Energy storage regulation and control decision-making method and system based on energy dynamic balance
By constructing a feature set and model of energy storage devices, the problem of poor accuracy and effectiveness of traditional energy storage regulation and decision-making schemes was solved, and the dynamic energy balance and grid stability of the energy storage system were realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-28
- Publication Date
- 2026-03-17
AI Technical Summary
Traditional energy storage regulation and control decision-making schemes suffer from low regulation accuracy and poor regulation effect, making it difficult to effectively aggregate and optimize distributed energy storage resources.
A feature set of energy storage devices is constructed, numerical conversion is performed, and an equivalent state of charge model, a time-of-day capacity calculation model, and a system power model are established. The balance conditions of the energy storage system are set, and alternative control schemes are determined through a multi-energy storage system control model. Control constraints and cost models are set for screening.
It improves the accuracy and effectiveness of energy storage regulation, realizes dynamic energy balance of energy storage node network, and ensures stable and safe operation of power grid.
Smart Images
Figure CN121689082A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of energy regulation, and in particular to an energy storage regulation decision-making method and system based on energy dynamic balance. BACKGROUND
[0002] With the rapid development of social economy, the demand for electricity of users has increased dramatically, and a large amount of distributed energy of power users has been connected to the grid. The intermittency and randomness of new energy such as solar photovoltaic and small wind power, combined with the uncertainty of user electricity consumption, have made it difficult for the power system to achieve real-time balance, and the safe and stable operation of the power grid has been under great pressure. Traditional regulation methods have been difficult to meet the demand.
[0003] The innovation and development of new energy storage technology and the gradual improvement of infrastructure have brought hope for alleviating the problem of power grid regulation. Energy storage systems can perform key functions such as peak shaving and frequency regulation in the power grid due to their fast response and flexible adjustment. However, the user-side energy storage resources are scattered and distributed, with significant differences in capacity and types, making it difficult to aggregate, calculate and regulate resources, and greatly limiting the overall benefits of energy storage.
[0004] In traditional energy storage regulation decision-making schemes, there are many technical shortcomings. Some small distributed energy aggregation models can analyze response potential, but ignore reasonable energy allocation; improved distributed algorithms promote energy storage resource sharing, but only stay at the level of mechanical integration and optimization, and the regulation effect is not satisfactory.
[0005] Therefore, the traditional energy storage regulation decision-making scheme has the technical problems of low regulation accuracy and poor regulation effect. SUMMARY
[0006] The present application provides an energy storage regulation decision-making method and system based on energy dynamic balance to solve the defects of low regulation accuracy and poor regulation effect in traditional energy storage regulation decision-making schemes.
[0007] In one aspect, the present application provides an energy storage regulation decision-making method based on energy dynamic balance, comprising: constructing an energy storage device feature set and numerically transforming the state features in the energy storage device feature set to obtain key energy storage features; constructing an equivalent state of charge model, an intra-day time period capacity calculation model, and a system power model of the energy storage system, and setting an energy storage system balance condition, and quantitatively solving the key energy storage features by the equivalent state of charge model, the intra-day time period capacity calculation model, and the system power model according to the energy storage system balance condition to obtain quantitative data of energy storage nodes; construct an energy balance hierarchical rule and a multi-energy storage system regulation model, determine an alternative regulation scheme through the multi-energy storage system regulation model according to the quantified data and the energy balance hierarchical rule; set a regulation constraint condition, construct a regulation cost model of the energy storage system participating in grid regulation, and screen the alternative regulation scheme according to the regulation constraint condition and the regulation cost model to obtain an energy storage regulation decision scheme.
[0008] According to the energy dynamic balance-based energy storage regulation decision method provided in the application, a set of energy storage device characteristics is constructed, and state characteristics in the set of energy storage device characteristics are numerically converted to obtain key energy storage characteristics, including: According to the influencing factors of energy storage participating in grid regulation on user-side energy storage, the state characteristics and the numerical characteristics of the energy storage device are determined; The state characteristics and the numerical characteristics are taken as the set of energy storage device characteristics; The different types of states in the state characteristics are valued to obtain converted characteristics; The numerical characteristics and the converted characteristics are taken as the key energy storage characteristics.
[0009] According to the energy dynamic balance-based energy storage regulation decision method provided in the application, an equivalent state of charge model of the energy storage system is constructed, including: The capacity of each energy storage device in the energy storage system and the state of charge of each energy storage device at any time are determined respectively; For each energy storage device at any time, the capacity and the state of charge are multiplied to obtain actual energy storage capacity; The actual energy storage capacities of all energy storage devices participating in regulation are added to obtain an energy storage capacity summation submodel; The capacities of all energy storage devices participating in regulation are added to obtain a capacity summation submodel; According to the energy storage capacity summation submodel and the capacity summation submodel, the equivalent state of charge model of the energy storage system is obtained.
[0010] According to the energy dynamic balance-based energy storage regulation decision method provided in the application, an energy storage system balance condition is set, including: In each regulation period, the initial equivalent state of charge of the energy storage system at the start time of the regulation period is set to be equal to the final equivalent state of charge at the end time.
