A heating load scheduling method and system considering power grid load characteristics and heating demand comprehensively

Abstract

A heating load scheduling method and system considering the grid load characteristics and heating demand, the heating load scheduling method comprising: obtaining grid load characteristic data and heating demand parameters; constructing an inverse load curve model based on the grid load characteristic data; calculating a heating demand coefficient according to the heating demand parameters, and then constructing a grid-friendly model based on the inverse load curve, the heating demand coefficient and the heating demand parameters; constructing a new energy-friendly model based on the grid load characteristic data; constructing a global optimal model based on the characteristics of the grid-friendly model and the new energy-friendly model; completing the construction of a simplified model according to the global optimal model; and selecting a suitable model from the global optimal model, the simplified model and a natural working condition operation model according to actual conditions, and using the selected model to optimize and schedule the heating load in the research area. The present application provides multi-level models from theoretical optimization to simplified implementation, which not only meets the technical and economic requirements, but also is convenient for practical popularization and application.

Description

Technical Field

[0001] This invention relates to the field of power grid planning technology, and in particular to a heating load scheduling method and system that comprehensively considers power grid load characteristics and heating demand. Background Technology

[0002] In high-altitude areas, heating safety and power supply safety are both important components of ensuring people's livelihood and safety. However, in high-altitude areas, the power grid construction is relatively weak, and the access of large-scale electric heating loads has brought new challenges to the power grid: Traditional heating load curves mostly adopt a fixed power operation mode, providing uniform heating throughout the day or passively adjusting according to outdoor temperature, without fully considering the characteristics of power grid load and the output characteristics of new energy. This operation mode has the following problems: (1) Increased peak load pressure on the power grid: Heating loads are usually concentrated in the morning and evening peak hours. After being superimposed with the original load of the power grid, the maximum load of the power grid increases significantly, putting pressure on the power grid supply capacity, which may require additional power grid investment. (2) Difficulty in the absorption of new energy: In areas with a high proportion of photovoltaic access, there is a power surplus during the midday photovoltaic peak generation period, but the traditional fixed heating load cannot flexibly respond to the power supply characteristics during this period, resulting in wasted solar power. (3) Insufficient load regulation capacity: The existing heating system lacks the ability to dynamically adjust according to the real-time status of the power grid, and cannot fully utilize the regulation potential of electric heating load as a flexible load.

[0003] Therefore, there is an urgent need for a heating load scheduling method that can comprehensively consider the characteristics of the power grid load, the characteristics of power output, and the heating demand, so as to minimize the impact on the power grid and promote the consumption of new energy sources while ensuring the quality of heating. Summary of the Invention

[0004] This invention provides a heating load scheduling method and system that comprehensively considers the characteristics of power grid load and heating demand, in order to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows: This invention provides a heating load scheduling method that comprehensively considers power grid load characteristics and heating demand, comprising the following steps: S1. Obtain power grid load characteristic data and heating demand parameters; S2. Construct an inverse load curve model based on grid load characteristic data, including the inverse load curve and the total inverse load for the whole day; S3. Calculate the heating demand coefficient based on the heating demand parameters, and then construct a grid-friendly model based on the anti-load curve, heating demand coefficient, and heating demand parameters. S4. Construct a renewable energy-friendly model based on grid load characteristic data; S5. Construct a globally optimal model based on the characteristics of the power grid-friendly model and the new energy-friendly model; S6. Based on the global optimal model, the day is divided into three time periods, each with three time periods. Then, the load values ​​for the three time periods are set respectively to complete the construction of the simplified model. S7. Select a suitable model from the global optimal model, simplified model and natural operating condition model according to the actual conditions, and use the selected model to optimize the scheduling of heating load in the study area.

[0006] In another aspect, the present invention provides a multi-objective optimization scheduling system that comprehensively considers the characteristics of the power grid load and the heating demand, configured to execute a heating load scheduling method that comprehensively considers the characteristics of the power grid load and the heating demand.

