Power system dispatching method and device for ensuring energy storage operation benefit, and storage medium

Abstract

The application discloses a power system scheduling method and device for guaranteeing energy storage operation benefit, and a storage medium, and belongs to the technical field of power system scheduling. The scheduling method comprises the following steps: establishing a scheduling initial model, including a target function and constraint conditions; the target function is used for obtaining the minimum operation cost of the power system; the constraint conditions comprise upper and lower limit constraints of operation parameters, energy storage energy quantity start-end energy balance constraints, energy storage life loss constraints and energy storage cumulative life loss upper limit constraints; an optimization algorithm is used to select parameters in the scheduling initial model to obtain a scheduling model; and a solution result of the scheduling model is used as a scheduling scheme. The energy storage life loss constraints limit the relationship between the energy storage life loss and the energy storage charging and discharging in a scheduling domain; the energy storage cumulative life loss upper limit constraints limit that the cumulative life loss in the scheduling domain cannot exceed the cumulative loss upper limit, so that the energy storage is prevented from being excessively scheduled, and the long-term operation benefit of the energy storage is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatching technology, and more specifically, relates to a power system dispatching method, device, and storage medium that ensures the operational benefits of energy storage. Background Technology

[0002] Energy storage has experienced rapid development in recent years due to its advantages in rapid response, high energy and power density, and environmental friendliness. With the large-scale deployment of energy storage in power systems, maximizing its benefits is a pressing issue. While energy storage charging and discharging is a short-term process, its lifespan deteriorates significantly over time, resulting in long-term cumulative benefits. Overusing energy storage in short-term scheduling may yield high short-term gains, but this shortens its lifespan, leading to low overall long-term benefits. Conversely, cautious use of energy storage in short-term scheduling, while ensuring its operational lifespan, may result in a high proportion of natural degradation over the long term, also leading to low long-term benefits. Therefore, constructing an optimized power system scheduling model that ensures the operational efficiency of energy storage while considering its long-term benefits in short-term scheduling is a crucial problem to solve. Summary of the Invention

[0003] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a power system dispatching method, device and storage medium to ensure the operational benefits of energy storage. Its purpose is to consider the long-term benefits of energy storage during the short-term dispatching of the power system, reduce the cost of short-term dispatching while ensuring the long-term benefits of energy storage.

[0004] To achieve the above objectives, according to one aspect of the present invention, a power system dispatching method for ensuring the operational efficiency of energy storage is provided. The power system includes thermal power units, renewable energy power units, an external power grid, and energy storage. The dispatching method includes:

[0005] Step S1: Establish an initial scheduling model, including an objective function and constraints. The objective function is to obtain the minimum operating cost of the power system. The constraints include upper and lower limits of operating parameters, energy balance constraints at the beginning and end of energy storage capacity, and energy storage lifetime loss constraints. In the formula, Let be the lifetime loss of energy storage b at time t. Let be the linear lifetime loss coefficient of energy storage b. Let be the charging power and discharging power of the energy storage b to be decided at time t; the upper limit constraint on the cumulative lifetime loss of energy storage; and the cumulative lifetime loss of any energy storage b within the scheduling domain 0 to T not exceeding its upper limit. parameter These are unknown parameters to be determined.

[0006] Step S2: Select the parameters in the initial scheduling model using an optimization algorithm. The scheduling model is obtained, and the solution result of the scheduling model is used as the scheduling scheme;

[0007] Among them, the optimization algorithm is used to select the parameters. include:

[0008] Step S21: For each parameter being searched Determine the corresponding parameters parameter Satisfy the parameter The deviation of the cumulative lifetime loss of each energy storage b obtained by the initial scheduling model decision in the scheduling domain does not exceed the error allowable range;

[0009] Step S22: Based on Substituting the minimum operating cost without energy storage and the minimum operating cost with available energy storage obtained from the initial scheduling model, we calculate the operating cost savings when all energy storage lifetimes are completely exhausted, and select the parameter that maximizes the operating cost savings.

[0010] In one embodiment, the optimization algorithm is any one of exhaustive search, particle swarm optimization, or genetic algorithm.