[0011] According to the energy dynamic balance-based energy storage regulation decision method provided in the application, an intra-day time period capacity measurement model is constructed, including: A first expression corresponding to the energy capacity of the energy storage system discharged from a certain time and a first charge and discharge capacity constraint condition are set to establish a first capacity measurement submodel; A second expression for the energy capacity of the energy storage system charging from a certain moment and a second charge / discharge constraint condition are set, and a second capacity measurement operator model is established. Determine the third expression corresponding to the minimum capacity of energy storage to participate in grid regulation, and establish a third capacity measurement operator model; The first capacity measurement sub-model, the second capacity measurement sub-model, and the third capacity measurement sub-model are used as intraday time-segmented capacity measurement models.
[0012] According to the energy storage regulation decision-making method based on dynamic energy balance provided by the present invention, a system power model is constructed, including: Determine the high-frequency components of the energy storage system after normalization, the capacity of key equipment in the previous control cycle, and the base point power of the energy storage equipment. A mapping relationship is established between the system power of the energy storage system and the normalized high-frequency component, the capacity of the key equipment in the previous control cycle, and the base point power of the energy storage equipment to obtain the system power model.
[0013] According to the energy storage regulation decision-making method based on dynamic energy balance provided by the present invention, an energy balance hierarchical rule is constructed, including: By setting the state of charge constraints corresponding to the over-discharge warning region and the overcharge warning region, the overcharge and over-discharge warning conditions are obtained. Based on the historical charge status data of the daily cycle settlement node of the energy storage system within a set time period, determine the charge status deviation value of the daily cycle settlement node; Based on the overcharge and over-discharge warning conditions and the degree to which the charge state deviation exceeds a preset threshold, multi-level warning rules are set to obtain energy balance classification rules.
[0014] According to the energy storage regulation decision-making method based on dynamic energy balance provided by the present invention, regulation constraints are set, including: Based on the balance between the overall power of grid regulation and control, the overall power of energy storage systems in alternative regulation schemes, and the overall power of other adjustable resources in the grid excluding energy storage systems, power balance constraints are established. Based on the fact that the regulation and response capability of an energy storage system is jointly affected by its current state of charge and charging and discharging behavior, system energy constraints are established. Based on the fact that the energy charging and discharging of energy storage systems are limited by overcharge and over-discharge protection, charging and discharging protection constraints are established; The power balance constraint, the system energy constraint, and the charge / discharge protection constraint are used as control constraints.
[0015] According to the energy storage regulation decision-making method based on dynamic energy balance provided by the present invention, the alternative regulation schemes are screened based on the regulation constraints and the regulation cost model to obtain an energy storage regulation decision scheme, including: Based on the aforementioned control constraints and the aforementioned control cost model, the control costs corresponding to each alternative control scheme are calculated respectively. The alternative control scheme with the lowest control cost will be used as the energy storage control decision scheme.
[0016] On the other hand, the present invention also provides an energy storage regulation and decision-making system based on dynamic energy balance, comprising: The feature module is used to construct a feature set of energy storage devices and to perform numerical transformation on the state features in the feature set of energy storage devices to obtain key energy storage features; The quantization module is used to construct the equivalent state of charge model, the intraday time-of-day capacity calculation model, and the system power model of the energy storage system, and to set the balance conditions of the energy storage system. Based on the balance conditions of the energy storage system, the key energy storage characteristics are quantitatively solved through the equivalent state of charge model, the intraday time-of-day capacity calculation model, and the system power model to obtain the quantitative data of the energy storage nodes. The grading module is used to construct energy balance grading rules and multi-energy storage system control models. Based on the quantitative data and the energy balance grading rules, alternative control schemes are determined through the multi-energy storage system control models. The decision module is used to set control constraints and construct a control cost model for the energy storage system to participate in the power grid. Based on the control constraints and the control cost model, the alternative control schemes are screened to obtain the energy storage control decision scheme.
[0017] The present invention provides an energy storage regulation and decision-making method and system based on dynamic energy balance. This method and system constructs a feature set of energy storage devices and performs numerical transformation on the state characteristics within this set to obtain key energy storage characteristics. It then constructs an equivalent state-of-charge model, a time-of-day capacity calculation model, and a system power model for the energy storage system, and sets balance conditions for the energy storage system. Based on these balance conditions, the key energy storage characteristics are quantitatively solved using the equivalent state-of-charge model, the time-of-day capacity calculation model, and the system power model to obtain quantitative data for the energy storage nodes. Furthermore, it constructs energy balance classification rules and a multi-energy storage system regulation model. Based on the quantitative data and the energy balance classification rules, alternative regulation schemes are determined using the multi-energy storage system regulation model. Finally, it sets regulation constraints and constructs a regulation cost model for the energy storage system's participation in the power grid. Based on the regulation constraints and the regulation cost model, alternative regulation schemes are screened to obtain an energy storage regulation decision scheme. Because the control process first quantifies the key energy storage characteristics and generates multiple alternative control schemes based on the quantified data of the energy storage nodes, and then combines the principle of cost optimization to obtain the optimal energy storage control decision scheme, the control accuracy is effectively improved, the dynamic energy balance of the energy storage node network is realized, the control effect is better, and thus it can provide a guarantee for the stable and safe operation of the power grid. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0019] Figure 1 This is a flowchart illustrating the energy storage regulation decision-making method based on dynamic energy balance provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the energy storage regulation and decision-making system based on dynamic energy balance provided in an embodiment of the present invention. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0021] The following is combined Figure 1 and Figure 2This invention describes the detailed scheme of the energy storage regulation decision-making method and system based on dynamic energy balance provided in the embodiments of the present invention.
