Day-before-day collaborative management and control method, system and device considering distribution network overcharge load fluctuation stabilization and high-quality power supply target and medium

Abstract

The invention is suitable for the field of power distribution network optimization scheduling, and discloses a day-ahead-day collaborative management and control method, system and device considering distribution network overcharge load fluctuation stabilization and a high-quality power supply target, and a medium, and the method comprises the steps: obtaining EV parameters, and employing a Monte Carlo method to simulate multiple types of EV loads; on the basis of the EV load, taking system operation economic optimization as an objective function, constructing a day-ahead optimization model in combination with multi-type constraints, and obtaining output instructions of OLTC, CB, a storage battery, photovoltaic and a super capacitor as day-ahead optimization results through a Gurobi solver; based on the updated EV load and the day-ahead optimization result, taking system economy and node voltage deviation as objective functions, combining the multi-type constraints, constructing an intra-day optimization model, obtaining optimized photovoltaic and super capacitor output instructions through a Gurobi solver, and combining output instructions of OLTC, CB and a storage battery to complete day-ahead-intra-day collaborative management and control. Source load fluctuation is stabilized, the service life of a storage battery is prolonged, and safe and economical operation of a power distribution network is promoted.

Description

Technical Field

[0001] This invention relates to the field of distribution network optimization scheduling, and in particular to a day-ahead-intraday coordinated management method, system, equipment and medium that takes into account the smoothing of distribution network overcharge load fluctuations and the goal of high-quality power supply. Background Technology

[0002] Electric vehicles (EVs) are gaining popularity due to their significant advantages of zero emissions and low energy consumption. To shorten EV charging time and improve user experience, supercharging stations are being connected to the power distribution network on a large scale. However, supercharging loads are characterized by high instantaneous power demand and strong fluctuations, leading to problems such as bidirectional current and voltage exceeding limits in the power distribution network, threatening its safe and stable operation. To address the issue of voltage exceeding limits in the power distribution network, existing grid-side resources, such as on-load tap changers (OLTCs) and capacitor banks (CBs), are being regulated. These devices can regulate voltage and compensate for reactive power, making the power distribution network more adaptable to the integration of new energy sources. However, discrete devices like OLTCs and CBs are slow to operate and cannot be frequently adjusted; they typically only allow for adjustments over longer timescales and cannot respond promptly to fluctuations in power supply and load output.

[0003] To address this, discrete devices such as OLTC and CB are coordinated with continuously adjustable devices such as energy storage systems (ESS) and photovoltaic (PV) systems to effectively handle power output fluctuations and improve the economics of the distribution network. However, the frequent and severe power fluctuations of EV overcharging loads cause batteries to charge and discharge frequently, even leading to deep discharge, which severely damages battery life and reduces the economics of the distribution network. Therefore, integrating hybrid energy storage into the distribution network is considered. This type of storage has both high energy density and high power density, enabling it to handle the impact loads from EVs and extend battery life. However, existing research on multi-timescale management typically treats hybrid energy storage as a continuous device, determining its control commands only within a single day. This intraday rolling local optimization cannot coordinate the energy dispatch needs of the entire cycle, resulting in poor overall system economics.

[0004] Therefore, a day-ahead and intraday coordinated management and control method is proposed, which considers both mitigating the fluctuations of overcharging load in the distribution network and achieving economical and high-quality power supply. Through day-ahead optimized scheduling and intraday rolling optimization, it can extend the battery life, improve system economy, and effectively ensure the safe and stable operation of the distribution network. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides a day-ahead-intraday coordinated management method and system that takes into account the goals of smoothing out distribution network overcharging load fluctuations and achieving high-quality power supply. This addresses the mismatch between the response characteristics of existing equipment and the characteristics of load fluctuations; the difficulty for a single energy storage device to balance fluctuation smoothing and ensuring economic efficiency; and the problem of fragmented optimization strategies on a time scale.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a day-ahead and intraday coordinated management method that takes into account both the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply, including: Obtain EV parameters and simulate multiple types of EV loads using the Monte Carlo method; Based on the aforementioned EV load, and with the objective function of optimizing system operation economy, a day-ahead optimization model is constructed by combining multiple types of constraints; Based on the day-ahead optimization model, the output commands of the voltage regulator, photovoltaic system and supercapacitor are obtained by the Gurobi solver as the day-ahead optimization results. Based on the updated EV load and day-ahead optimization results, an intraday optimization model is constructed with system economy and node voltage deviation as objective functions, combined with the aforementioned multi-type constraints; Based on the intraday optimization model, optimized photovoltaic and supercapacitor output commands are obtained through the Gurobi solver. Combined with the output commands of the voltage regulation device, day-ahead and intraday coordinated control is achieved.

[0008] As a preferred embodiment of the day-to-day coordinated management method for the distribution network's overcharging load fluctuation mitigation and high-quality power supply objectives described in this invention, the method includes: acquiring EV parameters and simulating multiple types of EV loads using the Monte Carlo method, including: Obtain the initial state of charge of electric vehicles (EVs) in slow charging, fast charging, and supercharging modes, and establish the initial state of charge probability density function. Based on the time-of-use electricity price, the starting time for slow charging mode is set to the start of the off-peak electricity price period; Based on charging demand, the starting charging time for fast charging and supercharging modes is set to follow a multi-peak normal distribution. Based on the initial charge probability density function and the initial charging time, the load of multiple types of EVs is obtained by accumulating the charging power of each EV.