[0011] In one embodiment, in step S21, for each parameter Determine the corresponding parameters include:

[0012] Step S211: Set parameters Substitute into the initial scheduling model;

[0013] Step S212: Randomly initialize parameters

[0014] Step S213: Solve the initial scheduling model based on the day-ahead forecast information of the power system to obtain the state of charge of each energy storage b at each scheduling time t within the scheduling domain. and lifespan loss

[0015] Step S214: Based on the energy storage state of charge The accurate lifetime loss of each energy storage unit b at each scheduling time t within the scheduling domain was calculated using the rainflow counting method.

[0016] Step S215: Calculate the cumulative lifetime loss of each energy storage b within the dispatch domain. and The relative error is used to determine whether the obtained relative error is within the set error allowable range:

[0017] If so, then output the parameters.

[0018] If not Then reduce the parameter Then proceed to step S213;

[0019] If not Then increase the parameter Then proceed to step S213.

[0020] In one embodiment, the formula for calculating the operating cost savings when all energy storage lifetimes are completely exhausted in step S22 is as follows:

[0021]

[0022] In the formula, ΔJ represents the saved operating cost, and J 0 Let B be the minimum operating cost excluding energy storage, and let J be the set of energy storage within the current dispatch domain. b This represents the minimum operating cost when b energy storage units remain in the power system. This represents the number of dispatch domains in the power system where the remaining b energy storage units can participate in dispatch simultaneously.

[0023] In one embodiment, The calculation formula is:

[0024]

[0025] In the formula, The number of operable scheduling domains for energy storage b, arranged in descending order of remaining lifetime:

[0026]

[0027] In the formula, SL b Let b be the remaining lifetime of energy storage.

[0028] In one embodiment, in the initial scheduling model, the energy balance constraint for the initial and final energy storage capacity is:

[0029]

[0030] In the formula, This represents the state of charge of energy storage b at time T in the previous scheduling domain. Let B be the initial capacity of energy storage b in the next scheduling domain, where B is the set of energy storage.

[0031] In the aforementioned scheduling model, the initial and final energy balance constraints of the energy storage capacity are reconstructed in a single time period to obtain a real-time scheduling model that can be scheduled according to the real-time status of the power system. The reconstructed single-time energy storage initial and final energy balance constraints are expressed as follows:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] In the formula, Let be the lower limit of energy storage capacity constrained by energy storage power, the lower limit of energy storage capacity constrained by energy storage lifetime, the upper limit of energy storage capacity constrained by energy storage power, and the upper limit of energy storage capacity constrained by energy storage lifetime at time t, respectively. Let b be the lower and upper limits of the energy storage capacity. Let b be the initial capacity of energy storage. The maximum charging power and maximum discharging power of energy storage b are given. Let Δt be the maximum remaining energy loss at time t that guarantees the energy storage b can return to its initial capacity, and let Δt be the scheduling step size within the scheduling domain.

[0039] In one embodiment, in step S1, the upper and lower limits of operating parameters include: upper and lower limits of thermal power unit output, upper and lower limits of power trading between the power system and the external power grid, upper and lower limits of wind power, upper and lower limits of photovoltaic power, upper and lower limits of power system node voltage, upper and lower limits of power system node phase angle, upper and lower limits of generator unit reactive power output, upper and lower limits of energy storage charging power, upper and lower limits of energy storage discharging power, upper and lower limits of energy storage capacity, and upper and lower limits of power system power flow.

[0040] The constraints also include: thermal power unit ramping constraints, energy storage state-of-charge transition constraints, power system active power balance constraints, and power system reactive power balance constraints.

[0041] In one embodiment, the expression for the objective function is:

[0042]

[0043] In the formula, J is the minimum operating cost, Δt is the scheduling step size within the scheduling domain T, and p t Let P be the electricity price at time t.t e Let P be the power exchanged between the power system and the external power grid at time t. t g Let a be the output of thermal power unit g at time t. g b g c g Let G be the cost coefficient for thermal power units, and G be the set of thermal power units.