[0022] like Figure 1 As shown in the figure, the energy storage regulation decision-making method based on dynamic energy balance provided by the present invention mainly includes the following steps: Step 110: Construct a feature set of energy storage devices, and perform numerical transformation on the state features in the feature set of energy storage devices to obtain key energy storage features.
[0023] In this embodiment, based on the energy storage charging and discharging characteristics, energy storage device features such as charging and discharging characteristics and stored energy characteristics can be extracted, as well as energy storage node features such as charging and discharging characteristics and multi-node association characteristics.
[0024] It is understandable that the feature information in the feature set of energy storage devices can be divided into two categories: one is state features and the other is numerical features. In order to meet the data format requirements of subsequent data processing, this embodiment performs numerical conversion on the state features, so that all features in the feature set of energy storage devices are numerical features, thereby obtaining key energy storage features.
[0025] Step 120: Construct the equivalent state of charge model, intraday time-of-day capacity calculation model, and system power model of the energy storage system, and set the energy storage system balance conditions. Based on the energy storage system balance conditions, use the equivalent state of charge model, intraday time-of-day capacity calculation model, and system power model to quantitatively solve the key energy storage characteristics and obtain the quantitative data of the energy storage nodes.
[0026] This embodiment mainly starts from the energy storage system of a single energy storage device set. Based on the charging and discharging characteristics in the key energy storage features, it constructs a quantitative model related to the charging and discharging behavior of the energy storage system participating in grid regulation, realizes the mathematical abstraction of the energy storage participation in grid regulation behavior, and simplifies the charging and discharging fluctuations of the energy storage system.
[0027] Step 130: Construct energy balance classification rules and multi-energy storage system control models. Based on quantitative data and energy balance classification rules, determine alternative control schemes through the multi-energy storage system control models.
[0028] This embodiment mainly starts from the power grid control requirements, quantifies the power grid's demand for grid-connected energy storage systems to participate in power grid control, and generates alternative control schemes for energy storage and power grid interaction balance based on the energy balance requirements of the energy storage system.
[0029] Step 140: Set control constraints and construct a control cost model for the energy storage system's participation in the power grid. Based on the control constraints and the control cost model, screen the alternative control schemes to obtain the energy storage control decision scheme.
[0030] In this embodiment, based on the actual situation of power grid operation and energy storage equipment, the energy storage control scheme needs to meet objective conditions such as power balance, energy limitation, and rated capacity. Under these constraints, it is necessary to further optimize the obtained alternative control schemes to obtain the energy storage control decision scheme with the lowest cost.
[0031] In one embodiment, a feature set of energy storage devices is constructed, and the state features in the feature set are numerically transformed to obtain key energy storage features, specifically including: The first step is to determine the state characteristics and numerical characteristics of energy storage devices based on the factors affecting the participation of energy storage in grid regulation on user-side energy storage.
[0032] In this embodiment, based on the control requirements of power grid regulation, energy storage participation in power grid regulation mainly changes the grid-connected / off-grid status, state of charge, and charging / discharging power of user-side energy storage. These are the influencing factors of energy storage participation in power grid regulation on user-side energy storage. Therefore, the characteristic set of energy storage devices includes characteristic information such as charging power, discharging power, capacity, response time, energy cycle efficiency, grid-connected / off-grid status, and charging / discharging status. Among them, the state characteristics such as grid-connected / off-grid status, charging / discharging status, and response time are discrete values; while the numerical characteristics such as charging power, discharging power, capacity, response time, and energy cycle efficiency are continuous values. The specific characteristic classification can be found in Table 1 below.
[0033] Table 1. Feature Classification Information in the Feature Set of Energy Storage Devices
[0034] The second step is to use the state characteristics and numerical characteristics as a set of characteristics for energy storage devices.
[0035] The third step is to assign values to different types of states in the state features to obtain the transformation features.
[0036] In practical applications, for the model sample input logic, the grid-connected state of the energy storage device can be set to 1, and the off-grid state to -1; the charging state can be set to 1, and the discharging state to -1; and the response time can be set to 1 for the day-ahead response and 2 for the intraday response. See Table 2 below for specific assignments.
[0037] Table 2 State Feature Assignment Settings
[0038] The fourth step is to use numerical characteristics and conversion characteristics as key energy storage characteristics.
