Method and device for coordinating, optimizing and regulating light storage and charging power of resident power distribution area

Abstract

The invention provides a light storage and charging power coordinated optimization regulation and control method and device for a resident power distribution area. The method comprises the following steps: acquiring optimal day-ahead scheduling plan data of the resident power distribution area comprising a distributed photovoltaic system, an energy storage device and an electric vehicle charging device; determining an SOC plan value based on the SOC plan values of the energy storage device at the end of the previous and later time periods; determining a net load deviation value of the resident power distribution area at the current moment based on the photovoltaic power generation power plan value and the energy storage power plan value at the current time period; determining an energy storage power regulation and control value at the current moment based on the electric power time period to which the current moment belongs, the net load deviation value and the SOC plan value; and determining a photovoltaic power generation power regulation value and an electric vehicle charging power regulation value at the current moment based on the net load deviation value, the actual power supply power of the power grid, the energy storage power regulation value, the photovoltaic power generation power plan value and the electric vehicle charging power plan value at the current time period. According to the invention, the light storage charging power coordination optimization regulation and control effect can be improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a method and device for coordinated optimization and control of photovoltaic storage and charging power in a residential distribution station area. Background Art

[0002] With the rapid development of renewable energy and the increasing popularity of electric vehicles, electricity demand in residential distribution areas is becoming increasingly diverse and complex. Solar photovoltaic power generation, as a key component of distributed energy, has been gradually adopted in residential areas. Simultaneously, electric vehicle charging facilities are also becoming increasingly widespread in residential communities. To further improve energy efficiency and reduce the impact of photovoltaic power generation and electric vehicle charging on the grid, energy storage has been introduced into residential distribution systems. By storing and releasing energy, it can smooth load fluctuations and shift peaks and valleys. The efficient operation of solar-storage-charging systems in residential distribution areas relies on optimal energy management and coordinated control. Photovoltaic power generation is affected by factors such as weather and time of day and exhibits discontinuity, while energy storage devices have limited capacity and battery life is affected by the number of charge and discharge cycles. Furthermore, the random and sudden nature of electric vehicle charging demand leads to significant fluctuations in charging loads. Distributing energy among the diverse energy devices in residential distribution areas and achieving power coordination and optimal control among photovoltaic power generation, energy storage, and charging facilities have become pressing technical challenges.

[0003] Currently, methods for optimizing and coordinating the power of photovoltaic, storage, and charging are complex, difficult to implement, or often ineffective. Specifically, the operation of photovoltaic, storage, and charging systems relies on forecasts of future demand, such as the future output of photovoltaic power generation and the charging needs of electric vehicles. However, these factors are significantly affected by external conditions and difficult to accurately predict, resulting in poor results in optimizing the power of photovoltaic, storage, and charging. Summary of the Invention

[0004] The embodiments of the present invention provide a method and device for coordinated optimization and control of photovoltaic, storage and charging power in a residential distribution station area, so as to solve the problem of poor coordinated optimization and control effect of photovoltaic, storage and charging power.

[0005] In a first aspect, an embodiment of the present invention provides a method for coordinated optimization and control of photovoltaic storage and charging power in a residential distribution station area, comprising:

[0006] Obtain optimal day-ahead scheduling plan data for a residential distribution substation containing a distributed photovoltaic system, an energy storage device, and an electric vehicle charging device, the optimal day-ahead scheduling plan data including a planned photovoltaic power generation value, a planned energy storage power value, a planned electric vehicle charging power value, and a planned SOC value of the energy storage device at the end of each time period; determine the planned SOC value at the current moment based on the planned SOC values ​​of the energy storage device at the end of the previous and subsequent time periods; determine the net load deviation value corresponding to the residential distribution substation at the current moment based on the planned photovoltaic power generation value and the planned energy storage power value of the current time period; determine the energy storage power control value of the energy storage device at the current moment based on the power time period to which the current moment belongs, the net load deviation value, and the planned SOC value; determine the photovoltaic power generation power control value and the electric vehicle charging power control value at the current moment based on the net load deviation value of the current time period, the actual power supply power of the power grid, the energy storage power control value, the planned photovoltaic power generation power value, and the planned electric vehicle charging power value.

