An optimized scheduling method and apparatus based on load-side flexibility resources

Abstract

This invention relates to the field of power dispatching technology and discloses an optimized dispatching method and apparatus based on load-side flexibility resources. When a power shortage request is received, the method determines the location and power demand of the power shortage node; using the location of the power shortage node as the search starting point, it searches for power supply nodes with power supply capacity within a preset search range, forming a first set; sequentially using the location of each power supply node as the search starting point, it searches for power demand nodes within a preset search range, forming a second set; using the second set, it sequentially evaluates the power supply of each power supply node to obtain the power supply coefficient of each power supply node; based on the power supply coefficient of each power supply node, it selects several power supply nodes to form a target set; and it controls each power supply node in the target set to provide power to the power shortage node. This invention improves the flexibility and rationality of power allocation, enhances power efficiency, and achieves low-carbon power consumption.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to an optimized dispatching method and apparatus based on load-side flexibility resources. Background Technology

[0002] Research on load-side large-scale flexible power resource aggregation services explores aspects such as more refined aggregation management, more intelligent collaborative control, more diversified business ecosystem expansion, and adaptation to massive-scale resource access, aiming to accelerate the development of distributed source-load aggregation services. Based on virtual power plants, it can flexibly aggregate user entities with different load characteristics, utilizing the peak-shaving and complementary effects of various resources in terms of daily load factor, daily peak-valley difference, and daily maximum utilization time to achieve complementary regulation.

[0003] However, the following problems still exist in the load distribution between adjacent user nodes: 1. A single power supply node that can supply power to other power-consuming nodes is often connected to multiple power-consuming nodes with power demand. Power demand may be sequential in time, and the current power distribution is based on the order of requests, which is not flexible and intelligent enough; 2. Power-consuming nodes with power demand may be connected to multiple different power supply nodes. The sources of excess power for different power supply nodes are different. The current method of distribution based on transmission distance cannot achieve the lowest possible carbon footprint. Summary of the Invention

[0004] This invention provides an optimized scheduling method and apparatus based on load-side flexibility resources, which improves the flexibility and rationality of power allocation, enhances power efficiency, and achieves low-carbon power consumption.

[0005] To address the aforementioned technical problems, this invention provides an optimized scheduling method based on load-side flexibility resources, comprising:

[0006] When a power shortage request is received, the location of the power shortage node and the power demand are determined based on the power shortage request;

[0007] Starting from the location of the power-deficient node, a power search is performed within a preset search range, and power supply nodes with power supply capability are added to the first set.

[0008] Using the location of each power supply node as the starting point, a power search is performed within a preset search range, and power nodes with power demand are added to the second set.

[0009] The power supply of each power supply node in the first set is evaluated sequentially using the second set to obtain the power supply coefficient of each power supply node.

[0010] Based on the power supply coefficient of each power supply node, select several power supply nodes from the first set to form a target set;

[0011] Control each power supply node in the target set to provide power to the power-deficient node.

[0012] Furthermore, the preset search range is specifically as follows:

[0013] The transmission distance is determined by the preset allowable range of transmission loss;

[0014] The search range is determined based on the transmission distance.

[0015] Furthermore, the step of performing a power search within a preset search range specifically involves:

[0016] Within the preset electricity consumption time range, the electricity consumption data of each node within the search range is obtained sequentially;

[0017] The electricity consumption of each node is predicted based on the electricity consumption data, and the power search results of each node are determined based on the electricity consumption.

[0018] If the power search result for the node is "power outage", then the node is determined to be a power supply node with power supply capability.

[0019] If the power search result for a node is "surplus power", then the node is determined to be a node with power demand.