[0039] In this embodiment, key energy storage characteristics can serve as a prerequisite for the quantification of energy storage nodes.
[0040] In one embodiment, constructing an equivalent state-of-charge model of the energy storage system includes: The first step is to determine the capacity of each energy storage device in the energy storage system and the state of charge of each energy storage device at any given time.
[0041] The second step is to multiply the capacity and state of charge of each energy storage device at any given time to obtain the actual stored energy capacity.
[0042] The third step is to sum the actual energy storage capacity of all energy storage devices involved in the regulation to obtain a sub-model for summing energy storage capacity.
[0043] The fourth step is to sum the capacities of all energy storage devices involved in the regulation to obtain a capacity summation sub-model.
[0044] The fifth step is to obtain the equivalent state of charge model of the energy storage system based on the energy storage power summation sub-model and the capacity summation sub-model.
[0045] In this embodiment, for the control period T, assuming that there are n energy storage devices participating in the control in the energy storage system, and taking the entire energy storage system as a whole, the equivalent state of charge model of the energy storage system can be represented as follows: (1) in, The equivalent state of charge of the energy storage system at time t. and Let be the state of charge and capacity of the i-th energy storage device at time t, respectively.
[0046] In one embodiment, setting the balance conditions for the energy storage system specifically includes: Within each control cycle, the initial equivalent state of charge of the energy storage system at the beginning of the control cycle is set to be equal to the final equivalent state of charge at the end of the control cycle.
[0047] Understandably, since the operation of the power grid can be simply regarded as a cycle on a daily basis, the energy storage system needs to complete the capacity reset at the beginning and end of a control cycle, that is, the initial equivalent state of charge at the initial moment is equal to the final equivalent state of charge at the end moment.
[0048] In one embodiment, a time-segmented capacity measurement model is constructed, specifically including: The first step is to define the first expression for the energy capacity of the energy storage system that discharges from a certain moment, as well as the first charging and discharging constraints, and establish the first capacity measurement operator model.
[0049] Based on the balance conditions of the energy storage system, the energy storage capacity for each time period during the day starting from time t is calculated. When the grid experiences excess power supply, the energy storage device needs to absorb the excess energy, i.e., the energy storage device is in a charging state. At this time, it only needs to be restored to its initial state within the allowable charging and discharging range of the energy storage device and before the end of the daily cycle. Therefore, the conditions during the discharge phase are more stringent, and energy storage participation in grid regulation should primarily focus on this phase.
[0050] Calculate the energy storage capacity of the energy storage system starting from time t. During this period, the charge and discharge quantities of the energy storage system must meet the following first charge and discharge quantity constraint condition: (2) in, Let be the charge / discharge amount at time t. Initial time Net charge / discharge amount up to time t , These represent the maximum and minimum states of charge of the energy storage system, respectively. This is the initial state of charge.
[0051] Furthermore, at the initial moment The net charge / discharge amount up to time t can be expressed as follows: (3) in, For a moment The corresponding charge / discharge amounts.
[0052] Therefore, the energy capacity of the energy storage system that can participate in the regulation of the power grid system at time t during discharge, i.e., the first expression, can be represented as follows: (4) in, The energy capacity of the energy storage system that is discharging energy at time t is available for participation in the regulation of the power grid system.
[0053] The second step is to set a second expression for the energy capacity of the energy storage system that is being charged from a certain moment, as well as a second charge and discharge constraint condition, and to establish a second capacity measurement operator model.
[0054] In this embodiment, the energy capacity of the energy storage system is calculated from time t. During this period, the charging and discharging amount of the energy storage system must meet the following second charging and discharging amount constraint condition: (5) in, The charge / discharge amount is from time t to the end time T.
[0055] Furthermore, the charge / discharge amounts from time t to the final time T can be expressed as follows: (6) Therefore, the energy capacity of the charging energy storage system available for grid system regulation at time t, i.e., the second expression, can be represented as follows: (7) in, The energy capacity of the energy storage system that can participate in the regulation of the power grid system at time t, which is being charged.
[0056] The third step is to determine the third expression corresponding to the minimum capacity of energy storage to participate in grid regulation and to establish the third capacity measurement operator model.
[0057] For all time periods within the daily cycle, based on the control periods covered by the control scheme, the maximum capacity of the energy storage system that can participate in grid control is selected as the capacity constraint condition of the energy storage control scheme. A third expression corresponding to the minimum capacity of energy storage participating in grid control is then established, namely: (8) in, N represents the minimum capacity for energy storage to participate in grid regulation, and N represents the maximum number of minimum time units within the regulation period.
[0058] The fourth step is to use the first capacity measurement sub-model, the second capacity measurement sub-model, and the third capacity measurement sub-model as the intraday time-segmented capacity measurement model.
[0059] In one embodiment, constructing a system power model specifically includes: First, determine the high-frequency components of the energy storage system after normalization, the capacity of key equipment in the previous control cycle, and the base power of the energy storage equipment.
