A multi-objective low-carbon scheduling method for power systems with energy storage considering carbon capture equipment and peak shaving initiative

Abstract

The present application relates to the field of energy storage auxiliary thermal power unit deep peak regulation, and is a multi-objective low-carbon scheduling method for a power system with energy storage considering carbon capture equipment and peak regulation initiative. The present application proposes a hierarchical model to fully utilize the peak regulation advantages of the double regulation capacity of pumped storage, to exert the peak regulation capacity and carbon capture level of thermal power, and to determine the pumped storage and thermal power distribution scheme. The upper layer utilizes the rapid throughput power capacity and large capacity characteristics of pumped storage to follow the fluctuations of wind power and load, considers full wind power consumption, optimizes the pumped storage unit output with the minimum net load fluctuation and the maximum pumped storage calling revenue as the target, and reduces the peak load shaving and valley filling pressure of thermal power units on the optimized load. The lower layer determines the internal power distribution scheme of thermal power based on the peak regulation capacity optimized by the upper layer, considers the peak regulation initiative constraint, and alternately iteratively solves the target of the minimum total system operation cost and the minimum wind power abandonment to determine the internal power distribution scheme of thermal power that meets the peak regulation initiative constraint and considers the economy and wind power consumption level of the system.

Description

Technical Field

[0001] This invention relates to the field of deep peak shaving of thermal power units assisted by energy storage, and specifically to a multi-objective low-carbon dispatching method for power systems with energy storage that takes into account carbon capture equipment and the initiative of peak shaving. Background Technology

[0002] In recent years, wind power installed capacity has increased year by year, and the proportion of new energy sources has significantly improved. By the end of 2024, the cumulative installed power capacity in China reached 3.35 billion kilowatts, of which wind power accounted for 520 million kilowatts, a year-on-year increase of 18.0%. However, the strong volatility and randomness of wind power have increased the peak-shaving burden on the system, widening the gap between net load peak and valley. To alleviate the peak-shaving dilemma, various power grids have launched deep peak-shaving for thermal power units. However, deep peak-shaving has brought about a series of problems, such as decreased economic efficiency of thermal power operation, increased lifespan loss of thermal power units, and increased carbon emissions. In addition, energy storage, with its ability to rapidly process power and the increasing maturity of large-scale energy storage technology, has also become an important peak-shaving means. Energy storage-assisted deep peak-shaving of thermal power units has become a research hotspot, but the high cost of energy storage technology is one of the key factors affecting 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, improve the absorption of renewable energy while meeting load demand, and achieve low-carbon, economical, and efficient operation of the power system. Summary of the Invention

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

[0004] This invention provides a multi-objective low-carbon dispatch method for power systems with energy storage that considers carbon capture equipment and proactive peak shaving, comprising:

[0005] An upper-level model is established with the goal of minimizing net load fluctuations and maximizing the benefits of pumped storage and dispatch.

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

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

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

[0009] Based on the standardized evaluation matrix, the weights of each objective in the multi-objective model are determined using the improved CRITIC method.

[0010] Based on the standardized evaluation matrix, the positive and negative ideal solutions are determined, and the geometric distance and overall satisfaction of each Pareto solution in the Pareto solution set with the positive and negative ideal solutions are calculated.

[0011] The solution set with the highest overall satisfaction in the Pareto solution set is determined as the optimal compromise solution and used as the system scheduling scheme.

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

[0013]

[0014] In the formula: P netload,t P represents the net load power at time t. netload,ave N represents the average net load power. pss This refers to the number of pumped storage units; and , where represents the power generation / pumping power of pumped storage unit k at time t; T represents the total number of sampling points during the dispatch day;

[0015] The objective function that maximizes the efficiency of pumping is:

[0016]

[0017] In the formula: P netload,t P represents the net load power at time t. netload,ave N represents the average net load power. pss This refers to the number of pumped storage units; Let the power efficiency of the k-th pumped-storage unit at time t be denoted as . Let $t$ be 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] In the formula: C i,t Let t be the peak-shaving cost of thermal power plants; The cost of wind curtailment at time t; For energy storage operating costs; For carbon trading costs;

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

[0022]

[0023] In the formula, P represents the predicted wind power at time t; t wind Let t be the grid-connected power of wind power.

