Variable-period fire-storage monthly scheduling method considering daily maximum discharge energy demand

Abstract

The invention provides a variable-time-period fire-storage monthly scheduling method considering daily maximum discharge energy demand. The method comprises the following steps: acquiring a net load power prediction mean value of a plurality of continuous time periods; carrying out clustering processing on the net load power prediction mean value; combining the adjacent time periods, belonging to the same category, of the net load power prediction mean value into the same unit start-stop decision period; according to the pre-obtained maximum discharge power and minimum chargeable power of the energy storage system, constructing energy storage charge and discharge constraints of the energy storage system; and based on the energy storage charging and discharging constraint, determining a start-stop scheme in the unit start-stop decision period according to a scheduling model. According to the method, clustering analysis is carried out on the net load prediction mean value, so that the number of unit start-stop decision time periods is reduced, a monthly scheduling model is reduced, energy storage charging and discharging constraints under the most extreme condition are constructed, and therefore the purposes of optimizing the scheduling model scale and meeting the energy storage charging requirement under the extreme net load scene are achieved.

Description

Technical Field

[0001] The invention relates to a time-varying thermal-storage monthly dispatching method taking into account the daily maximum discharge energy demand, and belongs to the technical field of coordinated dispatching of thermal power and energy storage. Background Art

[0002] Developing renewable energy is a necessary way to alleviate the energy and environmental crisis. However, the continuous growth of renewable energy penetration has weakened the adjustability of the source, causing a mismatch between the supply and demand of power in the system. Especially in extreme cold weather and other times when the output of renewable energy is severely affected, the mismatch between the supply and demand of power in the system is more prominent, which can easily lead to power shortages for several hours or even across days. At this time, how to dispatch on a longer monthly time scale and reasonably formulate operation plans for adjustable resources such as thermal power and energy storage to make up for the shortage of power supply and demand is the key to ensuring the balance of power supply and demand in the system. Summary of the invention

[0003] The present invention provides a variable-time period fire-storage monthly scheduling method taking into account the daily maximum discharge energy demand. By performing cluster analysis on the net load forecast mean, the number of unit start-stop decision periods is reduced, and the monthly scheduling model is reduced. At the same time, based on the maximum discharge power and minimum rechargeable power of the energy storage system, energy storage charging and discharging constraints under the most extreme conditions are constructed, thereby achieving the purpose of optimizing the scheduling model scale and meeting the charging demand of energy storage under extreme net load scenarios.

[0004] In order to achieve the above objectives / solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0005] On the one hand, the present invention provides a method for monthly dispatching of a time-varying fire-storage system taking into account the daily maximum discharge energy demand, which comprises:

[0006] Obtain the average net load power forecast for multiple consecutive time periods;

[0007] Performing clustering processing on the net load power prediction mean value to divide the net load power prediction mean value of each time period into multiple categories;

[0008] Merging adjacent time periods in which the net load power forecast mean value belongs to the same category into the same unit start / stop decision period;

[0009] Constructing energy storage charging and discharging constraints for the energy storage system according to the pre-acquired maximum discharge power and minimum chargeable power of the energy storage system;

[0010] Based on the energy storage charging and discharging constraints, the start-stop plan of the unit within the start-stop decision period is determined according to a pre-constructed fire-storage monthly scheduling model.

[0011] Optionally, clustering the net load power prediction mean value includes:

[0012] Normalizing the net load power prediction mean value to obtain a normalized net load power prediction mean value;

[0013] A cluster analysis is performed on the normalized net load power prediction mean.

[0014] Optionally, normalizing the net load power prediction mean to obtain the normalized net load power prediction mean includes:

[0015]

[0016]

[0017] In the formula, is the set of all time periods, each time period is 1h, and T is the number of time periods; They are the load power forecast mean, renewable energy output forecast mean, and net load power forecast mean at time t respectively; is the normalized net load power forecast mean value, is the set of all time periods on the dth day, T h is the number of time periods per day; D = {1, ..., D} is the set of all days in the current scheduling month, and D is the number of days.

