Peak shaving model with participation of multiple types of energy storage in low-voltage transformer area

Abstract

The invention discloses a peak regulation model with participation of multi-type energy storage in a low-voltage transformer area. According to the model, the operation characteristics and charging and discharging constraints of a pumped storage power station and electric vehicle aggregation energy storage system and various energy storage resources in a low-voltage transformer area are comprehensively considered, an optimization model of multi-type energy storage collaborative peak regulation is established, and by coordinating the charging and discharging behaviors of various types of energy storage devices and the relation between the energy storage devices and renewable energy sources and power loads, the optimal peak regulation of the energy storage devices is achieved. And the flexible adjustment of the transformer area power grid and the improvement of the new energy consumption capability are realized. The method can enhance the peak regulation capability of the power system, improve the utilization rate of renewable energy sources, guarantee safe and economical operation of a power grid, and promote green low-carbon energy transformation.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of power system operation and control, more particularly to a multi-type energy storage participating peak regulation model for low-voltage transformer area. BACKGROUND

[0002] With the continuous growth of renewable energy installed capacity and the large-scale access of distributed energy, the operation environment of power system has changed. Wind and solar energy have become the focus of promoting energy structure transformation due to their clean and low-carbon properties. Affected by natural conditions, wind and solar power generation has obvious intermittency and volatility characteristics, strong randomness, and uncontrollable output, etc. Higher requirements are put forward for the stable and safe operation of power grid and peak regulation capacity. Compared with traditional thermal power units, the output prediction accuracy of new energy is low, and due to large-scale centralized grid connection, it is easy to cause frequency fluctuation, voltage out-of-limit problem of power system, and even threaten the safety of power grid. At the same time, with the continuous rise of urban and rural power load, the peak-valley difference of load curve is expanding, and the daily peak regulation pressure is increasing, and the original peak regulation means gradually cannot meet the demand.

[0003] As a traditional efficient large-scale energy storage device, pumped storage power station has the characteristics of fast response speed, large storage capacity and high conversion efficiency. It has played an important role in power system peak regulation, frequency regulation and emergency backup for a long time. Under the background of continuous growth of new energy, pumped storage power station can store excess power in the form of potential energy by pumping water during periods of power surplus, and release it in time during peak power consumption, effectively smoothing the fluctuations of new energy output and improving the system regulation margin, relying on the ability of its reversible unit to flexibly switch between pumping and power generation conditions.

[0004] Due to the layout and dispatch of pumped storage power station being restricted by geographical environment, water resource conditions and construction cost, it is difficult to expand quickly to meet the demand of rapid growth of distributed new energy. The operation mode, unit start-stop, power ramping and reservoir water level of pumped storage power station have complex dynamic constraints, which need to be scientifically and reasonably dispatched and optimized by efficient energy algorithm.

[0005] Multi-type small-scale distributed energy storage devices (including: electrochemical energy storage, electric vehicle aggregated energy storage system) are increasingly popular in low-voltage distribution areas. These distributed energy storage resources are distributed in each node of the distribution network, have flexible charge and discharge management capabilities, and can autonomously adjust the operating state according to real-time electricity price signals or load changes. In particular, the application of electric vehicle virtual aggregators enables the intelligent and aggregated management of distributed energy storage, providing more refined and diversified peak shaving resources for the power grid. Compared with pumped storage, distributed energy storage has faster response speed, flexible layout, and can penetrate into low-voltage distribution areas, improving the level of source-load interaction. It also effectively supports the local consumption of new energy. However, the operating characteristics of multi-type energy storage systems differ greatly. The capacity, lifespan, power, and energy constraints are different, and distributed energy storage has strong randomness and weak aggregation, posing new challenges for unified dispatching and coordinated control of the power system.

[0006] Under the above background, establishing a low-voltage distribution area peak shaving model with multi-type energy storage collaborative participation becomes an important means to solve the current operational bottlenecks of the power system. On the one hand, the large-capacity long-regulation capability of pumped storage power stations and the fast and flexible response advantage of distributed energy storage are fully utilized, and the charge and discharge strategies of various types of energy storage devices are dynamically optimized according to the actual demand of the power system, effectively improving the level of new energy consumption and reducing the phenomenon of curtailment of wind and solar power.

[0007] At the same time, new flexible resources such as electric vehicle aggregated energy storage systems are introduced, and centralized monitoring and collaborative optimization of distributed energy storage resources are realized through the virtual power plant platform, enhancing the adaptability of the distribution network to load fluctuations and new energy output fluctuations.

[0008] On the other hand, due to the non-homogeneous and highly time-varying characteristics of the distribution and variation of low-voltage distribution area loads, the traditional centralized energy storage peak shaving mode cannot meet the actual demand, and a unified operating model that integrates pumped storage, EVA, and other multi-type energy storage needs to be established in combination with multi-source information and actual operating constraints. SUMMARY

[0009] In view of the problems faced by the existing power system under the background of high proportion of new energy access and continuous growth of electricity load, such as increased system peak shaving pressure, insufficient clean energy consumption capacity, and large differences in operating characteristics of multi-type energy storage resources and high difficulty of collaborative dispatching, a peak shaving model with multi-type energy storage participation in low-voltage distribution areas is proposed. The operating constraints and charge and discharge strategies of various types of energy storage devices are effectively coordinated to improve the flexible regulation capability of the distribution network, and efficient consumption of new energy and safe, economic, and stable operation of the power system are realized.