[0024] Preferably, the formulas for calculating the geometric distance and overall satisfaction are as follows:

[0025]

[0026] in, Let x represent the standardized objective function value of the i-th Pareto solution. ij To the ideal solution geometric distance, This represents the standardized objective function value x of the Pareto solution set. ij To the negative ideal solution The geometric distance, r i This represents the overall satisfaction level of the i-th Pareto solution.

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

[0028] Based on the concept of the ε-constraint method, the multi-objective model can be represented as:

[0029]

[0030] In the formula, x is the variable to be optimized, representing the pumping and discharging power of the pumped storage unit; f1(x) and f2(x) are two objective functions of the upper-level model, representing the net load fluctuation and the pumped storage dispatch benefit, respectively; A(x) and B(x) are the equality constraint and inequality constraint, respectively, representing the power equality constraint and the pumped storage operation constraint;

[0031] Let f1(x) be the primary objective function and f2(x) be the secondary objective function. This is transformed into an inequality constraint. The lower limit is taken as the f2(x) corresponding to x obtained by minimizing f1(x) as the single objective. When maxf2(x) is used as the upper bound for solving a single objective problem, f2(x) is taken as the upper bound.

[0032] Transform the multi-objective model into:

[0033]

[0034] In the formula, m represents the number of segments into which the range of values ​​for f2 is divided;

[0035] The multi-objective Pareto solution set is obtained by updating ε, and is represented as:

[0036]

[0037] In the formula, y ij This is the j-th objective function value corresponding to the i-th Pareto optimal solution.

[0038] As a preferred embodiment, the standardized evaluation matrix X after standardization is represented as follows:

[0039] X = (x ij ) N×2

[0040] Among these, for benefit objectives that are as large as possible,

[0041]

[0042] For cost-oriented objectives where smaller is better

[0043]

[0044] As a preferred method, the improved CRITIC method is used to determine the weights of each objective in the multi-objective model, with the weight N of objective j being... j The following formula can be used to obtain:

[0045]

[0046] In the formula, C j This indicates the amount of information contained in target j.

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

[0048]

[0049] In the formula, σ j Let σ be the Gini coefficient of target j. j ∈[0,1], σ j The larger the value, the greater the contrast intensity of the target; ζ j η is the information entropy utility value of target j; 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 as follows:

[0051]

[0052] Where max(X(:,1) represents the maximum value in the first column after standardization, obtained by the ε-constraint method. and Let represent the positive and negative ideal solutions of objective 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 dispatching method for power systems with energy storage that considers carbon capture equipment and proactive peak shaving. The upper-level model utilizes the rapid power throughput and large capacity of pumped storage (PSD) to follow the fluctuations of wind power and load, considering full wind power absorption. It optimizes PSD unit output with the objectives of minimizing net load fluctuations and maximizing PSD utilization benefits, thereby reducing the peak-shaving and valley-filling pressure on the optimized load from thermal power units. The lower-level model, based on the optimized peak-shaving capacity of 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. It aims to minimize the total system operating cost and wind curtailment, using proactive peak-shaving constraints to ensure that all stakeholders benefit from peak-shaving transactions. Through iterative solving, it determines an internal power allocation scheme for thermal power that balances system economy and wind power absorption levels, achieving maximum utilization of all peak-shaving resources. Attached Figure Description

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

[0056] Figure 2 This is a flowchart of the model solution process in this invention;

[0057] Figure 3 This is the wind power-load forecast curve in this invention;

[0058] Figure 4 This is a diagram illustrating the peak-shaving effect of the pumping and storage system in this invention.