[0018] Optionally, clustering the normalized net load power prediction mean value includes:

[0019] Step 1: Define the sample set for cluster analysis as

[0020] Step 2: Determine the number of clusters N and select N sample points as the initial cluster centers. represents the i-th cluster center;

[0021] Step 3: For each sample point Calculate it and all cluster centers Distance:

[0022]

[0023] In the formula, d represents the distance, represents the tth sample point, Represents the i-th cluster center corresponding to the t-th sample point;

[0024] Step 4: Sample points Assign to the cluster center with the shortest distance Form clusters corresponding to N cluster centers, and the net load data set of the i-th cluster is

[0025] Step 5: Update the cluster center and calculate the average value of all sample points in each cluster as the new cluster center

[0026]

[0027] Where M i is the i-th cluster The number of samples in is the new cluster center of the cluster; all sample points in each cluster belong to the same category;

[0028] Step 6: Determine the cluster center of each cluster after update Compared with the cluster center before update Is there any change? If yes, repeat steps 4 and 5, otherwise the classification ends.

[0029] Optionally, the maximum discharge power and the minimum chargeable power of the energy storage system are calculated as follows:

[0030]

[0031] In the formula, are the maximum discharge power and minimum chargeable power of the i-th energy storage system at time t, is the set of all thermal power units, N g is the number of units, is the set of all energy storage systems, N b is the number of energy storage systems; and They are the maximum load forecast value, the maximum renewable energy output forecast value and the corresponding load loss power at time t respectively; are the maximum output of the i-th thermal power unit and the start / stop state of the i-th thermal power unit in the τ-th start / stop decision period, respectively. Indicates startup. Indicates shutdown, g is the thermal power unit identifier; It represents the reduction of all thermal power units compared to their total maximum output at time t, P g.i is the minimum output of the ith thermal power unit.

[0032] Optionally, the energy storage charging and discharging constraints are:

[0033]

[0034]

[0035]

[0036] In the formula, are the discharge power and charging power of the i-th energy storage system at time t respectively; η d.i , η c.i are the discharge efficiency and charging efficiency of the i-th energy storage system respectively; γ i is the charge and discharge energy coefficient of the i-th energy storage system, γ i ≤1; is the energy upper limit of the i-th energy storage system; T' is the number of time periods corresponding to the charging and discharging energy constraint of the i-th energy storage system.

[0037] Optionally, the objective function of the fire-storage monthly scheduling model is:

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] In the formula, are the thermal power output of the i-th thermal power unit at time t, the charging and discharging power of the i-th energy storage system, and the load loss power, respectively. are the thermal power cost of the i-th thermal power unit, the energy storage operation cost of the i-th energy storage system, and the load loss cost; k g.i 、c g.i are the marginal cost of thermal power generation and the fixed operating cost per unit time of the i-th thermal power unit; k b.i , k L are the unit charging and discharging power cost and the unit load loss power cost of the i-th energy storage system respectively; ω, are the probability of occurrence of average net load and maximum net load respectively; c SU.i 、c SD.i are the startup and shutdown costs of the i-th thermal power unit respectively; are auxiliary decision variables, representing the startup and shutdown actions of the i-th thermal power unit in the τ-th start-stop decision period.

[0044] In a second aspect, the present invention provides a variable-time-period fire-storage monthly scheduling device taking into account the daily maximum discharge energy demand, comprising:

[0045] An acquisition module, used to obtain the average value of net load power prediction for multiple consecutive time periods;

[0046] A clustering processing module, used for clustering the net load power prediction mean value to divide the net load power prediction mean value of each time period into multiple categories;

[0047] A merging module, used to merge adjacent time periods whose net load power forecast mean values ​​belong to the same category into the same unit start / stop decision period;

[0048] A construction module, used to construct energy storage charging and discharging constraints of the energy storage system according to the maximum discharge power and the minimum chargeable power of the energy storage system acquired in advance;

[0049] A determination module is used to determine the start-stop plan of the unit within the start-stop decision period based on the energy storage charging and discharging constraints and according to a pre-built fire-storage monthly scheduling model.

[0050] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, it implements the variable-time period fire-storage monthly scheduling method that takes into account the daily maximum discharge energy demand.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The present invention provides a method for monthly dispatching of thermal power units with time periods varying according to the maximum daily discharge energy demand. In view of the large scale of the monthly thermal power dispatching model and the influence of the actual net load on the energy storage charging amount, the method reduces the number of start-stop decision periods of the units and optimizes the start-stop variables of the thermal power units to reduce the monthly dispatching scale. On the basis of considering the different net load fluctuation characteristics, a method for reducing the number of start-stop decision periods based on net load fluctuation is designed, which reduces the number of start-stop decision variables of the thermal power units and realizes efficient solution of the model.