[0044] According to another aspect of the present invention, a power system dispatching device for ensuring the operational efficiency of energy storage is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0045] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0046] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0047] 1. The power system dispatching method for ensuring the operational benefits of energy storage in this invention includes establishing a dispatching model, solving the model, and outputting a dispatching scheme. The objective of the dispatching model is to minimize the operating cost of the power system. Based on this objective function, the operating cost of short-term dispatching can be kept low. Furthermore, in addition to the constraints of conventional power systems, the dispatching model adds constraints on energy storage lifetime loss and an upper limit constraint on cumulative energy storage lifetime loss. The energy storage lifetime loss constraint limits the relationship between energy storage lifetime loss and energy storage charging and discharging within the dispatching domain; that is, each dispatching action directly affects the degree of energy storage lifetime loss. The upper limit constraint on cumulative energy storage lifetime loss limits the cumulative lifetime loss within the dispatching domain from exceeding the upper limit, thus avoiding over-dispatch of energy storage and ensuring its long-term operational benefits. Based on the objective function of minimizing cost and the two newly added constraints, by balancing the relationship between short-term and long-term benefits of energy storage, the long-term benefits of energy storage can be considered during short-term power system dispatching, reducing short-term dispatching costs while ensuring long-term energy storage benefits.

[0048] 2. When constructing constraints on energy storage lifetime loss and the upper limit of cumulative energy storage lifetime loss, the upper limit of cumulative energy storage lifetime loss within this scheduling domain is involved. and linear lifetime loss coefficient The determination of parameters This needs to be determined based on the specific circumstances of the current scheduling domain. This invention employs an optimization algorithm to find the optimal [scheduling method / method]. The scheduling model is obtained. First, the optimization algorithm will... Perform a traversal, for each parameter Find the corresponding parameters So that in the parameters The deviation of the cumulative lifetime loss obtained from the scheduling model decision does not exceed the error allowable range, so as to make the relationship between energy storage lifetime loss and energy storage charge and discharge as accurate as possible in the lifetime loss constraint. Then, the parameters of each group are calculated. The scheduling model with the highest cost savings when the energy storage lifespan is completely exhausted is selected as the final scheduling model, and the scheduling scheme is obtained by solving based on this model. In this invention, the parameters in the scheduling model are determined by the above method. This allows the energy storage lifetime loss constraint to accurately define the relationship between energy storage lifetime loss and energy storage charging and discharging, and ensures that the upper limit constraint of cumulative energy storage lifetime loss can guarantee the long-term operational benefits of energy storage.

[0049] 3. Furthermore, a decoupling method for the beginning and end energy balance constraints of multi-period energy storage is proposed, which reconstructs the beginning and end energy balance constraints of energy storage across time periods into single-period constraints, so that they conform to the real-time dispatch process of the power system, thereby establishing a real-time optimized dispatch model of the power system and performing dispatch planning separately at each dispatch time. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the steps of a power system dispatching method to ensure the operational efficiency of energy storage, as described in one embodiment.

[0051] Figure 2 One embodiment employs an optimization algorithm to select parameters in the initial scheduling model. Step-by-step flowchart;

[0052] Figure 3 For each parameter in one embodiment Determine the corresponding parameters Step-by-step flowchart;

[0053] Figure 4 This is a schematic diagram of a power system structure according to one embodiment;

[0054] Figure 5 This is a schematic diagram illustrating the change in energy storage capacity under real-time beginning and end energy balance constraints in one embodiment. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0056] Example 1

[0057] like Figure 1 The diagram shows the steps of a power system dispatching method to ensure the operational efficiency of energy storage. The main steps include:

[0058] Step S1: Establish the initial scheduling model, including the objective function and constraints. The objective function is to obtain the minimum operating cost of the power system. The constraints include upper and lower limits of operating parameters, energy balance constraints of energy storage capacity at the beginning and end, energy storage lifetime loss constraints, and upper limit constraints of cumulative energy storage lifetime loss.

[0059] Understandably, before modeling, it is necessary to collect the economic and technical parameters of each component involved in the dispatch model of the power system under study.

[0060] The components involved in the power system dispatch model generally include tie lines, nodal loads, thermal power units, external power grids, energy storage, wind power units, and photovoltaic units.

[0061] In one embodiment, the parameters of each component include:

[0062] 1) Number of nodes in the power system I bus Wind power P t w,F Photovoltaic P t s,F ,load and electricity price Predicted value;

[0063] 2) Number of power lines I branch Line start and end node numbers, line reactance per unit value x; upper limit of power transmission between power system node m and node n, system base capacity S;

[0064] 3) Node number of the thermal power unit, and upper and lower limits of the thermal power unit's output. and Maximum gradient and Cost coefficient a of thermal power units g b g c g .