[0060] Then, a mapping relationship is established between the system power of the energy storage system and the normalized high-frequency components, the capacity of the key equipment in the previous control cycle, and the base point power of the energy storage equipment, thus obtaining the system power model.
[0061] In this embodiment, since the energy storage system has both high-frequency and low-frequency components during operation, the mathematical expression for the overall power of the energy storage system, i.e., the system power model, can be expressed as follows: (9) in, The total power of the energy storage system, The high-frequency components of this energy storage system are normalized. This refers to the capacity of the key equipment reported by the energy storage system in the previous control cycle. The baseline power of energy storage devices is regularly monitored and updated for the entire power grid.
[0062] It can be understood that when the energy storage system switches from the normal working area to the overcharge and over-discharge areas, the charging and discharging power of the energy storage system will cause large fluctuations in the power of the regional power grid system. Therefore, when constructing the energy balance grading rules, the overcharge and over-discharge areas can be set as warning areas, and overcharge and over-discharge warning conditions can be established as the first warning mechanism. At the same time, considering the energy balance requirements of the energy storage system, based on the historical charge state data of the energy storage system in the past week, a second warning mechanism can be set according to the degree to which the charge state deviation value exceeds the preset threshold.
[0063] In one embodiment, constructing the energy balance grading rules specifically includes: The first step is to set the state of charge constraint conditions corresponding to the over-discharge warning area and the overcharge warning area respectively to obtain the overcharge and over-discharge warning conditions.
[0064] In this embodiment, according to the overcharge and over-discharge areas of common energy storage devices, it is set that exceeding 80% of the maximum state of charge is overcharge, and lower than 20% of the maximum state of charge is over-discharge. Therefore, in this embodiment, the state of charge constraint condition can be set to satisfy 20% < SOC < 40% as the over-discharge warning area, and the state of charge constraint condition satisfies 60% < SOC < 80% as the overcharge warning area.
[0065] The second step is to determine the state of charge deviation value of the daily cycle settlement node according to the historical charge state data of the daily cycle settlement node of the energy storage system within the set period.
[0066] In this embodiment, according to the historical charge state data of the daily cycle settlement node in the energy storage system in the past week, the state of charge deviation value of the daily cycle settlement node can be calculated as follows: (10) Where, is the state of charge deviation value of the daily cycle settlement node, is the state of charge of the daily cycle settlement node in the energy storage system on the current day, is the state of charge of the daily cycle settlement node in the energy storage system on the previous day.
[0067] The third step is to set multi-level warning rules according to the overcharge and over-discharge warning conditions and the degree to which the state of charge deviation value exceeds the preset threshold to obtain the energy balance grading rules.
[0068] In this embodiment, multi-level warning rules can be set according to the energy balance requirements of the energy storage system as follows: When the overcharge and over-discharge warnings are triggered by meeting the overcharge and over-discharge warning conditions, the warning level is the first level.
[0069] When the charge status deviation of the daily cycle settlement node of the energy storage system exceeds 10% of the preset standard value for more than 5 days in the past week, or when the charge status deviation of the daily cycle settlement node of the energy storage system exceeds 20% of the preset standard value on the previous day, the warning level is set to Level II.
[0070] When the charge status deviation of the daily cycle settlement node of the energy storage system exceeds 10% of the preset standard value for more than 3 days in the past week, the warning level is three.
[0071] In some embodiments, a multi-energy storage system control model is constructed, specifically including: First, a single energy storage system control model is constructed. Specifically, it can be based on the power grid topology and constructed using the DMF (Deep Matrix Factorization) algorithm. The DMF algorithm is a matrix factorization method combined with neural network technology, primarily used in recommendation systems. It achieves high-order feature interaction through deep networks, supports joint modeling of explicit and implicit data, adapts to diverse user behavior data, and optimizes low-dimensional vectors of users and items through nonlinear mapping, thereby improving representation capabilities. In this embodiment, each energy storage system is used as the power transmission node for power grid control. Let the overall control demand at time t be denoted as... The execution interval is The electrical energy needs to be adjusted during the interval. The change in self-generated energy of the energy storage system within the interval is .exist At time i, the formula for calculating the demand to be regulated after adjustment by node i is as follows: (11) in, In order to be in The demand to be regulated after being regulated by node i at any given time. This represents the system power of the energy storage system.
[0072] Algorithm. For calculating the interval time. To enhance the regulation capability and further realize the design of the overall scheme within the regulation time, this embodiment adaptively modifies the DMF algorithm to construct a multi-energy storage system regulation model. The specific algorithm flow is as follows: The first step is to record the adjustment time point as... .
[0073] The second step is to measure the first-level early warning energy storage system at time T, and to calculate the regulating energy based on the charge state deviation value of the overcharged or over-discharged energy storage system yesterday.
[0074] The third step is to determine whether the unit time control requirement at time T is met. If not, proceed to the fourth step; if it is met, proceed to the seventh step.
[0075] The fourth step is to update the remaining control quantity and calculate the control energy based on the historical charge state deviation of the energy storage system.