[0053] 2. Aiming at the charging demand of energy storage under different net load levels, the present invention constructs a model of the maximum daily discharge energy demand of energy storage under extreme net load scenarios to ensure that the energy storage system has sufficient charging capacity under various load conditions.

[0054] 3. The present invention comprehensively considers the operation cost of fire-storage, the load loss cost and the multi-time section coupling constraint of fire-storage, and constructs a variable-time-period fire-storage monthly scheduling model with the maximum daily discharge energy demand. This method can reasonably formulate the monthly operation plan of thermal power and energy storage, significantly shorten the model solution time, and meet the energy storage charging demand under extreme net load scenarios. It has the advantages of scientific rationality, strong applicability and good effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Monthly scheduling framework diagram of the fire-storage variable period of the fire-storage monthly scheduling method in Example 1;

[0056] Figure 2 This is a flow chart of merging and reducing the start and stop decision time periods of the unit in Example 1;

[0057] Figure 3 This is a schematic diagram of the effect of reducing the start and stop decision period of the unit in Example 1;

[0058] Figure 4 This is a schematic diagram of calculating the minimum chargeable amount and maximum discharge amount of energy storage per day in Example 1;

[0059] Figure 5 This is a comparison chart of the total maximum output results of the units before and after the start and stop periods of the units in Example 1 are reduced;

[0060] Figure 6 This is a comparison chart of the maximum output of thermal power units with and without considering the maximum discharge energy demand of energy storage in Example 1;

[0061] Figure 7 This is a comparison chart of hydrogen energy storage energy with and without considering the maximum discharge energy demand of energy storage under maximum load power of Example 1. DETAILED DESCRIPTION

[0062] It should be noted that:

[0063] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0064] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0065] Example 1

[0066] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] This embodiment is a variable-time fire-storage monthly scheduling method taking into account the daily maximum discharge energy demand. Figure 1As shown, the following steps are included:

[0068] Step 1: Algorithm for reducing the number of unit start-stop periods based on net load fluctuation characteristics

[0069] 1) Considering that the number of unit starts and stops is small when the net load changes slightly, it is approximately considered that the unit start and stop state remains unchanged when the load changes slightly. Several adjacent moments with small load changes can be reduced to one unit start and stop period, reducing the number of unit start and stop state decision quantities; the net load power prediction mean is normalized to obtain the normalized net load power prediction mean including

[0070]

[0071] In the formula, is the set of all time periods, each time period is 1h, and T is the number of time periods; They are the load power forecast mean, renewable energy output forecast mean, and net load power forecast mean at time t respectively; is the normalized net load power forecast mean value, is the set of all time periods on the dth day, T h is the number of time periods per day; is the set of all days in the current scheduling month, and D is the number of days.

[0072] Based on k-means algorithm Clustering is performed, namely:

[0073] ① Define the net load sample set for cluster analysis as

[0074] ②Determine the number of clusters N and select N sample points as the initial cluster centers. Represents the i-th cluster center.

[0075] ③For each sample point Calculate it and all cluster centers Distance:

[0076]

[0077] In the formula, d represents the distance, represents the tth sample point, Represents the i-th cluster center corresponding to the t-th sample point;

[0078] ④ The sample Assign to the cluster center with the shortest distance Form clusters corresponding to N cluster centers, and the net load data set of the i-th cluster is

[0079] ⑤ Update the cluster center and calculate the average value of all sample points in each cluster as the new cluster center

[0080]

[0081] Where M i is the i-th cluster The number of samples in is the new cluster center of the cluster, and all sample points in each cluster belong to the same category;

[0082] ⑥ Determine the clustering center of each cluster after update Compared with the cluster center before update Is there any change? If yes, repeat steps ④ and ⑤. Otherwise, the classification is completed.