[0065] 4) The upper and lower limits of power exchange between the power system and the external power grid at the node where the external power grid is located. and

[0066] 5) The upper and lower limits of the charging and discharging power of the energy storage node. and Upper and lower limits of energy storage capacity and and initial capacity

[0067] 6) Nodes of wind turbines and photovoltaic units.

[0068] 7) Daily forecasts of wind power, photovoltaic power, load, and electricity price.

[0069] The objective function of the initial scheduling model is to obtain the minimum operating cost of the power system. This objective function can be constructed based on the actual architecture of the power system. Generally, the operating cost of a power system mainly includes the cost of purchasing electricity and the generation cost of thermal power units, and may also include other operating costs. The calculation of the generation cost of thermal power units can be selected based on the actual situation of the thermal power units.

[0070] In one embodiment, considering only the cost of electricity purchase and the cost of generating electricity from thermal power units, the objective function can be expressed as:

[0071]

[0072] In the formula, T represents the duration of the entire scheduling domain, with each scheduling domain ranging from 0 to T. The start time of a scheduling domain is designated as time 0, and the end time is designated as time T. The scheduling step size is Δt, where T / Δt is an integer. Starting from the initial time 0, scheduling is performed once every step size Δt. The end time of the previous scheduling domain becomes the start time of the next scheduling domain.

[0073] In the formula, p t Let P be the electricity price at time t. t e Let P be the power exchanged between the power system and the external power grid at time t. t g Let a be the output of thermal power unit g at time t. g b g c g Let G be the cost coefficient for thermal power units, and G be the set of thermal power units.

[0074] In this invention, the constraints are based on conventional constraints and include constraints on energy storage lifetime loss and upper limit constraints on cumulative energy storage lifetime loss.

[0075] Specifically, conventional constraints include upper and lower limits for operating parameters, energy balance constraints for energy storage capacity, and also constraints for thermal power unit ramping, energy storage charge state transition, power system active power balance, and power system reactive power balance. Upper and lower limits for operating parameters specifically include upper and lower limits for thermal power unit output, power system and external grid trading power, wind power, photovoltaic power, power system node voltage, power system node phase angle, generator reactive power output, energy storage charging power, energy storage discharging power, energy storage capacity, and power system power flow. Each constraint is specifically represented as follows:

[0076] Thermal power unit ramping constraints:

[0077] Upper and lower limits of thermal power unit output constraints:

[0078] Upper and lower limits of power trading between the power system and the external power grid:

[0079] Wind power upper and lower limit constraints:

[0080] Photovoltaic power upper and lower limit constraints:

[0081] Upper and lower limits of power system node voltage constraints:

[0082] Power system node phase angle upper and lower limits constraints:

[0083] upper and lower limits of generator reactive power output constraints:

[0084] Energy storage charge state transition constraints:

[0085] Upper and lower limits of energy storage charging power constraints:

[0086] Upper and lower limits of energy storage discharge power constraints:

[0087] Upper and lower limits of energy storage capacity constraints:

[0088] Energy balance constraints of stored energy from start to finish:

[0089] Power system active power balance constraints:

[0090] Reactive power balance constraints in power systems:

[0091] Power system power flow upper and lower limits constraints:

[0092] In this invention, the added energy storage lifetime loss constraint is:

[0093]

[0094] In the formula, Let be the lifetime loss of energy storage b at time t. Let be the linear lifetime loss coefficient of energy storage b. Let be the charging power and discharging power of the energy storage b to be decided at time t, respectively. The energy storage lifetime loss constraint essentially correlates the degree of energy storage lifetime loss with the energy storage's charge / discharge state. At this point, the linear lifetime loss coefficient... These are parameters to be determined.

[0095] In this invention, the upper limit constraint on the cumulative lifetime loss of energy storage limits the cumulative lifetime loss of any energy storage b within the scheduling domain 0 to T to not exceed its upper limit. Specifically, this can be expressed as:

[0096]

[0097] In the formula, Let B be the upper limit of the cumulative lifetime loss of energy storage b within the entire scheduling domain, where B is the set of energy storage systems. These are also parameters to be determined. That is, parameters. These are unknown parameters to be determined. Once they are determined, a complete scheduling model can be obtained and solved to obtain a scheduling scheme, and to determine the power purchase, output of thermal power units, and charging and discharging power of each energy storage unit at each scheduling time t.

[0098] Step S2: Use an optimization algorithm to select the parameters in the initial scheduling model. The scheduling model is obtained, and the solution result of the scheduling model is used as the scheduling scheme.