[0059] Figure 5 This is a diagram showing the optimized output results of the generator units in various scenarios in this invention. Detailed Implementation

[0060] It should be noted that the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features described herein are detailed descriptions of the technical solution of the present invention, not limitations thereof. Where there is no conflict, the embodiments and technical features described herein can be combined with each other. The term "and / or" merely describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0061] Example 1

[0062] To make the purpose, technical solution and advantages of this invention patent clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0063] This invention proposes a multi-objective low-carbon dispatch method for power systems with energy storage that considers carbon capture equipment and proactive peak-shaving. The objective is to achieve the desired power output P of pumped-storage hydropower generation. t pss and the internal power P of thermal power plants t G A reasonable allocation of resources can fully leverage the peak-shaving capacity of each entity while ensuring the efficiency of pumped storage and the initiative of thermal power in deep regulation, thereby improving the economic efficiency of system operation, promoting wind power consumption, and reducing carbon emissions. It includes the following:

[0064] 1) Two-layer model of the joint system

[0065] Given that wind-thermal-storage joint peak shaving is a complex integer nonlinear programming problem, in order to simplify computational complexity and improve solution speed, we combine... Figures 1-2 Based on 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 pumped storage's double regulation capacity, leverage the peak-shaving capacity and carbon capture level of thermal power, and determine the power allocation scheme between pumped storage and thermal power.

[0066] The upper layer utilizes the rapid power throughput and large capacity of pumped storage to follow the fluctuations of wind power and load, taking into account the full absorption of wind power, and optimizes the output of pumped storage units with the goal of minimizing net load fluctuations and maximizing the benefits of pumped storage dispatch, thereby reducing the peak shaving and valley filling pressure of thermal power units on the optimized load.

[0067] The lower layer, based on the optimized peak-shaving capacity of the upper layer and taking into account the peak-shaving initiative constraint, aims to minimize the total system operating cost and the wind curtailment volume. It iteratively solves the problem to determine the internal power allocation scheme of thermal power that satisfies the peak-shaving initiative constraint while taking into account the system's economy and wind power absorption level.

[0068] The dual-layer model structure diagram proposed in this embodiment is as follows: Figure 1 As shown.

[0069] 2) Optimize the objective function of the model

[0070] (a) Objective function of the upper-level model

[0071] The upper-level model optimizes the output of pumped storage units with the objectives of minimizing net load fluctuations and maximizing pumped storage dispatch benefits. Its sub-objective functions are as follows:

[0072] (1) Minimal fluctuation in net load

[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-ups or wide-range regulation of thermal power plants, and effectively extend unit lifespan while improving overall system economy, this embodiment establishes the following objective function:

[0074]

[0075] In the formula: P netload,t P represents the net load power at time t. netload,ave N represents the average net load power. pss This refers to the number of pumped storage units; and These represent the power generation / pumping capacity of pumped-storage unit k at time t; T is the total number of sampling points during the dispatching day; P t d and Let t be the load and the predicted wind power.

[0076] (2) The pumping and mobilization of water storage has the greatest benefit.

[0077] Pumped-storage units experience power loss due to varying water-to-electricity conversion coefficients under different operating conditions. The power efficiency of the k-th pumped-storage unit at time t is calculated. for:

[0078]

[0079] In the formula: P price,t Let t be the time-of-use electricity price of the power grid at time t; Δt is the dispatch time interval.

[0080] Pumped-storage units incur physical losses during frequent start-ups and shutdowns. The start-up and shutdown loss cost of the k-th pumped-storage unit at time t is... for:

[0081]

[0082] In the formula: and These are the losses incurred during a single start-up and shutdown of the pumped-storage unit, respectively. These are Boolean variables representing whether pumped storage unit k is in power generation or pumping mode at time t, with 1 indicating yes and 0 indicating no.