[0009] As a preferred embodiment of the day-ahead-intraday coordinated management method for considering the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply as described in this invention, the method comprises: based on the EV load, taking the optimal economic operation of the system as the objective function, and constructing a day-ahead optimization model by combining multiple types of constraints, including: Based on multiple types of EV load, base load and photovoltaic load, with the objective function of optimizing system operation economy, and with the preset first time period as the day-ahead optimization time scale, a day-ahead optimization model is constructed by combining multiple types of constraints. Among them, the various types of constraints include: power flow constraints of distribution networks, power grid safe operation constraints, photovoltaic power output constraints, energy storage operation constraints, on-load tap changer (OLTC) operation constraints, and parallel capacitor bank (CB) operation constraints.

[0010] As a preferred embodiment of the day-to-day coordinated management method for considering the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply as described in this invention, wherein: the objective function of optimizing system operation economy includes: The economics of system operation include: electricity purchase cost, distribution network loss cost, photovoltaic operation and maintenance cost, hybrid energy storage operation and maintenance cost, OLTC operation cost, CB operation cost, curtailment cost, and battery life loss cost; Among them, the battery life loss cost is obtained by constructing an energy storage life loss model based on the actual number of battery cycles and the depth of discharge.

[0011] As a preferred embodiment of the day-ahead and intraday coordinated management method for considering the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply as described in this invention, the intraday optimization model is constructed based on the updated EV load and day-ahead optimization results, using system economy and node voltage deviation as objective functions, and in conjunction with the aforementioned multi-type constraints, including: Using a preset second time period as the intraday optimization time scale and a preset third time period as the rolling optimization window, updated EV load, base load, and photovoltaic load are obtained; Based on the updated EV load, base load, photovoltaic load, and day-ahead optimization results, an intraday optimization model is constructed with system economy and node voltage deviation as objective functions. The constraints include: distribution network power flow constraints, grid safe operation constraints, photovoltaic output constraints, and supercapacitor operation constraints.

[0012] As a preferred embodiment of the day-to-day coordinated management method for considering the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply as described in this invention, wherein: the objective function is system economy and node voltage deviation, including: Based on the electricity purchase cost, grid loss cost, curtailment cost, and operation and maintenance cost of photovoltaic and supercapacitors, with the preset second time period as the intraday optimization time scale, a system economic objective function is established; Based on the updated EV load, base load, and photovoltaic load, a node voltage deviation objective function is established.

[0013] As a preferred embodiment of the day-ahead-intraday coordinated management method for considering the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply as described in this invention, the intraday optimization model is constructed based on the day-ahead optimization results, with system economy and node voltage deviation as objective functions, including: The current optimization results, combined with the objective function of system economy and node voltage deviation, are used as the normalization benchmark. Based on the normalized benchmark and objective function, an intraday optimization model is constructed using the weighting coefficient method.

[0014] Secondly, this invention provides a day-ahead-intraday coordinated management and control system that considers both the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply, comprising: The simulated load module is used to acquire EV parameters and simulate multiple types of EV loads using the Monte Carlo method; The day-ahead optimization model construction module is used to construct a day-ahead optimization model based on the EV load, with the objective function of optimizing system operation economy, and incorporating multiple types of constraints. The day-ahead optimization results module is used to obtain the output commands of the voltage regulator, photovoltaic and supercapacitor as day-ahead optimization results based on the day-ahead optimization model and through the Gurobi solver. The intraday optimization model construction module is used to construct an intraday optimization model based on the updated EV load and the day-ahead optimization results, with system economy and node voltage deviation as objective functions, combined with the aforementioned multi-type constraints; The collaborative control module is used to obtain optimized photovoltaic and supercapacitor output commands based on the intraday optimization model through the Gurobi solver, and combine them with the output commands of the voltage regulation device to complete the day-to-day collaborative management and control.

[0015] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the day-to-day coordinated management method that takes into account the smoothing of distribution network overcharge load fluctuations and the goal of high-quality power supply are implemented.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the day-to-day coordinated management method for considering the overcharging load fluctuation smoothing and high-quality power supply targets of the distribution network.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: By determining the output command of the battery at the day-ahead stage, this invention avoids the impact of EV load power fluctuations during intraday operation, reducing the life-cycle cost of the battery and achieving the goal of economical and high-quality power supply; at the same time, the introduction of supercapacitors, working in synergy with photovoltaics in intraday rolling optimization, achieves real-time and precise smoothing of overcharge load fluctuations, effectively preventing the problem of node voltage exceeding limits; through the synergistic control of day-ahead global optimization and intraday local correction, it effectively ensures the safe and stable operation of the distribution network while extending the battery life and improving system economy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the overall process of the day-to-day coordinated management method for the distribution network overcharging load fluctuation suppression and high-quality power supply target, as described in one embodiment of the present invention.

[0020] Figure 2 This is a diagram of the IEEE 33-node system architecture of the day-to-day collaborative management method for the distribution network overcharging load fluctuation mitigation and high-quality power supply objectives, as described in one embodiment of the present invention.