[0010] To achieve the above purpose, the present application is implemented by using the following technical solutions: the peak shaving model comprises: 1. Pumped storage unit operating condition model: Each unit keeps a single operating mode in a certain period, which is characterized by the following model: where, , are binary decision variables of the kth unit in pumping and generating state, respectively; , are the maximum and minimum power values of unit k in pumping condition; , represent the actual output power of unit k in pumping or generating operation; , are the pumping and generating power of unit k in period t, respectively; , reflects the unified operating mode of the power station in period t; The constraint conditions are as follows: (1) The constraint of the number of units started at the same time in the power station; The total number of units running at the same time must not exceed the maximum number of units allowed to start in the power station K; (2) The water level and change limit of the reservoir; Considering the limited effective volume of the upper and lower reservoirs, different unit operation strategies will cause the water level of the reservoir to change over time, which is constrained and modeled according to the following relationship: where, and represent the upper and lower limits of the water level of the upstream and downstream reservoirs, respectively; and correspond to the maximum and minimum water level values allowed in the operation of the upper and lower reservoirs, respectively; and represent the actual measured water level height of the upper and lower reservoirs at time t; and represent the efficiency parameters of the water and electricity conversion during the pumping and generating processes of the unit, respectively; 2. Operation modeling of battery energy storage power station (1) Battery charging and discharging power and state constraints At any time, each energy storage battery can only choose one of the charging or discharging states, and its charging and discharging power needs to be limited within a predetermined range to ensure the safe operation and efficiency of the equipment, and the related model is as follows: where, and respectively represent the binary decision variable of the i-th energy storage unit in charging or discharging state at time period t; and respectively represent the maximum and minimum charging power allowed for energy storage battery i in charging mode; and respectively represent the maximum and minimum discharging power that energy storage battery i can reach in discharging mode; and respectively represent the actual charging and discharging power of energy storage battery i at time period t; 3. State of Charge (SOC) evolution model of battery The state of charge of battery will change over time, and its dynamic evolution can be described by the following formula: In the formula: represents the state of charge of the i-th battery at time period t; ζ is the daily self-discharge rate of the battery; ζ is the daily self-discharge rate of the battery; , respectively represent the charging and discharging efficiency parameters; is the rated capacity of the i-th battery; 4. Electric Vehicle (EV) operation model (1) Modeling of flexible regulation capacity of single EV When electric vehicles are connected to the power grid, their charging and discharging behavior exhibits flexibility in interacting with the power system. Due to the energy bi-directional flow function (i.e. V2G) of vehicles, the charging and discharging flexibility of a single EV can be characterized by four key indicators: , , Among them: , are the time points when the vehicle arrives and leaves the charging station; , represent the state of charge (SOC) of the vehicle when it arrives and leaves, which are obtained in real time through the charging station reservation platform; (2) Modeling of adjustable capacity of large-scale EVs Given the large number of electric vehicles, centralized single vehicle management will greatly increase the computational burden, resulting in decreased scheduling efficiency. Therefore, an electric vehicle aggregator (EVA) is introduced as the unified coordination subject. EVA collects and summarizes the charging and discharging characteristics of all subordinate electric vehicles, and uses the energy-power boundary method to superimpose the upper and lower power boundaries of each single vehicle, thereby directly obtaining the upper and lower adjustable capacities of the entire aggregation at any time period. The modeling formula at the group level is as follows: , , In the formula: The number of vehicles managed by the nth EVA; and These represent the upper and lower limits of energy, respectively. and These represent the upper and lower boundaries of the polymerization power, respectively.

[0011] Furthermore, the constraints of the peak-shaving model are as follows: (1) Output limit of thermal power unit During the basic peak-shaving period of thermal power units, optimization is usually based primarily on the direct cost of power generation. When the unit enters deep peak-shaving mode and the output is significantly reduced, additional economic compensation measures are needed due to low-load operation control and auxiliary fuel consumption. In actual operation, we use the Big M method to linearize the output constraints. , , In the formula, m is the thermal power unit number, and t is the dispatching period index. For thermal power units During the period The actual contribution of the people. This represents the minimum active power output of thermal power unit R under normal operating conditions. For thermal power units Maximum active power output under normal operating conditions For thermal power units In The lower bound of active power output corresponding to the operating state, i.e. Output boundary of peak shaving or reserve area; For thermal power units In The active power output level or output boundary corresponding to the operating status is used to describe a deeper level of peak shaving or standby operation zone; For thermal power units During the period Binary variables that are currently powered on and running. For thermal power units During the period Binary variables that are in the normal operating range For thermal power units During the period In Operating status, i.e., binary variables of the first peak-shaving or standby area; For thermal power units During the period Binary variable representing the operation state, i.e. the second or deeper peak regulation region; M is a preset constant to realize the linearization of the constraint condition; (2) Piecewise constraint of the cost of thermal power units for peak regulation; During deep peak regulation, the operation cost of thermal power units presents a piecewise increasing characteristic, so the corresponding piecewise constraint condition needs to be introduced in the model; by dividing the operation interval of the unit into multiple cost segments, the cost change at different output levels can be more accurately reflected, and the economy and feasibility of peak regulation scheduling can be ensured: , In the formula, is the linearized variable of the operation cost of the thermal power unit in the time period , which is used to depict the cost level of the unit in the time period in the optimization model; is the cost function value corresponding to the output of the unit when it is in the normal operation interval; is the cost function value corresponding to the output of the unit when it is in the operation state ; is the cost function value corresponding to the output of the unit when it is in the operation state ; (3) Constraints of unit start-up and shut-down, minimum operation time and ramp rate , , , wherein, is a positive taking operator, which is used to identify the start-up or shut-down time according to the state change of the unit; is the rated maximum active output of the thermal power unit , is a conversion coefficient related to the regulation capacity of the thermal power unit, which is used to convert the maximum output of the unit into available regulation capacity; is the length of time that the thermal power unit has been continuously started and operated in the time period , is the length of time that the thermal power unit has been continuously shut down in the time period , and are the minimum continuous start-up and minimum continuous shut-down time requirements of the unit, and are the minimum continuous start-up and minimum continuous shut-down time requirements of the unit, are the minimum continuous start-up and minimum continuous shut-down time requirements of the unit,These are the unit's maximum uphill and maximum downhill rates, respectively. (4) Reservoir capacity constraints of hydropower stations: At any given time, the reservoir capacity of the hydropower station must be maintained at the design minimum and maximum allowable values. , )between; This represents the actual reservoir capacity of hydropower station s during time period t; (5) Constraints of water balance The inflow of water into the reservoir and the water consumption for power generation by each generating unit must always be kept in dynamic balance. In the formula: Let represent the natural inflow of the s-th hydropower station at time t. This represents the water flow rate for power generation of the l-th generating unit during the t-th time period. This represents the total number of generating units in the s-th hydropower station; (6) The conversion relationship between water energy The output power of the hydro turbine unit is related to the flow rate of the water used for power generation, and can be characterized by the water energy conversion coefficients (r1,l, r2,l, r3,l): , In the formula, Let be the output power of the i-th generating unit in the S-th hydropower station during time period t. For the first The first of the hydropower stations The flow rate through each unit For the first The first hydropower station in Water head height during the period is the linear coefficient of flow rate with respect to output power. is the linearity coefficient of head with respect to output power. For constant terms; (7) EVA charging and discharging power constraints On the aggregator platform, the charge / discharge state of each EVA at any given time is defined by a binary variable. , In the formula, and Let be the binary variables representing the charging and discharging states of the nth electric vehicle aggregator during time period t. For the first A single electric vehicle aggregator during the period The discharge power, For the first A single electric vehicle aggregator during the period The charging power, For the first A single electric vehicle aggregator during the period The upper limit of the rated charge and discharge power achieved; (8) EVA polymerization energy state constraint The total energy of each EVA is always constrained by its capacity and historical charge / discharge behavior: , In the formula, Let n be the total aggregated electricity volume of the nth electric vehicle aggregator during time period t. and The first A single electric vehicle aggregator during the period The lower and upper limits of the aggregated capacity are used to constrain the minimum / maximum allowable state of charge of the battery pack; For the first An electric vehicle aggregator in the previous period The aggregated power, For the first An electric vehicle aggregator in the previous period The charging power, For the first An electric vehicle aggregator in the previous period The discharge power, Δt is the length of the adjacent scheduling period, usually expressed in hours, used to convert power into electricity; (9) Output restrictions of new energy power plants: The power output of new energy power plants must not exceed the forecast value, and wind and solar curtailment must be clearly included: , In the formula; The power output is allocated to the r-th renewable energy power station during time t. Let r be the predicted power generation capacity of the r-th renewable energy power station. This represents the amount of renewable energy power abandoned by the r-th renewable energy power station at time t. (10) Power balance constraints of energy storage grid system: The system maintains a power balance in each time period: , In the formula, For the first Taiwan thermal power units during the period Those who have made contributions For the first The hydropower station during the period Those who have made contributions For the first Pumped storage power station during the period Power generation capacity For the first Pumped storage power station during the period Pumping power, For the first An electrochemical energy storage device during a certain period The discharge power For the first An electrochemical energy storage device during a certain period charging power, For the first An electric vehicle aggregator in the previous period The discharge power For the first An electric vehicle aggregator in the previous period charging power, For the system in time period Total active power load demand, , , , , and These include the number of thermal power units, hydropower units, photovoltaic power stations, pumped storage power stations, electrochemical energy storage devices, and electric vehicle aggregators.