[0083] The benefits of pumped storage can be expressed as the revenue from the pumped electricity. Start-up and shutdown costs The difference can be expressed as:

[0084]

[0085] In the formula: P netload,t P represents the net load power at time t. netload,ave N represents the average net load power. pss This refers to the number of pumped storage units; and These represent the power generation / pumping capacity of pumped-storage unit k at time t; T is the total number of sampling points during the dispatching day; P t d and Let t be the load and the predicted wind power.

[0086] (b) Objective function of the lower-level model

[0087] If the sole objective is optimal economic efficiency, the peak-shaving depth of the generating units will be minimized, resulting in significant wind curtailment. Conversely, if the objective is only 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 both the total system operating cost and the amount of wind curtailment.

[0088] (1) Lowest total system operating cost

[0089] The lower-level model optimizes the total system operating cost, including thermal power peak-shaving costs, wind curtailment penalty costs, CO2 storage costs, energy storage operation costs, and carbon trading costs, with the objective function of minimizing the total system operating cost. This can be expressed as:

[0090]

[0091] In the formula: γ ES P represents the charging and discharging cost coefficient of energy storage. t c ,P t d Let t represent the charging power and discharging power of the stored energy at time t, respectively.

[0092] In the formula: C i,t Let t be the peak-shaving cost of thermal power plants; The cost of wind curtailment at time t; For energy storage operating costs; The carbon trading cost is calculated using a tiered carbon trading model.

[0093] The total operating cost of thermal power units can be expressed as follows at different peak-shaving depth stages:

[0094]

[0095] In the formula: This represents the coal consumption cost of thermal power unit i at time t; The generating capacity of thermal power units; a i b i c i The coefficients of the consumption characteristic function of the i-th thermal power unit; S represents the loss cost of thermal power unit i at time t, and β is the actual operating loss coefficient of the thermal power plant; i The purchase cost of thermal power unit i; N fFor the rotor cracking cycle of a thermal power unit, N f (P)=0.005778P 3 -2.682P 2 +484.8P-8411; This represents the fuel injection cost of the th generating unit at time t. S represents the amount of oil supplied to thermal power unit i at time t; oil This refers to the oil price for the current season.

[0096]

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

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

[0099]

[0100] In the formula: θ is the cost of sealing a unit of CO2, E i,t,r Let t be the total amount of CO2 captured by unit i at time t.

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

[0102]

[0103] (2) Minimum wind curtailment

[0104]

[0105] 3) Optimization model solution method

[0106] Considering that both the upper and lower level models are multi-objective problems and there is no unique global optimum, this embodiment designs an improved ε-constraint-approximation ideal solution ranking method (TOPSIS) to obtain a compromise optimum. First, the ε-constraint method is introduced and ε is updated to obtain a multi-objective Pareto solution set. Second, to obtain the optimal compromise solution for each objective function, the TOPSIS method is used to calculate the fitting progress 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 weights, and then weighting the satisfaction of each group of Pareto solutions to determine the overall satisfaction of that group of solutions.

[0107] (a) ε-constraint method

[0108] Based on the concept of the ε-constraint method, the bi-objective model can be expressed as:

[0109]

[0110] Taking the upper-level model as an example, x is the variable to be optimized, representing the pumping and discharging power of the pumped storage unit; f1(x) and f2(x) are two objective functions, representing the net load fluctuation and the pumped storage dispatch benefit, respectively; A(x) and B(x) are equality constraints and inequality constraints, respectively, representing the power equality constraint and the pumped storage operation constraint.

[0111] Let f1(x) be the primary objective function and f2(x) be the secondary objective function. This is transformed into an inequality constraint. The lower limit is taken as the f2(x) corresponding to x obtained by minimizing f1(x) as the single objective. When maxf2(x) is used as the upper bound for solving a single objective problem, f2(x) is taken as the upper bound. The original expression can then be transformed into:

[0112]

[0113] In the formula, N represents the number of segments into which the range of values ​​for 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] In the formula, y ij This is the j-th objective function value corresponding to the i-th Pareto optimal solution.