[0020] Furthermore, the step of using the second set to sequentially evaluate the power supply of each power supply node in the first set to obtain the power supply coefficient of each power supply node is specifically as follows:

[0021] Obtain the total available power of each power supply node in the first set;

[0022] Obtain the total power demand of each power consumption node in the second set;

[0023] Based on the total available power at each power supply node and the total power demand at each power consumption node, the power supply coefficient for each power supply node is calculated as follows:

[0024]

[0025] In the formula, Se i It is the power supply coefficient of power supply node i; and These are the preset first and second weighting coefficients, respectively; E i Q represents the total available power at power supply node i; kis the total power demand of power consumption node k; m is the number of power consumption nodes in the second set; n0 is the preset number of standard transmission lines; R is the preset conversion function.

[0026] Furthermore, the step of selecting several power supply nodes from the first set to form a target set based on the power supply coefficient of each power supply node specifically involves:

[0027] The power supply nodes are sorted according to their power supply coefficients.

[0028] Under the premise of meeting the preset power supply requirements, select a number of undetermined power supply nodes with the smallest power supply coefficient to form a target set.

[0029] Furthermore, under the premise of meeting the preset power supply requirements, selecting several undetermined power supply nodes with the smallest power supply coefficients to form a target set specifically involves:

[0030] The power supply percentage of each power supply node is obtained based on the power supply coefficient of each power supply node;

[0031] Starting with the power supply node with the lowest power supply coefficient, add power supply nodes to the target set one by one until the power supply nodes in the target set meet the preset power supply requirements;

[0032] The preset power supply requirements are as follows:

[0033]

[0034] In the formula, σ j It represents the percentage of power supplied to power node j; q j Q0 is the total available power of power supply node j; Q0 is the power demand of the power-deficient node; n is the number of power supply nodes in the target set; N is the preset correction factor.

[0035] Furthermore, the step of obtaining the power supply percentage of each power supply node based on the power supply coefficient of each power supply node specifically involves:

[0036] The power supply coefficient of each power supply node is compared with several preset coefficient ranges to determine the coefficient range corresponding to each power supply node; wherein, the preset coefficient range stores the corresponding percentage value.

[0037] The percentage values ​​stored in the coefficient range corresponding to each power supply node are determined as the power supply percentage of each power supply node.

[0038] Furthermore, the preset correction factor ranges from [1, 1.5].

[0039] Furthermore, the control of each power supply node in the target set to provide power to the power-deficient node specifically involves:

[0040] Obtain the carbon emissions per unit of power supply for each power supply node in the target set;

[0041] The power supply nodes in the target set are reordered based on the carbon emissions per unit of electricity supplied.

[0042] Starting with the power supply node with the lowest carbon emissions per unit of power supply, each power supply node in the control target set supplies power to the power-deficient node one by one.

[0043] This invention provides an optimized scheduling method based on load-side flexible resources. When a power shortage request is received, the location and power demand of the power shortage node are determined. Starting from the location of the power shortage node, a search is conducted within a preset search range for power supply nodes with power supply capacity, forming a first set. Then, starting from the location of each power supply node, a search is conducted within the preset search range for power demand nodes, forming a second set. The power supply of each power supply node is evaluated using the second set to obtain its power supply coefficient. Based on the power supply coefficients of each power supply node, several power supply nodes are selected to form a target set. Each power supply node in the target set is controlled to provide power to the power shortage node. This invention, by controlling each power supply node in the target set to provide power to the power shortage node, fully aggregates and mines the load-side response of a virtual power plant. Through cloud-based overall scheduling, it achieves efficient aggregation and flexible control of large-scale flexible power resources on the load side, improving power efficiency and realizing low-carbon electricity use.

[0044] Accordingly, the present invention provides an optimized scheduling device based on load-side flexibility resources, comprising: a request receiving module, a first search module, a second search module, an evaluation module, a filtering module, and a control module;

[0045] The request receiving module is used to determine the location of the power shortage node and the power demand based on the power shortage request when it receives a power shortage request.

[0046] The first search module is used to perform a power search within a preset search range, starting from the location of the power-deficient node, and add power supply nodes with power supply capability to the first set;

[0047] The second search module is used to sequentially search for electricity within a preset search range, starting from the location of each power supply node, and add the power-consuming nodes with electricity demand to the second set.