[0007] The beneficial effects of this invention are: This invention takes into account the characteristics of power grid load, power output, and heating demand, and comprehensively considers the relationship between heating load and power grid operation. It proposes a multi-level model for electric heating in high-altitude areas with weak power grids, from theoretical optimization to simplified implementation, taking into account both technicality and operability: it meets both technical and economic requirements and is easy to promote and apply in practice. Attached Figure Description

[0008] Figure 1 This is a flowchart of the heating load scheduling method in this invention; Figure 2 This is an example diagram of a power grid-friendly model in an embodiment of the present invention; Figure 3 This is an example diagram of a new energy-friendly model in an embodiment of the present invention; Figure 4 This is an example diagram of the globally optimal model in an embodiment of the present invention; Figure 5 This is an example diagram of a simplified model in an embodiment of the present invention. Detailed Implementation

[0009] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many other different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0010] Reference Figure 1 This application provides a heating load scheduling method that comprehensively considers power grid load characteristics and heating demand, including the following steps: S1. Obtain power grid load characteristic data and heating demand parameters; S2. Construct an inverse load curve model based on grid load characteristic data, including the inverse load curve and the total inverse load for the whole day; S3. Calculate the heating demand coefficient based on the heating demand parameters, and then construct a grid-friendly model based on the inverse load curve, heating demand coefficient, and heating demand parameters; see the example diagram of the grid-friendly model. Figure 2 As shown; S4. Construct a renewable energy-friendly model based on grid load characteristic data, including the second heating load curve, midday heating electricity consumption, and heating load during other times; see example diagram of the renewable energy-friendly model. Figure 3 As shown; S5. Construct a globally optimal model based on the characteristics of the grid-friendly model and the renewable energy-friendly model; see the example diagram of the globally optimal model. Figure 4 As shown; S6. Based on the global optimal model, the day is divided into three time periods, with three time periods for each period. Then, the load values ​​for each of the three time periods are set to complete the construction of the simplified model. See the example diagram of the simplified model below. Figure 5 As shown; S7. Select a suitable model from the global optimal model, simplified model and natural operating condition model according to the actual conditions, and use the selected model to optimize the scheduling of heating load in the study area.

[0011] This invention takes into account the characteristics of power grid load, power output, and heating demand, and comprehensively considers the relationship between heating load and power grid operation. It proposes a multi-level model for electric heating in high-altitude areas with weak power grids, from theoretical optimization to simplified implementation, taking into account both technicality and operability: it meets both technical and economic requirements and is easy to promote and apply in practice.

[0012] In some embodiments, S1 specifically includes the following steps: S11. Obtain grid load characteristic data, including typical daily load curves, typical daily power output curves, and maximum daily grid load values ​​for the target area. ; S12. Superimpose the typical daily load curve and the typical daily power output curve of the target area to obtain the grid load curve of the target area. ; S13. Obtain heating demand parameters, including the maximum load of heating equipment. and daily heating demand electricity .

[0013] In some embodiments, the expression for the anti-load curve model is as follows: ; ; in, Indicates the reverse load curve. This indicates the total electricity consumption in reverse curve form throughout the day. For time intervals, time intervals It is usually taken for 24 hours.

[0014] In some embodiments, S3 specifically includes the following steps: S31, Based on the total daily reverse curve electricity consumption And the daily heating demand electricity within the heating demand parameters The heating demand coefficient is calculated using the following expression: ; in, This represents the heating demand coefficient; S32, Based on the reverse load curve Heating demand coefficient and the maximum load of heating equipment The first heating load curve is constructed as follows: ; in, This represents the first heating load curve; S33. Superimpose the first heating load curve with the conventional load to obtain the overall load curve, as shown in the following expression: ; in, This represents the overall load curve, which is a grid-friendly model. The characteristics of a grid-friendly model are: under ideal conditions, it does not increase the peak load on the grid, and its impact on the grid's power supply capacity is less than a set value, which can be understood as having virtually no impact.

[0015] In some embodiments, S4 specifically includes the following steps: S41. Identify the load curve of the target area. During periods of electricity surplus, the heating load is set to the maximum load of the heating equipment. The second heating load curve is obtained; S42. Calculate the electricity consumption for heating at midday. The calculation formula is as follows: ; in, This indicates the electricity consumption for heating at midday. Indicates the number of hours the machine operates during midday; S43. Calculate the heating load for the remaining time periods. This completes the construction of the renewable energy-friendly model. The calculation formula for the heating load for the remaining time periods is as follows: ; in, This indicates the heating load during other time periods.