[0076] Fifth step: Determine whether the unit time control requirement at time T is met. If not, proceed to the sixth step; if it is met, proceed to the seventh step.
[0077] The sixth step is to query the remaining energy storage systems and select the appropriate energy storage systems for control based on their current adjustable capacity, from largest to smallest.
[0078] Step 7: Determine whether the overall control requirements are met. If not, proceed to step 8; if so, proceed to step 9.
[0079] Step 8: Adjust the timing point downwards. At that moment, proceed to the second step.
[0080] Step 9: End, output alternative control schemes.
[0081] In one embodiment, setting control constraints specifically includes: The first step is to establish power balance constraints based on the balance between the overall power of grid regulation and the overall power of energy storage systems in alternative regulation schemes and the overall power of other adjustable resources in the grid excluding energy storage systems.
[0082] In this embodiment, the participation of energy storage in grid regulation needs to be coordinated and balanced with the overall demand of grid regulation and other adjustable resources. Therefore, the power balance constraints are as follows: (12) in, To regulate the overall power output of the power grid, The overall power of the energy storage system within the alternative control scheme, This refers to the total power of adjustable resources within the power grid, excluding energy storage systems.
[0083] The second step is to establish system energy constraints based on the fact that the regulation and response capability of the energy storage system is affected by the current state of charge and charging and discharging behavior.
[0084] In this embodiment, the regulation and response capability of the energy storage system is jointly affected by the current state of charge and charging and discharging behavior. Therefore, the specific energy constraints of the system are as follows: (13) in, The energy stored in the energy storage system at time t+1. Let t be the stored electrical energy of the energy storage system. To improve the charging efficiency of energy storage systems. For the discharge efficiency of the energy storage system For charging power, This represents the discharge power.
[0085] The third step is to establish charging and discharging protection constraints based on the overcharging and over-discharging protection limitations of the energy storage system.
[0086] In this embodiment, the energy charging and discharging of the energy storage system is limited by overcharge and over-discharge protection. Therefore, the specific charging and discharging protection constraints are as follows: (14) in, Let t be the stored electrical energy of the energy storage system. This is the upper limit of electrical energy for overcharge and over-discharge protection.
[0087] The fourth step is to use the power balance constraint, system energy constraint, and charge / discharge protection constraint as the control constraints.
[0088] In one embodiment, based on the control constraints and control cost model, alternative control schemes are screened to obtain an energy storage control decision scheme, specifically including: First, based on the control constraints and control cost model, the control costs corresponding to each alternative control scheme are calculated.
[0089] The main control costs of energy storage systems participating in grid regulation are charging loss costs and discharging loss costs, as detailed below: (15) in, The regulation cost of energy storage systems participating in grid regulation, Let i be the regulation cost of the i-th energy storage device. Let $ be the charging loss cost of the i-th energy storage device. Let $\frac{i}{i}$ be the discharge loss cost of the $i$-th energy storage device.
[0090] Based on charge and discharge efficiency, the control cost model can be specifically expressed as follows: (16) in, The weighting coefficients corresponding to charging efficiency. The correlation coefficient for charging time. Let i be the charging time corresponding to the i-th energy storage device. The weighting coefficients corresponding to the discharge efficiency are: The correlation coefficient is the discharge duration. Let be the discharge duration corresponding to the i-th energy storage device.
[0091] Then, the alternative control scheme with the lowest control cost will be used as the energy storage control decision scheme.
[0092] Understandably, the control costs of each alternative scheme can be calculated according to the above control cost model. Subsequently, the alternative control schemes are ranked according to the control costs of the alternative control schemes for the energy storage system to participate in grid control. Based on the principle of lowest cost, the target alternative control scheme with the lowest cost is output first as the energy storage control decision scheme.
[0093] The following example illustrates in detail the implementation process of the energy storage regulation decision-making method based on dynamic energy balance.
[0094] It should be noted that there are two main ways to obtain the model algorithm parameters in this embodiment: one is based on the file information reported by each energy storage device when it is registered and connected to the grid; the other is to calculate based on historical data from the past 7 days for energy storage devices with missing information. After constructing the feature set of energy storage devices and performing numerical transformation on the state features in the feature set to obtain the key energy storage features, the model deployment and verification phase begins.
[0095] First, according to market-based regulation rules, the frequency regulation day is divided into 24 frequency regulation periods, each lasting 1 hour. Furthermore, the instruction interval is 5 minutes. When the frequency regulation capacity demand issued by the dispatch center exceeds the frequency regulation capacity of the aggregator, the frequency regulation instruction is distributed to the energy storage cluster based on the maximum value.
[0096] The specific implementation process of the energy storage regulation decision-making method for multi-energy storage systems is as follows: Step 1: In the algorithm initialization phase, set the energy of the source node and the target node to... The energy of other nodes is set according to the initial threshold of the previous day. Then, the key energy storage characteristics are input to determine whether there are any unexhaustive node schemes. If there are, continue to Step 2; otherwise, proceed to Step 6.
