Power system energy storage capacity and operation scheduling optimization method and system and storage medium

Abstract

The present application relates to the technical field of building energy management and power demand response, and particularly relates to a power system energy storage capacity and operation scheduling optimization method, system and storage medium, the method comprising: performing energy storage and release scheduling strategy according to the power difference between the power supply of the power grid and the power required on the user side, obtaining system flexible adjustment capability parameters and equipment utilization rate parameters; constructing an objective function according to the system flexible adjustment capability parameters and the equipment utilization rate parameters, and optimizing the power system energy storage capacity and operation scheduling by minimizing the objective function. The present application aims at the problems of static configuration of traditional power system energy storage capacity, rigid operation strategy and weak response capability, constructs an objective function with the system flexible adjustment capability parameters and the equipment utilization rate parameters as optimization variables, realizes the "energy storage and supply simultaneously" strategy adjusted dynamically by hours, and breaks away from the rigid mode of traditional "energy storage at night and release during the day".

Description

Technical Field

[0001] This invention relates to the field of building energy management and electricity demand response technology, and in particular to methods, systems and storage media for optimizing power system energy storage capacity and operation scheduling. Background Technology

[0002] With the accelerated construction of my country's new power system, the installed capacity of renewable energy sources such as wind power and photovoltaics continues to expand, becoming an important component of power supply. However, the output of these renewable energy sources is significantly affected by meteorological conditions, exhibiting seasonal fluctuations and diurnal periodic variations, resulting in a strong dynamic characteristic of the overall power system supply capacity. Especially during the midday period when sunlight is strong and renewable energy output is high, local power oversupply often occurs, while the pressure of power shortages may arise during the rapid load increase after sunset. This structural change in source-side output capacity creates a time mismatch with the operating patterns of building loads, further exacerbating the difficulty of source-load coordination in the system.

[0003] Against this backdrop, building air conditioning systems, due to their high operating power, long adjustment cycle, and strong regularity of operation time, are considered one of the most promising flexible loads in the power system. In traditional operation modes, air conditioning energy storage systems generally adopt a strategy of "centralized energy storage at night and centralized energy release during the day" to achieve load peak shaving and reduced capacity of cooling and heating sources. However, in power supply systems dominated by a high proportion of renewable energy, peak building loads may overlap with peak energy supply periods, and the original operating logic can no longer adapt to changes in the system's operating rhythm.

[0004] Existing building air conditioning energy storage systems generally employ a fixed-time-period control strategy, which involves activating cooling or heating source equipment during pre-set nighttime periods to store cold or heat, and releasing the stored energy during designated daytime periods to meet load demand. This strategy relies on a relatively stable power supply and demand relationship and lacks sufficient consideration of the intraday fluctuations and seasonal differences in the power system. Consequently, given the future high proportion of renewable energy output, the adaptability and flexibility of system dispatching are limited. Specific shortcomings of existing technologies include:

[0005] 1. Lack of a unified evaluation index that takes into account both system regulation capacity and equipment utilization efficiency: Existing energy storage capacity optimization methods usually use a single index as the optimization target, such as only using peak and valley load transfer or operating cost as the evaluation basis, ignoring the utilization efficiency of energy storage devices. This may result in the capacity configuration result being that the equipment utilization rate is low when the load regulation capacity is strong, or the regulation capacity is insufficient when the utilization rate is high, lacking comprehensiveness and balance.

[0006] 2. The scheduling strategy for chiller and heat source units lacks the ability to dynamically match the load: In actual operation, the start-up, shutdown, and load allocation of chiller and heat source units often adopt a fixed mode or simple priority judgment, failing to make flexible adjustments based on hourly load characteristics (hourly load refers to the average load of systems such as power systems, air conditioning systems, or heating systems in each hour). For example, when the system is equipped with multiple air source heat pumps and chiller units, there is a lack of a decision-making mechanism to flexibly switch the operating mode (direct supply or energy storage) according to the characteristics of different units and load changes, resulting in some units operating in low-efficiency or suboptimal load areas for extended periods.