[0099] The key to this step lies in determining the appropriate parameters. This allows us to determine the scheduling model.

[0100] like Figure 2 As shown, an optimization algorithm is used to select the parameters in the initial scheduling model. include:

[0101] Step S21: For each parameter being searched Determine the corresponding parameters parameter Satisfy the parameter The deviation of the cumulative lifetime loss of each energy storage b in the scheduling domain obtained from the initial scheduling model decision does not exceed the error allowable range.

[0102] Specifically, the parameters can be traversed using optimization algorithms such as exhaustive search, particle swarm optimization, and genetic algorithms. For each parameter Determine the corresponding parameters For each Selected parameters All of these can result in the cumulative lifetime loss of each energy storage unit b within the scheduling domain, as determined by the corresponding initial scheduling model decision. The deviation does not exceed the allowable error range, that is, for each The determined parameters This ensures that the relationship between the energy storage lifetime loss level and the energy storage charge / discharge state, as defined by the energy storage lifetime loss constraint in the model, is accurate.

[0103] Understandably, the parameters After substituting the initial scheduling model, and based on the day-ahead forecast information, the model is solved to obtain the state of charge of energy storage b at each time t within the entire scheduling domain. and lifespan loss Lifespan loss at various times By summing the results, we can obtain the cumulative lifetime loss of energy storage b within the dispatch domain. Determine the cumulative lifespan loss of energy storage The deviation needs to be addressed by adjusting the cumulative lifetime loss obtained from the model solution. With accurate cumulative lifespan loss A comparison is then made. The state of charge of energy storage b at each time t within the entire scheduling domain is obtained. Then, the accurate lifetime loss of energy storage b at each time t can be calculated using the conventional rainflow counting method. Lifespan loss at various times By summing the results, we can obtain the cumulative lifetime loss of energy storage b within the dispatch domain. adjust Make and If the relative error does not exceed the allowable error range, then output the corresponding...

[0104] In one embodiment, such as Figure 3 As shown, step S21 specifically includes:

[0105] Step S211: Set parameters Substitute into the initial scheduling model.

[0106] Step S212: Randomly initialize parameters

[0107] Step S213: Solve the initial scheduling model based on the day-ahead forecast information of the power system to obtain the state of charge of each energy storage b at each scheduling time t within the scheduling domain. and lifespan loss

[0108] Specifically, it can be expressed as:

[0109]

[0110] Step S214: Based on the energy storage state of charge The accurate lifetime loss of each energy storage unit b at each scheduling time t within the scheduling domain was calculated using the rainflow counting method.

[0111] Step S215: Calculate the cumulative lifetime loss of each energy storage b within the dispatch domain. and The relative error is used to determine whether the obtained relative error is within the set error allowable range:

[0112] If so, then output the parameters.

[0113] If not Then reduce the parameter Then proceed to step S213;

[0114] If not Then increase the parameter Then proceed to step S213.

[0115] Specifically, in step S215, the loss is determined. and Whether the relative error is within the set error allowable range is determined by judging whether the following formula holds true:

[0116]

[0117] In the formula, err loss The set error allowable threshold value.

[0118] Step S22: Based on Substituting the minimum operating cost (excluding energy storage) and the minimum operating cost (including available energy storage) obtained from the initial scheduling model, we calculate the operating cost savings when all energy storage lifetimes are completely exhausted, and select the parameter that maximizes the operating cost savings.

[0119] In other words, the goal of optimization is to maximize the operating cost savings from energy storage scheduling from the start of the current scheduling domain until all energy storage is exhausted, that is, to maximize the long-term operating benefits of energy storage.

[0120] Understandable, After substituting the initial scheduling model, the model can be solved, and the minimum operating cost J can be calculated.

