Controllable load day-ahead regulation capability evaluation method for clean energy consumption

Abstract

The present application relates to a kind of controllable load day-ahead regulation capability evaluation method for clean energy consumption, establish the objective function of controllable load tracking wind light waveform fluctuation capability index model, and load tracking clean energy consumption regulation amount function, obtain the contribution index model of controllable load clean energy consumption according to objective function and regulation amount function, the ability of different controllable load tracking wind light output curve is obtained, and the quantification of consumption regulation capability.The present application has the advantages that: establish the objective function of controllable load tracking wind light waveform fluctuation capability index model, and load tracking clean energy consumption regulation amount function, obtain the contribution index model of controllable load clean energy consumption according to objective function and regulation amount function;The day-ahead flexibility regulation capability of different controllable load is quantified, the optimal load planning scheme of each period can be obtained, and reference is provided for the implementation of corresponding incentive policy of electric power system.

Description

Technical Field

[0001] This invention relates to the field of controllable load regulation, and in particular to a method for evaluating the day-ahead regulation capacity of controllable loads for clean energy consumption. Background Technology

[0002] The concentrated distribution of wind and solar resources has dictated my country's large-scale, centralized wind power development path. However, with the increase in my country's large-scale, centralized installed capacity of wind and solar power, insufficient local wind and solar power consumption capacity and obstructed transmission channels have led to a significant amount of wind and solar power curtailment. The essence of the wind power consumption problem is that the large-scale, randomly fluctuating wind power grid significantly increases the demand for grid flexibility, while the existing power structure and dispatching methods lack sufficient flexibility. Due to my country's relatively simple power structure and limited number of generating units capable of flexible start-up and shutdown, the grid's flexibility has become a bottleneck restricting wind power consumption. Given the insufficient flexibility on the generation side, compared to the huge investment in energy storage equipment, involving demand-side loads in achieving local consumption of wind and solar power is a more economical choice. Therefore, managing demand and incentivizing controllable loads to participate in the power system's flexibility will be a crucial way to achieve peak shaving and valley filling, and local wind power consumption. Load dispatching methods that utilize load tracking of wind power output changes are expected to become a trend in future power system operations.

[0003] Data from actual production shows that the load variation curve often differs significantly from the wind and solar power output curve over the same period, leading to "unabsorbable intervals" at many times. Specifically, for example... Figure 1 As shown. To achieve efficient load absorption of new energy sources like wind and solar, it's necessary to adjust controllable loads to adapt to fluctuations in clean energy output. This adjustment alters the load curve, better aligning it with the wind and solar power output curves, thus improving absorption. The uncertainty of clean energy output also places significant demands on the flexible adjustment capabilities of controllable loads. Due to the increasing demand for load flexibility in power systems, the intermittent, volatile, and unpredictable nature of wind and other renewable energy generation renders traditional load-tracking dispatch methods unsustainable. There's a need to enhance the flexible adjustment capabilities of controllable loads to adapt to clean energy power fluctuations. However, different controllable loads exist within the power system, each with varying flexibility adjustment capabilities. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a method for evaluating the day-ahead regulation capacity of controllable loads for clean energy consumption, and to obtain a contribution index model for clean energy consumption of controllable loads.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for evaluating the day-ahead regulation capacity of controllable loads for clean energy consumption is proposed. The method establishes an objective function for the controllable load tracking wind and solar waveform fluctuation capability index model and a regulation function for load tracking clean energy consumption. Based on the objective function and regulation function, the contribution index model of controllable loads to clean energy consumption is obtained, and the ability of different controllable loads to track wind and solar power output curves is obtained, as well as the quantification of the consumption regulation capacity.

[0007] The specific steps are as follows:

[0008] S1. Establish an index model to characterize the ability of controllable loads to track wind and solar waveform fluctuations. The steps are as follows:

[0009] S101. Establish the objective function of the index model for the controllable load tracking wind and solar waveform fluctuation capability; the smaller the objective function, the closer the controllable load curve is to the wind and solar power output curve, the stronger the controllable load tracking wind and solar power output capability, that is, the stronger the load absorption and regulation capability.

[0010] S102. Establish the constraints for the indicator model;

[0011] S2. Establish an index model to characterize the controllable load peak-shifting regulation capacity to absorb wind and solar power, and quantify the controllable load peak-shifting regulation capacity to absorb wind and solar power through the load tracking and clean energy absorption regulation function.