[0007] 3. The operational priority control logic for energy storage and direct supply is simplistic: Existing scheduling methods often use simple fixed time periods or fixed threshold rules when determining whether to store or release energy, failing to dynamically prioritize based on real-time power system supply and demand curves. For example, the strategy of prioritizing energy release when there is a large supply-demand gap and prioritizing energy storage when there is a supply surplus lacks sophisticated logical implementation in existing schemes, making it difficult to fully leverage the role of energy storage systems in balancing supply and demand fluctuations. Summary of the Invention

[0008] The purpose of this invention is to overcome the above-mentioned deficiencies in the prior art and to provide a method, system and storage medium for optimizing the energy storage capacity and operation scheduling of power systems.

[0009] In a first aspect, the present invention provides a method for optimizing the energy storage capacity and operation scheduling of a power system, comprising:

[0010] The energy storage and release dispatch strategy is executed based on the power difference between the power supplied by the power grid and the power required by the user side, and the system's flexible regulation capability parameters and equipment utilization parameters are obtained.

[0011] The system's flexible regulation capability parameters and equipment utilization parameters include the power of the main unit in direct supply mode, the hourly direct supply of the main unit in direct supply mode, the power of the main unit in energy storage mode, the hourly stored energy of the main unit in energy storage mode, the power of the energy storage device, and the hourly released energy of the energy storage device.

[0012] Among them, the power of the host in direct supply mode, the power of the host in energy storage mode, the power of the energy storage device, and the hourly energy release of the energy storage device are used for power system energy storage capacity optimization.

[0013] The hourly direct supply of the host in direct supply mode, the hourly stored energy of the host in energy storage mode, and the hourly released energy of the energy storage device are used for power system operation and scheduling optimization.

[0014] An objective function is constructed based on the system's flexible regulation capability parameters and equipment utilization parameters. The optimization results of the power system's energy storage capacity and the corresponding operation and scheduling optimization results are obtained by minimizing the objective function.

[0015] By introducing a dual objective function, the system's regulation capacity and equipment utilization rate are jointly optimized, effectively avoiding the problems of over-matching leading to investment waste and under-matching leading to insufficient regulation in the selection of energy storage capacity.

[0016] Preferably, if the power difference Δ P < Deviation Threshold m If the value is negative, the direct energy release scheduling strategy is executed. The execution process is as follows: the energy stored in the energy storage device covers Δ P < Deviation Threshold m During periods of negative air conditioning load, and during the energy release process, the hourly energy and power released by the energy storage device are obtained;

[0017] If the energy storage device is insufficient after energy release, the remaining air conditioning load will be met by the main unit in direct supply mode, and the hourly direct supply quantity and power of the main unit in direct supply mode will be obtained during the direct supply process.

[0018] Preferably, if the power difference satisfies - m ≤Δ P ≤ m If the direct supply scheduling strategy is executed, the process is as follows: the air conditioning load for that period is met by directly supplying energy to the main unit, and the direct supply amount of the main unit is equal to the air conditioning load for that period. m This is the deviation threshold.

[0019] Preferably, if the power difference Δ P Deviation threshold m If the energy storage direct supply scheduling strategy is executed, the execution process includes:

[0020] Calculate Δ on the day P Deviation threshold m The main unit in energy storage mode during the period of time operates at full capacity and stores energy. X And the next day ΔP Air conditioning load required during period <0 Q d And combined with maximum energy storage X max Determine the energy storage for the second day X t ;

[0021] When the power grid has surplus power, the system prioritizes the high-efficiency main unit to enter the energy storage state; when the power grid is under pressure, the system prioritizes the release of energy storage equipment to supplement the load, and participates in load compensation according to the start-up and shutdown conditions of the main unit, which significantly enhances the air conditioning system's flexible regulation capability to the power grid.

[0022] Preferably, the host in the energy storage state is determined in real time by the host capacity and hourly load.

[0023] The start-up, shutdown, and allocation are flexibly adjusted according to the hourly load to ensure that each main unit is always in a high-efficiency operating range.

[0024] Preferably, the iterative variable energy storage rate is introduced. x traversing the energy storage rate x Calculate the range of values ​​for different energy storage capacities. X max The objective function value is given below, and the optimal energy storage rate corresponding to the minimum value of the objective function is obtained. x and optimal energy storage capacity X max .

[0025] Traversing the storage rate x By determining the range of values ​​for the optimal energy storage capacity, the problem of over-allocation leading to wasted investment or under-allocation leading to insufficient regulation can be effectively avoided in the selection of energy storage capacity.