[0012] Furthermore, the peak-shaving model further includes a pumped storage power station operation model. The pumped storage power station operation model requires that each pumped storage unit can only be in one state of pumping or generating at any given time, and is constrained according to the maximum and minimum power ranges corresponding to the pumping and generating conditions. It also requires that all units maintain a single operating condition during the same period to avoid system pressure fluctuations caused by different operating states.

[0013] Furthermore, the total number of all units operating simultaneously must not exceed the maximum number of units allowed to be started by the power station. In conjunction with the reservoir water level and change restrictions, the reservoir water level must always be between the upper and lower limits to ensure that the state balance of the upper and lower reservoirs meets the actual operating requirements under different unit operating strategies. Moreover, the water flow for unit power generation is affected by both the natural inflow and the number of generator units.

[0014] Furthermore, the charging and discharging states of each EVA at each time period are defined by independent binary variables, and the total energy of the EVA is required to always be within the capacity range. Its historical charging and discharging behavior is fully included in the energy calculation, effectively ensuring that the aggregated energy state of the EVA is constrained by capacity and actual operation.

[0015] Further, the target function of the peak regulation model is to minimize the comprehensive operation cost in the entire scheduling period under the premise of meeting various operation constraints, wherein the comprehensive operation cost at least includes fuel cost and start-stop cost of thermal power units, water quantity opportunity cost of hydropower stations and pumped storage power stations, battery life loss cost of electrochemical energy storage power stations and electric vehicle aggregators, and wind and light curtailment penalty cost and load reduction penalty cost.

[0016] Further, the peak regulation model further sets low-voltage distribution network safety constraints, wherein in any time period t, the apparent power of the distribution transformer does not exceed the rated capacity, the current on each low-voltage feeder does not exceed the allowable current of the corresponding line, and each node voltage is limited within the preset upper and lower voltage limit range, so as to ensure that the centralized charging and discharging of multiple types of energy storage does not cause the overload of the distribution transformer or the voltage out-of-limit.

[0017] Further, the peak regulation model further includes system reserve capacity constraints, wherein in each time period t, the upward adjustment capacity of all thermal power units and the downward adjustment capacity and the upward and downward adjustment margins provided by the pumped storage power stations, electrochemical energy storage power stations and electric vehicle aggregators within their respective power boundary ranges are not less than the system specified upward reserve demand and downward reserve demand, so as to achieve peak regulation while meeting the power grid reserve safety standard.

[0018] Further, the peak regulation model is solved by a mixed integer linear programming method, adopts a rolling optimization mode with a day as a cycle, divides the 24-hour scheduling period into a plurality of equal time intervals Δt, and updates the model parameters and solves again in each scheduling period according to the latest load prediction results and new energy generation prediction results, so as to obtain the optimal output plan of the thermal power units, hydropower stations, pumped storage power stations, electrochemical energy storage power stations and electric vehicle aggregators in each time period.

[0019] Advantages of the present application: 1. Multi-type energy storage resource collaborative modeling, improving low-voltage distribution area peak regulation capability: the present application simultaneously introduces three levels of energy storage resources of pumped storage units, battery energy storage power stations and electric vehicle aggregators in a unified optimization framework, and models them jointly with thermal power units, hydropower stations and new energy power stations, to realize integrated source-load-storage dispatching. Compared with the scheme of only considering a single energy storage type or only regulating peak at the high-voltage side, the present application can significantly improve the peak regulation depth and flexibility at the low-voltage distribution area level, reduce the peak regulation pressure at the high-voltage side, and improve the new energy consumption capacity.

[0020] 2. Fine constraint of pumped storage unit working condition, guaranteeing reservoir safety and operation economy: The application constrains the "pumping / generating" single working condition of pumped storage unit and the number of simultaneously started units in power station, and models in combination with the upper and lower limits of reservoir water level and water balance relationship, which ensures that each unit is only in one definite working condition at any time, and ensures that the water level is always within the allowable range under different operation strategies, thereby preventing reservoir over-limit operation and unreasonable water regulation, and taking into account the safety of power station and the efficiency of water energy utilization.

[0021] 3. Strict modeling of battery energy storage charging and discharging and SOC evolution, prolonging battery life: The application describes the mutual exclusive relationship of battery charging and discharging through binary variables, sets the upper and lower limits of charging and discharging power, and introduces the SOC dynamic evolution equation considering self-discharge rate and charging and discharging efficiency, so that the state of charge of the battery is always constrained by capacity boundary and historical behavior, effectively avoiding overcharging, overdischarging and other working conditions, delaying battery performance degradation, and improving the life and life cycle economy of battery energy storage power station.

[0022] 4. Two-stage model of electric vehicles from single vehicle to aggregation, fully tapping the adjustment potential of EVA: The application first describes flexibility at the level of a single electric vehicle using key parameters such as arrival and departure time and arrival and departure SOC, and then uses the energy-power boundary superposition method to obtain the upper and lower adjustable capacity of the group at the level of electric vehicle aggregator, and establishes evolution and capacity constraints for aggregated energy state, which can accurately quantify and call the adjustable capacity of large-scale electric vehicles without affecting the travel needs of vehicle owners, and improve the proportion of low-voltage distribution area load side participating in peak shaving.

[0023] 5. Multi-working condition segmented cost and start / stop / climbing constraints of thermal power unit, improving economy and feasibility: The application divides the output range of thermal power unit into normal operation, first peak shaving area and second deep peak shaving area, and realizes linearization of segmented constraints using large M method, while introducing start / stop judgment, minimum start / stop time and upper / lower climbing rate and other operation constraints, so that the model can accurately describe the cost change and operation boundary under different output levels, avoid unrealistic deep peak shaving instructions, and ensure that the peak shaving scheme is both economical and executable.

[0024] 6. Introducing distribution network safety and backup constraints to improve power supply reliability and operation safety: The application can further superimpose safety constraints such as low-voltage distribution area main transformer and feeder capacity, voltage out-of-limit, and system upward and downward backup demand constraints in the peak shaving model, to ensure that concentrated charging and discharging of multiple types of energy storage will not cause transformer overload and voltage out-of-limit, while meeting the peak-valley difference regulation while maintaining the necessary backup margin, thereby improving the safety and robustness of distribution network operation.