[0016] If the heating load exceeds the maximum load of the heating equipment during other times Then take the maximum load of the heating equipment. If the heating load for the remaining time periods cannot meet the heating demand throughout the day without increasing the maximum load of the heating system, then it is permissible to moderately exceed the maximum load limit.

[0017] The characteristics of the renewable energy friendly model are: it only operates under two conditions throughout the day, namely full load and normal load, with low operation and maintenance requirements, and can effectively absorb photovoltaic power at noon.

[0018] In some embodiments, S5 specifically includes the following steps: S51. First, define all surplus periods as photovoltaic peak periods, which are the periods during which all photovoltaic power is consumed; then set the evening peak period and other periods based on the photovoltaic peak periods. S52. Calculate the heating power during peak photovoltaic power generation periods using a renewable energy-friendly model, and set the heating load curve for the evening peak period. The required heating power is zero, meaning the heating power demand during the evening peak hours is zero. Then, the heating power demand for the remaining periods is calculated using a grid-friendly model. Thus, a preliminary global optimal model is constructed; S53. Determine whether the preliminary global optimal model meets the set constraints. If not, reduce the peak photovoltaic power generation period, evening peak period, and other periods, and return to S52. If it meets the constraints, output the determined peak photovoltaic power generation period, evening peak period, and other periods. At this point, the construction of the global optimal model is complete.

[0019] In some embodiments, the constraints set in S53 include: a first constraint and a second constraint; The first constraint is to minimize the impact on the power grid, and the expressions are as follows: ; The second constraint is that the photovoltaic power consumption is optimal at noon, and the expressions are as follows: ; in, This indicates the amount of electricity used for heating during other times of the day; Indicates the number of hours during the midday peak solar power generation period; This indicates the number of hours during the evening rush hour.

[0020] This invention constructs a globally optimal model based on the inverse curve principle. Through a multi-period coordination strategy of "full load absorption of photovoltaic power at noon, shutdown and peak shaving during the evening peak (i.e., setting the required heating power during the evening peak period to zero), and smooth adjustment of heating power during other periods (i.e., smooth adjustment of heating power during other periods through the second constraint and the inverse load curve)," it achieves the optimal matching between heating load and grid characteristics, taking into account both technical economy and operation and maintenance operability.

[0021] In some embodiments, S6 specifically includes the following steps: S61. Based on the global optimal model, the day is divided into three time periods: shutdown period (18:00-23:00), full load period (11:00-17:00), and flat load period (00:00-10:00). S62. During the shutdown period, the fourth heating load curve will be... The load value is set to zero; the fourth heating load curve is set during the full load period. The load value is set to the maximum load of the heating equipment. To absorb the photovoltaic power generation; S63, Fourth heating load curve during flat load period The load value setting satisfies the following formula: ; in, This indicates the electricity consumption during the full-load period; Indicates the number of hours during the flat load period; Meanwhile, the 10:00 time period during the flat load period is specially handled based on the calculation results of the global optimal model: if the calculated load is less than the maximum load of the heating equipment... If the set percentage (e.g., 80%) is used, then 0 is used; if it is greater than or equal to the set percentage, then the maximum load of the heating equipment is used. The percentage is set as an empirical value and can be flexibly selected according to the actual capacity and operation of the power grid.

[0022] Once the settings are complete, the simplified model will be built.

[0023] In some embodiments, the natural operating condition model is a natural operating condition curve. This curve calculates the heating load in real time based on outdoor temperature and heating demand, without active adjustment, and meets power supply requirements through grid expansion. The natural operating condition model is suitable for heating projects without auxiliary heat sources or thermal storage systems.

[0024] In some embodiments, S7 is implemented as follows: A simplified model is being used during the recent rollout phase to reduce operational and maintenance complexity. The global optimal model is adopted during the optimized operation phase to further reduce the power grid supply pressure; For projects that do not have heat storage capacity, a natural operating condition model is adopted, and power supply demand is met by expanding the grid capacity.

[0025] The heating load scheduling method of this invention is illustrated by the following examples: Example 1: Assuming the target area's total heating load in 2027 is 1000MW, daily heating electricity consumption is 15000MWh, and the maximum daily load utilization hours are 15h. Based on the region's power grid load characteristics and power output characteristics, the calculation results of each model are shown in Table 1.