[0007] In a second aspect, an embodiment of the present invention provides a device for coordinating and optimizing the photovoltaic storage and charging power in a residential distribution station area, comprising:

[0008] A day-ahead scheduling plan data determination module is used to obtain the optimal day-ahead scheduling plan data for a residential distribution station area containing a distributed photovoltaic system, an energy storage device, and an electric vehicle charging device. The optimal day-ahead scheduling plan data includes the planned photovoltaic power generation power value, the planned energy storage power value, the planned electric vehicle charging power value, and the planned state of charge (SOC) value of the energy storage device at the end of each time period;

[0009] The energy storage SOC control module is used to determine the planned SOC value at the current moment based on the planned SOC values ​​of the energy storage device at the end of the previous and next time periods;

[0010] The net load deviation value calculation module is used to determine the net load deviation value corresponding to the residential distribution station area at the current moment based on the photovoltaic power generation power plan value and the energy storage power plan value of the current period;

[0011] An energy storage power control value determination module is used to determine the energy storage power control value of the energy storage device at the current moment based on the power time period, net load deviation value and SOC plan value at the current moment;

[0012] The photovoltaic charging control module is used to determine the photovoltaic power generation power control value and electric vehicle charging power control value at the current moment based on the net load deviation value of the current period, the actual power supply power of the power grid, the energy storage power control value, the photovoltaic power generation power plan value and the electric vehicle charging power plan value.

[0013] An embodiment of the present invention provides a method for coordinated optimization and control of photovoltaic, energy storage, and charging power in a residential distribution area. The method first obtains optimal day-ahead scheduling plan data for a residential distribution area containing a distributed photovoltaic system, an energy storage device, and an electric vehicle charging device. The method then determines the SOC plan value at the current moment based on the SOC plan value of the energy storage device at the end of the previous and subsequent time periods. The method also determines the net load deviation value corresponding to the residential distribution area at the current moment based on the photovoltaic power generation power plan value and the energy storage power plan value in the current time period. The method also determines the energy storage power control value of the energy storage device at the current moment based on the power time period to which the current moment belongs, the net load deviation value, and the SOC plan value. The method also determines the photovoltaic power generation power control value and the electric vehicle charging power control value at the current moment based on the net load deviation value, the actual power supply power of the power grid, the energy storage power control value, the photovoltaic power generation power plan value, and the electric vehicle charging power plan value. This embodiment can fully utilize the limited capacity of energy storage to smooth out power fluctuations in the distribution area, avoid excessive charge and discharge cycles of the energy storage battery, and not damage the energy storage battery life. It also maximizes the utilization of photovoltaic power generation, reduces photovoltaic energy waste, and improves the coordinated optimization and control effect of photovoltaic, energy storage, and charging power. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 This is a flowchart of the implementation of the method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution station areas provided by an embodiment of the present invention;

[0016] Figure 2 Schematic diagram of the structure of the photovoltaic storage and charging power coordination optimization control device for residential distribution station area provided by an embodiment of the present invention;

[0017] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0019] In the description of this application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0020] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0021] In addition, the “plurality” mentioned in the embodiments of the present application should be interpreted as two or more.

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0023] See also Figure 2 , which shows a flow chart for implementing a method for coordinating and optimizing the photovoltaic storage and charging power in a residential distribution station area provided by an embodiment of the present invention, as detailed below:

[0024] S101: Obtain optimal day-ahead scheduling plan data for a residential distribution station area including a distributed photovoltaic system, an energy storage device, and an electric vehicle charging device. The optimal day-ahead scheduling plan data includes a planned photovoltaic power generation power value, a planned energy storage power value, a planned electric vehicle charging power value, and a planned SOC value of the energy storage device at the end of each time period.