[0026] In a second aspect, the present invention provides a power system energy storage capacity and operation scheduling optimization system for executing the method described in the first aspect, including an energy storage and release scheduling strategy execution module for obtaining system flexible regulation capability parameters and equipment utilization parameters based on the power difference between the power supplied by the power grid and the power required by the user side.

[0027] The energy storage capacity and operation scheduling optimization module is used to construct an objective function based on the system's flexible regulation capability parameters and equipment utilization parameters, and to obtain the target parameters for power system energy storage capacity and operation scheduling optimization by minimizing the objective function.

[0028] Preferably, it also includes a host operation strategy judgment module, which is used to determine whether the host is currently in direct power supply or energy storage mode.

[0029] In a third aspect, the present invention also provides a computer storage medium storing program instructions, which, when executed by at least one processor, are used to implement the power system energy storage capacity and operation scheduling optimization method as described in the first aspect.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] 1. This invention obtains system flexibility adjustment capability parameters and equipment utilization parameters through energy storage and release scheduling strategy, significantly enhancing the air conditioning system's flexibility adjustment capability to the power grid, and realizing a "storage and supply simultaneously" strategy with hourly dynamic adjustment;

[0032] 2. This invention addresses the problems of static capacity configuration, rigid operating strategies, weak response capabilities, and low main unit operating efficiency in traditional air conditioning energy storage systems. By establishing an objective function with the system's flexible adjustment capability and equipment utilization rate as optimization objectives, the adjustment capability and equipment usage frequency are jointly optimized, thereby improving the utilization rate of the energy storage device while ensuring the system's adjustment capability. Attached Figure Description

[0033] Figure 1 This is a flowchart of the power system energy storage capacity and operation scheduling optimization method in Embodiment 1 of the present invention;

[0034] Figure 2 This is a flowchart of a specific power system energy storage capacity and operation scheduling optimization method in Embodiment 2 of the present invention;

[0035] Figure 3 This is a schematic diagram of the host structure and operation mode in Embodiment 3 of the present invention;

[0036] Figure 4 This is a simulation flowchart of the power system energy storage capacity and operation scheduling optimization method in Embodiment 4 of the present invention;

[0037] Figure 5 This is a schematic diagram of the power system energy storage capacity and operation scheduling optimization system in Embodiment 5 of the present invention. Detailed Implementation

[0038] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0039] Example 1

[0040] like Figure 1 As shown in the figure, this embodiment provides a method for optimizing the energy storage capacity and operation scheduling of a power system, including:

[0041] According to the power supplied by the input power grid P g Power required by the user side P d The difference is used to execute the energy storage and release scheduling strategy and obtain the system's flexible regulation capability parameters and equipment utilization parameters.

[0042] An objective function is constructed based on the system's flexible regulation capability parameters and equipment utilization parameters. The optimization results of the power system's energy storage capacity and the corresponding operation and scheduling optimization results are obtained by minimizing the objective function.

[0043] It is understandable that optimizing the energy storage capacity and operation scheduling of a power system by minimizing the objective function means minimizing the objective function to obtain configuration parameters related to the system load regulation capacity and equipment utilization. Based on these configuration parameters, the energy storage capacity of the power system can be optimized. Under different energy storage capacities, the main unit capacity, the number of main units in operation, and the status allocation can be determined, thereby obtaining the operation scheduling optimization result under the energy storage capacity optimization result.

[0044] Therefore, in this embodiment, the objective function is used as a unified evaluation index to simultaneously measure the system's flexible adjustment capability parameter and equipment utilization rate. By flexibly adjusting the start-up, shutdown and allocation of parameters acquired hourly, the host is always in a high-efficiency operating range, while ensuring that the system's energy storage capacity is fully utilized.

[0045] Example 2

[0046] like Figures 1-2 As shown, this embodiment is a further optimization based on embodiment 1.

[0047] In one possible implementation, the system flexibility adjustment capability parameters and equipment utilization parameters include the power of the host in direct supply mode, the hourly direct supply of the host in direct supply mode, the power of the host in energy storage mode, the hourly stored energy of the host in energy storage mode, the power of the energy storage device, and the hourly released energy of the energy storage device.

[0048] Among them, the power of the host in direct supply mode, the power of the host in energy storage mode, the power of the energy storage device, and the hourly energy release of the energy storage device are used for power system energy storage capacity optimization.

[0049] The hourly direct supply of the host in direct supply mode, the hourly stored energy of the host in energy storage mode, and the hourly released energy of the energy storage device are used for power system operation and scheduling optimization.