[0121] Considering the different remaining lifetimes of different energy storage systems, the B energy storage systems will be depleted and rendered unusable sequentially according to their remaining lifetimes. Therefore, over time, the number of energy storage systems available for scheduling gradually decreases until it reaches zero. To calculate the operating cost ΔJ saved from the start of the current scheduling domain until all energy storage systems have reached the end of their lifetimes, we can calculate it in segments, with the number of available energy storage systems remaining constant in each segment. This can be represented as follows:

[0122]

[0123] Where B represents the possible energy storage within the current scheduling domain, and J... 0 J is the minimum operating cost obtained by solving the initial scheduling model without energy storage. b This represents the minimum operating cost obtained from the initial scheduling model when b energy storage units remain in the power system. In the long term, if there are B available energy storage units in the current scheduling domain, the next scheduling domain may only have B-1 available energy storage units (the energy storage units with the shortest lifespan have been depleted and cannot be used for subsequent scheduling). Let J be the number of dispatch domains that can simultaneously participate in dispatching for the remaining b energy storage units in the power system. Taking b=2 as an example, J... 2 J is the minimum operating cost obtained by solving the initial scheduling model when there are 2 energy storage devices remaining in the power system. 0 -J 2 This represents the cost savings achieved by having the remaining two energy storage devices in the power system participate in dispatch within a single dispatch domain, multiplied by the number of dispatch domains where the remaining two energy storage devices can participate in dispatch simultaneously. Received This represents the cost savings achieved when the remaining two energy storage devices in the power system participate in dispatching together throughout the entire operation. And so on. This represents the total operating cost saved by the B possible energy storage units operating over a long period and gradually being depleted.

[0124] In one embodiment, the number of scheduling domains The calculation formula is as follows:

[0125]

[0126] In the formula, Let B energy storage systems be arranged in descending order of remaining lifetime, and let b be the number of operable scheduling domains for the b-th energy storage system. This represents the number of operable scheduling domains for the (b+1)th energy storage. This represents the number of dispatch domains experienced between the depletion of the (b+1)th energy storage and the depletion of the bth energy storage, which is also the number of dispatch domains that the remaining b energy storage units (energy storage unit 1 to energy storage unit b) in the power system can participate in dispatching simultaneously.

[0127] The calculation formula can be expressed as:

[0128]

[0129] In the formula, SL b Let be the remaining lifetime of energy storage b, and be a known quantity. To determine the upper limit of cumulative lifetime loss of energy storage within a scheduling domain, the above formula can be used to perform an equivalent estimation, thus obtaining the number of scheduling domains.

[0130] At this point, the optimization objective can be expressed as:

[0131]

[0132] That is, select the parameter that maximizes the savings in operating costs. Substituting the data into the model as the scheduling model, and solving the scheduling model based on the day-ahead forecasts of wind power, photovoltaic power, electricity prices, and loads, we can obtain the scheduling schemes for each scheduling time within the current scheduling domain.

[0133] Example 2

[0134] In one embodiment, in determining the parameters in the scheduling model Subsequently, the energy balance constraint of the energy storage capacity at the beginning and end can be reconstructed into a single time period constraint.

[0135] Original multi-stage energy storage beginning and end energy balance constraints

[0136]

[0137] Based on the multi-time period energy storage start-end energy balance constraint, it is necessary to plan the scheduling at each scheduling moment in the entire scheduling domain simultaneously. In this embodiment, a decoupling method for the multi-time period energy storage start-end energy balance constraint is proposed, which reconstructs the cross-time period energy storage start-end energy balance constraint into a single time period constraint, so that it conforms to the real-time scheduling process of the power system, thereby establishing a real-time optimized scheduling model of the power system, and performing scheduling planning separately at each scheduling moment.

[0138] Reconstructed single-period energy storage start-end energy balance constraints:

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] In the formula, Let be the lower limit of energy storage capacity constrained by energy storage power, the lower limit of energy storage capacity constrained by energy storage lifetime, the upper limit of energy storage capacity constrained by energy storage power, and the upper limit of energy storage capacity constrained by energy storage lifetime at time t, respectively. Let b be the lower and upper limits of the energy storage capacity. Let b be the initial capacity of energy storage. The maximum charging power and maximum discharging power of energy storage b are given. Let Δt be the maximum remaining energy loss at time t that guarantees the energy storage b can return to its initial capacity, and let Δt be the scheduling step size within the scheduling domain.

[0146] Example 3

[0147] The present invention also relates to a power system dispatching device for ensuring the operational efficiency of energy storage, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0148] The power system dispatching device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The memory can be used to store computer programs and / or modules. The processor performs various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory.

[0149] Example 4

[0150] The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0151] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0152] Example 5

[0153] This example uses a 123-node power system for analysis, such as... Figure 4 As shown in the figure (the Arabic numerals in the figure represent node numbers). The system includes 5 thermal power units, 4 wind power units, 4 photovoltaic units, 3 energy storage units, and an interface for trading power with an external power grid.