[0083] 2) Algorithm for reducing the unit start-stop decision period

[0084] Combined with the algorithm described in step 1), the net load is clustered into several categories. The power of adjacent net loads of the same category is relatively close. It is approximately considered that the start and stop state of the unit remains unchanged. The corresponding time period can be reduced to a unit start and stop decision period. The process of reducing the unit start and stop decision period is as follows: Figure 2 As shown in the figure, by continuously rolling forward the calculation, the corresponding periods of the same type of net load power at adjacent moments are merged into one unit start-stop decision period to reduce the number of unit start-stop decision periods. The effect of reducing the unit start-stop period is shown in Figure 3 (In the figure, is the set of hours in the τth unit start / stop decision period; t st.τ 、T τ are the starting hour and number of hours of the τth unit start / stop decision period respectively; is the set of unit start-stop decision periods, L = {L1, ..., L K}, K is the number of unit start-stop decision periods, The number of unit start-stop decision periods after reduction is significantly less than the number of hours, which can greatly reduce the variable scale of unit start-stop 01 control and shorten the solution time of the monthly scheduling model.

[0085] Step 2: The maximum discharge energy demand of the energy storage is at the maximum net load. The discharge power of the energy storage is the maximum and the chargeable power is the minimum. If the charge and discharge power integral meets the energy constraint at this time, the charging demand of the energy storage under various net loads can be met. Thus, the chargeable capacity constraint of the energy storage under the maximum net load can be constructed;

[0086] 1) Calculate the maximum discharge power and minimum rechargeable power of energy storage

[0087] According to the net load fluctuation characteristics, the maximum discharge power and minimum rechargeable power of energy storage are analyzed, such as Figure 4 As shown, when the maximum net load When the maximum net load is greater than the maximum output of thermal power, energy storage needs to be discharged; when the maximum net load is less than the maximum output of thermal power, energy storage can be charged. are the maximum discharge power and minimum chargeable power of the i-th energy storage system at time t respectively.

[0088]

[0089] In the formula, is the set of all units, N g is the number of units, is the set of all energy storage systems, N b is the number of energy storage systems; and They are the maximum load forecast value, the maximum renewable energy output forecast value and the corresponding load loss power at time t respectively; are the maximum output of the i-th thermal power unit and the start / stop status of the i-th unit in the τ-th unit start / stop decision period, Indicates startup. Indicates shutdown, g is the thermal power unit identifier; It represents the reduction of all thermal power units compared to their total maximum output at time t, P g.i is the minimum output of the ith thermal power unit.

[0090] 2) Construct the energy constraints of energy storage charging and discharging under the most extreme net load, requiring that under the maximum net load, the difference between the minimum chargeable amount and the maximum discharge amount of energy storage per day meets the energy constraint and is greater than the energy storage energy at the end time of each day in the monthly decision;

[0091]

[0092]

[0093]

[0094] In the formula, are the discharge power and charging power of the i-th energy storage system at time t respectively; η d.i , η c.i are the discharge efficiency and charging efficiency of the i-th energy storage system respectively; γ i is the charge and discharge energy coefficient of the i-th energy storage system, γ i ≤1; is the energy upper limit of the i-th energy storage system; T' is the number of time periods corresponding to the charging and discharging energy constraint of the i-th energy storage system.

[0095] Step 3: Build a monthly fire-storage scheduling model

[0096] Combined with the reduction of the start-up and shutdown periods of the units and the construction of the maximum daily discharge energy demand of the energy storage, the monthly dispatch model of the fire-storage system is constructed by considering the constraints such as the fire-storage operation cost, the load loss cost and the fire-storage operation.

[0097] 1) The objective function aims to minimize the sum of the operating costs of thermal power and energy storage and the cost of load loss;

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] In the formula, are the thermal power output of the i-th thermal power unit at time t, the charging and discharging power of the i-th energy storage system, and the load loss power, respectively. are the thermal power cost of the i-th thermal power unit, the energy storage operation cost of the i-th energy storage system, and the load loss cost; k g.i 、c g.i are the marginal cost of thermal power generation and the fixed operating cost per unit time of the i-th thermal power unit; k b.i , k L are the unit charging and discharging power cost and the unit load loss power cost of the i-th energy storage system respectively; ω, are the probability of occurrence of average net load and maximum net load respectively; c SU.i 、c SD.i are the startup and shutdown costs of the i-th thermal power unit respectively; are auxiliary decision variables, representing the startup and shutdown actions of the i-th thermal power unit in the τ-th start-stop decision period.