[0012] S3. Based on the index model of the controllable load's ability to track wind and solar waveform fluctuations and the index model of the controllable load's ability to absorb wind and solar energy through peak-shaving regulation, obtain the contribution index model of the controllable load to clean energy absorption.

[0013] In step S101, the objective function is as follows:

[0014]

[0015] In formula ①, B j Let be the objective function; S be the set of all possible running scenarios; s be a possible scenario; ρ s Let be the probability of occurrence of time-series scenario s; T be the total number of time periods; Let t be the load of controllable load j in time period t under time series scenario s for region i; Let be the maximum load of controllable load j in time period t under time series scenario s for region i. To provide power output for new energy sources during time period t in the i-time series scenario s of the region; The maximum output of new energy during time period t under the time series scenario s of region i.

[0016] In step S102, the constraints of the index model are established, including the following:

[0017] 1) The controllable load for each time period is the baseline load plus the increased load, then minus the decreased load, as shown in the following formula:

[0018]

[0019] In formula ②, The baseline load of controllable load j in time period t under time series scenario s for region i; The increase in load of controllable load j in time period t under the time series scenario s of region i; The load reduction amount of controllable load j in time period t under the time series scenario s of region i;

[0020] 2) Controllable loads do not require a reduction in residential demand or an interruption of production tasks. Instead, they adjust energy consumption time to maintain a constant total electricity consumption throughout the cycle. In other words, the increase in load and the decrease in load must be balanced to ensure that residential demand and total production are not affected. The formula is as follows:

[0021]

[0022] 3) Controllable loads have a certain amount of uncontrollable base load; only a portion of the load can be adjusted, meaning that the amount of load increase and decrease in each time period is limited:

[0023]

[0024]

[0025]

[0026] In formula ④-⑥, and These represent the load capacity of controllable load j in region i and the minimum load requirement to maintain production, respectively. and These are the state variables representing the increase and decrease in power of controllable load j during time period t, under time series scenario s for region i. This indicates that in the time series scenario s of region i, the controllable load j increases its power during the time period t; This indicates that the controllable load j reduces its power during time period t under time sequence scenario s in region i.

[0027] In step S2, the capacity of load shifting regulation to absorb wind and solar power is calculated using the following formula:

[0028]

[0029] In formula ⑦, G j For load tracking of the clean energy consumption regulation function, G jThe smaller the value, the less adjustment is needed to control the load to absorb clean energy during peak periods, and the stronger the load's ability to track fluctuations in wind and solar power output, meaning the stronger the load's absorption and regulation capabilities.

[0030] In step S3, the contribution index of clean energy consumption of controllable load is calculated using the following formula:

[0031] A j ×B j G j ⑧

[0032] In formula ⑧, A j Contribution indicators for the consumption of clean energy under controllable loads.

[0033] In step S102, the load capacity is the maximum power of the electrical equipment.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] 1. It solves the problem of reduced overall regulation capacity and economy caused by the inability to optimize load regulation schemes due to the inability to assess load flexibility in traditional controllable load regulation;

[0036] 2. Establish the objective function of the controllable load tracking wind and solar waveform fluctuation capability index model and the load tracking clean energy consumption adjustment function. Based on the objective function and adjustment function, obtain the contribution index model of controllable load to clean energy consumption.

[0037] 3. Quantifying the day-ahead flexibility adjustment capability of different controllable loads can yield the optimal load planning scheme for each time period, providing a reference for the power system to implement corresponding incentive policies. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the areas where wind and solar energy can be absorbed and those that cannot.

[0039] Figure 2 This is a schematic diagram of baseline load and load adjustment.

[0040] Figure 3 This is a schematic diagram comparing the effects of load curve regulation. Detailed Implementation

[0041] The present invention will now be described in detail with reference to the accompanying drawings, but it should be noted that the implementation of the present invention is not limited to the following embodiments.

[0042] The following embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments. Unless otherwise specified, the methods used in the following embodiments are conventional methods.

[0043]

Example 1

[0044] See Figure 1 ,See Figure 2 This paper presents a method for evaluating the day-ahead regulation capacity of controllable loads for clean energy consumption. It establishes an objective function for a controllable load tracking wind and solar waveform fluctuation index model and a regulation function for load tracking and clean energy consumption. Based on the objective function and regulation function, it obtains a contribution index model for clean energy consumption of controllable loads, derives the ability of different controllable loads to track wind and solar power output curves, and quantifies the consumption regulation capacity. For controllable loads with strong regulation capacity, more incentives are implemented accordingly to achieve optimal day-ahead load regulation in each time period.