[0050] In one possible implementation, such as Figure 2 As shown, based on the power supplied by the input power grid P g Power required by the user side P d The difference between electricity supply and demand can be obtained. ΔP=P g - P d .

[0051] (1) If the power difference Δ P If <-m, then the direct energy release scheduling strategy is executed, and the execution process is as follows: ΔP<m According to | ΔP| The energy release priority is sorted by size, | ΔP|It has a higher energy release priority at larger times, prioritizes the release of energy from the energy storage device, obtains the hourly released energy and power of the energy storage device during the energy release process, and directly supplies the remaining insufficient part to the host after the energy release is complete, and obtains the hourly direct supply amount of the host in the direct supply state during the direct supply process.

[0052] (2) If the power difference is - m ≤Δ P ≤ m If the direct supply scheduling strategy is executed, the execution process is as follows: the air conditioning load for that period is met by directly supplying energy to the main unit, and the direct supply amount of the main unit is equal to the air conditioning load for that period.

[0053] (3) If the power difference Δ P If the value is greater than m, then the direct energy storage scheduling strategy will be executed, and its execution process includes:

[0054] Calculate Δ on the day P >m-period full-capacity energy storage of the host unit in energy storage state X And the next day ΔP Air conditioning load required for time period <-m Q d And combined with maximum energy storage X max Determine the energy storage for the second day X t ;

[0055] Preferably, ΔP >m-period full-capacity energy storage of the host unit in energy storage state X The calculation formula is:

[0056]

[0057] in, X To store energy for the main unit to operate at full capacity when it is in a storage state; w i The rated operating capacity of the main unit currently in energy storage mode; Indicates the day ΔP > m The moment;

[0058] Day 2 ΔP <- m Air conditioning load required during the period Q d The calculation formula is:

[0059]

[0060] in, t -m For the second day ΔP< -m At that moment, i For time markers, The hourly load of the air conditioner for the next day;

[0061] First compare X and Q d ,like X≤Q d ,but X t =X ,otherwise X t =Q d ;

[0062] Compare again X t and X max ,like X t ≤X max ,but X t The value remains unchanged, otherwise X t =X max ;

[0063] Based on the energy storage demand on the second day X t The energy storage unit stores energy at maximum power until it meets the energy storage requirements for the next day, thus determining the hourly energy storage of the unit in the energy storage state.

[0064] ΔP The air conditioning load during the >m time period is directly supplied by the main unit, according to ΔP The hourly air conditioning load during the time period m determines the hourly direct supply of the main unit in direct supply mode.

[0065] Through the above-described energy storage and release scheduling strategy, the hourly direct supply, stored energy, and released energy can be obtained. This allows for the calculation of the hourly power distribution of the main unit in direct supply mode, the hourly power distribution of the main unit in energy storage mode, and the hourly power distribution of the energy storage device. This distribution can then be used as the maximum stored energy. X max The corresponding power system operation and scheduling optimization results.

[0066] Example 3

[0067] This embodiment is a further optimization based on embodiment 2.

[0068] Please refer to the following: Figure 3As shown in Example 2, it can be understood that the above-mentioned energy storage and release scheduling strategy is based on the direct participation of the host in the direct supply state and the host in the energy storage state.

[0069] As one possible implementation, the host in energy storage mode operates at full capacity to store energy. X Through Δ P > m The duration of the time period is determined by the main unit that is in the energy storage state during the corresponding time period;

[0070] The host in the energy storage state is determined in real time by the host capacity and hourly load. It can be understood that the energy storage device can be understood as a container, and its capacity refers to the amount of cold or heat it can hold. The host capacity refers to the maximum amount of cold or heat that the host can output.

[0071] In one possible implementation, a schematic diagram of the host structure and operation is provided as follows: Figure 3 As shown. Based on as Figure 3 The main unit configuration shown includes two air source heat pumps and one chiller. The air source heat pumps must meet the maximum daily heat load in winter, while the air source heat pumps and chiller must meet the maximum daily cooling load in summer. Their capacity allocation is as follows:

[0072]

[0073] in, w a1 This is the rated operating capacity of the first air source heat pump. w a2 This is the rated operating capacity of the second air source heat pump; The capacity ratio coefficient of the air source heat pump is taken in this embodiment. >0.5, w c This refers to the rated operating capacity of the chiller unit. q csi For Architecture Design Day i Always cold load, q hsi For Architecture Design Day i Constant heat load.