[0021] Figure 3 This is a 1-hour level base load, EV load, and photovoltaic power prediction curve of the day-to-day coordinated management method for the distribution network supercharging load fluctuation smoothing and high-quality power supply target described in one embodiment of the present invention.

[0022] Figure 4 This is a 1-minute EV load power diagram of the day-to-day coordinated management method for the distribution network overcharging load fluctuation smoothing and high-quality power supply target, as described in one embodiment of the present invention.

[0023] Figure 5 This is a battery output power diagram showing different schemes of the day-to-day coordinated management method for the distribution network overcharging load fluctuation suppression and high-quality power supply target, as described in one embodiment of the present invention.

[0024] Figure 6 The diagram shows the node voltage diagrams for different schemes of the day-to-day coordinated management method for the distribution network overcharging load fluctuation suppression and high-quality power supply target, as described in one embodiment of the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] Example 1, referring to Figure 1 As an embodiment of the present invention, a day-ahead-intraday coordinated management method considering both distribution network overcharging load fluctuation mitigation and high-quality power supply objectives is provided, comprising: S100: Obtain EV parameters and simulate multiple types of EV loads using the Monte Carlo method; S200: Based on the aforementioned EV load, and with the objective function of optimizing system operation economy, a day-ahead optimization model is constructed by combining multiple types of constraints; S300: Based on the day-ahead optimization model, the output commands of the voltage regulator, photovoltaic and supercapacitor are obtained through the Gurobi solver as the day-ahead optimization results; S400: Based on the updated EV load and day-ahead optimization results, an intraday optimization model is constructed with system economy and node voltage deviation as objective functions, combined with the aforementioned multi-type constraints; S500: Based on the intraday optimization model, the optimized output commands for photovoltaic and supercapacitors are obtained through the Gurobi solver. Combined with the output commands of the voltage regulation device, the day-ahead and intraday coordinated control is completed.

[0027] It should be noted that the connection of overcharging loads in the distribution network is characterized by large instantaneous power and high fluctuation frequency. During its operation, parameters such as load power, node voltage, and energy storage state of charge change drastically, posing a severe challenge to the system's power balance and voltage stability. Affected by the uncertainty of electric vehicle user behavior, the spatiotemporal distribution of the load curve also exhibits strong randomness, making accurate perception and control of the operating status difficult, and safety control often has a lag. At the same time, the frequent impact of the impulsive power of overcharging loads on the energy storage system leads to accelerated degradation of battery cycle life, thereby affecting the economic operation of the system. The complex characteristics of multi-timescale coupling place high demands on the real-time performance of optimization decisions, and power quality problems may also occur due to strategy mismatch. Therefore, the advanced perception and collaborative optimization of the distribution network's operating status are particularly important.

[0028] Therefore, to address the aforementioned issues of operational prediction and collaborative optimization, the Monte Carlo method is employed to simulate various types of EV loads through steps S100-S500. With optimal system economics as the objective function, a day-ahead optimization model is constructed in conjunction with multiple constraints. During the day-ahead optimization phase, output commands for the voltage regulator, photovoltaic system, and supercapacitor are obtained. Based on the updated EV load and the day-ahead optimization results, an intraday optimization model is constructed with system economy and node voltage deviation as objective functions, yielding optimized output commands for the photovoltaic system and supercapacitor. Therefore, during the intraday optimization phase, the voltage regulator strictly executes the output commands, while the optimized output commands for the photovoltaic system and supercapacitor are corrected, achieving day-ahead and intraday collaborative control.

[0029] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, a day-ahead-intraday coordinated management method is provided that takes into account the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply.

[0030] In this embodiment of the application, step S100, which involves obtaining EV parameters and simulating multiple types of EV loads using the Monte Carlo method, includes the following steps A1-A4: A1: Obtain the initial state of charge of electric vehicles (EVs) in slow charging, fast charging, and supercharging modes, and establish the initial state of charge probability density function. Specifically, the charging demand of electric vehicles is closely related to their initial State of Charge (SOC). The initial SOC of an EV during charging can be characterized by a normal distribution, and its probability density function is: In the formula, For EV starting SOC; , These are the mean and standard deviation of the initial SOC of EV, respectively.

[0031] A2: Based on the time-of-use electricity price, set the starting charging time of the slow charging mode to the start time of the low electricity price period; Specifically, in slow charging mode, considering the impact of time-of-use electricity pricing on user charging behavior, it is assumed that the starting charging time for all EVs is at the beginning of the off-peak electricity price period.

[0032] A3: Based on charging demand, the starting charging time for fast charging and supercharging modes follows a multi-peak normal distribution; Specifically, in fast charging and supercharging modes, considering the strong randomness of load charging time, it is assumed that the initial charging time follows a multi-peaked normal distribution, obtained by weighting the probability density functions of multiple unimodal normal distributions. This reflects the spatiotemporal distribution characteristics of the user's actual charging needs.

[0033] Table 1 Load characteristics of different types of EVs

[0034] Refer to Table 1 for the load characteristics of different types of EVs. This refers to the initial charging time. , These represent the charging speeds in fast charging and supercharging modes, respectively. h、s The probability weights of each peak satisfy the following conditions: ; , , , The first two charging modes are fast charging and supercharging modes respectively. h , s The mean and standard deviation of each peak.