[0048] The evaluation module is used to evaluate the power supply of each power supply node in the first set in sequence using the second set, and to obtain the power supply coefficient of each power supply node.

[0049] The filtering module is used to select several power supply nodes from the first set according to the power supply coefficient of each power supply node to form a target set;

[0050] The control module is used to control each power supply node in the target set to provide power to the power-deficient node.

[0051] This invention provides an optimized scheduling device based on load-side flexible resources. Based on the organic integration of modules, when a power shortage request is received, the location and power demand of the power shortage node are determined. Starting from the location of the power shortage node, a search is conducted within a preset search range for power supply nodes with power supply capacity, forming a first set. Then, starting from the location of each power supply node, a search is conducted within the preset search range for power demand nodes, forming a second set. The second set is used to sequentially evaluate the power supply of each power supply node, obtaining the power supply coefficient of each node. Based on the power supply coefficients of each power supply node, several power supply nodes are selected to form a target set. Each power supply node in the target set is controlled to provide power to the power shortage node. This invention, by controlling each power supply node in the target set to provide power to the power shortage node, fully aggregates and mines the load-side response of a virtual power plant. Through cloud-based overall scheduling, it achieves efficient aggregation and flexible control of large-scale flexible power resources on the load side, improving power efficiency and realizing low-carbon electricity use. Attached Figure Description

[0052] Figure 1 A flowchart illustrating an embodiment of the optimized scheduling method based on load-side flexibility resources provided by the present invention;

[0053] Figure 2 This is a schematic diagram of an embodiment of the optimized scheduling device based on load-side flexibility resources provided by the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0056] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0057] Example 1

[0058] See Figure 1 This is a flowchart illustrating an embodiment of the optimized scheduling method based on load-side flexibility resources provided by the present invention. The method includes steps 101 to 106, each step being as follows:

[0059] Step 101: When a power shortage request is received, determine the location of the power shortage node and the power demand based on the power shortage request.

[0060] In the first embodiment of the present invention, upon receiving a power shortage request, the power shortage node corresponding to the power shortage request and the power demand of the power shortage request can be located based on the power shortage request. Power supply nodes with power supply capabilities in the vicinity are searched based on the location of the power shortage node, thereby rationally and flexibly allocating the load status between adjacent or nearby power grid nodes.

[0061] Step 102: Using the location of the power-deficient node as the starting point of the search, perform a power search within a preset search range, and add power supply nodes with power supply capability to the first set.

[0062] Step 103: Using the location of each power supply node as the starting point, perform a power search within a preset search range, and add the power-consuming nodes with power demand to the second set.

[0063] Furthermore, in the first embodiment of the present invention, the preset search range is specifically as follows:

[0064] The transmission distance is determined by the preset allowable range of transmission loss;

[0065] The search range is determined based on the transmission distance.

[0066] In the first embodiment of the present invention, when the transmission distance between the search starting point and the target point is less than the search range, the transmission loss can be ignored. By setting an allowable transmission loss, the search range can be increased. Therefore, the transmission distance can be obtained based on the preset allowable range of transmission loss, thereby determining the search range for power search.

[0067] Furthermore, in the first embodiment of the present invention, power search is performed within a preset search range, specifically as follows:

[0068] Within the preset electricity consumption time range, the electricity consumption data of each node within the search range is obtained sequentially;

[0069] The electricity consumption of each node is predicted based on the electricity consumption data, and the power search results of each node are determined based on the electricity consumption.

[0070] If the power search result for the node is "power outage", then the node is determined to be a power supply node with power supply capability.

[0071] If the power search result for a node is "surplus power", then the node is determined to be a node with power demand.