[0074] In one possible implementation, based on the relative size of the host capacity, this embodiment determines the following host operation strategy as shown in Table 1:

[0075] Table 1. Classification of Host Operation Strategies

[0076]

[0077] As a preferred embodiment, the host in the energy storage state and the host in the direct supply state determine the corresponding host operation strategy based on the air source heat pump capacity, the chiller unit capacity, and the hourly cooling load and hourly heating load of the building within one year.

[0078] As one possible implementation, this embodiment provides a corresponding host status table for each host operation strategy to determine which hosts are in energy storage mode and which are in direct supply mode under different seasons and conditions. Each host operation strategy is shown in Tables 2-8:

[0079] Table 2 Host Status Table Corresponding to Host Operation Strategy 1

[0080]

[0081] Table 3 Host Operation Strategy 2 Host Status Table

[0082]

[0083] Table 4 Host Status Table Corresponding to Host Operation Strategy 3

[0084]

[0085] Table 5 Host Status Table Corresponding to Host Operation Strategy 4

[0086]

[0087] Table 6 Host Status Table Corresponding to Host Operation Strategy 5

[0088]

[0089] Table 7 Host Status Table Corresponding to Host Operation Policy 6

[0090]

[0091] Table 8 Host Status Table Corresponding to Host Operation Strategy 7

[0092]

[0093] in, q ci and q hi These are the hourly cooling load and hourly heating load of the building over 8760 hours (i.e., one year), which can be obtained through historical data or simulation prediction. The table above is used to determine the main unit in energy storage and direct supply states under different seasons and different hourly cooling and heating loads.

[0094] As one possible implementation, an objective function is constructed based on the system's flexible adjustment capability parameters and equipment utilization parameters.F It can be represented as:

[0095]

[0096] in, To emphasize the system's flexible adjustment capability, weighting coefficients, To emphasize the weighting factor of the system's energy storage device utilization rate, The cumulative regulation ratio of the system is used to characterize the system's flexible regulation capability. R c To improve the utilization rate of energy storage devices during the cooling season, R h The utilization rate of energy storage devices during the heating season, of which:

[0097]

[0098] in, X max This refers to the maximum energy that the energy storage device can store during one energy storage and release cycle. t 1 represents the number of days in the cooling season; t 2 represents the number of days in the heating season. Indicates the system at the 1st j TianΔ P <- m The baseline cumulative energy level when there is no energy storage device at any given time. For the system number j The actual cumulative electricity generated under the condition that there is an energy storage device at the time ΔP < -m is affected by the combined influence of the power of the energy storage device, the power of the host in direct supply mode, and the power of the host in energy storage mode. Based on Δ P <- m The hourly power of the energy storage device, the hourly direct power of the host in direct supply mode, or the hourly energy storage power of the host in energy storage mode are calculated. E cj This refers to the hourly energy release of the energy storage device during the cooling season. E hj This refers to the hourly energy release of the energy storage device during the heating season.

[0099] As one possible implementation, the utilization rate of the energy storage device in this embodiment is measured in days, with one day constituting an energy storage / discharge cycle, thereby calculating the maximum energy storage capacity of the energy storage device within one energy storage / discharge cycle. X max .

[0100] As a preferred embodiment, the iterative variable energy storage rate is introduced. x traversing the energy storage rate x Calculate the range of values ​​for different energy storage capacities.X max The objective function value is given below, and the optimal energy storage rate corresponding to the minimum value of the objective function is obtained. x and optimal energy storage capacity X max As the result of optimizing the energy storage capacity of the power system, the optimal energy storage capacity is... X max The corresponding operation scheduling optimization scheme is the optimal operation scheduling optimization scheme.

[0101] The process of optimizing power system energy storage capacity and operation scheduling through an objective function is as follows:

[0102] Calculate the energy storage rate x Maximum energy storage X max The calculation formula is as follows:

[0103]

[0104] in, x The energy storage rate has a value range of [0,1]. q si For the hourly load of the design date; C p The specific heat capacity of water, The density of water, This refers to the temperature difference between the inlet and outlet water of the energy storage device.