[0035] Among them, the unimodal normal distribution The probability density function can be expressed as: In the formula, T The scheduling period is [number].

[0036] A4: Based on the initial charge probability density function and the initial charging time, the load of multiple types of EVs is obtained by accumulating the charging power of each EV.

[0037] Specifically, the charging time can be obtained based on the EV's initial SOC and initial charging time: In the formula, The SOC of the EV at the end of charging; For EV battery capacity; Improve EV charging efficiency; The rated charging power of the EV charging station.

[0038] The EV charging end time is: In the formula, The moment when charging of the EV ends.

[0039] Furthermore, the charging power of each EV can be obtained: Furthermore, by summing the charging power of each EV over time, the total charging load power of different types of electric vehicles can be obtained: In the formula, , , These represent the number of EVs using slow charging, fast charging, and supercharging, respectively.

[0040] In an optional implementation, the construction of the initial state of charge and the initial charging time in step S100 can also be carried out using a dynamic parameter EV load modeling method. The probability density function parameter of the initial SOC and / or the multi-peak normal distribution parameter of the initial charging time are based on historical data mining and are adaptively adjusted according to the date type. For example, a "date type-parameter" mapping library can be established: the fast charging peak on weekdays is during the morning and evening commuting hours, while on weekends it may be distributed in the afternoon and evening.

[0041] In another optional implementation, the construction of the initial state of charge and the initial charging time in step S100 can also be carried out using a dynamic parameter EV load modeling method that is adaptively adjusted according to seasonal factors and special events. For example, weather influence factors can be introduced: under extreme weather conditions, users' travel intentions and charging behaviors will change, and the distribution parameters can be dynamically corrected through data-driven approaches; or an online update mechanism can be used: the system can continuously update the parameter library based on the actual charging data collected recently, so that the model always approximates the real user behavior.

[0042] In this embodiment of the application, step S200, based on the EV load, uses the economic optimization of system operation as the objective function and combines multiple types of constraints to construct a day-ahead optimization model, including the following steps B1-B4: Specifically, based on multiple types of EV loads, base loads, and photovoltaic loads, and with the objective function of optimizing system operation economy, a day-ahead optimization model is constructed using a preset first time period of 1 hour as the day-ahead optimization time scale and multiple types of constraints.

[0043] B1: The objective function is to achieve optimal economic operation of the system. Specifically, the system operates economically. Including: electricity purchase cost Power distribution network loss costs Photovoltaic operation and maintenance costs Hybrid energy storage operation and maintenance costs OLTC action cost CB action cost Cost of abandoned light Battery lifespan depletion costs .

[0044] Furthermore, the cost of battery life depletion. The energy storage life loss model is obtained by constructing a model based on the actual number of battery cycles and the depth of discharge: The relationship between the actual number of battery cycles and the depth of discharge can be obtained through fitting, and is expressed as: In the formula, This refers to the actual number of battery cycles. , These are the coefficients of the fitted curve. This represents the actual depth of discharge of the battery. The rated number of cycles for the battery; This refers to the rated depth of discharge of the battery.

[0045] For ease of calculation, the actual number of cycles at different discharge depths is converted to the equivalent number of cycles at the rated discharge depth: In the formula, This represents the equivalent cycle number of the battery.

[0046] The cost of battery life loss can then be expressed as: In the formula, The cost per unit capacity of the battery; This refers to the rated capacity of the battery. The actual cycle life of a battery is a non-linear function, and therefore the life-cycle cost is also a non-linear function. A piecewise linearization method is used to linearize it. Due to the variables... The value of is within [0,1]. Divide the interval [0,1] into N-1 segments, corresponding to N segmentation points. , , ···, Introducing continuous variables within [0,1] The linearized representation of the energy storage lifetime model is as follows: In one optional implementation, the energy storage life loss model constructed in step S200 to obtain the battery life loss cost can also be used to construct a dynamic life model. Based on the original model, a state variable with the healthy state (SOH) as the core is introduced. SOH is no longer a fixed initial value, but a dynamic quantity that evolves with the operating history.

[0047] In another optional implementation, the energy storage life loss model constructed in step S200 to obtain the battery life loss cost can also construct a dynamic life model, rewriting the original battery life loss cost in a form related to real-time SOH. For example, when the SOH is low, the loss cost per cycle is higher, guiding the system to "protect" the aging battery.

[0048] Furthermore, electricity purchase costs Power distribution network loss costs Photovoltaic operation and maintenance costs Hybrid energy storage operation and maintenance costs OLTC action cost CB action cost Cost of abandoned light The calculation formula is as follows: In the formula, Optimize the time scale for the present day; t For a specific moment; j For system nodes; ij For system branches; E It is the collection of all branches of the distribution network; , , , , , , , These are the cost coefficients for each part; This refers to the power purchased by the distribution network from the main grid. For nodes i Flow to Node j The current value; For the line ij The resistance value; This represents the predicted output value of photovoltaic power generation. This represents the actual amount of photovoltaic power absorbed. , , , These are the charging and discharging power of the battery and the supercapacitor, respectively. branch road ij The OLTC tap position; for t Time period nodes j The number of capacitor banks in operation.