[0154] In this embodiment, the particle swarm optimization method is used to search for and determine the optimal parameters. From a long-term perspective, the cost savings (long-term energy storage benefits) from energy storage participation in dispatch are estimated at $80,347. Table 1 shows the energy storage benefits calculated under three different dispatch models that consider energy storage lifetime degradation. The levelized degradation cost model is derived from the PLC model. J. S.Arnaltes G′omez, M. Plaza and APAsensio, “A review on the degradation implementation for the operation of battery energy storage systems,” *Batteries*, vol. 8, no. 9, p. 110, 2022. It can be seen that although the proposed real-time optimal dispatch model for power systems that guarantees the operational efficiency of energy storage sacrifices some short-term (daily) benefits in short-term (single-day) dispatch, from the perspective of overall energy storage benefits, the proposed model achieves the greatest benefit, guaranteeing the efficiency of energy storage. Simultaneously, in short-term dispatch, energy storage also minimizes daily operating costs within a given daily lifetime loss limit. This achieves a balance between short-term and long-term dispatch.

[0155] Table 1

[0156] No energy storage 285.0227 \ Leveling degradation cost model 262.6311 72210 The model proposed in this invention 265.7318 80347

[0157] At the same time, the parameters determined in step S21 were also verified. The accuracy of the parameters is shown in Table 2, which compares the lifetime loss of the three types of energy storage with the actual lifetime loss. The errors are 7.7233e-4, 8.3177e-4, and 7.9863e-4, respectively, indicating that the parameters are accurate. The relationship between the degree of energy storage lifespan loss and the energy storage charge / discharge state is accurate.

[0158] Table 2

[0159] Energy Storage 1 1.22e-4 1.2209e-4 7.7233e-4 Energy Storage 2 1.205e-4 1.206e-4 8.3177e-4 Energy Storage 3 9.5e-5 9.5076e-5 7.9863e-4

[0160] Simultaneously, it was verified that after reconstructing the initial and final energy balance constraints of energy storage into single-period constraints, energy storage can ensure that the energy level remains consistent with the initial energy level at the final scheduling moment during real-time scheduling. Figure 5 As shown, BES1, BES2, and BES3 respectively represent Figure 4 Energy storage is located at nodes 7, 57, and 116.

[0161] Those skilled in the art will readily understand that the above description is merely 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 scope of protection of the present invention.

Claims

1. A power system dispatching method to ensure the operational efficiency of energy storage, wherein the power system includes thermal power units, renewable energy power units, an external power grid, and energy storage, characterized in that, The scheduling method includes: Step S1: Establish an initial scheduling model, including an objective function and constraints. The objective function is to obtain the minimum operating cost of the power system. The constraints include upper and lower limits of operating parameters, energy balance constraints of energy storage capacity at the beginning and end, energy storage lifetime loss constraints, and upper limit constraints of cumulative energy storage lifetime loss. Any energy storage within the scheduling domain 0~T... b The cumulative lifespan loss does not exceed its upper limit. The energy storage lifespan loss constraint is: , for t Energy storage at all times b Lifespan loss, For energy storage b The linear lifetime loss coefficient, , Energy storage to be decided b exist t The charging power and discharging power at each moment; where the parameter { , } represents the unknown parameters to be determined; Step S2: Select the parameters { in the initial scheduling model using an optimization algorithm. , The scheduling model is obtained, and the solution result of the scheduling model is used as the scheduling scheme. Among them, the optimization algorithm is used to select the parameters { , },include: Step S21: For each parameter being searched Determine the corresponding parameters ,parameter Satisfying the condition with parameters { , The deviation of the cumulative lifetime loss of each energy storage b obtained by the initial scheduling model decision in the scheduling domain does not exceed the error allowable range; Step S22: Based on { , Substituting the minimum operating cost without energy storage and the minimum operating cost with available energy storage into the initial scheduling model, calculate the operating cost savings when all energy storage lifetimes are exhausted, and select the parameter that maximizes the operating cost savings. , }; In step S22, the formula for calculating the operating cost savings when all energy storage resources have been completely depleted is as follows: In the formula, To save on operating costs, B represents the minimum operating cost excluding energy storage, and B represents the total amount of energy storage in the current dispatch domain. This represents the minimum operating cost when b energy storage units remain in the power system. This represents the number of dispatch domains in the power system where the remaining b energy storage units can participate in dispatch simultaneously. The calculation formula is: In the formula, This represents the number of operable scheduling domains for the b-th energy storage unit when its remaining lifetime is arranged in descending order. This represents the number of scheduling domains that the (b+1)th energy storage unit can operate in. This represents the number of scheduling domains traversed from the (b+1)th energy storage unit being depleted until the bth energy storage unit is depleted: In the formula, Let b be the remaining lifetime of energy storage.