[0025] 7. The mixed integer linear programming and rolling optimization mechanism is adopted to facilitate engineering deployment and online application: the complex multi-source-multi-storage-multi-period peak regulation problem is uniformly converted into a mixed integer linear programming model, the mature MILP solver is adopted to quickly solve in the scheduling background or edge computing platform, and the latest load prediction and new energy prediction information is introduced through the daily rolling optimization mode to realize online updating and adaptive scheduling of the model, and the method has good engineering implementability and real-time application prospect.

[0026] Through the comprehensive action of the above technical features, the application can realize the coordinated optimization scheduling of multiple types of energy storage at the low-voltage area level, effectively suppress the load fluctuation, improve the new energy consumption level, reduce the frequency and amplitude of deep peak regulation of thermal power, and at the same time, the safety and economy of equipment are taken into account, and the operation quality and comprehensive benefit of the distribution network are improved as a whole. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 The figure is a model diagram of the application; Figure 2 The figure is a diagram in which the energy and power boundaries of a single EV in vehicle-network interaction are respectively constrained by the upper and lower limits of the battery SOC and the maximum charging and discharging power; Figure 3 The figure is an operation platform of an energy storage aggregator of the application. DETAILED DESCRIPTION

[0028] In order to facilitate the understanding of the application, the application will be described more fully below with reference to the related drawings. The drawings show typical embodiments of the application. However, the application can be implemented in many different forms and is not limited to the embodiments described in the application. On the contrary, the purpose of providing these embodiments is to make the disclosure of the application more thorough and comprehensive.

[0029] Unless otherwise defined, all technical and scientific terms used in the application have the same meaning as understood by those skilled in the art to which the application belongs. The terms used in the specification of the application are only for the purpose of describing the specific embodiments of the application and are not intended to limit the application. In order to facilitate the understanding of the application, the application will be described more fully below with reference to the related drawings. The drawings show typical embodiments of the application. However, the application can be implemented in many different forms and is not limited to the embodiments described in the application. On the contrary, the purpose of providing these embodiments is to make the disclosure of the application more thorough and comprehensive.

[0030] As Figure 1As shown, a multi-type energy storage participating peak regulation model of low-voltage transformer area, mainly facing the low-voltage transformer area scene of distribution network, comprehensively considers multiple types of energy storage resources such as thermal power units, hydropower stations, pumped storage power stations, battery energy storage power stations, and electric vehicle storage accessed through electric vehicle aggregators, and realizes economic, safe, and flexible peak regulation of low-voltage transformer area under the premise of meeting the operation constraints of various devices, power grid safety constraints, and user energy demand.

[0031] In this embodiment, the peak regulation model mainly includes the following parts: I. Pumped storage power station operation model: 1. Pumped storage unit operation condition model: At present, most pumped storage power stations use water pump turbine generator sets with reversible function. The unit can switch between pumping and power generation. At any time, each unit is only allowed to be in one of the pumping or power generation states, and different conditions correspond to different power constraint intervals. Considering that pumped storage units are usually connected in series through a shared water delivery system, if there are some units pumping and some units generating at the same time, it will cause pressure fluctuations in the hydraulic system, affecting the safety and efficiency of the equipment. Therefore, this embodiment requires each unit to maintain a single condition within each dispatching period, and its operation mode can be characterized by the unit condition model shown in the following formula: , In the formula: 、 are binary decision variables of the kth unit in the pumping and power generation states, respectively; 、 represent the maximum and minimum power values allowed for unit k under pumping conditions; 、 represent the actual output power of unit k under pumping or power generation operation; 、 are the pumping and power generation powers of unit k in period t, respectively; 、 reflects the unified operation mode of the power station in period t; In the above formula, the binary decision variable of the pumping / power generation state is used to indicate whether the kth unit is in the pumping or power generation state in period t; the upper and lower limits of the pumping power are used to limit the minimum and maximum allowed power of the unit in the pumping mode; the pumped storage power and the power generation power represent the actual output under the corresponding condition; and the power station unified operation mode variable is used to ensure that all units maintain consistent pumping or power generation conditions in the same period, thereby avoiding mixed flow states in the water delivery system.

[0032] 2. Constraint on the number of units started at the same time in the power station: To prevent the water and electrical system from being impacted by too many units being put into operation at once, the number of units in operation at the same time is restricted in this embodiment. Specifically, the number of units in operation at time t is calculated by the following formula, and is limited to not exceed the maximum number of units in operation K allowed by the power station, so as to ensure that the unit start-stop process conforms to the design capacity and scheduling strategy of the power station: .

[0033] 3. Reservoir water level and change limit modeling: Due to the limited effective capacity of the upper and lower reservoirs, pumping and power generation behavior will cause the reservoir water level to change dynamically over time. To ensure the long-term safe operation of the reservoir, the upper and lower limits of the water level of the upper and lower reservoirs are respectively restricted in this embodiment, and the unit output is linked to the water level change through the water level-storage capacity relationship: , , In the formula: and represent the upper and lower limits of the water level of the upper and lower reservoirs, respectively; and correspond to the maximum and minimum water levels allowed by the upper and lower reservoirs when in operation; and represent the actual measured water level of the upper and lower reservoirs at time t; and represent the efficiency parameters of the unit in the process of pumping and power generation when water energy and electrical energy are converted into each other; Specifically, the above formula gives the water level constraints of the upper and lower reservoirs at time t, requiring the actual water level to always be between the maximum and minimum allowed water levels; at the same time, the pumping efficiency and power generation efficiency parameters are used to convert the unit power and water quantity, so as to reflect the water level evolution process corresponding to different scheduling strategies in the time dimension.

[0034] II. Operation modeling of battery energy storage power station 1. Battery charging and discharging power and state constraints.

[0035] At any scheduling time, each energy storage battery unit can only be in one of the charging or discharging states, in order to avoid unreasonable working conditions such as "self-charging and self-discharging", this embodiment sets mutually exclusive binary state variables, and imposes upper and lower limit constraints on the charging and discharging power of each binary state variable: , In the formula: and represent the binary decision variables of the i-th energy storage unit in the charging or discharging state at time t; and respectively represent the maximum and minimum output power that the energy storage battery i can reach when discharging; and respectively represent the maximum and minimum output power that the energy storage battery i can reach when discharging; and respectively represent the actual charging and discharging power of the energy storage battery i in time period t; The above formula gives the charging and discharging power constraints of the battery in time period t: when the charging state variable is 1, the charging power is limited between the given minimum and maximum charging power; when the discharging state variable is 1, the discharging power is limited within the corresponding discharging power range; at the same time, through a mutual exclusion constraint, it is ensured that the charging state and the discharging state cannot take 1 at the same time in the same time period, so as to ensure the rationality and safety of the battery operating condition.