[0025] In one possible implementation, the specific implementation process of S101 includes:

[0026] Taking the minimization of the cost of purchasing electricity from the upper distribution network and the operating cost of the energy storage device as the optimization objectives, a day-ahead optimal scheduling model for residential distribution stations that includes distributed photovoltaic systems, energy storage devices, and electric vehicle charging devices is constructed.

[0027] The day-ahead optimization scheduling model is solved based on the particle swarm optimization algorithm to obtain the optimal day-ahead scheduling plan data for the residential power distribution station area.

[0028] The executor of this embodiment is the substation smart terminal. Taking a single day as a whole cycle and 15 minutes as a time period, the substation smart terminal collects the photovoltaic power generation power, energy storage charging and discharging power, charging pile charging power and distribution network power supply power of the residential distribution substation from each photovoltaic power generation equipment, energy storage device and charging pile in the substation starting from midnight and recording it every 15 minutes. It also statistically calculates the total photovoltaic power generation power, total energy storage charging and discharging power, total electric vehicle charging power and distribution network power supply power of the residential distribution substation, and covers the storage of the total photovoltaic power generation power, total energy storage charging and discharging power, total electric vehicle charging power and distribution network power supply power of the residential distribution substation every 15 minutes for the past 7 days.

[0029] Using historical data, the estimated power values ​​of the total load and photovoltaic output in the substation area at each time period of the next day are predicted.

[0030] Specifically, if the next day is a working day, the estimated value of the total load in the substation is set to the total load of the previous working day (i.e., conventional load + charging pile load); if the next day is a rest day, the estimated value of the total load in the substation is set to the total load of the previous rest day; the estimated value of the total photovoltaic output power is set to the total photovoltaic output power of the previous day.

[0031] After collecting the data, the conventional load, photovoltaic power generation power, electric vehicle charging power, energy storage power, and SOC value of the energy storage device in the substation are aggregated separately. According to the estimated values ​​of the total load power (conventional load + total electric vehicle charging power) and photovoltaic power generation power, factors such as time-of-day electricity prices are considered, and the operating cost (electricity purchase and sales costs + energy storage operating costs) is minimized. With the constraints of not overloading the distribution transformer, a day-ahead optimization scheduling model is established. The day-ahead optimization scheduling model is solved to obtain the optimal day-ahead scheduling plan for the power of photovoltaic power generation, energy storage charging and discharging, and electric vehicle charging.

[0032] In one possible implementation, the objective function of the day-ahead optimization scheduling model is:

[0033]

[0034] Among them, F Grid F represents the electricity purchase and sales cost of the residential distribution station area, which is the difference between the electricity purchase cost and the electricity sales profit of the residential distribution station area and the upper power grid; ESS Represents the energy storage operating cost, which is the loss cost of the energy storage device in the residential distribution station area during the charging and discharging process; C Buy (n) represents the electricity purchase price of the residential distribution station area in the nth period, P Buy (n) represents the purchased power in the nth period, C Sell (n) represents the electricity price of the residential distribution area in the nth period, P Sell(n) represents the electricity sales power of the residential distribution station area in the nth period; P C (n) represents the charging power of the energy storage device in the nth period; P D (n) represents the discharge power of the energy storage device in the nth period; C ESS It represents the unit operating cost of the energy storage device during the charging and discharging process; N represents the number of time periods in a single cycle, N = 96.

[0035] In one possible implementation, the constraints of the day-ahead optimization scheduling model include power balance constraints for residential distribution stations, power supply constraints, energy storage charging and discharging power constraints, energy storage SOC transfer relationship constraints, energy storage SOC state upper and lower limit constraints, energy storage SOC state balance constraints at the beginning and end of the scheduling period, photovoltaic output constraints, and electric vehicle charging power constraints.