[0026] Table 1: Comprehensive Technical and Economic Comparison of Various Models; ; As shown in Table 1, the simplified model yields the largest load increase of 727MW, with no surplus in photovoltaic absorption. The operation and maintenance difficulty of both the renewable energy friendly model and the simplified model is easy, while the operation and maintenance difficulty of the grid friendly model and the global optimal model is not as high as that of the renewable energy friendly model and the simplified model.

[0027] Example 2: The project utilizes an air-source heat pump + hot water storage tank + photovoltaic power station, with a maximum heating load of 6.9MW. It is equipped with a 4-hour hot water storage tank and has load regulation capabilities. In this project, using the global optimal model and the simplified model will have the following impacts on the power grid, as detailed in Table 2. Table 2: Impact Analysis of Example 1 on the Power Grid; ; When using the global optimal model: it operates at full load during midday (11:00-17:00) to absorb photovoltaic power; heating equipment is shut down during the evening peak (19:00-22:00); and smooth adjustment is applied during other times; the maximum load on the power grid is increased by about 0.6MW, which has almost no impact on the power grid.

[0028] When using the simplified model: 0-10 hours, the transformer operates at the same load; 11-17 hours, it operates at full load (6.9MW); 18-23 hours, it shuts down; the maximum load on the power grid increases by about 6MW, which is still within the main transformer's carrying capacity.

[0029] In another aspect, the present invention provides a multi-objective optimization scheduling system that comprehensively considers the characteristics of the power grid load and the heating demand, configured to execute a heating load scheduling method that comprehensively considers the characteristics of the power grid load and the heating demand.

[0030] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A heating load dispatching method which comprehensively considers the load characteristics of a power grid and heating demand, characterized by, Includes the following steps: S1. Obtain power grid load characteristic data and heating demand parameters; S2. Construct an inverse load curve model based on grid load characteristic data, including the inverse load curve and the total inverse load for the whole day; S3. Calculate the heating demand coefficient based on the heating demand parameters, and then construct a grid-friendly model based on the anti-load curve, heating demand coefficient, and heating demand parameters. S4. Construct a renewable energy-friendly model based on grid load characteristic data; S5. Construct a globally optimal model based on the characteristics of the power grid-friendly model and the new energy-friendly model; S6. Based on the global optimal model, the day is divided into three time periods, and then the load values ​​for the three time periods are set respectively to complete the construction of the simplified model. S7. Select a suitable model from the global optimal model, simplified model and natural operating condition model according to the actual conditions, and use the selected model to optimize the scheduling of heating load in the study area. 2.The heating load scheduling method considering grid load characteristics and heating demand according to claim 1, wherein, S1 specifically includes the following steps: S11. Obtain grid load characteristic data, including typical daily load curves, typical daily power output curves, and maximum daily grid load values ​​for the target area. ; S12. Superimpose the typical daily load curve and the typical daily power output curve of the target area to obtain the grid load curve of the target area. ; S13. Obtain heating demand parameters, including the maximum load of heating equipment. and daily heating demand electricity .

3. A heating load dispatching method that comprehensively considers power grid load characteristics and heating demand according to claim 2, characterized in that, The expression for the reverse load curve model is as follows: ； ； in, Indicates the reverse load curve. This indicates the total electricity consumption in reverse curve form throughout the day. For time intervals.

4. A heating load dispatching method that comprehensively considers power grid load characteristics and heating demand according to claim 3, characterized in that, S3 specifically includes the following steps: S31, Based on the total daily reverse curve electricity consumption And the daily heating demand electricity within the heating demand parameters The heating demand coefficient is calculated using the following expression: ； in, This represents the heating demand coefficient; S32, Based on the reverse load curve Heating demand coefficient and the maximum load of heating equipment The first heating load curve is constructed as follows: ； in, This represents the first heating load curve; S33. Superimpose the first heating load curve with the conventional load to obtain the overall load curve, as shown in the following expression: ； in, This represents the overall load curve, which is a grid-friendly model. The characteristics of the grid-friendly model are: under ideal conditions, it does not increase the peak load of the grid, and its impact on the grid's power supply capacity is less than the set value.