[0105] Under the current energy storage and storage scheduling strategy and host operation strategy, the energy storage rate is traversed. x Calculate the range of values ​​for different energy storage capacities. X max The objective function value is given below, and the optimal energy storage rate corresponding to the minimum value of the objective function is obtained. x and optimal energy storage capacity X max It outputs hourly direct supply and energy storage / discharge strategies as the result of power system energy storage capacity optimization.

[0106] Example 4

[0107] Please refer to the example below. Figure 3 and Figure 4 This embodiment optimizes the energy storage capacity and operation scheduling of the power system based on simulation, including the following steps:

[0108] Step 1: Perform initialization;

[0109] Input the hourly air conditioning load of the building design. q si Iteration variable energy storage rate x Calculate the energy storage rate. x Maximum energy storageX max The calculation formula is as follows:

[0110]

[0111] Where x is the energy storage rate, and its value ranges from [0,1]. q si For the hourly load of the design date; C p The specific heat capacity of water, The density of water, The temperature difference between the inlet and outlet water of the energy storage device;

[0112] Step 2: Allocate host capacity and number of running units;

[0113] Main unit capacity allocation: For example, the main unit includes two air source heat pumps and one chiller. The air source heat pumps need to meet the maximum daily heat load in winter, and the air source heat pumps and chiller need to jointly meet the maximum daily cooling load in summer. The capacity allocation is as follows:

[0114]

[0115] in, w a1 This is the rated operating capacity of the first air source heat pump. w a2 This is the rated operating capacity of the second air source heat pump. w a1 and w a2 These are the rated operating capacities of air source heat pump 1 and air source heat pump 2, respectively. This is the capacity ratio coefficient for the air source heat pump; in this case, it is the capacity ratio coefficient for air source heat pump 1. >0.5, w c This refers to the rated operating capacity of the chiller unit. q csi For Architecture Design Day i Constant cooling load, this is the building's summer design day. i Always under cold load, q hsi For Architecture Design Day i Constant heat load, this is the building's winter design day. i Constant heat load.

[0116] Host number allocation: Based on the relative size of host capacity, it can be divided into the following situations as shown in Table 9:

[0117] Table 9. Relative Relationship of Host Capacity

[0118]

[0119] Based on the capacity of the air source heat pump and chiller units, and combined with the building's hourly heating and cooling load data throughout the year, a corresponding allocation scheme for the number of main units is determined according to the relative capacity of each type of main unit, so as to clarify the number of each main unit in direct supply and energy storage modes. The specific allocation schemes will be explained one by one in Tables 10-16.

[0120] Table 10: Relative Relationship of Host Capacity 1 - Allocation of Host Counts (for Table 1)

[0121]

[0122] Table 11 shows the allocation of the number of operating hosts corresponding to the relative relationship between host capacity 2.

[0123]

[0124] Table 12 shows the relative relationship between host capacity and the allocation of the number of host machines corresponding to Table 3.

[0125]

[0126] Table 13 shows the relative relationship between host capacity and the corresponding allocation of the number of host machines.

[0127]

[0128] Table 14 shows the relative relationship between host capacity and the allocation of the number of host machines corresponding to 5.

[0129]

[0130] Table 15 shows the relative relationship between host capacity and the corresponding allocation of the number of host machines.

[0131]

[0132] Table 16 shows the relative relationship between host capacity and the corresponding allocation of the number of running hosts.

[0133]

[0134] in, q ci and q hi These are the hourly cooling load and hourly heating load of the building over 8760 hours (i.e., one year), which can be obtained through historical data or simulation prediction. The table above is used to determine the allocation of the main unit in energy storage and direct supply states under different seasons and different hourly cooling and heating loads.

[0135] Step 3: Execution of direct supply and energy storage / release dispatch strategies:

[0136] According to the power supplied by the input power grid P g Power required by the user side P d The difference between electricity supply and demand can be obtained. ΔP= P g - P d According to Δ P The magnitude of the signal determines the supply and demand relationship between the power grid and the user side, and executes the corresponding dispatching strategy:

[0137] (1) If the power difference Δ P If <-m, then the direct energy release scheduling strategy is executed, and the execution process is as follows: ΔP<m According to | ΔP| The energy release priority is sorted by size, | ΔP| It has a higher energy release priority at larger times, prioritizes the release of energy from the energy storage device, obtains the hourly released energy and power of the energy storage device during the energy release process, and directly supplies the remaining insufficient part to the host after the energy release is complete, and obtains the hourly direct supply amount of the host in the direct supply state during the direct supply process.