[0036] The power balance constraint of the residential distribution station area is:

[0037] P Grid (n)+P PV (n)+P ESS (n) = P EV (n)+P Load (n)

[0038] Among them, P Grid (n) represents the planned power supply value of the power grid in the residential distribution area in the nth period; P PV (n) represents the planned photovoltaic power generation value of the residential distribution station area in the nth period; P ESS (n) represents the planned energy storage power value for the nth period (positive during discharge and negative during charge); P EV (n) represents the planned charging power value of the electric vehicle in the nth period, P Load (n) represents the planned value of conventional load power in the nth period; where the grid power supply power P Grid (n) = P Buy (n)-P Sell (n), in any period, the purchased power P Buy (n), electricity sales power P Sell (n) are not less than 0, and at most one of them is greater than 0; the total energy storage power P ESS (n) = P D (n)-P C (n), in any period, the energy storage discharge power P D (t), charging power P C (t) are both not less than 0, and at most one of them can be greater than 0;

[0039] The power supply constraints are:

[0040] P Gmin ≤P Grid (n)≤P Gmax

[0041] Among them, P Gmin Indicates the lower limit of the power supply, P Gmax Indicates the upper limit of power supply;

[0042] The energy storage charging and discharging power constraints are:

[0043]

[0044] Among them, P c (n) represents the planned charging power value of the energy storage device in the nth time period; P c (n) represents the planned discharge power value of the energy storage device in the nth time period; P Cmax Indicates the upper limit of the charging power of the energy storage device, P Dmax Indicates the upper limit of the discharge power of the energy storage device;

[0045] The energy storage SOC transfer relationship constraint is:

[0046]

[0047] Where SOC(n) represents the planned SOC value of the energy storage device at the end of the nth period; η C represents the charging efficiency of the energy storage device, η D Indicates the discharge efficiency of the energy storage device; E N represents the rated capacity of the energy storage device; ΔT represents the scheduling time interval, which is set to 15 minutes;

[0048] The upper and lower limits of the energy storage SOC state are:

[0049]

[0050] Among them, SOC(0) represents the SOC planned value of the energy storage device at the beginning of the first period; SOC ini Indicates the initial SOC value of the energy storage device for day-ahead scheduling; SOC max Indicates the SOC upper limit of the energy storage device; SOC min Indicates the lower limit of the SOC of the energy storage device;

[0051] The energy storage SOC state balance constraint at the beginning and end of the scheduling period is:

[0052] SOC(0)=SOC(N)

[0053] The photovoltaic output constraint is:

[0054] 0≤PPV (n)≤P PVmax (n)

[0055] Among them, P PVmax (n) represents the estimated value of photovoltaic power generation in the residential distribution area in the nth period;

[0056] The electric vehicle charging power constraint is:

[0057] 0≤P EV (n)≤P EVmax (n)

[0058] Among them, P EVmax (n) represents the upper limit of the charging power of electric vehicles in the residential distribution station area in the nth period.

[0059] After the day-ahead optimization scheduling model is constructed, the general particle swarm optimization algorithm is used to solve the day-ahead optimization scheduling model to obtain the optimal day-ahead scheduling power, that is, the photovoltaic power generation power P PV (n), energy storage power P ESS (n) and electric vehicle charging power P EV (n), and the SOC(n) value at the end of each storage period, where n = 1, 2, 3, …, N.

[0060] S102: Determine the planned SOC value at the current moment based on the planned SOC values ​​of the energy storage device at the end of the previous and next time periods.

[0061] Considering that during actual operation, the actual power of photovoltaics, charging piles, and conventional loads may differ from the planned dispatch power or upper limit value, the following rules are used to adjust the energy storage SOC in real time.

[0062] In one possible implementation, the specific implementation process of S102 includes:

[0063] Based on the formula Determine the SOC value at the current moment;

[0064] Wherein, ceil() represents a round-up function; t represents the t-th moment of the current cycle, n represents the n-th period of the current cycle, SOC(n) represents the planned SOC value at the end of the n-th period; SOC(n-1) represents the planned SOC value at the end of the n-1-th period.