[0049] B2: Multiple types of constraints include: power flow constraints of distribution network, power grid safe operation constraints, photovoltaic power output constraints, energy storage operation constraints, on-load tap changer (OLTC) operation constraints, and parallel capacitor bank (CB) operation constraints.

[0050] Specifically, ① Distribution network power flow constraints: In the formula, , The active and reactive power are injected into the nodes, respectively. and These are the active and reactive power of the branch circuit, respectively. Represents a node j The set of downstream nodes; Represents a node j The set of upstream nodes; This refers to the node voltage value. This represents the branch circuit reactance value.

[0051] In the formula, , These are the active and reactive power of the load, respectively. For EV load; This refers to the reactive power of the parallel capacitor bank.

[0052] ② Constraints on safe operation of power grid: In the formula, and These are the lower and upper limits of the node voltage; This represents the upper limit of the branch current.

[0053] ③ Photovoltaic output constraints: The actual output power of photovoltaic power is constrained by the predicted output power: ④ Energy storage operation constraints: The output power of a hybrid energy storage unit must not exceed its rated power. Furthermore, to prevent overcharging and over-discharging of the energy storage components, which could accelerate dielectric aging, its remaining capacity needs to be limited to a set upper and lower limit. Taking a battery as an example, the constraints are as follows: In the formula, , These are binary variables representing the charging and discharging states of the battery, respectively. A value of 1 indicates that the energy storage is in the charging state, and a value of 0 indicates that the energy storage is in the discharging state, thus preventing the battery from being in both charging and discharging states simultaneously. This refers to the remaining capacity of the battery. and These represent the upper and lower limits of the remaining capacity of the battery. The constraints for supercapacitors are the same as for batteries.

[0054] ⑤ Operating constraints of on-load tap-changing transformers: In the formula, The adjustable ratio for OLTC; and These are the standard turns ratio and adjustment step size of the OLTC, respectively. This represents the maximum value of the adjustable gears in the OLTC.

[0055] ⑥ Operational constraints of parallel capacitor banks In the formula, This refers to the reactive power output of a single capacitor bank. This is the upper limit for the number of capacitor banks.

[0056] In an optional implementation, the power flow constraints of the distribution network in step S200 can also be based on the DistFlow model, which can be transformed into a linear or convex problem. The nonlinear branch current square terms and voltage square terms are substituted with variables, and second-order cone relaxation constraints are applied to transform the original problem into a mixed integer second-order cone programming (MISOCP) model that can be solved efficiently.

[0057] In this embodiment of the application, step S300, based on the day-ahead optimization model, obtains the output commands of the voltage regulating device, photovoltaic system, and supercapacitor as the day-ahead optimization result using the Gurobi solver, including: Specifically, based on the day-ahead optimization model, the output commands of the voltage regulator, photovoltaic and supercapacitor are obtained by calling the Gurobi solver through MATLAB as the day-ahead optimization results. The voltage regulator includes: OLTC, CB and battery.

[0058] In an optional implementation, the output instructions obtained by calling the Gurobi solver using MATLAB in step S300 can also employ a hierarchical optimization framework. The output instructions of OLTC are used as the upper-level master problem, while the output instructions of CB, storage battery, photovoltaic, and supercapacitor are used as lower-level sub-problems. The upper-level master problem and the lower-level sub-problems are iterated using the Alternating Directional Multiplier Method (ADMM) until convergence to a globally optimal or near-optimal solution. Simultaneously, each sub-problem can be called in parallel using solvers such as Gurobi, greatly improving computational efficiency.

[0059] In this embodiment of the application, step S400, based on the updated EV load and the day-ahead optimization results, uses system economy and node voltage deviation as objective functions, and combines the aforementioned multi-type constraints to construct an intraday optimization model, including the following steps C1-C3: Specifically, the second time period of 1 minute is used as the intraday optimization time scale, and the third time period of 15 minutes is used as the rolling optimization window to obtain updated EV load, base load and photovoltaic load. The intraday optimization model is constructed with system economy and node voltage deviation as objective functions. The constraints include: distribution network power flow constraints, grid safe operation constraints, photovoltaic output constraints and supercapacitor operation constraints.

[0060] C1: The objective function is the system economy and node voltage deviation; Taking into account the system's electricity purchase cost, grid loss cost, curtailment cost, and operation and maintenance costs of photovoltaic and supercapacitor systems, the following intraday system economic targets are established: The specific calculation formulas for each part are as follows: In the formula, Optimize the time scale for intraday use.

[0061] To address the voltage over-limit problem caused by the fluctuations and uncertainties in source and load outputs, the voltage regulation objective function is established as follows: In the formula, This is the nominal value of the node voltage.

[0062] C2: The objective function of combining the day-ahead optimization results with the system economy and node voltage deviation is used as the normalization benchmark; based on the normalization benchmark and the objective function, the weighting coefficient method is used to construct the intraday optimization model.

[0063] A weighted coefficient method is used to process the multi-objective function. Meanwhile, to avoid discrepancies in the solution results due to different units, the intraday objective function is normalized using the day-ahead optimization results. In the formula, , These are the weighting coefficients for each objective. ; , These are the two objective function values ​​calculated based on the optimization results of the previous day.