[0097] Step 2: In the stage of updating the energy of the energy storage node and the energy storage system capacity of the multi-energy storage device set, the number of energy to be regulated at the source node is set to the amount of electricity to be regulated in this power grid, and the regulation time is set to 1 hour.
[0098] Step 3: Traverse all energy storage nodes and select energy storage devices to participate in grid regulation step by step according to the multi-level early warning rules. If an energy storage node has multiple selectable next-hop nodes, filter them step by step according to the amount of remaining energy.
[0099] Step 4: Based on the matching results obtained in Step 3, calculate and update the control quantity to be matched, and record the updated control scheme.
[0100] Step 5: Determine whether the matched control quantity meets the control requirements. If not, repeat Step 3; if it does, use it as an alternative control scheme and execute Step 1.
[0101] Step 6: Based on the alternative control schemes output in Steps 1-5, calculate the control cost of each alternative control scheme, sort them from low to high control cost, and output the energy storage control decision scheme with the lowest control cost.
[0102] Next, three regional power grids were selected to test the model and compare the control costs of energy storage systems participating in power grid regulation. The specific cost values can be found in Table 3 below.
[0103] Table 3. Regulation Costs of Energy Storage Systems Participating in Grid Regulation
[0104] The model operation results of the regional power grid show that the energy storage regulation decision-making method provided in this embodiment will reduce the regulation cost by about 6.5%, indicating that the method can effectively reduce the overall regulation cost of the power grid after it is implemented.
[0105] In summary, the solution provided by this invention, based on the needs of grid regulation and the characteristics of energy storage systems, aggregates energy storage systems and combines algorithms such as DMF to form a grid regulation scheme that satisfies the intraday energy balance of multi-level energy storage systems. This enhances the sustainability of energy storage systems' participation in grid regulation and provides reliable guarantees for the operation, maintenance, and safety protection of energy storage devices, further reconciling the contradiction between the operation and maintenance of energy storage devices and grid regulation response. Furthermore, this method has minimal impact on the operation of the energy storage devices themselves, has low repetition with existing methods for energy storage device output prediction and planning, and energy storage device investment and construction, and can operate in parallel with other methods. Its deployment and operation have minimal impact on the existing system and are low-cost, better meeting the low-cost requirements of energy storage regulation.
[0106] Based on the same general inventive concept, this invention also protects an energy storage regulation and decision-making system based on dynamic energy balance. The energy storage regulation and decision-making system based on dynamic energy balance provided by this invention will be described below. The energy storage regulation and decision-making system based on dynamic energy balance described below can be referred to in correspondence with the energy storage regulation and decision-making method based on dynamic energy balance described above.
[0107] like Figure 2 As shown, the energy storage regulation and decision-making system based on dynamic energy balance provided in this embodiment of the invention specifically includes: Feature module 210 is used to construct a feature set of energy storage devices and to perform numerical transformation on the state features in the feature set of energy storage devices to obtain key energy storage features.
[0108] The quantization module 220 is used to construct the equivalent state of charge model, intraday time-of-day capacity calculation model, and system power model of the energy storage system, and to set the balance conditions of the energy storage system. Based on the balance conditions of the energy storage system, the key energy storage characteristics are quantitatively solved through the equivalent state of charge model, intraday time-of-day capacity calculation model, and system power model to obtain the quantitative data of the energy storage nodes.
[0109] The grading module 230 is used to construct energy balance grading rules and multi-energy storage system control models. Based on quantitative data and energy balance grading rules, alternative control schemes are determined through the multi-energy storage system control model.
[0110] The decision module 240 is used to set control constraints and construct a control cost model for the energy storage system to participate in the power grid. Based on the control constraints and control cost model, it screens alternative control schemes to obtain an energy storage control decision scheme.
[0111] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments of the relevant methods, and will not be elaborated further here.
[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. An energy dynamic balance-based energy storage regulation decision method, characterized in that, The method comprises the following steps: a characteristic set of energy storage devices is constructed, and state characteristics in the characteristic set of energy storage devices are numerically converted to obtain key energy storage characteristics; an equivalent state of charge model of the energy storage system, an intra-day time interval capacity calculation model, and a system power model are constructed, and an energy storage system balance condition is set, and the key energy storage characteristics are quantitatively solved by the equivalent state of charge model, the intra-day time interval capacity calculation model, and the system power model according to the energy storage system balance condition to obtain quantitative data of the energy storage node; an energy balance hierarchical rule and a multi-energy storage system regulation model are constructed, and the quantitative data and the energy balance hierarchical rule are used to determine an alternative regulation scheme by the multi-energy storage system regulation model; a regulation constraint condition is set, a regulation cost model of the energy storage system participating in grid regulation is constructed, and the alternative regulation scheme is screened according to the regulation constraint condition and the regulation cost model to obtain an energy storage regulation decision scheme.