[0065] Specifically, the time t is the number of minutes from the time 0:00.

[0066] S103: Based on the planned photovoltaic power generation value and the planned energy storage power value in the current period, determine the net load deviation value corresponding to the residential distribution station area at the current moment.

[0067] In one possible implementation, the specific implementation process of S103 includes:

[0068] Based on the formula p net (t) = p Grid (t)-p ESS-f (t) Determine the actual value of the net load of the residential distribution station area at the current moment;

[0069] Based on the formula P net (n) = P Grid (n)-P ESS (n) Determine the planned net load value of the residential distribution area during the current period;

[0070] Based on the formula ΔP net (t) = p net (t)-P net (n) Determine the net load deviation value corresponding to the residential distribution station area at the current moment;

[0071] Among them, p Grid (t) represents the actual grid power supply at the current time t, p ESS-f (t) represents the actual energy storage power at the current time t; P Grid (n) represents the planned value of the power supply power of the power grid in the residential distribution area in the current period; P ESS (n) represents the planned energy storage power value of the residential distribution station area in the current period; ΔP net (t) represents the net load deviation value corresponding to the residential distribution station area at time t.

[0072] S104: Determine the energy storage power control value of the energy storage device at the current moment based on the power time period, the net load deviation value, and the SOC plan value.

[0073] In one possible implementation, assuming that the current time t is within the nth time period of the day-ahead scheduling, the specific implementation process of S104 includes:

[0074] When ΔP net (t)<0, the nth period belongs to the peak period and soc(t)≤SOC min , then p ESS (t) = min{P ESS (n),0};

[0075] When ΔP net (t)<0, the nth period belongs to the peak period and SOC min <soc(t)<SOC max , then p ESS (t) = P ESS (n);

[0076] When ΔP net (t) < 0, the nth period belongs to the peak period and soc(t) ≥ SOC max , then p ESS (t) = max{P ESS (n), 0};

[0077] If ΔP net (t) < 0, the nth period belongs to the normal period, and soc(t) ≤ SOC min , then p ESS (t) = min{max[P ESS (n) + ΔP net (t), -P Cmax , 0};

[0078] If ΔP net (t) < 0, the nth period belongs to the normal period, and SOC min < soc(t) < SOC(t), then p ESS (t) = max{P ESS (n) + ΔP net (t), -P Cmax};

[0079] If ΔP net (t) < 0, the nth period belongs to the normal period, and SOC(t) < soc(t) < SOC max , then p ESS (t) = P ESS (n);

[0080] If ΔP net (t) < 0, the nth period belongs to the normal period, and soc(t) > SOC max , then p ESS (t) = max[P ESS (n), 0];

[0081] If ΔP net (t) < 0, the nth period belongs to the valley period and soc(t) ≤ SOC<0, min , then p ESS (t) = min{max[P ESS (n) + ΔP ; net [[ID="]](t), -P Cmax , 0};

[0082] If ΔP net (t) < 0, the nth period belongs to the valley period and SOC min < soc(t) < SOC max , then p ESS (t) = max[P ESS(n) + ΔP net , -P Cmax ;

[0083] If ΔP net (t) < 0, the nth period belongs to the valley period and soc(t) ≥ SOC max , then p ESS (t) = max{P ESS (n) + ΔP net (t), 0};

[0084] If ΔP net (t) ≥ 0, the nth period belongs to the peak period, and soc(t) ≤ SOC min , then p ESS (t) = min{P ESS (n) + ΔP net (t), 0};

[0085] If ΔP net (t) ≥ 0, the nth period belongs to the peak period, and SOC min < soc(t) < SOC max , then p<​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ESS (t) = min{P ESS (n)+ΔP net (t), P Dmax};

[0090] If ΔP net (t)≥0, the nth period belongs to the normal period and soc(t)≥SOC max , then p ESS (t) = max{min[P ESS (n)+ΔP net (t), P Dmax ],0};