[0104] 2) Constraints

[0105] Power balance constraints, namely:

[0106]

[0107] Alternative constraints, namely:

[0108]

[0109] Thermal power operation constraints, namely:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] Energy storage operation constraints, namely:

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] Loss of load constraint, that is:

[0125]

[0126] In the formula, They are the load power forecast mean and renewable energy output forecast mean at time t respectively; They are the upper reserve demand and lower reserve demand at time t respectively; P g.i are the maximum output and minimum output of the i-th thermal power unit respectively; are the maximum power increase rate and the maximum power increase rate decrease rate of the i-th thermal power unit respectively; are the maximum startup power change rate and the maximum shutdown power change rate of the thermal power unit i, respectively; S on.i (τ), S off.i (τ) are the number of start-stop periods that the i-th thermal power unit needs to maintain starting and shutting down from the τ start-stop periods. Since the time window length of each start-stop period is different, S on.i (τ), S off.i (τ) is related to the start-stop period; T on.i , T off.i are the minimum start-up time and minimum shutdown time of the i-th thermal power unit respectively;

[0127] In a specific embodiment, the energy storage in this embodiment includes two energy storage systems, namely an electrochemical energy storage system and a hydrogen energy storage system. Considering the unified operation of the electrochemical energy storage and hydrogen energy storage, are the maximum discharge power and maximum charging power of the i-th energy storage system respectively; is the energy storage capacity of the i-th energy storage system at time t, is the energy storage capacity of the i-th energy storage system at the initial moment, is the energy storage capacity of the i-th energy storage system in the T-th period; for different types of energy storage (electrochemical energy storage system and hydrogen energy storage system), They correspond to different physical meanings, for example, for hydrogen energy storage, they represent the equivalent power of the fuel cell power, the hydrogen electrolyzer power, and the hydrogen storage capacity of the hydrogen storage tank; is the maximum load loss power allowed at time t.

[0128] Application Examples

[0129] Consider a high-ratio wind-solar system, with a thermal power installed capacity of 2305MW, wind power and photovoltaic installed capacity of 1100MW and 935MW respectively, and battery energy storage and hydrogen energy storage configurations of 200MW×2h and 200MW×10h respectively. The thermal power and energy storage parameters are shown in Table 1 and Table 2. Extremely cold weather occurred on the 22nd and 23rd days, and the load increased and the wind and solar output decreased significantly. The maximum allowable load loss power is 5% of the load in the corresponding period on the 22nd and 23rd days, and 0 on the other days.

[0130] Table 1 Thermal power unit operating parameters

[0131]

[0132]

[0133] Table 2 Energy storage operation parameters

[0134]

[0135] By adopting the method of the present invention, the monthly scheduling results are obtained. In the period when the net load fluctuation amplitude is relatively large, the number of start-stop decision periods is reduced. Therefore, the start-stop of thermal power units can be allowed to respond to the sharp fluctuation of net load, and adapt to the characteristics of frequent start-stop of thermal power units in the period when the net load fluctuation amplitude is relatively large; and the number of start-stop decision periods in the period when the net load fluctuation amplitude is relatively small is small. Under the premise of meeting the start-stop requirements of thermal power units as much as possible, the number of start-stop decision variables can be reduced as much as possible. According to statistics, the number of start-stop decision periods after reduction is 149. At this time, the number of 01 integer decision variables (start-stop status, auxiliary decision variables) is reduced from 2106 (24×28×3) to 447 (149×3). The number of 01 integer decision variables is greatly reduced, which can significantly reduce the scale of the monthly decision model. The total maximum output of the unit before / after the reduction of the unit start-stop period, the monthly scheduling cost and the calculation time are respectively as follows: Figure 5 , as shown in Table 3. Figure 5 It can be seen that the change trend of the total maximum output of the unit after the start-stop period is reduced is consistent with that before the reduction, indicating that the reduction of the start-stop period has little impact on the start-stop decision of the unit. As shown in Table 3, the monthly scheduling model solution time before and after the start-stop period is reduced is 7565 seconds and 597 seconds respectively, and the cost is 170.5 million yuan and 171.6 million yuan respectively. Compared with before the start-stop period is reduced, the calculation time of the monthly scheduling model solution after the reduction is reduced by 92.1%, while the scheduling cost is only increased by 0.006%. It can be seen that the reduction of the start-stop period can greatly shorten the monthly scheduling model solution time without significantly increasing the scheduling cost, and effectively improve the efficiency of scheduling plan formulation.