[0036] 2. Evolution model of the state of charge (SOC) of the battery: The state of charge (SOC) of the battery will change with time due to charging and discharging behavior and self-discharge effect, and the following formula is used in the embodiment to model the dynamic evolution of the SOC of the battery: In the formula: denotes the state of charge of the i-th energy storage battery in time period t; ζ is the daily self-discharge rate of the battery; ζ is the daily self-discharge rate of the battery; 、 respectively represent the charging and discharging efficiency parameters; is the rated capacity of the i-th battery; In the above formula, the SOC of the i-th energy storage battery in time period t is determined by the SOC of the previous time period, the current charging and discharging power, and the self-discharge rate of the battery; the charging and discharging efficiency coefficients reflect the energy loss in the charging and discharging process; the rated capacity is used to convert the power-time product into the change amount of SOC, and by setting the upper and lower limit constraints of SOC, overcharging and overdischarging of the battery can be effectively avoided, and the service life of the battery can be prolonged.

[0037] III. Electric vehicle (EV) operation model: 1. Modeling of flexible energy regulation capability of a single EV: When an electric vehicle is connected to the power grid, its charging and discharging behavior exhibits flexibility in interaction with the power system. Due to the bidirectional energy flow function (i.e. V2G) of the vehicle, the charging and discharging flexibility of a single EV can be characterized by four key indicators ( 、 、 ), wherein: 、 are the time points at which the vehicle arrives at and leaves the charging station; , SOC represents the battery state of charge when the vehicle arrives and leaves, which can be obtained by the charging station reservation platform in real time or from historical operation data.

[0038] Based on the above key data, the energy-power boundary method can be used to describe the interaction range between a single EV and the grid, thereby establishing the upper and lower limits of the achievable charging and discharging power and energy of the jth vehicle from arrival to departure . The cumulative charging and discharging amount and the instantaneous charging and discharging power of the jth EV in the time period must be located within the feasible region defined by the upper limit ) and the lower limit ). This region fully considers the maximum and minimum constraints of the battery SOC, Figure 2 and represent the upper and lower limits of the SOC (state of charge) allowed by the power battery, respectively; and correspond to the maximum power values allowed by the electric vehicle during charging and discharging, respectively.

[0039] 2. Modeling of the adjustable capacity of large-scale EVs: When the number of electric vehicles connected to the grid is huge, if each vehicle is modeled and optimized separately, the scale of the dispatching problem will expand dramatically, making it difficult to solve within the engineering real-time requirements. Therefore, this embodiment introduces an electric vehicle aggregator (EVA) as the coordination subject; The EVA aggregates the single-vehicle energy-power boundaries of all electric vehicles within its management scope, and obtains the upper and lower boundaries of the adjustable power of the aggregate in any time period by superimposing the upper and lower power boundaries of each vehicle. At the same time, the upper and lower limits of the energy that can be adjusted by each vehicle are counted to obtain the aggregated energy boundary of the aggregate. The corresponding mathematical expression is shown in the following formula, where the number of vehicles managed by the EVA and the upper and lower limits of energy and power comprehensively depict the aggregated adjustment capability of the EVA in each time period: , , In the formula: is the number of vehicles managed by the nth EVA; and are the upper and lower limits of energy, respectively; and are the upper and lower boundaries of the aggregated power, respectively.

[0040] Four, constraint conditions and system operation model: 1. Output limit of thermal power unit: ​​In this embodiment, the thermal power unit mainly undertakes basic peak-shaving tasks. Within the normal operating range, the optimization objective is primarily fuel cost; when the unit enters deep peak-shaving mode and its output falls below a certain threshold, additional compensation is required due to the additional losses and auxiliary fuel consumption introduced by low-load operation. This embodiment uses the Big M method to linearize the unit's multi-condition output constraints; the relevant constraints are shown in the equation: , , In the formula, m is the thermal power unit number, and t is the dispatching period index. For thermal power units During the period The actual contribution of the people. This represents the minimum active power output of thermal power unit R under normal operating conditions. For thermal power units Maximum active power output under normal operating conditions For thermal power units In The lower bound of active power output corresponding to the operating state, i.e. Output boundary of peak shaving or reserve area; For thermal power units In The active power output level or output boundary corresponding to the operating status is used to describe a deeper level of peak shaving or standby operation zone; For thermal power units During the period Binary variables that are currently powered on and running. For thermal power units During the period Binary variables that are in the normal operating range For thermal power units During the period In Operating status, i.e., binary variables of the first peak-shaving or standby area; For thermal power units During the period The binary variable is in the running state, i.e., the second or deeper peak-shaving region; M is a preset constant to achieve linearization of the constraint conditions; By introducing binary variables representing the start-up state, normal operation state, first peak shaving zone state, and second (deep) peak shaving zone state, and combining them with the upper and lower limits of normal output, the output boundary of the peak shaving zone, and the preset large M constant, the mutual exclusion and switching constraints between different output ranges of the unit are realized, ensuring that the model can be solved linearly in mathematics and conforms to the operating characteristics of the unit in physical terms.

[0041] 2. Segmented constraints on peak-shaving costs of thermal power units: For different cost levels of thermal power units in normal operation area, first peak regulation area and second peak regulation area, the embodiment constructs a segmented linear cost function as shown in the following formula. The linearized cost variable of the unit at time period t is associated with the cost function value of different intervals by the big M method, so as to ensure that when the actual output of the unit falls in a segmented interval, the cost variable is equal to the cost function value of the interval, thereby accurately reflecting the cost increasing characteristics in deep peak regulation process: , In the formula, is the linearized variable of the operation cost of the thermal power unit at time period , which is used to depict the cost level of the unit at the time period in the optimization model; is the cost function value corresponding to the output of when the unit is in the normal operation interval; is the cost function value corresponding to the output of when the unit is in the operation state ; is the cost function value corresponding to the output of when the unit is in the operation state ; is the cost function value corresponding to the output of when the unit is in the operation state .

[0042] 3. Unit start-stop, minimum operation time and climbing speed constraints: In order to avoid damage to the unit equipment caused by frequent start-stop, the embodiment sets the minimum continuous start time and minimum continuous stop time constraints of the thermal power unit; at the same time, in order to ensure the smooth change of unit load, the upper and lower climbing rate constraints are introduced, and the related constraints are shown in the formula: , , , Among them, is a positive operator, which is used to identify the start time or stop time according to the state change of the unit; is the rated maximum active power of the thermal power unit , is a conversion coefficient related to the regulation capacity of the thermal power unit, which is used to convert the maximum output of the unit into available regulation capacity; is the time length of the continuous start operation of the thermal power unit at time period , is the time length of the continuous stop of the thermal power unit at time period , and respectively the minimum continuous on-time and minimum continuous off-time requirements of the units, and respectively the maximum up-ramp rate and maximum down-ramp rate of the units; In the above constraints, the positive operator is used to identify the time instants at which the units are switched from off to on, or from on to off, in order to update the continuous on-off time counts; the rated maximum output and the conversion factor jointly determine the up and down regulation capacity of the units; the up-ramp rate and down-ramp rate limits the output difference between adjacent time periods, so as to ensure that the load change of the units does not exceed the allowed range.