[0045] The specific steps are as follows:

[0046] S1. Establish an index model to characterize the ability of controllable loads to track wind and solar waveform fluctuations. The steps are as follows:

[0047] S101. Establish the objective function of the index model for the controllable load tracking wind and solar waveform fluctuation capability; the smaller the objective function, the closer the controllable load curve is to the wind and solar power output curve, and the stronger the controllable load's ability to track wind and solar power output, that is, the stronger the load absorption and regulation capability. The objective function is as follows:

[0048]

[0049] In formula ①, B j Let be the objective function; S be the set of all possible running scenarios; s be a possible scenario; ρ s Let be the probability of occurrence of time-series scenario s; T be the total number of time periods; Let t be the load of controllable load j in time period t under time series scenario s for region i; Let be the maximum load of controllable load j in time period t under time series scenario s for region i. To provide power output for new energy sources during time period t in the i-time series scenario s of the region; For the maximum output of new energy in the time period t under the time series scenario s of region i;

[0050] S102. Establish the constraints for the indicator model, including the following:

[0051] 1) The controllable load for each time period is the baseline load plus the increased load, then minus the decreased load, as shown in the following formula:

[0052]

[0053] In formula ②, The baseline load of controllable load j in time period t under time series scenario s for region i; The increase in load of controllable load j in time period t under the time series scenario s of region i; The load reduction amount of controllable load j in time period t under the time series scenario s of region i;

[0054] 2) Controllable loads do not require a reduction in residential demand or an interruption of production tasks. Instead, they adjust energy consumption time to maintain a constant total electricity consumption throughout the cycle. In other words, the increase in load and the decrease in load must be balanced to ensure that residential demand and total production are not affected. The formula is as follows:

[0055]

[0056] 3) Controllable loads have a certain amount of uncontrollable base load; only a portion of the load can be adjusted, meaning that the amount of load increase and decrease in each time period is limited:

[0057]

[0058]

[0059]

[0060] In formula ④-⑥, and These are the load capacity of controllable load j in region i and the minimum load requirement to maintain production, respectively, where the load capacity is the maximum power of the electrical equipment. and These are the state variables representing the increase and decrease in power of controllable load j during time period t, under time series scenario s for region i. This indicates that in the time series scenario s of region i, the controllable load j increases its power during the time period t; This indicates that the controllable load j reduces its power during time period t under time sequence scenario s in region i.

[0061] S2. Establish an index model to characterize the controllable load peak-shifting regulation capacity to absorb wind and solar power. Quantify this capacity using a load tracking-based clean energy absorption regulation function, as shown in the following formula:

[0062]

[0063] In formula ⑦, G j For load tracking of the clean energy consumption regulation function, G j The smaller the value, the less adjustment is needed to control the load to absorb clean energy during peak periods, and the stronger the load's ability to track fluctuations in wind and solar power output, meaning the stronger the load's absorption and regulation capabilities.

[0064] S3. Based on the index models of the controllable load's ability to track wind and solar waveform fluctuations and the controllable load's ability to absorb wind and solar energy through peak-shaving regulation, a contribution index model for the clean energy absorption of controllable loads is obtained. That is, by evaluating the controllable load's ability to absorb and regulate clean energy, guidance can be provided for the power system to take incentive measures. The formula is as follows:

[0065] A j =B j G j ⑧

[0066] In formula ⑧, A j Contribution indicators for the consumption of clean energy under controllable loads.

[0067] Quantifying the day-ahead flexibility adjustment capability of different controllable loads can yield the optimal load planning scheme for each time period, helping the power system implement corresponding incentive policies.

[0068] The actual power grid data of a certain region in northern China is used as the case study to be evaluated. The installed wind power capacity is 500MW and the load power is 500MW, including residential load and commercial load that are involved in regulation.

[0069] See Figure 3 By simulating the regulatory effect of controllable load adjustment resources on the system load curve, it is easy to see that the adjustment and improvement effect of the load curve is obvious after evaluating the clean energy absorption and regulation capacity of controllable load. It can better track the wind power output curve, provide guidance for the power system to take incentive measures, effectively promote the absorption of renewable energy, and improve the load curve.