[0064] Constraints: The constraints during the intraday optimization phase include distribution network power flow constraints, grid safety operation constraints, photovoltaic power output constraints, and supercapacitor operation constraints, which are consistent with those before the day.

[0065] In an optional implementation, the weighting coefficient method used in step S400 to process the multi-objective function can also be improved by introducing a dynamic adaptive weighting coefficient, making the weighting coefficient a function that changes with time. For example, when the risk of voltage exceeding the limit is high, the weight of voltage deviation should be automatically increased; during peak electricity price periods, more emphasis should be placed on economic efficiency.

[0066] In an optional implementation, the weight coefficient method used in step S400 to process the multi-objective function can also be used to design a fuzzy logic controller as a weight decision-maker. For example, precise values ​​such as "minimum node voltage", "average electricity price", and "overcharging load change gradient" can be converted into fuzzy linguistic variables such as "low", "normal", "high", and "drastic", so that a fuzzy rule base can be established and the fuzzy inference results can be converted into precise weight values.

[0067] In the implementation of this application, in step S500, based on the intraday optimization model, the optimized output commands of photovoltaic and supercapacitors are obtained by calling the Gurobi solver using MATLAB. Combined with the output commands of OLTC, CB and battery, the day-ahead and intraday coordinated management and control are completed.

[0068] Example 3, referring to Figures 2-4 As an embodiment of the present invention, based on the above embodiment, a simulation experiment is provided on a day-to-day coordinated management method that takes into account the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply, in order to verify its feasibility and effectiveness; The IEEE 33-node system is used for computational demonstration. The system structure is as follows: Figure 2 As shown, the base capacity is 1 MVA, the base voltage is 12.66 kVA, and the adjustable range of the voltage transformation ratio on both sides of the on-load tap-changing transformer is 0.95~1.05 pu. The access locations and related parameters of each resource are shown in Table 2. The system time-of-use pricing is shown in Table 3, the cost coefficients of each component are shown in Table 4, and the parameters of the Monte Carlo model are shown in Table 5.

[0069] Table 2 Equipment Connection Locations and Rated Capacities

[0070] Table 3 Time-of-use electricity prices

[0071] Table 4 Cost Coefficients

[0072] Table 5 Monte Carlo simulation parameters

[0073] The current base load, EV load, and normalized PV power forecast curves are as follows: Figure 3 As shown, the fluctuation range for intraday photovoltaic and load data is set to ±5% of their predicted values. The total EV load power on a 1-minute timescale is shown below. Figure 4As shown. In slow charging mode, all vehicles start charging at 11 PM, and the load power quickly climbs to about 0.2MW, continuing until 5 AM the next day before slowly decreasing. In fast charging and supercharging modes, the EV load curve shows short-term power peaks on a min time scale, reflecting users' random daytime charging behavior, which can easily cause significant impact on the power distribution network.

[0074] Example 4, refer to Figures 5-6 As an embodiment of the present invention, based on the above embodiment, a comparative analysis of a day-to-day coordinated management method that takes into account the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply is provided to verify its feasibility and effectiveness; To verify the effectiveness of the proposed two-stage optimization method for hybrid energy storage synergy in improving battery life and system voltage levels, three schemes were compared and analyzed: Option 1: Regulate based on battery energy storage, determine OTLC and CB instructions in the near future, and update battery and photovoltaic instructions within the day; Option 2: Consider the coordinated operation of hybrid energy storage. Determine the output power of OTLC and CB in the near future, and replace batteries, supercapacitors, and photovoltaic modules within the day, taking into account the lifespan of the batteries. Option 3: Considering the coordinated operation of hybrid energy storage, the OTLC, CB, and battery instructions are determined in the current day based on the battery life loss, and the supercapacitor and photovoltaic instructions are updated within the day, which is the option in this article.

[0075] Table 6 compares the system economics of different schemes. Compared to Scheme 1, which uses only a single battery, Schemes 2 and 3, through the synergistic optimization of hybrid energy storage, not only promote peak shaving and valley filling, reducing system electricity purchase costs, but also reduce power imbalances in the grid, lowering system network loss costs. Specifically, compared to Scheme 1, Scheme 3 reduces the total system cost by 15.60%. Compared to Scheme 2's intraday local optimization, Scheme 3, by determining the battery output power before the day, is more conducive to global system optimization, achieving optimal economic scheduling and reducing the total system cost by 6.67%. In other words, this scheme effectively improves the system's operational economy.

[0076] Table 6 Comparison of System Economics

[0077] like Figure 5The battery output power curves are shown under different schemes. In Scheme 1, the battery output power fluctuates frequently due to source and load fluctuations, resulting in drastic fluctuations in the battery power curve. In Scheme 2, the high cost of battery lifespan leads to fewer battery operations, which reduces lifespan costs but weakens the energy storage effect. In Scheme 3, the supercapacitor mainly smooths out intraday source and load fluctuations, and the battery power is determined during the day-ahead phase, extending its lifespan while fully utilizing its peak-shaving and valley-filling capabilities. Table 5 shows that compared to Scheme 1, Scheme 3 reduces battery lifespan costs by 95.77%; compared to Scheme 2, Scheme 3 reduces battery lifespan costs by 78.25%. Therefore, the scheme proposed in this paper, which determines the battery output power curve during the day-ahead phase, can effectively extend battery lifespan.