[0138] (2) If the power difference is - m ≤Δ P ≤ m If the direct supply scheduling strategy is executed, the execution process is as follows: the air conditioning load for that period is met by directly supplying energy to the main unit, and the direct supply amount of the main unit is equal to the air conditioning load for that period.

[0139] (3) If the power difference Δ P If the value is greater than m, then the direct energy storage scheduling strategy will be executed, and its execution process includes:

[0140] Calculate Δ on the day P >m-period full-capacity energy storage of the host unit in energy storage state X And the next day ΔP Air conditioning load required for time period <-m Q d And combined with maximum energy storage X max Determine the energy storage for the second day X t ;

[0141] Preferably, ΔP >m-period full-capacity energy storage of the host unit in energy storage state X The calculation formula is:

[0142]

[0143] in, XTo store energy for the main unit to operate at full capacity when it is in a storage state; w i The rated operating capacity of the main unit currently in energy storage mode; Indicates the day ΔP > m The moment;

[0144] Day 2 ΔP <- m Air conditioning load required during the period Q d The calculation formula is:

[0145]

[0146] in, t -m For the second day ΔP< - m At that moment, i For time markers, The hourly load of the air conditioner for the next day;

[0147] First compare X and Q d ,like X≤Q d ,but X t =X ,otherwise X t =Q d ;

[0148] Compare again X t and X max ,like X t ≤X max ,but X t The value remains unchanged, otherwise X t =X max ;

[0149] Based on the energy storage demand on the second day X t The energy storage unit stores energy at maximum power until it meets the energy storage requirements for the next day, thus determining the hourly energy storage of the unit in the energy storage state.

[0150] ΔP The air conditioning load during the >m time period is directly supplied by the main unit, according to ΔPThe hourly air conditioning load during the time period m determines the hourly direct supply of the main unit in direct supply mode.

[0151] Through the above-described energy storage and release scheduling strategy, the hourly direct supply, stored energy, and released energy can be obtained. This allows for the calculation of the hourly power distribution of the main unit in direct supply mode, the hourly power distribution of the main unit in energy storage mode, and the hourly power distribution of the energy storage device. This distribution can then be used as the maximum stored energy. X max The corresponding power system operation and scheduling optimization results.

[0152] Step 4: Construct the objective function and obtain the capacity optimization result by minimizing the objective function;

[0153] Step 5: Iterate and output the optimal capacity optimization result and the corresponding energy storage and release strategy as the operation optimization result.

[0154] Example 5

[0155] This embodiment provides a power system energy storage capacity and operation scheduling optimization system for executing the method provided in any one of embodiments 1-4, including:

[0156] The energy storage and release dispatch strategy execution module is used to obtain system flexibility regulation capability parameters and equipment utilization parameters based on the power difference between the power supplied by the grid and the power required by the user side.

[0157] The energy storage capacity and operation scheduling optimization module is used to construct an objective function based on the system's flexible regulation capability parameters and equipment utilization parameters, and to obtain the target parameters for power system energy storage capacity and operation scheduling optimization by minimizing the objective function.

[0158] As a preferred embodiment of this invention, such as Figure 5 As shown, the system also includes a host operation strategy judgment module, which is used to determine whether the host is in direct supply mode or energy storage mode at the current moment.

[0159] Example 6

[0160] In another aspect, the present invention provides a computer storage medium storing program instructions, which, when executed by at least one processor, are used to implement the power system energy storage capacity and operation scheduling optimization method described in any one of embodiments 1-4.

[0161] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0162] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0163] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM).

[0164] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0165] It should be understood that the system disclosed in this invention can be implemented in other ways. For example, the division of modules is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the communication connection between modules can be through some interfaces, indirect coupling or communication connections between servers or units, and can be electrical or other forms.