[0136] Table 3 Comparison of scheduling costs and calculation time before and after the reduction of unit start-up and shutdown periods

[0137]

[0138]

[0139] Considering / not considering the maximum discharge energy demand of energy storage, the total maximum output of thermal power units is as follows: Figure 6 As shown. Hydrogen energy storage with a longer charge and discharge cycle is selected for analysis. Figure 6 It can be seen that compared with not considering the maximum discharge energy demand, the maximum output of the thermal power unit obtained by considering the maximum discharge energy demand during the daily net load valley period is slightly higher, and the number of thermal power units in operation is greater, which can reserve sufficient rechargeable space for energy storage. Figure 7It can be seen that when the net load is large, the energy storage can charge more electricity to meet future discharge needs, and load loss only occurs during extremely cold weather periods; without considering the maximum discharge energy demand, the energy storage has insufficient charging space under the maximum net load, and the power shortage matching effect when thermal power and wind and solar power generation are insufficient is poor. There are load losses in extremely cold weather and other periods, and the load loss power is relatively large. Therefore, based on the proposed monthly strategy considering the maximum discharge energy demand, the decision result can still reserve enough charging space for energy storage, better match the energy storage discharge demand caused by the large actual net load value, reduce the possible load loss, and improve the system power supply reliability.

[0140] Although the above describes the specific implementation of the present invention in conjunction with the accompanying drawings, it does not limit the protection scope of the present invention. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the protection scope of the present invention.

[0141] Example 2

[0142] Based on the same inventive concept as that of Example 1, this example introduces a variable-time-period fire-storage monthly scheduling device taking into account the daily maximum discharge energy demand, including:

[0143] An acquisition module, used to obtain the average value of net load power prediction for multiple consecutive time periods;

[0144] A clustering processing module, used for clustering the net load power prediction mean value to divide the net load power prediction mean value of each time period into multiple categories;

[0145] A merging module, used to merge adjacent time periods whose net load power forecast mean values ​​belong to the same category into the same unit start / stop decision period;

[0146] A construction module, used to construct energy storage charging and discharging constraints of the energy storage system according to the maximum discharge power and the minimum chargeable power of the energy storage system acquired in advance;

[0147] A determination module is used to determine the start-stop plan of the unit within the start-stop decision period based on the energy storage charging and discharging constraints and according to a pre-built fire-storage monthly scheduling model.

[0148] Example 3

[0149] Based on the same inventive concept as Example 1, this embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the variable-time-period fire-storage monthly scheduling method that takes into account the daily maximum discharge energy demand.

[0150] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0152] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0154] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for monthly dispatching of fire-storage power with variable time periods taking into account the maximum daily discharge energy demand, characterized in that: include: Obtain the average net load power forecast for multiple consecutive time periods; Performing clustering processing on the net load power prediction mean value to divide the net load power prediction mean value of each time period into multiple categories; Merging adjacent time periods in which the net load power forecast mean value belongs to the same category into the same unit start / stop decision period; Constructing energy storage charging and discharging constraints for the energy storage system according to the pre-acquired maximum discharge power and minimum chargeable power of the energy storage system; Based on the energy storage charging and discharging constraints, the start-stop plan of the unit within the start-stop decision period is determined according to a pre-constructed fire-storage monthly scheduling model.

2. A method for monthly dispatching of fire-storage with variable time periods taking into account the daily maximum discharge energy demand according to claim 1, characterized in that: The clustering process of the net load power prediction mean value includes: Normalizing the net load power prediction mean value to obtain a normalized net load power prediction mean value; A cluster analysis is performed on the normalized net load power prediction mean.

3. A method for monthly dispatching of fire-storage with variable time periods taking into account the daily maximum discharge energy demand according to claim 2, characterized in that: The net load power prediction mean value is normalized to obtain the normalized net load power prediction mean value, which includes: In the formula, is the set of all time periods, each time period is 1h, and T is the number of time periods; They are the load power forecast mean, renewable energy output forecast mean, and net load power forecast mean at time t respectively; is the normalized net load power forecast mean value, is the set of all time periods on the dth day, T h is the number of time periods per day; is the set of all days in the current scheduling month, and D is the number of days.