[0072] In the first embodiment of the present invention, the power supply capacity and power supply demand are both derived from analysis within a certain time range of electricity consumption. For example, if the electricity consumption time range is set to the current day, the electricity consumption data of each node within the search range of the current day is obtained. Based on the obtained electricity consumption data, the electricity consumption is predicted. Based on the prediction results of the electricity consumption, it can be determined whether each node is currently in a state of power shortage with power supply demand or has surplus power supply capacity.

[0073] Step 104: Use the second set to perform power supply evaluation on each power supply node of the first set in sequence, and obtain the power supply coefficient of each power supply node.

[0074] Furthermore, in the first embodiment of the present invention, the power supply of each power supply node in the first set is evaluated sequentially using the second set to obtain the power supply coefficient of each power supply node, specifically as follows:

[0075] Obtain the total available power of each power supply node in the first set;

[0076] Obtain the total power demand of each power consumption node in the second set;

[0077] Based on the total available power at each power supply node and the total power demand at each power consumption node, the power supply coefficient for each power supply node is calculated as follows:

[0078]

[0079] In the formula, Se i It is the power supply coefficient of power supply node i; and These are the preset first and second weighting coefficients, respectively; E i Q represents the total available power at power supply node i; k is the total power demand of power consumption node k; m is the number of power consumption nodes in the second set; n0 is the preset number of standard transmission lines; R is the preset conversion function.

[0080] In the first embodiment of this invention, the values ​​of the total available power at each power supply node and the total demand power at each power consumption node change in real time, both related to the power consumption time range, and are obtained through power forecasting and power planning results. In the above formula for calculating the power supply coefficient, m is the number of power consumption nodes in the second set, which is equivalent to the number of transmission lines corresponding to the power supply nodes in the first set. n0 is a preset number of standard transmission lines, which can be obtained based on empirical data. R is a preset conversion function, preferably a lookup table function, set based on experience. The conversion function is used to... and In this case, the data is transformed and corrected to make it... The value is between [1, 2].

[0081] In the first embodiment of the present invention, the power supply coefficient is obtained with the total power demand of the power-consuming nodes in the second set and the total available power of the power supply nodes in the first set as the main variable. The greater the total power demand of the power-consuming nodes in the second set is compared with the total available power of the power supply nodes in the first set, the larger the power supply coefficient is. The corresponding power that the node can provide to the power-deficient node that issues a power shortage request is set to be smaller. The power supply coefficient is used to reasonably control the power supplied to the power-deficient node, while also meeting the power demand of other power-consuming nodes near the power supply node.

[0082] Step 105: Based on the power supply coefficient of each power supply node, select several power supply nodes from the first set to form a target set.

[0083] Furthermore, in the first embodiment of the present invention, based on the power supply coefficient of each power supply node, a number of power supply nodes are selected from the first set to form a target set, specifically as follows:

[0084] The power supply nodes are sorted according to their power supply coefficients.

[0085] Under the premise of meeting the preset power supply requirements, select a number of undetermined power supply nodes with the smallest power supply coefficient to form a target set.

[0086] Furthermore, in the first embodiment of the present invention, under the premise of meeting the preset power supply requirements, a target set is formed by selecting several undetermined power supply nodes with the smallest power supply coefficient, specifically as follows:

[0087] The power supply percentage of each power supply node is obtained based on the power supply coefficient of each power supply node;

[0088] Starting with the power supply node with the lowest power supply coefficient, add power supply nodes to the target set one by one until the power supply nodes in the target set meet the preset power supply requirements;

[0089] The preset power supply requirements are as follows:

[0090]

[0091] In the formula, σ j It represents the percentage of power supplied to power node j; q j Q0 is the total available power of power supply node j; Q0 is the power demand of the power-deficient node; n is the number of power supply nodes in the target set; N is the preset correction factor.

[0092] In the first embodiment of the present invention, when selecting an appropriate number of power supply nodes from the first set as the target set, the power supply nodes are first sorted according to the size of the power supply coefficient. Starting from the power supply node with the smallest power supply coefficient, power supply nodes are added to the target set one by one. After each addition of a power supply node, the target set is judged to meet the preset power supply requirements based on the power supply percentage and the total available power of each power supply node in the target set. When it is determined that the power supply requirements are met, the addition of power supply nodes to the target set is stopped.