[0091] If ΔP net (t)≥0, the nth period belongs to the valley period, and soc(t)≤SOC min , then p ESS (t) = min{P ESS (n),0};

[0092] If ΔP net (t)≥0, the nth period belongs to the valley period, and SOC min <soc(t)<SOC max , then p ESS (t) = P ESS (n);

[0093] If ΔP net (t)≥0, the nth period belongs to the valley period, and soc(t)≥SOC max , then p ESS (t) = max{P ESS (n),0};

[0094] Where ΔP net (t) represents the net load deviation value corresponding to the residential distribution station area at time t, soc(t) represents the SOC value of the energy storage power at time t; SOC max Indicates the SOC upper limit of the energy storage device; SOC min Indicates the lower limit of SOC of the energy storage device; p ESS (t) represents the energy storage power dispatch value corresponding to the energy storage device at time t; p ESS (n) represents the energy storage power plan value corresponding to the energy storage device in the nth time period; P Dmax Indicates the upper limit of the discharge power of the energy storage device; P Cmax (n) represents the upper limit of the charging power of the energy storage device.

[0095] S105: Determine the photovoltaic power generation power control value and the electric vehicle charging power control value at the current moment based on the net load deviation value of the current period, the actual power supply power of the power grid, the energy storage power control value, the photovoltaic power generation power plan value and the electric vehicle charging power plan value.

[0096] In one possible implementation, the implementation process of S105 includes:

[0097] If the net load deviation value is less than zero and the grid power supply power of the residential distribution station area at the current moment is less than the power supply power lower limit, then reduce the energy storage power control value at the current moment until the grid power supply power of the residential distribution station area at the current moment is not less than the power supply power lower limit;

[0098] If the energy storage power control value of the energy storage device at the current moment is reduced to the first target threshold or the SOC value at the current moment is greater than or equal to the SOC upper limit of the energy storage device, and the grid power supply power of the residential distribution station area at the current moment is still less than the power supply power lower limit, then the photovoltaic power generation power control value at the current moment is reduced until the grid power supply power of the residential distribution station area at the current moment is not less than the power supply power lower limit; the first target threshold is the inverse of the charging power upper limit of the energy storage device;

[0099] If the net load deviation value is not less than zero and the grid power supply power of the residential distribution station area at the current moment is greater than the power supply power upper limit, then increase the energy storage power control value at the current moment until the grid power supply power of the residential distribution station area at the current moment is no greater than the power supply power upper limit;

[0100] If the energy storage power control value of the energy storage device at the current moment is reduced to the discharge power upper limit of the energy storage device or the SOC value at the current moment is less than or equal to the SOC lower limit of the energy storage device, and the grid power supply power of the residential distribution station area at the current moment is still greater than the power supply power upper limit, then the electric vehicle charging power control value at the current moment is reduced until the grid power supply power of the residential distribution station area at the current moment is no greater than the power supply power upper limit.

[0101] In this embodiment, when ΔP net (t)<0: If p Grid (t) <P Gmin , then reduce p ESS (t) until p Grid (t)≥P Gmin If p ESS (t) is reduced to -P Cmax or soc(t)≥SOC max After still p Grid (t) <P Gmin , then reduce the photovoltaic power generation power p PV(t) until p Grid (t)≥P Gmin ;

[0102] When ΔP net (t)≥0: If p Grid (t)>P Gmax , then increase p ESS (t) until p Grid (t)≤P G·max If p ESS (t) Increase to P Dmax or soc(t)≤SOC min After still p Grid (t)>P Gmax , then reduce the electric vehicle charging power p EV (t) until p Grid (t)≤P Gmax .

[0103] Every minute, the total photovoltaic power generation control value, the total energy storage power control value, and the total electric vehicle charging power control value determined by the above method will be distributed to each photovoltaic power generation device, energy storage device, and electric vehicle charging device in proportion according to the rated power of each photovoltaic power generation device, the rated power of each energy storage device, and the rated power of each electric vehicle charging device, so as to realize real-time control of the photovoltaic storage and charging system.