[0070] This invention solves the problem of reduced overall regulation capacity and economy caused by the inability to optimize load regulation schemes due to the inability to assess load flexibility in traditional controllable load regulation. It establishes an objective function for a controllable load tracking wind and solar waveform fluctuation capability index model and a load tracking clean energy absorption regulation function. Based on the objective function and regulation function, a contribution index model for clean energy absorption of controllable loads is obtained. By quantifying the day-ahead flexibility regulation capability of different controllable loads, the optimal load planning scheme for each time period can be obtained, providing a reference for the implementation of corresponding incentive policies in the power system.

Claims

1. A method for evaluating the controllable load day-ahead regulation capacity for clean energy consumption, characterized in that, Establish the objective function of the controllable load tracking wind and solar waveform fluctuation capability index model and the load tracking clean energy consumption adjustment function. Based on the objective function and adjustment function, obtain the contribution index model of the controllable load to clean energy consumption, obtain the ability of different controllable loads to track wind and solar power output curves, and quantify the consumption adjustment capability. The specific steps are as follows: S1. Establish an index model to characterize the ability of controllable loads to track wind and solar waveform fluctuations. The steps are as follows: S101. Establish the objective function of the index model for the controllable load tracking wind and solar waveform fluctuation capability; the smaller the objective function, the closer the controllable load curve is to the wind and solar power output curve, the stronger the controllable load tracking wind and solar power output capability, that is, the stronger the load absorption and regulation capability. S102. Establish the constraints for the indicator model; S2. Establish an index model to characterize the controllable load peak-shifting regulation capacity to absorb wind and solar power, and quantify the controllable load peak-shifting regulation capacity to absorb wind and solar power through the load tracking and clean energy absorption regulation function. S3. Based on the index model of the controllable load’s ability to track wind and solar waveform fluctuations and the index model of the controllable load’s ability to absorb wind and solar energy through peak-shaving regulation, obtain the contribution index model of the controllable load’s clean energy absorption. In step S101, the objective function is as follows: ① In formula ①, The objective function is... The set of all possible operating scenarios; For a possible scenario; For time sequence scenarios The probability of occurrence; Total number of time periods; For the region Time sequence scene Lower controllable load exist Load during a specific time period; For the region Time sequence scene Lower controllable load exist Maximum load during the period; For the region Time sequence scene Down New energy output during specific periods; For the region Time sequence scene Down Maximum output of new energy sources during a given period; In step S102, the constraints for establishing the index model include the following: 1) The controllable load for each time period is the baseline load plus the increased load, then minus the decreased load, as shown in the following formula: ② In formula ②, For the region Time sequence scene Lower controllable load exist Baseline load for the time period; area Time sequence scene Lower controllable load exist Increase in load during a given period; area Time sequence scene Lower controllable load exist The amount of load reduction during a given period; 2) Controllable loads do not require a reduction in residential demand or an interruption of production tasks. Instead, they adjust energy consumption time to maintain a constant total electricity consumption throughout the cycle. In other words, the increase in load and the decrease in load must be balanced to ensure that residential demand and total production are not affected. The formula is as follows: ③； 3) Controllable loads have a certain amount of uncontrollable base load; only a portion of the load can be adjusted, meaning that the amount of load increase and decrease in each time period is limited: ④ ⑤ ⑥ In formula ④-⑥, and They are respectively regions Controllable load The load capacity and the minimum load requirement to maintain production; and They are respectively regions Time sequence scene Lower controllable load exist State variables representing power increases and decreases during specific time periods; Indicates the region Time sequence scene Lower controllable load exist Increase power during the period; Indicates the region Time sequence scene Lower controllable load exist Power reduction during certain periods; In step S2, the capacity of load shifting regulation to absorb wind and solar power is defined by the following formula: ⑦ In formula ⑦, To provide a function for regulating the absorption of clean energy during load tracking, The smaller the value, the smaller the amount of adjustment needed to controllable load peak shifting and absorb clean energy, the stronger the load tracking ability to fluctuate wind and solar power output, and the stronger the load absorption and regulation capability. In step S3, the contribution index of the controllable load to clean energy consumption is calculated using the following formula: ⑧ In formula ⑧, Contribution indicators for the consumption of clean energy under controllable loads.

2. The method for evaluating the controllable load day-ahead regulation capacity for clean energy consumption according to claim 1, characterized in that, In step S102, the load capacity is the maximum power of the electrical equipment.

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