[0093] Furthermore, in the first embodiment of the present invention, the preset correction factor ranges from [1, 1.5].

[0094] In the first embodiment of this invention, the correction factor is not static, but dynamically adjusted in real time based on factors such as electricity demand, energy prices, carbon emission intensity of power supply nodes, and other relevant factors. For example, when electricity demand is high, the correction factor is increased to incentivize power supply nodes to reduce carbon emissions; when electricity supply is sufficient and prices are low, the correction factor is appropriately decreased. By setting different correction factors to change the number of power supply nodes within the target set, it is possible to select power supply nodes with low-carbon emission reduction advantages for priority power supply from a wider range, thereby achieving low-carbon electricity use.

[0095] Furthermore, in the first embodiment of the present invention, the power supply percentage of each power supply node is obtained based on the power supply coefficient of each power supply node, specifically as follows:

[0096] The power supply coefficient of each power supply node is compared with several preset coefficient ranges to determine the coefficient range corresponding to each power supply node; wherein, the preset coefficient range stores the corresponding percentage value.

[0097] The percentage values ​​stored in the coefficient range corresponding to each power supply node are determined as the power supply percentage of each power supply node.

[0098] Step 106: Control each power supply node in the target set to provide power to the power-deficient node.

[0099] Furthermore, in the first embodiment of the present invention, controlling each power supply node in the target set to provide power to the power-deficient node specifically involves:

[0100] Obtain the carbon emissions per unit of power supply for each power supply node in the target set;

[0101] The power supply nodes in the target set are reordered based on the carbon emissions per unit of electricity supplied.

[0102] Starting with the power supply node with the lowest carbon emissions per unit of power supply, each power supply node in the control target set supplies power to the power-deficient node one by one.

[0103] In the first embodiment of this invention, after determining the power supply nodes in the target set, the power supply nodes in the target set are reordered according to the carbon emissions per unit of power supply, so that power supply nodes with low carbon emission reduction advantages are given priority to supply power, thereby achieving low-carbon electricity use. This invention avoids the situation where a single power supply node supplies power to other power-consuming nodes near that node, which may not receive power supply assistance due to late power requests, by controlling each power supply node in the target set to provide power to the nodes in need. It improves electricity efficiency by rationally and flexibly allocating the load status between adjacent or nearby grid nodes.

[0104] In the first embodiment of this invention, while controlling the power supply nodes to provide power, the carbon emissions, power supply stability, and other relevant indicators of the power supply nodes are continuously monitored and evaluated. Based on the monitoring and evaluation results, the correction factor and other relevant parameters are adjusted in a timely manner to ensure that the selected power supply nodes always meet the goal of low-carbon electricity use. This invention can be combined with a carbon trading market. By allocating carbon allowances to power supply nodes and allowing them to buy and sell carbon allowances on the market, it can further incentivize power supply nodes to reduce carbon emissions, thereby achieving the goal of low-carbon electricity use.

[0105] In summary, the first embodiment of this invention provides an optimized scheduling method based on load-side flexible resources. When a power shortage request is received, the location and power demand of the power shortage node are determined. Using the location of the power shortage node as the search starting point, power supply nodes with power supply capacity are searched within a preset search range to form a first set. Then, using the location of each power supply node as the search starting point, power demand nodes with power consumption are searched within a preset search range to form a second set. The power supply of each power supply node is evaluated using the second set to obtain the power supply coefficient of each power supply node. Based on the power supply coefficient of each power supply node, several power supply nodes are selected to form a target set. Each power supply node in the target set is controlled to provide power to the power shortage node. This invention, by controlling each power supply node in the target set to provide power to the power shortage node, fully aggregates and mines the load-side response of the virtual power plant. Through cloud-based overall scheduling, it achieves efficient aggregation and flexible control of large-scale flexible power resources on the load side, improving power efficiency and realizing low-carbon electricity use.