[0117] (b) Improved Approximation Ideal Solution Ranking Method

[0118] First, a standardized decision matrix is ​​established. Since the selected evaluation targets vary in type and scale, this embodiment first standardizes the target values ​​to obtain a standardized evaluation matrix X = (x... ij ) m×2 The standardized processing method is as follows:

[0119] 1) Benefit-oriented goals (the higher the better)

[0120]

[0121] 2) Cost-related objectives (the smaller the better)

[0122]

[0123] Secondly, the weights of each objective are determined. The traditional CRITIC method suffers from problems such as dimensional standard deviation, potentially negative correlation coefficients, and an inability to measure the dispersion of the objective function value distribution. To address these issues, this embodiment improves upon the traditional CRITIC method by introducing the Gini coefficient and information entropy utility value to measure the contrast strength of the objectives and the dispersion of the objective function value distribution. Based on the Pareto solution set obtained using the ε-constraint method, the volatility and dispersion of the same objective (vertical) and the conflict between different objectives (horizontal) are quantified mathematically to determine the weights of each objective. The weight ω of objective j... j It can be obtained by the following formula:

[0124]

[0125] In the formula, C j σ represents the amount of information contained in target j; j Let σ be the Gini coefficient of target j. j ∈[0,1],σ j The larger the value, the greater the contrast intensity of the target; ζ j η is the information entropy utility value of target j; ij The correlation coefficient between target i and target j. Next, determine the positive and negative ideal solutions using the following formula:

[0126]

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

[0128] Finally, the geometric distance and overall satisfaction of each Pareto solution with respect to the positive and negative ideal solutions are calculated using the following formulas:

[0129]

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

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

[0132] 4) Constraints

[0133] (a) Pumped storage units are subject to the following operational constraints due to their own characteristics: reservoir capacity constraint, flow rate constraint, single operating condition constraint, and number of start-stop cycles constraint.

[0134]

[0135] In the formula: η is the capacity of the upper reservoir of the pumped storage power station at time t; p and η g These are the water-to-electricity conversion coefficients for the unit under pumping and power generation conditions, respectively. and These are the maximum and minimum capacities of the upper reservoir, respectively. and The initial and final capacities of the upper reservoir at the end of the scheduling cycle are respectively; k represents the upper and lower limits of the pumped-storage unit's power output under power generation and pumping conditions, respectively; M represents the maximum number of start-ups and shutdowns for a single pumped-storage unit.

[0136] (b) Power balance constraints

[0137]

[0138] In the formula: Let be the fixed energy consumption of unit i at time t; Let λ be the unit's operating energy consumption at time t; i E represents the carbon emissions per unit output of the unit i; i,t E represents the total CO2 produced by unit i at time t. i,t,r Let t be the total amount of CO2 captured by unit i at time t; For the carbon capture efficiency of unit i; γ E This refers to the energy consumption required for the unit to capture CO2.

[0139] (c) Thermal power plant operation constraints

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

[0141]

[0142] In the formula: and These represent the maximum upward and downward output variation limits of unit i, respectively; T i,off and T i,on These represent the minimum continuous shutdown and operating time of unit i, respectively; α load and α w These are the reserve factors considering load and wind power uncertainties, respectively, and are set to 0.05 and 0.1.

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

[0144] CCS equipment is subject to ramp rate constraints and maximum operating energy consumption constraints.

[0145]

[0146] In the formula: This indicates the maximum operating energy consumption of the CCS equipment; and These represent the upper and lower limits of the ramp rate for carbon capture unit i, respectively.