2. The energy dynamic balance based energy storage regulation decision method according to claim 1, characterized in that, The method comprises the following steps: state characteristics and numerical characteristics of energy storage devices are determined according to factors influencing user-side energy storage participating in grid regulation; the state characteristics and the numerical characteristics are taken as a characteristic set of energy storage devices; different types of states in the state characteristics are valued to obtain converted characteristics; the numerical characteristics and the converted characteristics are taken as key energy storage characteristics.
3. The energy dynamic balance based energy storage regulation decision method according to claim 1, characterized in that, The method comprises the following steps: the capacity of each energy storage device in the energy storage system and the state of charge of each energy storage device at any time are determined respectively; for each energy storage device at any time, the capacity and the state of charge are multiplied to obtain actual energy storage capacity; the actual energy storage capacities of all energy storage devices participating in regulation are added to obtain an energy storage capacity summation sub-model; the capacities of all energy storage devices participating in regulation are added to obtain a capacity summation sub-model; the equivalent state of charge model of the energy storage system is obtained according to the energy storage capacity summation sub-model and the capacity summation sub-model.
4. The energy dynamic balance based energy storage regulation decision method according to claim 1, characterized in that, The method comprises the following steps: in each regulation period, the initial equivalent state of charge of the energy storage system at the start time of the regulation period is set to be equal to the final equivalent state of charge at the end time.
5. The energy dynamic balance based energy storage regulation decision method according to claim 1, characterized in that, The method comprises the following steps: a first capacity calculation sub-model is established by setting a first expression corresponding to the energy capacity of the energy storage system discharging from a certain time and a first charge and discharge capacity constraint condition; a second capacity calculation sub-model is established by setting a second expression corresponding to the energy capacity of the energy storage system charging from a certain time and a second charge and discharge capacity constraint condition; a third capacity calculation sub-model is established by setting a third expression corresponding to the minimum capacity of the energy storage participating in grid regulation; the first capacity calculation sub-model, the second capacity calculation sub-model, and the third capacity calculation sub-model are taken as the intra-day time interval capacity calculation model.
6. The energy dynamic balance based energy storage regulation decision method according to claim 1, wherein, The method comprises the following steps: determine a high-frequency component of the energy storage system after normalization processing, a key device capacity of a last regulation period, and a base point power of the energy storage device; establish a mapping relationship between system power of the energy storage system and the high-frequency component after normalization processing, the key device capacity of the last regulation period, and the base point power of the energy storage device, to obtain a system power model.
7. The energy dynamic balance based energy storage regulation decision method according to claim 1, characterized in that, construct an energy balance hierarchical rule, including: set state of charge constraint conditions corresponding to over-discharge and over-charge warning regions respectively, to obtain over-charge and over-discharge warning conditions; determine a state of charge deviation value of a daily cycle settlement node of the energy storage system according to historical state of charge data of the daily cycle settlement node in a set period; set a multi-level warning rule according to the over-charge and over-discharge warning conditions and a degree to which the state of charge deviation value exceeds a preset threshold, to obtain the energy balance hierarchical rule.
8. The energy dynamic balance based energy storage regulation decision method according to claim 1, wherein, set regulation constraint conditions, including: establish a power balance constraint condition according to balance between overall power of grid regulation and overall power of the energy storage system in a candidate regulation scheme and overall power of other adjustable resources in the grid except the energy storage system; establish a system energy constraint condition according to the fact that regulation response capability of the energy storage system is jointly affected by a current state of charge and charging and discharging behavior; establish a charging and discharging protection constraint condition according to the fact that charging and discharging of the energy storage system is limited by over-charge and over-discharge protection; use the power balance constraint condition, the system energy constraint condition, and the charging and discharging protection constraint condition as the regulation constraint conditions.
9. The energy dynamic balance based energy storage regulation decision method according to claim 1, wherein, screen the candidate regulation scheme according to the regulation constraint conditions and the regulation cost model, to obtain an energy storage regulation decision scheme, including: calculate a regulation cost corresponding to each candidate regulation scheme according to the regulation constraint conditions and the regulation cost model; use a candidate regulation scheme with the minimum regulation cost as the energy storage regulation decision scheme.
10. An energy dynamic balance-based energy storage regulation decision system, characterized in that, including: a feature module for constructing an energy storage device feature set and performing numerical conversion on state features in the energy storage device feature set to obtain key energy storage features; a quantization module for constructing an equivalent state of charge model, an intra-day time period capacity measurement model, and a system power model of the energy storage system, setting an energy storage system balance condition, and performing quantization solving on the key energy storage features through the equivalent state of charge model, the intra-day time period capacity measurement model, and the system power model to obtain quantization data of an energy storage node; a hierarchical module for constructing an energy balance hierarchical rule and a multi-energy storage system regulation model, determining a candidate regulation scheme through the multi-energy storage system regulation model according to the quantization data and the energy balance hierarchical rule; a decision module for setting regulation constraint conditions, constructing a regulation cost model of the energy storage system participating in grid regulation, screening the candidate regulation scheme according to the regulation constraint conditions and the regulation cost model, and obtaining an energy storage regulation decision scheme.