[0078] like Figure 6 The system node voltage diagrams under different schemes are shown. Compared with Scheme 1, which uses only a single battery, Schemes 2 and 3, which introduce hybrid energy storage for synergistic optimization, exhibit stronger power regulation flexibility and can effectively smooth voltage fluctuations caused by source and load fluctuations, thereby reducing system node voltage deviation. Compared with Scheme 1, the total node voltage deviation of Scheme 3 is reduced by 2.38%. The proposed scheme determines the battery output power curve before the day. Although the intraday node voltage regulation capability is slightly worse than that of Scheme 2, it still maintains the node voltage within the safe range of [0.95, 1.05]. That is, the proposed scheme can effectively mitigate the impact of EV overcharging load fluctuations, prevent node voltage from exceeding limits, and ensure the safe and stable operation of the system.

[0079] In summary, this invention employs the Monte Carlo method to simulate multiple types of EV loads, using optimal system operation economy as the objective function. A day-ahead optimization model is constructed by combining multiple types of constraints. During the day-ahead optimization phase, output commands for OLTC, CB, batteries, photovoltaics, and supercapacitors are obtained, reducing the lifespan cost of batteries and achieving the goal of economical and high-quality power supply. Simultaneously, supercapacitors and photovoltaics work synergistically in intraday rolling optimization to achieve real-time and precise smoothing of supercharging load fluctuations, effectively preventing node voltage exceeding limits. Based on the updated EV load and day-ahead optimization results, an intraday optimization model is constructed with system economy and node voltage deviation as objective functions, obtaining optimized output commands for photovoltaics and supercapacitors. Therefore, during the intraday optimization phase, OLTC, CB, and batteries strictly execute the output commands, while the optimized output commands for photovoltaics and supercapacitors are corrected, achieving synergistic control of day-ahead global optimization and intraday local correction, effectively ensuring the safe and stable operation of the distribution network.

[0080] Example 5 illustrates a day-to-day coordinated management method for both distribution network overload fluctuation mitigation and high-quality power supply targets. It should be noted that the technical solution of this system for day-to-day coordinated management of distribution network overload fluctuation mitigation and high-quality power supply targets is based on the same concept as the aforementioned method for day-to-day coordinated management of distribution network overload fluctuation mitigation and high-quality power supply targets. Details not described in detail in this example can be found in the description of the aforementioned method for day-to-day coordinated management of distribution network overload fluctuation mitigation and high-quality power supply targets.

[0081] This embodiment also provides a day-ahead-intraday collaborative management and control system that takes into account both the smoothing of distribution network overcharging load fluctuations and the goal of high-quality power supply, including: The simulated load module is used to acquire EV parameters and simulate multiple types of EV loads using the Monte Carlo method; The day-ahead optimization model construction module is used to construct a day-ahead optimization model based on the EV load, with the objective function of optimizing system operation economy, and incorporating multiple types of constraints. The day-ahead optimization results module is used to obtain the output commands of the voltage regulator, photovoltaic and supercapacitor as day-ahead optimization results based on the day-ahead optimization model and through the Gurobi solver. The intraday optimization model construction module is used to construct an intraday optimization model based on the updated EV load and the day-ahead optimization results, with system economy and node voltage deviation as objective functions, combined with the aforementioned multi-type constraints; The collaborative control module is used to obtain optimized photovoltaic and supercapacitor output commands based on the intraday optimization model through the Gurobi solver, and combine them with the output commands of the voltage regulation device to complete the day-to-day collaborative management and control.

[0082] This embodiment also provides an electronic device applicable to the day-to-day coordinated management of distribution network overcharging load fluctuations and high-quality power supply targets, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to realize the day-to-day coordinated management method of distribution network overcharging load fluctuations and high-quality power supply targets as proposed in the above embodiment.

[0083] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the day-to-day collaborative management method proposed in the above embodiments for achieving the goals of smoothing out distribution network overcharge load fluctuations and high-quality power supply.