[0043] 4. Reservoir capacity and water balance constraints: In order to ensure the long-term safe and stable operation of the hydropower station, the reservoir capacity (or water level) is constrained by the following formula in this embodiment, which requires that at any time, the reservoir capacity of the hydropower station is maintained between the designed minimum and maximum allowed values: , In the formula: Qs,t represents the natural inflow of the st hydropower station at the tth time, Ql,t represents the water flow for power generation of the lth unit in the tth time period, Ns represents the total number of generating units of the st hydropower station.

[0044] Further, the following formula gives the water balance constraint, which requires that in each time period, the reservoir inflow and the sum of the water flow for power generation of each unit are in dynamic balance, in which the natural inflow can be obtained by hydrological prediction or measurement, and the water flow for power generation of the unit is calculated from its output and water head parameters. Through the water balance constraint, the water conservation of the reservoir under different dispatching schemes can be ensured, and excessive water use or water abandonment in the long term can be avoided: , In the formula: Qs,t represents the natural inflow of the st hydropower station at the tth time, Ql,t represents the water flow for power generation of the lth unit in the tth time period, Ns represents the total number of generating units of the st hydropower station.

[0045] 5. Relationship between water energy and electric energy: In a hydropower station, the output power of the water turbine unit is closely related to the flow rate through the machine and the water head height. In order to reflect the conversion characteristics between water energy and electric energy, this embodiment uses a linear approximation model, as shown in the following formula, which represents the output power of the unit as a linear combination of the flow rate through the machine and the water head, where each linear coefficient is obtained by fitting the unit efficiency curve; this relationship is easy to couple with the overall peak shaving model, and can improve the solving efficiency on the premise of ensuring accuracy: , In the formula, Let be the output power of the i-th generating unit in the S-th hydropower station during time period t. For the first The first of the hydropower stations The flow rate through each unit For the first The first hydropower station in Water head height during the period is the linear coefficient of flow rate with respect to output power. is the linearity coefficient of head with respect to output power. This is a constant term.

[0046] 6. EVA charging and discharging power and polymerization energy constraints: Under the aggregation platform, the charging and discharging states of each EVA at any given time period t are limited by binary variables, and the actual charging and discharging power is guaranteed not to exceed the aggregated available capacity by the upper limit constraint formula (17). To prevent abnormal behavior of charging and discharging coexisting in the same time period, mutual exclusion constraints are further set so that the sum of the charging state variables and the discharging state variables does not exceed 1. , In the formula, and Let be the binary variables representing the charging and discharging states of the nth electric vehicle aggregator during time period t. For the first A single electric vehicle aggregator during the period The discharge power, For the first A single electric vehicle aggregator during the period The charging power, For the first A single electric vehicle aggregator during the period The upper limit of the rated charge and discharge power achieved; Furthermore, the state of mass accumulation (SOC) of EVA must simultaneously satisfy both capacity constraints and energy conservation constraints. The following formula gives the evolution relationship of the SOC: the current SOC is accumulated by multiplying the SOC of the previous SOC by the SOC of the previous SOC and the charge / discharge power of the previous SOC, and is limited to the upper and lower limits of the allowable energy, thereby ensuring that the overall SOC of the aggregate is reasonable. , In the formula, Let n be the total aggregated electricity volume of the nth electric vehicle aggregator during time period t. and The first A single electric vehicle aggregator during the period The lower and upper limits of the aggregated capacity are used to constrain the minimum / maximum allowable state of charge of the battery pack; For the first An electric vehicle aggregator in the previous period The aggregated power, For the first An electric vehicle aggregator in the previous period The charging power, For the first An electric vehicle aggregator in the previous period The discharge power is Δt, which is the length of the adjacent scheduling period, usually expressed in hours, and is used to convert power into electricity.

[0047] 7. Output restrictions for new energy power plants: For renewable energy power plants such as wind and solar power, this embodiment requires that their dispatched output does not exceed the corresponding predicted power, and the excess is included in the curtailment of wind and solar power. The following formula gives the output limit constraints of renewable energy power plants in each time period. By explicitly introducing the curtailment variable, the curtailment behavior can be penalized in the objective function to promote the priority consumption of renewable energy: , In the formula; The power output is allocated to the r-th renewable energy power station during time t. Let r be the predicted power generation capacity of the r-th renewable energy power station. This represents the amount of renewable energy power abandoned by the r-th renewable energy power station at time t.

[0048] 8. Energy storage – power balance constraints of the power grid system: At the system level, this embodiment establishes the power balance relationship through the following formula: In each scheduling period, the sum of the output of thermal power units, hydropower, pumped storage power generation, net output of electrochemical energy storage and electric vehicle aggregation, and scheduling output of new energy power plants should equal the total load demand for that period; where the quantities of various power sources, energy storage and loads are represented by corresponding superscripts; this power balance equation is one of the core constraints of the entire peak-shaving model, ensuring that the system is in a state of supply and demand balance in each period: , In the formula, For the first Taiwan thermal power units during the period Those who have made contributions For the first The hydropower station during the period Those who have made contributions For the first Pumped storage power station during the period Power generation capacity For the first Pumped storage power station during the period Pumping power, For the first An electrochemical energy storage device during a certain period The discharge power For the first An electrochemical energy storage device during a certain period charging power, For the first An electric vehicle aggregator in the previous period The discharge power For the first An electric vehicle aggregator in the previous period charging power, For the system in time period Total active power load demand, , , , , and These include the number of thermal power units, hydropower units, photovoltaic power stations, pumped storage power stations, electrochemical energy storage devices, and electric vehicle aggregators.

[0049] V. For example Figure 3 As shown, based on the above peak-shaving model, this embodiment further proposes an energy storage aggregation operation platform based on adaptive equilibrium regulation. The operation platform adopts a layered architecture, consisting of a regulation and control layer, an aggregation and coordination layer, and a bottom response layer from top to bottom. The functions of each layer are as follows.

[0050] 1. Regulation and control layer: The regulation and control layer is connected to the power grid dispatch center or distribution master station. Based on the daily / day-ahead dispatch plan issued by the superior, real-time load forecast, and new energy power generation forecast results, it constructs the above-mentioned peak-shaving optimization model involving multiple types of energy storage. It solves the model using methods such as mixed integer linear programming to obtain the target output plans of thermal power units, hydropower stations, pumped storage power stations, battery energy storage power stations, and each EVA in each time period.

[0051] Meanwhile, the regulation and control layer has a built-in adaptive equalization regulation algorithm that dynamically corrects the charging and discharging commands within the group based on the remaining available capacity, lifespan degradation, geographical location, and constraint margin of each energy storage unit. This achieves load and lifespan balance among different energy storage units, preventing some units from operating at high intensity for a long time and retiring prematurely.

[0052] 2. Aggregation Coordination Layer: The aggregation coordination layer is mainly directed to pumped storage power stations, battery energy storage power stations, and multiple EVA and other aggregation resources. According to the power and energy instructions issued by the regulation and control layer, in combination with the real-time state (SOC, power margin, accessible duration, etc.) of each aggregation internal unit, the aggregation and grouping algorithm is used to perform zoning management on the internal distributed energy storage, and the global scheduling target is decomposed into local instructions executable by each unit.