2. The power system dispatching method for ensuring the operational efficiency of energy storage as described in claim 1, characterized in that, The optimization algorithm can be any one of exhaustive search, particle swarm optimization, or genetic algorithm.

3. The power system dispatching method for ensuring the operational efficiency of energy storage as described in claim 1, characterized in that, In step S21, for each parameter being searched... Determine the corresponding parameters ,include: Step S211: Set parameters Substitute into the initial scheduling model; Step S212: Randomly initialize parameters ; Step S213: Solve the initial scheduling model based on the day-ahead forecast information of the power system to obtain the state of charge of each energy storage b at each scheduling time t within the scheduling domain. and lifespan loss ; Step S214: Based on the energy storage state of charge The accurate lifetime loss of each energy storage unit b at each scheduling time t within the scheduling domain was calculated using the rainflow counting method. ; Step S215: Calculate the cumulative lifetime loss of each energy storage b within the dispatch domain. and accurate cumulative lifespan loss The relative error is used to determine whether the obtained relative error is within the set error allowable range: If so, then output the parameters. ; If not Then reduce the parameter Then proceed to step S213; If not Then increase the parameter Then proceed to step S213. Let T be the scheduling step size within the scheduling domain T.

4. The power system dispatching method for ensuring the operational efficiency of energy storage as described in claim 1, characterized in that, In the initial scheduling model, the energy balance constraint for the initial and final energy storage capacity is: In the formula, Energy storage at time T within the previous scheduling domain b The state of charge, Let b be the initial capacity of energy storage in the next scheduling domain. A collection of energy storage systems; In the aforementioned scheduling model, the initial and final energy balance constraints of the energy storage capacity are reconstructed in a single time period to obtain a real-time scheduling model that can be scheduled according to the real-time status of the power system. The reconstructed single-time energy storage initial and final energy balance constraints are expressed as follows: In the formula, , , , Let be the lower limit of energy storage capacity constrained by energy storage power, the lower limit of energy storage capacity constrained by energy storage lifetime, the upper limit of energy storage capacity constrained by energy storage power, and the upper limit of energy storage capacity constrained by energy storage lifetime at time t, respectively. , Let b be the lower and upper limits of the energy storage capacity. Let b be the initial capacity of energy storage. , The maximum charging power and maximum discharging power of energy storage b are given. Let b be the energy storage capacity at time t that guarantees it can return to its initial capacity. Let T be the scheduling step size within the scheduling domain. Let be the state of charge of energy storage b at time t within the scheduling domain.

5. The power system dispatching method for ensuring the operational efficiency of energy storage as described in claim 1, characterized in that, In step S1, the upper and lower limits of operating parameters include: upper and lower limits of thermal power unit output, upper and lower limits of power trading between the power system and the external power grid, upper and lower limits of wind power, upper and lower limits of photovoltaic power, upper and lower limits of power system node voltage, upper and lower limits of power system node phase angle, upper and lower limits of generator unit reactive power output, upper and lower limits of energy storage charging power, upper and lower limits of energy storage discharging power, upper and lower limits of energy storage capacity, and upper and lower limits of power system power flow. The constraints also include: thermal power unit ramping constraints, energy storage state-of-charge transition constraints, power system active power balance constraints, and power system reactive power balance constraints.

6. The power system dispatching method for ensuring the operational efficiency of energy storage as described in claim 1, characterized in that, The expression for the objective function is: In the formula, To minimize operating costs, Let T be the scheduling step size within the scheduling domain. Let be the electricity price at time t. Let t be the power exchanged between the power system and the external power grid. Let g be the output of the thermal power unit at time t. , , Let G be the cost coefficient for thermal power units, and G be the set of thermal power units.

7. A power system dispatching device for ensuring the operational efficiency of energy storage, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

8. 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 steps of the method according to any one of claims 1 to 6.

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