[0106] Example 2

[0107] See Figure 2 This is a schematic diagram of an embodiment of the optimized scheduling device based on load-side flexibility resources provided by the present invention. The device includes a request receiving module 201, a first search module 202, a second search module 203, an evaluation module 204, a filtering module 205, and a control module 206.

[0108] The request receiving module 201 is used to determine the location of the power shortage node and the power demand based on the power shortage request when it receives a power shortage request.

[0109] The first search module 202 is used to perform a power search within a preset search range, starting from the location of the power-deficient node, and add power supply nodes with power supply capability to the first set;

[0110] The second search module 203 is used to sequentially search for electricity within a preset search range, starting from the location of each power supply node, and add the power-consuming nodes with electricity demand to the second set.

[0111] The evaluation module 204 is used to evaluate the power supply of each power supply node in the first set in sequence using the second set, and to obtain the power supply coefficient of each power supply node.

[0112] The filtering module 205 is used to select several power supply nodes from the first set according to the power supply coefficient of each power supply node to form a target set;

[0113] The control module 206 is used to control each power supply node in the target set to provide power to the power-deficient node.

[0114] Furthermore, in the second embodiment of the present invention, the preset search range is specifically as follows:

[0115] The transmission distance is determined by the preset allowable range of transmission loss;

[0116] The search range is determined based on the transmission distance.

[0117] Furthermore, in the second embodiment of the present invention, power search is performed within a preset search range, specifically as follows:

[0118] Within the preset electricity consumption time range, the electricity consumption data of each node within the search range is obtained sequentially;

[0119] The electricity consumption of each node is predicted based on the electricity consumption data, and the power search results of each node are determined based on the electricity consumption.

[0120] If the power search result for the node is "power outage", then the node is determined to be a power supply node with power supply capability.

[0121] If the power search result for a node is "surplus power", then the node is determined to be a node with power demand.

[0122] Furthermore, in the second embodiment of the present invention, the power supply of each power supply node in the first set is evaluated sequentially using the second set to obtain the power supply coefficient of each power supply node, specifically as follows:

[0123] Obtain the total available power of each power supply node in the first set;

[0124] Obtain the total power demand of each power consumption node in the second set;

[0125] Based on the total available power at each power supply node and the total power demand at each power consumption node, the power supply coefficient for each power supply node is calculated as follows:

[0126]

[0127] In the formula, Se i It is the power supply coefficient of power supply node i; and These are the preset first and second weighting coefficients, respectively; E i Q represents the total available power at power supply node i; k is the total power demand of power consumption node k; m is the number of power consumption nodes in the second set; n0 is the preset number of standard transmission lines; R is the preset conversion function.

[0128] Furthermore, in the second embodiment of the present invention, based on the power supply coefficient of each power supply node, a number of power supply nodes are selected from the first set to form a target set, specifically as follows:

[0129] The power supply nodes are sorted according to their power supply coefficients.

[0130] Under the premise of meeting the preset power supply requirements, select a number of undetermined power supply nodes with the smallest power supply coefficient to form a target set.

[0131] Furthermore, in the second embodiment of the present invention, under the premise of meeting the preset power supply requirements, a target set is formed by selecting several undetermined power supply nodes with the smallest power supply coefficient, specifically as follows:

[0132] The power supply percentage of each power supply node is obtained based on the power supply coefficient of each power supply node;

[0133] Starting with the power supply node with the lowest power supply coefficient, add power supply nodes to the target set one by one until the power supply nodes in the target set meet the preset power supply requirements;

[0134] The preset power supply requirements are as follows:

[0135]

[0136] In the formula, σ j It represents the percentage of power supplied to power node j; q j Q0 is the total available power of power supply node j; Q0 is the power demand of the power-deficient node; n is the number of power supply nodes in the target set; N is the preset correction factor.