[0053] In the embodiment, the aggregation coordination layer groups the energy storage units with similar operating attributes (such as capacity size, life stage, and feeder location) to achieve balanced regulation within the group and differentiated scheduling between groups, thereby meeting the system peak shaving demand while improving the overall utilization efficiency of the energy storage resources.

[0054] 3. Bottom response layer: The bottom response layer corresponds to specific physical energy storage units and field control devices, including local controllers of pumped storage units, battery energy storage converter control units, and electric vehicle terminals distributed in different charging stations. The layer receives the active / reactive power instructions and SOC reference values issued by the aggregation coordination layer, performs fast response control on local devices according to various operating constraints established in the embodiment, and reports real-time state information such as voltage, current, SOC, water level, and output.

[0055] Through the cooperative operation of the above three layers, the energy storage aggregation operation platform described in the embodiment can fully utilize the cooperative advantages of wide-area distributed energy storage and centralized large-capacity energy storage, and realize fine peak shaving, peak cutting and valley filling, and new energy-friendly grid connection at the low-voltage transformer area level.

[0056] Six, implementation and optional solutions: Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by a computer program to instruct relevant hardware to complete. The program can be stored in a computer-readable storage medium, and when executed by a processor, it realizes the functions of the peak shaving model solving, energy storage aggregation scheduling, and adaptive balancing control. The storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, or other media capable of storing program codes.

[0057] It should be noted that the above-mentioned embodiment is only one of the preferred embodiments of the present application. Those skilled in the art can replace or modify various parameters, variable names, time scales, and solving algorithms without departing from the spirit and essence of the present application, for example, using different time steps, different forms of cost functions, or introducing more types of distributed power sources, all of which are within the protection scope of the present application.

Claims

1. A multi-type energy storage participating peak regulation model for low-voltage transformer area, characterized by: The peak shaving model comprises: I. Pumped storage unit operation condition model: Each unit maintains a single condition within a certain period, and the operation mode is characterized by the following model: ， wherein: and are binary decision variables for the kth unit in pumping and generation states, respectively; and represent the maximum and minimum power values allowed for unit k to operate in pumping condition, respectively; and represent the actual output power of unit k in pumping or generation operation, respectively; and are the pumping and generation power of unit k in period t, respectively; and reflect the unified operation mode of the power station in period t; The constraint conditions are as follows: (1) The number of units started at the same time in the power station is constrained; ， The total number of units running at the same time must not exceed the maximum number of units allowed to start in the power station K; (2) Water level and change limit; Considering that the effective volume of the upper and lower reservoirs is limited, different unit operation strategies will cause the water level of the reservoir to change over time, and the following relationship is used for constraint modeling: ， ， wherein: and respectively represent the upper and lower limits of the water level of the upstream and downstream reservoirs; and respectively correspond to the maximum and minimum water level values allowed for the upstream and downstream reservoirs when in operation; and represent the actual measured water level height of the upstream and downstream reservoirs at time t; and respectively represent the efficiency parameters of the unit during the pumping and power generation processes when water energy and electrical energy are converted into each other; II. Operation modeling of battery energy storage power station: (1) Battery charging and discharging power and state constraints: At any time, each energy storage battery can only choose one of the charging or discharging states, and the charging and discharging power needs to be limited within a predetermined range to ensure the safe operation and efficiency of the equipment. The relevant model is as follows: ， wherein: and respectively represent the binary decision variable of the i-th energy storage unit when it is in charging or discharging state during time period t; and respectively represent the maximum and minimum charging power allowed for energy storage battery i in charging mode; and respectively represent the maximum and minimum discharging power that energy storage battery i can reach in discharging mode; and respectively represent the actual charging and discharging power of energy storage battery i during time period t; III. Evolution model of battery state of charge SOC; The state of charge of the battery will change over time, and its dynamic evolution is described by the following formula: wherein: S i(t) represents the state of charge of the i-th energy storage battery at time period t; ζ is the daily self-discharge rate of the battery; and and C i is the rated capacity of the i-th battery; IV. Electric vehicle operation model: (1) Modeling of the flexible adjustment capacity of a single electric vehicle: When the electric vehicle (EV) is connected to the power grid, its charging and discharging behavior exhibits flexibility in interacting with the power system. Due to the bidirectional energy flow function of the vehicle, the charging and discharging flexibility of a single EV is characterized by four key indicators: , , and , wherein: and are the time points when the vehicle arrives at and leaves the charging station, respectively; and represent the state of charge of the vehicle when it arrives and leaves, respectively, and the above parameters are obtained in real time through the charging station reservation platform. (2) Modeling of the adjustable capacity of a large number of electric vehicles: An electric vehicle aggregator is introduced as the unified coordination subject; the electric vehicle aggregator collects and summarizes the charging and discharging characteristics of all subordinate electric vehicles, uses the energy-power boundary method to superimpose the upper and lower power boundaries of each vehicle, and then directly obtains the upper and lower adjustable capacities of the entire aggregation within any period; the group-level modeling formula is as follows: ， ， where: is the number of vehicles managed by the nth electric vehicle aggregator; and are the upper and lower limits of energy, respectively; and are the upper and lower boundaries of aggregated power, respectively. 2.The multi-type energy storage participating peak regulation model of a low-voltage transformer area according to claim 1, characterized in that: The peak shaving model constraint conditions are: (1) Thermal power unit output limit: During the participation of thermal power units in basic peak shaving, the direct cost of power generation is mainly optimized; when the unit enters the deep peak shaving condition and the output is significantly reduced, the output constraint is linearized by using the large M method: ， ， In the formula, m is the thermal power unit number, and t is the dispatching period index. For thermal power units During the period The actual contribution of the people. This represents the minimum active power output of thermal power unit R under normal operating conditions. For thermal power units Maximum active power output under normal operating conditions For thermal power units In The lower bound of active power output corresponding to the operating state, i.e. Output boundary of peak shaving or reserve area; For thermal power units In The active power output level or output boundary corresponding to the operating status is used to describe a deeper level of peak shaving or standby operation zone; For thermal power units During the period Binary variables that are currently powered on and running. For thermal power units During the period Binary variables that are in the normal operating range For thermal power units During the period In Operating status, i.e., binary variables of the first peak-shaving or standby area; For thermal power units During the period The binary variable is in the running state, i.e., the second or deeper peak-shaving region; M is a preset constant to achieve linearization of the constraint conditions; (2) Thermal power unit peak shaving cost segmentation constraint: The operation range of the unit is divided into multiple cost segments to reflect the cost changes at different output levels and ensure the economy and feasibility of peak shaving scheduling: ， In the formula, for a thermal power unit In the time period linear variable of the operation cost, used to depict the cost level of the unit in the time period in the optimization model; for a unit In the normal operation interval, the output is the corresponding cost function value when for a unit In the operation state , the output is the corresponding cost function value when for a unit In the operation state , the output is the corresponding cost function value when (3) Unit start-stop, minimum running time and ramping speed constraint: ， ， ， wherein, is a positive operator for identifying the start-up or shut-down time according to the unit state change; is the rated maximum active power of the thermal power unit , is a conversion coefficient related to the regulation capacity of the thermal power unit, for converting the maximum output of the unit into the available regulation capacity; is the continuous operation time of the thermal power unit in the time period , is the continuous shutdown time of the thermal power unit in the time period , and are the minimum continuous start-up and minimum continuous shutdown time requirements of the unit, respectively, and are the maximum up-ramp rate and maximum down-ramp rate of the unit, respectively. (4) Water storage capacity constraint range of hydropower station: ， At any time, the reservoir capacity of the hydropower plant is maintained between the minimum and maximum allowed values of design; , ) ; represents the actual reservoir capacity of the hydropower plant s at time period t; Water balance constraint: The inflow of the reservoir and the water used for power generation of each unit are always in dynamic balance: ， In the formula: Qs,t represents the natural inflow of the s-th hydroelectric power station at the t-th time point, Ql,t represents the water flow for power generation of the l-th unit in the t-th period, Ns represents the total number of generating units of the s-th hydroelectric power station; The conversion relationship between water and energy: The output power of the hydraulic turbine unit is related to the water flow for power generation, which is characterized by a water energy conversion coefficient 、 and ​ ， In the formula, Let be the output power of the i-th generating unit in the S-th hydropower station during time period t. For the first The first of the hydropower stations The flow rate through each unit For the first The first hydropower station in Water head height during the period is the linear coefficient of flow rate with respect to output power. is the linearity coefficient of head with respect to output power. For constant terms; Electric vehicle aggregator charging and discharging power constraint: Under the aggregator platform, the charging and discharging state of each electric vehicle aggregator in any period is limited by a binary variable ， In the formula, and Let be the binary variables representing the charging and discharging states of the nth electric vehicle aggregator during time period t. For the first A single electric vehicle aggregator during the period The discharge power, For the first A single electric vehicle aggregator during the period The charging power, For the first A single electric vehicle aggregator during the period The upper limit of the rated charge and discharge power achieved; Electric vehicle aggregator aggregated energy state constraint: The total energy of each electric vehicle aggregator is always constrained by the capacity and historical charging and discharging behavior: ， In the formula, Let n be the total aggregated electricity volume of the nth electric vehicle aggregator during time period t. and The first A single electric vehicle aggregator during the period The lower and upper limits of the aggregated capacity are used to constrain the minimum / maximum allowable state of charge of the battery pack; For the first An electric vehicle aggregator in the previous period The aggregated power, For the first An electric vehicle aggregator in the previous period The charging power, For the first An electric vehicle aggregator in the previous period The discharge power, Δt is the length of the adjacent scheduling period, usually expressed in hours, used to convert power into electricity; New energy power station output limit: The output of the new energy power station must not exceed the predicted value, and the abandoned wind and light must be clearly included: ， In the formula, is the dispatch output of the rth new energy plant at the tth time interval; is the predicted power generation of the rth new energy plant, represents the abandoned new energy power of the rth new energy plant at the tth time. (10) Power balance constraint of energy storage power grid system: The system maintains power balance in each period: ， In the formula, For the first Taiwan thermal power units during the period Those who have made contributions For the first The hydropower station during the period Those who have made contributions For the first Pumped storage power station during the period Power generation capacity For the first Pumped storage power station during the period Pumping power, For the first An electrochemical energy storage device during a certain period The discharge power For the first An electrochemical energy storage device during a certain period charging power, For the first An electric vehicle aggregator in the previous period The discharge power For the first An electric vehicle aggregator in the previous period charging power, For the system in time period Total active power load demand, , , , , and These include the number of thermal power units, hydropower units, photovoltaic power stations, pumped storage power stations, electrochemical energy storage devices, and electric vehicle aggregators.