5. A heating load dispatching method that comprehensively considers power grid load characteristics and heating demand according to claim 4, characterized in that, S4 specifically includes the following steps: S41. Identify the load curve of the target area. During periods of electricity surplus, the heating load is set to the maximum load of the heating equipment. The second heating load curve is obtained; S42. Calculate the electricity consumption for heating at midday. The calculation formula is as follows: ； in, This indicates the electricity consumption for heating at midday. Indicates the number of hours the machine operates during midday; S43. Calculate the heating load for the remaining time periods. This completes the construction of the renewable energy-friendly model. The renewable energy-friendly model includes the second heating load curve, the midday heating electricity consumption, and the heating load for the remaining time periods. The renewable energy-friendly model can absorb midday photovoltaic power. The calculation formula for the heating load for the remaining time periods is as follows: ； in, This indicates the heating load during other time periods; If the heating load exceeds the maximum load of the heating equipment during other times Then take the maximum load of the heating equipment. If the heating load for the remaining time periods cannot meet the heating demand throughout the day without increasing the maximum load of the heating system, then it is permissible to moderately exceed the maximum load limit.

6. A heating load dispatching method that comprehensively considers power grid load characteristics and heating demand according to claim 5, characterized in that, S5 specifically includes the following steps: S51. First, define all surplus periods as photovoltaic peak periods, which are the periods during which all photovoltaic power is consumed; then set the evening peak period and other periods based on the photovoltaic peak periods. S52. Calculate the heating power during peak photovoltaic power generation periods using a renewable energy-friendly model, and set the heating load curve for the evening peak period. The required heating power is zero, meaning the heating power demand during the evening peak hours is zero. Then, the heating power demand for the remaining periods is calculated using a grid-friendly model. Thus, a preliminary global optimal model is constructed; S53. Determine whether the preliminary global optimal model meets the set constraints. If not, reduce the peak photovoltaic power generation period, evening peak period, and other periods, and return to S52. If it meets the constraints, output the determined peak photovoltaic power generation period, evening peak period, and other periods. At this point, the construction of the global optimal model is complete.

7. A heating load dispatching method that comprehensively considers power grid load characteristics and heating demand according to claim 6, characterized in that, The constraints set in S53 include: a first constraint and a second constraint. The first constraint is to minimize the impact on the power grid, and the expressions are as follows: ； The second constraint is that the photovoltaic power consumption is optimal at noon, and the expressions are as follows: ； in, This indicates the amount of electricity used for heating during other times of the day; Indicates the number of hours during the midday peak solar power generation period; This indicates the number of hours during the evening rush hour.

8. A heating load dispatching method that comprehensively considers power grid load characteristics and heating demand according to claim 7, characterized in that, S6 specifically includes the following steps: S61. Based on the global optimal model, the whole day is divided into three time periods: shutdown period, full load period, and flat load period. S62. During the shutdown period, the fourth heating load curve will be... The load value is set to zero; the fourth heating load curve is set during the full load period. The load value is set to the maximum load of the heating equipment. To absorb the photovoltaic power generation; S63, Fourth heating load curve during flat load period The load value setting satisfies the following formula: ； in, This indicates the electricity consumption during the full-load period; Indicates the number of hours during the flat load period; Meanwhile, the 10:00 time period during the flat load period is specially handled based on the calculation results of the global optimal model: if the calculated load is less than the maximum load of the heating equipment... If the load is less than or equal to the set percentage, the load is zero; if it is greater than or equal to the set percentage, the maximum load of the heating equipment is used. ; Once the settings are complete, the simplified model will be built.

9. A heating load dispatching method that comprehensively considers power grid load characteristics and heating demand according to claim 8, characterized in that, The natural operating condition model is a natural operating condition curve. The natural operating condition curve calculates the heating load in real time based on the outdoor temperature and heating demand, without making active adjustments, and meets the power supply demand through grid-side capacity expansion.

10. A multi-objective optimization dispatching system that comprehensively considers power grid load characteristics and heating demand, characterized in that, The method is configured to perform a heating load scheduling method that comprehensively considers the characteristics of the power grid load and the heating demand, as described in any one of claims 1 to 9.

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