[0147] (e) Energy storage-related constraints

[0148]

[0149] In the formula: E s This refers to the rated capacity of the energy storage. and These are the maximum charging power and the maximum discharging power, respectively. The state of charge of the energy storage system at time t; and These are the upper and lower limits of the state of charge of the energy storage system, respectively. and These represent the initial and final SOC states of energy storage; η c and η d , respectively, represent the charging and discharging efficiencies of the stored energy at time t; and These are Boolean variables representing the charging and discharging states of energy storage, respectively, with 1 indicating yes and 0 indicating no.

[0150] (f) Constraints on proactive peak shaving

[0151] To enhance the incentive for thermal power units to engage in deep peak shaving, the "Northeast Power Auxiliary Service Market Operation Rules" clearly state that during the non-heating season, the compensation price for deep peak shaving of thermal power units adopts a "tiered" pricing model, providing compensation to participating units based on the depth of peak shaving. The peak shaving compensation can be expressed as follows:

[0152]

[0153] In the formula: The compensation obtained by thermal power unit i participating in deep peak shaving at time t; δ g,peak Compensation fee for unit electricity consumption during deep peak shaving; For thermal power unit i, This represents the deep peak-shaving space of thermal power unit i at time t.

[0154] In deep peak shaving ancillary services, the compensation costs borne by thermal power units and wind power units that do not participate in deep peak shaving within the system are shared according to the proportion of on-grid electricity and the proportion of total wind power generation on the day, respectively. The peak shaving compensation sharing model is as follows:

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

[0156]

[0157] The compensation costs borne by the wind turbine generators are as follows:

[0158]

[0159] In the formula: The amount of electricity generated by thermal power unit i participating in regular peak shaving; N represents the grid-connected power of wind turbine i; G and N wind These represent the number of thermal power units and the number of wind power units, respectively.

[0160] This embodiment describes the peak-shaving intentions of each peak-shaving entity by establishing a revenue difference model before and after peak shaving:

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

[0162]

[0163] Where: δ g This indicates the on-grid price of thermal power. These represent the benefits of thermal power plants before and after participating in deep peak shaving, respectively.

[0164] (2) The initiative constraints of wind power plants in participating in deep peak shaving It can be represented as:

[0165]

[0166] Where: δ wind This indicates the on-grid price of wind power. These represent the benefits of thermal power plants before and after participating in deep peak shaving, respectively.

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

[0168] Table 1 Operating parameters of pumped storage units

[0169]

[0170] Table 2 Composition and Parameters of Thermal Power Units

[0171]

[0172] Because wind power output is random, this embodiment uses a fuzzy C-means clustering algorithm to cluster wind power output scenarios, setting the number of clusters to 5 to reduce the number of scenarios. Expected target calculations are performed on each scenario, and simulation analysis is conducted using the scenario with the highest probability. The local power grid wind power data and load power data are as follows: Figure 3As shown.

[0173] The peak-shaving effect obtained by the optimization method in this embodiment is as follows: Figure 4 As shown.

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

[0175] To compare and analyze the lower-level scheduling strategy in this embodiment, five different scenarios were set up to verify the effectiveness of the lower-level model.

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

[0177] Scenario 2: Thermal power units can perform deep peak shaving without adding carbon capture equipment or energy storage.

[0178] Scenario 3: Thermal power units can perform deep peak shaving without the need for carbon capture equipment, and include energy storage.

[0179] Scenario 4: Thermal power units can perform deep peak shaving and are equipped with carbon capture equipment, but do not include energy storage.

[0180] Scenario 5: Thermal power units can perform deep peak shaving by adding carbon capture equipment, including energy storage.

[0181] Optimization results for each scenario are as follows Figure 5 As shown in Table 3, the economic efficiency comparison of each scenario is presented.