3. The low-voltage area multi-type energy storage participating peak shaving model according to claim 1, characterized in that: The peak regulation model further comprises a pumped storage power station operation model, which requires each pumped storage unit to be in only one of pumping or power generation at any time, and is constrained by the corresponding maximum and minimum power ranges of the unit under pumping and power generation conditions, and requires all units to maintain a single operating condition within the same time period.

4. The low-voltage transformer area multi-type energy storage participating peak regulation model according to claim 1, characterized in that: The total number of all simultaneously operating units must not exceed the maximum number of units allowed to be started by the power station, and in combination with the reservoir water level and variation limits, the reservoir water level is required to always be between the upper and lower limits, ensuring that the upper and lower reservoir water balances meet the actual operation requirements under different unit operation strategies, and the unit power generation water flow is jointly affected by the natural inflow and the number of generator units.

5. The low-voltage transformer area multi-type energy storage participating peak regulation model according to claim 1, characterized in that: The charging and discharging states of each electric vehicle aggregator in each time period are limited by independent binary variables, and the total energy of the electric vehicle aggregator is always required to be within the capacity range, and the historical charging and discharging behavior of the electric vehicle aggregator is fully counted in the energy calculation, ensuring that the aggregation energy state of the electric vehicle aggregator is constrained by the capacity and actual operation.

6. The low-voltage transformer area multi-type energy storage participating peak regulation model according to claim 1, characterized in that: The objective function of the peak regulation model is to minimize the comprehensive operation cost within the entire dispatching period under the premise of meeting various operation constraints, which at least includes fuel cost and start-stop cost of thermal power units, water quantity opportunity cost of hydropower stations and pumped storage power stations, battery life impairment cost of electrochemical energy storage power stations and electric vehicle aggregators, as well as wind and light curtailment penalty cost and load reduction penalty cost.

7. The low-voltage transformer area multi-type energy storage participating peak regulation model according to claim 1, characterized in that: The peak regulation model further sets low-voltage distribution network safety constraints, in which the apparent power of the distribution transformer in any time period t does not exceed its rated capacity, the current on each low-voltage feeder does not exceed the allowable current of the corresponding line, and each node voltage is limited within the preset upper and lower voltage limit range, to ensure that the centralized charging and discharging of multiple types of energy storage will not cause transformer overload or voltage out-of-limit in the distribution area. 8.The multi-type energy storage participating peak regulation model of a low-voltage transformer area according to claim 1, characterized in that: The peak regulation model further comprises a system reserve capacity constraint, in each time period t, the upward regulation capacity of all thermal power units and the downward regulation capacity of all thermal power units and the sum of the upward and downward regulation margins provided by pumped storage power stations, electrochemical energy storage power stations and electric vehicle aggregators within their respective power boundary ranges is not less than the system specified upward reserve demand and downward reserve demand respectively, so as to achieve peak regulation while meeting the power grid reserve safety standard. 9.The multi-type energy storage participating peak regulation model of a low-voltage transformer area according to claim 1, characterized in that: The peak regulation model is solved by a mixed integer linear programming method, adopts a rolling optimization method with a daily cycle, divides the 24-hour dispatching period into several equal time intervals Δt, and updates the model parameters and solves them again in each dispatching period according to the latest load prediction results and new energy generation prediction results, to obtain the optimal output plan of thermal power units, hydropower stations, pumped storage power stations, electrochemical energy storage power stations and electric vehicle aggregators in each time period.

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