[0137] Furthermore, in the second embodiment of the present invention, the power supply percentage of each power supply node is obtained based on the power supply coefficient of each power supply node, specifically as follows:

[0138] The power supply coefficient of each power supply node is compared with several preset coefficient ranges to determine the coefficient range corresponding to each power supply node; wherein, the preset coefficient range stores the corresponding percentage value.

[0139] The percentage values ​​stored in the coefficient range corresponding to each power supply node are determined as the power supply percentage of each power supply node.

[0140] Furthermore, in the second embodiment of the present invention, the preset correction factor ranges from [1, 1.5].

[0141] Furthermore, in the second embodiment of the present invention, controlling each power supply node in the target set to provide power to the power-deficient node specifically involves:

[0142] Obtain the carbon emissions per unit of power supply for each power supply node in the target set;

[0143] The power supply nodes in the target set are reordered based on the carbon emissions per unit of electricity supplied.

[0144] Starting with the power supply node with the lowest carbon emissions per unit of power supply, each power supply node in the control target set supplies power to the power-deficient node one by one.

[0145] In summary, the second embodiment of this invention provides an optimized scheduling device based on load-side flexible resources. Based on the organic integration of modules, when a power shortage request is received, the location and power demand of the power shortage node are determined. Using the location of the power shortage node as the search starting point, power supply nodes with power supply capacity are searched within a preset search range to form a first set. Then, using the location of each power supply node as the search starting point, power demand nodes with power consumption are searched within a preset search range to form a second set. The power supply of each power supply node is evaluated using the second set to obtain the power supply coefficient of each power supply node. Based on the power supply coefficient of each power supply node, several power supply nodes are selected to form a target set. Each power supply node in the target set is controlled to provide power to the power shortage node. This invention, by controlling each power supply node in the target set to provide power to the power shortage node, fully aggregates and mines the load-side response of the virtual power plant. Through cloud-based overall scheduling, it achieves efficient aggregation and flexible control of large-scale flexible power resources on the load side, improving power efficiency and realizing low-carbon electricity use.

[0146] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. An optimized scheduling method based on load-side flexibility resources, characterized in that, include: When a power shortage request is received, the location of the power shortage node and the power demand are determined based on the power shortage request; Starting from the location of the power-deficient node, a power search is performed within a preset search range, and power supply nodes with power supply capability are added to the first set. Using the location of each power supply node as the starting point, a power search is performed within a preset search range, and power nodes with power demand are added to the second set. The power supply of each power supply node in the first set is evaluated sequentially using the second set to obtain the power supply coefficient of each power supply node. Based on the power supply coefficient of each power supply node, select several power supply nodes from the first set to form a target set; Control each power supply node in the target set to provide power to the power-deficient node; Specifically, the step of using the second set to sequentially evaluate the power supply of each power supply node in the first set to obtain the power supply coefficient of each power supply node is as follows: Obtain the total available power of each power supply node in the first set; Obtain the total power demand of each power consumption node in the second set; Based on the total available power at each power supply node and the total power demand at each power consumption node, the power supply coefficient for each power supply node is calculated as follows: In the formula, It is the power supply coefficient of power supply node i; and These are the preset first weighting coefficient and the second weighting coefficient, respectively; It represents the total available power at power supply node i; It is the total electricity demand of electricity consumption node k; It is the number of power-consuming nodes in the second set; This is the preset number of standard transmission lines; It is a preset conversion function.

2. The optimized scheduling method based on load-side flexibility resources according to claim 1, characterized in that, The preset search range is specifically as follows: The transmission distance is determined by the preset allowable range of transmission loss; The search range is determined based on the transmission distance.

3. The optimized scheduling method based on load-side flexibility resources according to claim 2, characterized in that, The process of performing a power search within a preset search range specifically includes: Within the preset electricity consumption time range, the electricity consumption data of each node within the search range is obtained sequentially; The electricity consumption of each node is predicted based on the electricity consumption data, and the power search results of each node are determined based on the electricity consumption. If the power search result for the node is "power shortage", then the node is determined to be a power supply node with power demand. If the power search result for the node is "surplus power", then the node is determined to be a power-consuming node with power supply capability.