[0182] Table 3. Economic Comparison of Different Scenarios

[0183]

[0184]

[0185] Scenario 5 involves adding carbon capture and energy storage devices to thermal power units with deep peak shaving capabilities. During off-peak periods, the combined effect of energy storage and carbon capture significantly reduces the peak shaving pressure on thermal power units, allowing them to operate in a conventional peak shaving state. This reduces peak shaving costs while allowing actual grid-connected power to be below the oil-fired peak shaving power (Pa), increasing wind power grid connection and reducing wind curtailment penalty costs by 59.4% compared to Scenario 2. During peak periods, the energy storage devices discharge, freeing up more power for the thermal power units to capture carbon, compensating for insufficient carbon capture levels. Carbon trading costs are reduced by RMB 10,300 compared to Scenario 4, resulting in lower overall system operating costs.

[0186] To verify the low-carbon advantages of the established optimization model in deep peak shaving scenarios, a comparative analysis of wind curtailment and carbon emissions under each scenario is shown in Table 4.

[0187] Table 4 Wind power absorption rate and carbon emissions for each scenario

[0188]

[0189] Scenario 5 shows the highest wind power grid connection capacity and the highest wind power absorption rate during off-peak hours. Compared to Scenario 2, the addition of carbon capture and energy storage devices in Scenario 5 not only increases the power generation capacity of thermal power units while meeting the proactive peak-shaving constraints and achieves economic optimization of system operating costs, but also deepens the peak-shaving depth of thermal power units, reduces the grid connection capacity of thermal power, and provides more grid connection space for wind power. The amount of wind curtailment is reduced by 1.41%, verifying the advantages of the established model in promoting the absorption of renewable energy while ensuring economic efficiency.

[0190] Compared to Scenario 2, Scenario 4 shows that adding carbon capture equipment to thermal power units can capture carbon emissions from these units, reducing system carbon emissions. It also deepens the peak-shaving depth of thermal power units, allowing them to accommodate more wind power. Compared to Scenario 4, Scenario 5 shows that considering both energy storage and carbon capture units for peak shaving, compared to considering only carbon capture units, reduces wind curtailment by 52.95%. The addition of energy storage compensates for the insufficient carbon capture level of thermal power during peak hours, reducing carbon emissions by 162.22 tons, demonstrating the superiority of combined peak shaving using carbon capture and energy storage.

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

[0192] To demonstrate the advantages of this embodiment model in enhancing 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 Shaving Benefits in Various Scenarios

[0194]

[0195] Under the same installed capacity, the wind curtailment rate and total system operating cost decrease sequentially from scenario 1 to scenario 5, while wind power revenue increases sequentially. Thermal power revenue, from lowest to highest, is shown in scenarios 2, 3, 1, 4, and 5. The model proposed in this embodiment can significantly improve the enthusiasm of various power generation companies to participate in peak shaving while ensuring the economic efficiency of system operation.

[0196] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0197] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0198] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0199] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0200] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A multi-objective low-carbon dispatching method for power systems with energy storage that considers carbon capture equipment and proactive peak-shaving, characterized in that, include: An upper-level model is established with the goal of minimizing net load fluctuations and maximizing the benefits of pumped storage and dispatch. The lower-level model is established with the objectives of minimizing the total system operating cost and minimizing the amount of wind curtailment. Both the upper-layer model and the lower-layer model are multi-objective models, based on... The constraint method is used to find the Pareto solution set of the multi-objective model; The objective function values ​​in the Pareto solution set are standardized to obtain a standardized evaluation matrix; Based on the standardized evaluation matrix, the weights of each objective in the multi-objective model are determined using the improved CRITIC method. Based on the standardized evaluation matrix, the positive and negative ideal solutions are determined, and the geometric distance and overall satisfaction of each Pareto solution in the Pareto solution set with the positive and negative ideal solutions are calculated. The solution set with the highest overall satisfaction in the Pareto solution set is determined as the optimal compromise solution and used as the system scheduling scheme. The objective function for minimizing net load volatility is: In the formula: for Net load power at any given time; This represents the average net load power. This refers to the number of pumped storage units; and They are respectively pumped storage unit The power generation / pumping capacity; T is the total number of sampling points within the dispatching day; The objective function that maximizes the efficiency of pumping is: In the formula: for Net load power at any given time; This represents the average net load power. This refers to the number of pumped storage units; for Time of the first Electricity efficiency of pumped storage units for Time of the first Start-up and shutdown losses of a pumped storage unit; The objective function for minimizing the total operating cost of the system is: In the formula: for Real-time peak-shaving costs of thermal power plants; for Constantly incurring the cost of wind curtailment penalties; For energy storage operating costs; For carbon trading costs; The objective function for minimizing the amount of wind curtailment is: In the formula, express Real-time wind power forecast; for Real-time wind power grid connection capacity.

