized evaluation matrix; According to the standardized evaluation matrix, the improved CRITIC method is used to determine the weight of each objective in the multi-objective model; Determine the positive ideal solution and the negative ideal solution according to the standardized evaluation matrix, and calculate the geometric distance and comprehensive satisfaction of each group of Pareto solutions in the Pareto solution set and the positive ideal solution and the negative ideal solution; The set of solutions with the highest comprehensive satisfaction in the Pareto solution set is determined as the optimal compromise solution to serve as the system scheduling solution.

2. The multi-objective low-carbon dispatching method for a power system with energy storage considering carbon capture equipment and peak load regulation initiative according to claim 1 is characterized in that: The objective function for minimizing the net load fluctuation is: Where: P netload,t is the net load power at time t; P netload,ave is the average net load power; N pss is the number of pumped storage units; and are the power generation / pumping power of pumped storage unit k at time t; T is the total number of sampling points within the scheduling day; The objective function to maximize the benefit of pumped storage is: Where: P netload,t is the net load power at time t; P netload,ave is the average net load power; N pss is the number of pumped storage units; is the electricity efficiency of the kth pumped storage unit at time t, is the start-up and shutdown loss cost of the kth pumped storage unit at time t; The objective function for minimizing the total operating cost of the system is: Where: C i,t is the peak regulation cost of thermal power at time t; is the penalty cost for wind curtailment at time t; The operating cost of energy storage; is the cost of carbon trading; The objective function for minimizing the amount of wind curtailment is: Where, P represents the predicted wind power at time t; t wind is the wind power grid connected at time t.

3. The multi-objective low-carbon dispatching method for a power system with energy storage considering carbon capture equipment and peak load regulation initiative according to claim 1 is characterized in that: The calculation formulas for the geometric distance and comprehensive satisfaction are: in, represents the standardized objective function value x of the i-th group of Pareto solutions ij To the positive ideal solution The geometric distance, Represents the standardized objective function value x of the Pareto solution group ij To the negative ideal solution The geometric distance, r i It represents the comprehensive satisfaction of the Pareto solution of group i.

4. The multi-objective low-carbon dispatch method for a power system with energy storage considering carbon capture equipment and peak load regulation initiative according to claim 3 is characterized in that: When the multi-objective model is an upper-layer model, the Pareto solution set of the multi-objective model obtained based on the ε-constraint method includes: According to the concept of ε-constraint method, the multi-objective model is expressed as: Where x is the variable to be optimized, representing the pumped-storage unit's pumped-discharge power; f1(x) and f2(x) are the two objective functions of the upper-level model, representing the net load volatility and the pumped-storage operation benefit, respectively; A(x) and B(x) are the equality and inequality constraints, representing the power equality constraint and the pumped-storage operation constraint, respectively. Let f1(x) be the main objective function, f2(x) be the secondary objective function, transform it into an inequality constraint, and take f2(x) corresponding to x obtained by minf1(x) as the single objective as the lower limit Take f2(x) obtained when solving maxf2(x) as a single objective as the upper limit Convert the multi-objective model into: Where, m represents the number of segments into which the value range of f2 is divided; By updating ε, we can obtain the multi-objective Pareto solution set, which is expressed as: Where y ij is the jth objective function value corresponding to the i-th Pareto optimal solution.

5. The multi-objective low-carbon dispatching method for a power system with energy storage considering carbon capture equipment and peak load regulation initiative according to claim 3 is characterized in that: The standardized evaluation matrix X after standardization is expressed as: X=(x ij ) m×2 Among them, for the efficiency goal of the bigger the better, For cost-oriented goals, the smaller the better.

6. The multi-objective low-carbon dispatch method for a power system with energy storage considering carbon capture equipment and peak load initiative according to claim 5, characterized in that: The improved CRITIC method is used to determine the weight of each target in the multi-target model, and the weight of target j ω j Obtained by the following formula: Where C j Indicates the amount of information contained in target j.

7. The multi-objective low-carbon dispatch method for a power system with energy storage considering carbon capture equipment and peak load initiative according to claim 6, characterized in that: The calculation formula for the amount of information contained in the target j is: Where σ j is the Gini coefficient of target j, σ j ∈[0,1],σ j The larger the value is, the greater the contrast intensity of the target is; j is the information entropy utility value of target j; η ij The correlation coefficient between target i and target j.

8. The multi-objective low-carbon dispatching method for a power system with energy storage considering carbon capture equipment and peak load regulation initiative according to claim 3 is characterized in that: The formulas for the positive ideal solution and the negative ideal solution are: Where max(X(:,1)) represents the maximum value in the first column after normalization obtained by the ε-constraint method; and denote the positive ideal solution and negative ideal solution of target j respectively.

Images