4. The optimized scheduling method based on load-side flexibility resources according to claim 1, characterized in that, The step of selecting several power supply nodes from the first set to form a target set based on the power supply coefficient of each power supply node is as follows: The power supply nodes are sorted according to their power supply coefficients. Under the premise of meeting the preset power supply requirements, select a number of undetermined power supply nodes with the smallest power supply coefficient to form a target set.

5. The optimized scheduling method based on load-side flexibility resources according to claim 4, characterized in that, Under the premise of meeting the preset power supply requirements, a target set is formed by selecting several undetermined power supply nodes with the smallest power supply coefficients. Specifically: The power supply percentage of each power supply node is obtained based on the power supply coefficient of each power supply node; Starting with the power supply node with the lowest power supply coefficient, add power supply nodes to the target set one by one until the power supply nodes in the target set meet the preset power supply requirements; The preset power supply requirements are as follows: In the formula, It represents the percentage of power supplied to power node j; It represents the total available power at power supply node j; This represents the electricity demand at power-deficient nodes. It is the number of power supply nodes in the target set; It is the preset correction factor.

6. The optimized scheduling method based on load-side flexibility resources according to claim 5, characterized in that, The process of obtaining the power supply percentage of each power supply node based on the power supply coefficient of each power supply node is as follows: The power supply coefficient of each power supply node is compared with several preset coefficient ranges to determine the coefficient range corresponding to each power supply node; wherein, the preset coefficient range stores the corresponding percentage value. The percentage values ​​stored in the coefficient range corresponding to each power supply node are determined as the power supply percentage of each power supply node.

7. The optimized scheduling method based on load-side flexibility resources according to claim 5, characterized in that, The preset correction factor has a range of values. .

8. The optimized scheduling method based on load-side flexibility resources according to claim 1, characterized in that, The control of each power supply node in the target set to provide power to the power-deficient node specifically involves: Obtain the carbon emissions per unit of power supply for each power supply node in the target set; The power supply nodes in the target set are reordered based on the carbon emissions per unit of electricity supplied. Starting with the power supply node with the lowest carbon emissions per unit of power supply, each power supply node in the control target set supplies power to the power-deficient node one by one.

9. An optimized scheduling device based on load-side flexibility resources, characterized in that, include: The system comprises a request receiving module, a first search module, a second search module, an evaluation module, a filtering module, and a control module. The request receiving module is used to determine the location of the power shortage node and the power demand based on the power shortage request when it receives a power shortage request. The first search module is used to perform a power search within a preset search range, starting from the location of the power-deficient node, and add power supply nodes with power supply capability to the first set; The second search module is used to sequentially search for electricity within a preset search range, starting from the location of each power supply node, and add the power-consuming nodes with electricity demand to the second set. The evaluation module is used to evaluate the power supply of each power supply node in the first set in sequence using the second set, and to obtain the power supply coefficient of each power supply node. The filtering module is used to select several power supply nodes from the first set according to the power supply coefficient of each power supply node to form a target set; The control module is used to control each power supply node in the target set to provide power to the power-deficient node; Specifically, the step of using the second set to sequentially evaluate the power supply of each power supply node in the first set to obtain the power supply coefficient of each power supply node is as follows: Obtain the total available power of each power supply node in the first set; Obtain the total power demand of each power consumption node in the second set; Based on the total available power at each power supply node and the total power demand at each power consumption node, the power supply coefficient for each power supply node is calculated as follows: In the formula, It is the power supply coefficient of power supply node i; and These are the preset first weighting coefficient and the second weighting coefficient, respectively; It represents the total available power at power supply node i; It is the total electricity demand of electricity consumption node k; It is the number of power-consuming nodes in the second set; This is the preset number of standard transmission lines; It is a preset conversion function.

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