[0166] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one processing unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0167] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0168] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing energy storage capacity and operation scheduling in power systems, characterized in that, include: The energy storage and release dispatch strategy is executed based on the power difference between the power supplied by the power grid and the power required by the user side, and the system's flexible regulation capability parameters and equipment utilization parameters are obtained. The system's flexible regulation capability parameters and equipment utilization parameters include the power of the main unit in direct supply mode, the hourly direct supply of the main unit in direct supply mode, the power of the main unit in energy storage mode, the hourly stored energy of the main unit in energy storage mode, the power of the energy storage device, and the hourly released energy of the energy storage device. Among them, the power of the host in direct supply mode, the power of the host in energy storage mode, the power of the energy storage device, and the hourly energy release of the energy storage device are used for power system energy storage capacity optimization. The hourly direct supply of the host in direct supply mode, the hourly stored energy of the host in energy storage mode, and the hourly released energy of the energy storage device are used for power system operation and scheduling optimization. An objective function is constructed based on the system's flexible regulation capability parameters and equipment utilization rate parameters. The optimization results of the power system's energy storage capacity and the corresponding operation and scheduling optimization results are obtained by minimizing the objective function. The objective function is: ； in, To emphasize the system's flexible adjustment capability, weighting coefficients, To emphasize the weighting factor of the system's energy storage device utilization rate, The cumulative regulation ratio of the system is used to characterize the system's flexible regulation capability. R c To improve the utilization rate of energy storage devices during the cooling season, R h To improve the utilization rate of energy storage devices during the heating season; Introducing the iterative variable energy storage rate x traversing the energy storage rate x Calculate the maximum energy storage capacity within the range of values. X max The objective function value is used to obtain the optimal energy storage rate and the best energy storage capacity corresponding to the minimum value of the objective function.

2. The method for optimizing power system energy storage capacity and operation scheduling according to claim 1, characterized in that, If the power difference ΔP is less than the negative value of the deviation threshold m, the direct energy release scheduling strategy is executed. The execution process is as follows: the energy stored in the energy storage device covers the air conditioning load during the period when ΔP is less than the negative value of the deviation threshold m, and the hourly energy release and power of the energy storage device are obtained during the energy release process; where m is the deviation threshold. If the energy storage device is insufficient after energy release, the remaining air conditioning load will be met by the main unit in direct supply mode, and the hourly direct supply quantity and power of the main unit in direct supply mode will be obtained during the direct supply process.

3. The method for optimizing power system energy storage capacity and operation scheduling according to claim 1, characterized in that, If the power difference satisfies -m≤ΔP≤m, then the direct supply scheduling strategy is executed. The execution process is as follows: the air conditioning load during the period when the power difference is -m≤ΔP≤m is directly supplied by the direct supply host. The direct supply amount of the host is equal to the air conditioning load during the period when the power difference is -m≤ΔP≤m, where m is the deviation threshold.

4. The method for optimizing power system energy storage capacity and operation scheduling according to claim 1, characterized in that, If the power difference ΔP > the deviation threshold m, then the energy storage direct supply scheduling strategy is executed, and its execution process includes: Calculate the full-capacity operating energy storage X of the main unit in energy storage state during the period when ΔP > deviation threshold m on the current day, and the air conditioning load Q required during the period when ΔP < 0 on the next day. d And determine the energy stored on the second day X by combining the maximum stored energy. t ; Based on the energy stored on the second day, Xt, determine the hourly stored energy and power of the host in the energy storage state.

5. The method for optimizing power system energy storage capacity and operation scheduling according to claim 4, characterized in that, The full-capacity operating energy storage X of the host in the energy storage state is determined by the duration of the period when ΔP > deviation threshold m and the host in the energy storage state during the corresponding period.

6. The method for optimizing power system energy storage capacity and operation scheduling according to claim 5, characterized in that, The host in the energy storage state is determined in real time by the host capacity and hourly load.

7. A power system energy storage capacity and operation dispatch optimization system, characterized in that, The method for optimizing power system energy storage capacity and operation scheduling according to any one of claims 1-6 includes: The energy storage and release dispatch strategy execution module is used to obtain system flexibility regulation capability parameters and equipment utilization parameters based on the power difference between the power supplied by the grid and the power required by the user side. The energy storage capacity and operation scheduling optimization module is used to construct an objective function based on the system's flexible regulation capability parameters and equipment utilization parameters, and to obtain the target parameters for power system energy storage capacity and operation scheduling optimization by minimizing the objective function.

8. The power system energy storage capacity and operation scheduling optimization system according to claim 7, characterized in that, It also includes a host operation strategy judgment module, which is used to determine whether the host is in direct power supply or energy storage mode at the current moment.

9. A computer storage medium, characterized in that, The computer storage medium stores program instructions, which, when executed by at least one processor, are used to implement the power system energy storage capacity and operation scheduling optimization method as described in any one of claims 1-6.

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