4. A method for monthly dispatching of fire-storage with variable time periods taking into account the daily maximum discharge energy demand according to claim 3, characterized in that: The clustering process of the normalized net load power prediction mean value includes: Step 1: Define the sample set for cluster analysis as Step 2: Determine the number of clusters N and select N sample points as the initial cluster centers. represents the i-th cluster center; Step 3: For each sample point Calculate it and all cluster centers Distance: In the formula, d represents the distance, represents the tth sample point, Represents the i-th cluster center corresponding to the t-th sample point; Step 4: Sample points Assign to the cluster center with the shortest distance Form clusters corresponding to N cluster centers, and the net load data set of the i-th cluster is Step 5: Update the cluster center and calculate the average value of all sample points in each cluster as the new cluster center Where M i is the i-th cluster The number of samples in is the new cluster center of the cluster; all sample points in each cluster belong to the same category; Step 6: Determine the cluster center of each cluster after update Compared with the cluster center before update Is there any change? If yes, repeat steps 4 and 5, otherwise the classification ends.

5. The method for monthly dispatching of fire-storage with variable time periods taking into account the daily maximum discharge energy demand according to claim 3, characterized in that: The maximum discharge power and minimum rechargeable power of the energy storage system are calculated as follows: In the formula, are the maximum discharge power and minimum chargeable power of the i-th energy storage system at time t, is the set of all thermal power units, N g is the number of units, is the set of all energy storage systems, N b is the number of energy storage systems; and They are the maximum load forecast value, the maximum renewable energy output forecast value and the corresponding load loss power at time t respectively; are the maximum output of the i-th thermal power unit and the start / stop state of the i-th thermal power unit in the τ-th start / stop decision period, respectively. Indicates startup. Indicates shutdown, g is the thermal power unit identifier; It represents the reduction of all thermal power units compared to their total maximum output at time t, P g.i is the minimum output of the ith thermal power unit.

6. A method for monthly dispatching of fire-storage with variable time periods taking into account the daily maximum discharge energy demand according to claim 5, characterized in that: The energy storage charge and discharge constraints are: In the formula, are the discharge power and charging power of the i-th energy storage system at time t respectively; η d.i , η c.i are the discharge efficiency and charging efficiency of the i-th energy storage system respectively; γ i is the charge and discharge energy coefficient of the i-th energy storage system, γ i ≤1; is the energy upper limit of the i-th energy storage system; T' is the number of time periods corresponding to the charging and discharging energy constraints of the i-th energy storage system.

7. A method for monthly dispatching of fire-storage with variable time periods taking into account the daily maximum discharge energy demand according to claim 6, characterized in that: The objective function of the fire-storage monthly scheduling model is: In the formula, are the thermal power output of the i-th thermal power unit at time t, the charging and discharging power of the i-th energy storage system, and the load loss power, respectively. are the thermal power cost of the i-th thermal power unit, the energy storage operation cost of the i-th energy storage system, and the load loss cost; k g.i 、c g.i are the marginal cost of thermal power generation and the fixed operating cost per unit time of the i-th thermal power unit; k b.i , k L are the unit charging and discharging power cost and the unit load loss power cost of the i-th energy storage system respectively; ω, are the probability of occurrence of average net load and maximum net load respectively; c SU.i 、c SD.i are the startup and shutdown costs of the i-th thermal power unit respectively; are auxiliary decision variables, representing the startup and shutdown actions of the i-th thermal power unit in the τ-th start-stop decision period.

8. A variable-time-period fire-storage monthly scheduling device taking into account the daily maximum discharge energy demand, characterized in that: include: An acquisition module, used to obtain the average value of net load power prediction for multiple consecutive time periods; A clustering processing module, used for clustering the net load power prediction mean value to divide the net load power prediction mean value of each time period into multiple categories; A merging module, used to merge adjacent time periods whose net load power forecast mean values ​​belong to the same category into the same unit start / stop decision period; A construction module, used to construct energy storage charging and discharging constraints of the energy storage system according to the maximum discharge power and the minimum chargeable power of the energy storage system acquired in advance; A determination module is used to determine the start-stop plan of the unit within the start-stop decision period based on the energy storage charging and discharging constraints and according to a pre-built fire-storage monthly scheduling model.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a variable-time-period fire-storage monthly scheduling method taking into account the daily maximum discharge energy demand as described in any one of claims 1-7 is implemented.

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