[0084] The storage medium proposed in this embodiment and the day-to-day coordinated management method for achieving the goals of smoothing out overcharge load fluctuations and high-quality power supply proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0085] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A day-ahead-day-of-day coordinated control method considering distribution network overload fluctuation smoothing and high-quality power supply targets, characterized in that, The application relates to a method for coordinated control of a power grid, and in particular to a method for coordinated control of a power grid based on Monte Carlo simulation of EV load. The method comprises the following steps: EV parameters are acquired, and a Monte Carlo method is used to simulate multi-type EV load; a day-ahead optimization model is constructed based on the EV load, with system operation economy optimization as a target function and in combination with multi-type constraints; based on the day-ahead optimization model, an output instruction of a voltage regulating device, photovoltaic and super capacitor is obtained through a Gurobi solver as a day-ahead optimization result; based on the updated EV load and the day-ahead optimization result, a day-ahead-intra-day coordinated control model is constructed, with system economy and node voltage deviation as target functions and in combination with the multi-type constraints; 2. The day-ahead-intraday collaborative management method considering distribution network overload fluctuation smoothing and high-quality power supply target according to claim 1, wherein, based on the day-ahead-intra-day coordinated control model, the optimized output instruction of the photovoltaic and super capacitor is obtained through the Gurobi solver, and the output instruction of the voltage regulating device is combined to complete the day-ahead-intra-day coordinated control. EV parameters are acquired, and a Monte Carlo method is used to simulate multi-type EV load, which comprises the following steps: the starting state of charge of an electric vehicle (EV) in a slow charging mode, a fast charging mode and an ultra-fast charging mode is acquired, and a starting state of charge probability density function is established; according to a time-of-use electricity price, the starting charging time of the EV in the slow charging mode is set as the starting time of the low valley section of the electricity price; according to charging demand, the starting charging time of the EV in the fast charging mode and the ultra-fast charging mode is set to be subject to a multi-peak normal distribution; 3.The day-ahead and day-ahead coordinated control method considering distribution network overload fluctuation smoothing and high-quality power supply target of claim 2, wherein, based on the starting state of charge probability density function and the starting charging time, the charging power of each EV is accumulated to obtain the multi-type EV load. The method comprises the following steps: based on the multi-type EV load, a basic load and a photovoltaic load, a day-ahead optimization model is constructed, with system operation economy optimization as a target function, with a preset first time period as a day-ahead optimization time scale, and in combination with multi-type constraints; 4. The day-ahead-intraday collaborative management method considering distribution network overload fluctuation smoothing and high-quality power supply target of claim 3, wherein, wherein the multi-type constraints comprise power grid flow constraints, power grid safe operation constraints, photovoltaic output constraints, energy storage operation constraints, on-load tap-changing transformer (OLTC) operation constraints and shunt capacitor bank (CB) operation constraints. The method comprises the following steps: system operation economy comprises electricity purchase cost, power distribution network loss cost, photovoltaic operation and maintenance cost, hybrid energy storage operation and maintenance cost, OLTC action cost, CB action cost, light rejection cost and battery life loss cost; 5. The day-ahead-intraday collaborative management method considering distribution network overload fluctuation smoothing and high-quality power supply target of claim 4, wherein, wherein the battery life loss cost is obtained by constructing an energy storage life loss model through the actual cycle number and the discharge depth of the battery. The method comprises the following steps: a preset second time period is set as a day-ahead-intra-day coordinated control time scale, and a preset third time period is set as a rolling optimization window to obtain updated EV load, basic load and photovoltaic load; based on the updated EV load, the basic load, the photovoltaic load and the day-ahead optimization result, a day-ahead-intra-day coordinated control model is constructed, with system economy and node voltage deviation as target functions; 6. The day-ahead-intraday collaborative management method considering distribution network overload fluctuation smoothing and high-quality power supply target of claim 5, wherein, constraint conditions comprise power grid flow constraints, power grid safe operation constraints, photovoltaic output constraints and super capacitor operation constraints. The method comprises the following steps: An economic objective function of the system is established based on the purchase cost, network loss cost, abandoned light cost, and operation and maintenance cost of the photovoltaic and super capacitor within a preset second time period as an intraday optimization time scale; A node voltage deviation objective function is established based on the updated EV load, base load, and photovoltaic load.

7. The day-ahead-intraday collaborative management method considering distribution network overload fluctuation smoothing and high-quality power supply target of claim 5, wherein, The intraday optimization model is constructed based on the day-ahead optimization result, the economic objective function of the system, and the node voltage deviation objective function, including: The day-ahead optimization result is combined with the economic objective function of the system and the node voltage deviation objective function as a normalized benchmark; The day-ahead optimization model is constructed based on the normalized benchmark and the objective function by using a weight coefficient method.

8. A day-ahead-day-of system for coordinated control considering the super-charging load fluctuation smoothing and high-quality power supply target, applying the method of any one of claims 1-7, characterized in that, It includes: The EV parameter is obtained by using a Monte Carlo method to simulate the multi-type EV load; The day-ahead optimization model is constructed based on the EV load, the economic optimization of the system as an objective function, and the multi-type constraint; The day-ahead optimization result module is used to obtain the output instruction of the voltage regulating device, photovoltaic, and super capacitor as the day-ahead optimization result based on the day-ahead optimization model and the Gurobi solver; The day-ahead optimization model is constructed based on the updated EV load and the day-ahead optimization result, the economic objective function of the system, and the node voltage deviation objective function, and the multi-type constraint is combined to construct the day-ahead optimization model; The collaborative control module is used to obtain the optimized output instruction of the photovoltaic and super capacitor based on the day-ahead optimization model and the Gurobi solver, and the output instruction of the voltage regulating device is combined to complete the day-ahead-intraday collaborative control.

9. An electronic device, comprising: It includes: A memory and a processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions, which realize the steps of the day-ahead-intraday collaborative control method considering the super charging load fluctuation suppression and high-quality power supply target of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It includes that the memory has stored computer executable instructions, and the computer executable instructions are executed by the processor to realize the steps of the day-ahead-intraday collaborative control method considering the super charging load fluctuation suppression and high-quality power supply target of any one of claims 1 to 7.

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