2. The multi-objective low-carbon dispatch method for power systems with energy storage that considers carbon capture equipment and proactive peak shaving, as described in claim 1, is characterized in that... The formulas for calculating the geometric distance and overall satisfaction are as follows: in, Denotes the standardized objective function value of the i-th Pareto solution. To the ideal solution geometric distance, This represents the standardized objective function value of the Pareto solution set. To the negative ideal solution geometric distance, This represents the overall satisfaction level of the i-th Pareto solution.

3. The multi-objective low-carbon dispatch method for power systems with energy storage that considers carbon capture equipment and proactive peak-shaving as described in claim 2, is characterized in that... When the multi-objective model is a higher-level model, the model based on... The constraint method yields the Pareto solution set of the multi-objective model, which includes: according to The concept of the constraint method represents a multi-objective model as follows: In the formula, Let be the variable to be optimized, representing the pumping discharge power of the pumped storage unit; These are the two objective functions of the upper-level model, representing net load volatility and pumping efficiency, respectively. These represent equality constraints and inequality constraints, respectively, indicating power equality constraints and pumped storage operation constraints; set up The main objective function is... The objective function is transformed into an inequality constraint. Obtained as a single objective Corresponding Take as the lower limit ,Will Obtained as a single-objective solution Take as the upper limit ; Transform the multi-objective model into: ； In the formula, , express The number of segments into which the value range is divided; By updating The multi-objective Pareto solution set is obtained as follows: In the formula, For the first The first Pareto optimal solution corresponds to the... The objective function value.

4. The multi-objective low-carbon dispatching method for power systems with energy storage that considers carbon capture equipment and proactive peak shaving, as described in claim 2, is characterized in that... The standardized evaluation matrix X after standardization is represented as follows: Among these, for benefit objectives that are as large as possible, For cost-oriented objectives where smaller is better ； in, For the first The first Pareto optimal solution corresponds to the... The objective function value.

5. The multi-objective low-carbon dispatching method for power systems with energy storage that considers carbon capture equipment and proactive peak-shaving as described in claim 4, characterized in that, The improved CRITIC method is used to determine the weights of each objective in a multi-objective model. weight The following formula can be used to obtain: In the formula, Indicate target The amount of information contained therein.

6. The multi-objective low-carbon dispatching method for power systems with energy storage that considers carbon capture equipment and proactive peak shaving, as described in claim 5, is characterized in that... The target The formula for calculating the amount of information contained is: In the formula, For the goal The Gini coefficient, , The larger the value, the greater the contrast intensity of the target; The information entropy utility value of target j; The correlation coefficient between target i and target j.

7. The multi-objective low-carbon dispatching method for power systems with energy storage that considers carbon capture equipment and proactive peak shaving as described in claim 2, characterized in that, The formulas for the positive ideal solution and the negative ideal solution are: In the formula, and Let represent the positive and negative ideal solutions of objective j, respectively; Indicates The maximum value in the standardized first column obtained by the constraint method. Indicates The maximum value in the standardized second column obtained by the constraint method. Indicates The minimum value in the first standardized column obtained by the constraint method. Indicates The minimum value in the standardized second column obtained by the constraint method.

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