[0104] This embodiment proposes a method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution stations. This method can reduce the amount of calculation required for optimization and control of photovoltaic storage and charging power, and can be implemented through the intelligent terminal in the station area; it does not need to communicate with the main station system, and can achieve complete autonomy of photovoltaic storage and charging in the station area, without increasing the calculation and communication pressure of the main station; secondly, the method provided by this embodiment makes full use of the limited capacity of energy storage to smooth out power fluctuations in the station area, and can avoid excessive charging and discharging times of energy storage batteries, thereby extending the service life of the battery; thirdly, this embodiment maximizes the use of photovoltaic power generation and reduces the waste of photovoltaic energy; it can reduce the electricity purchase cost and energy storage charging and discharging cost of the station area, and improve the economy of the station area operation; at the same time, it can ensure the safe operation of the low-voltage distribution network in the station area and avoid overload of the distribution transformer.

[0105] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0106] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0107] Figure 2The following is a schematic diagram showing the structure of a device for coordinating and optimizing the photovoltaic storage and charging power in a residential distribution station area provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows:

[0108] like Figure 2 As shown, the photovoltaic storage and charging power coordination optimization control device 100 in the residential distribution station area includes:

[0109] A day-ahead scheduling plan data determination module 110 is configured to obtain optimal day-ahead scheduling plan data for a residential distribution area including a distributed photovoltaic system, an energy storage device, and an electric vehicle charging device. The optimal day-ahead scheduling plan data includes a planned photovoltaic power generation power value, a planned energy storage power value, a planned electric vehicle charging power value, and a planned state of charge (SOC) value of the energy storage device at the end of each time period.

[0110] The energy storage SOC control module 120 is used to determine the planned SOC value at the current moment based on the planned SOC values ​​of the energy storage device at the end of the previous and next time periods;

[0111] The net load deviation value calculation module 130 is used to determine the net load deviation value corresponding to the residential distribution station area at the current moment based on the photovoltaic power generation power plan value and the energy storage power plan value of the current period;

[0112] The energy storage power control value determination module 140 is used to determine the energy storage power control value of the energy storage device at the current moment based on the power time period, the net load deviation value and the SOC plan value;

[0113] The photovoltaic charging control module 150 is used to determine the photovoltaic power generation power control value and the electric vehicle charging power control value at the current moment based on the net load deviation value of the current period, the actual power supply power of the power grid, the energy storage power control value, the photovoltaic power generation power plan value and the electric vehicle charging power plan value.

[0114] In one possible implementation, the energy storage SOC control module 120 includes:

[0115] Based on the formula Determine the SOC value at the current moment;

[0116] Wherein, ceil() represents a round-up function; t represents the t-th moment of the current cycle, n represents the n-th period of the current cycle, SOC(n) represents the planned SOC value at the end of the n-th period; SOC(n-1) represents the planned SOC value at the end of the n-1-th period.

[0117] In one possible implementation, the net load deviation value calculation module 130 includes:

[0118] Based on the formula p net (t) = pGrid (t)-p ESS-f (t) Determine the actual value of the net load of the residential distribution station area at the current moment;

[0119] Based on the formula P net (n) = P Grid (n)-P ESS (n) Determine the planned net load value of the residential distribution area during the current period;

[0120] Based on the formula ΔP net (t) = p net (t)-P net (n) Determine the net load deviation value corresponding to the residential distribution station area at the current moment;

[0121] Among them, p Grid (t) represents the actual grid power supply at the current time t, p ESS-f (t) represents the actual energy storage power at the current time t; P Grid (n) represents the planned value of the power supply power of the power grid in the residential distribution area in the current period; P ESS (n) represents the planned energy storage power value of the residential distribution station area in the current period; ΔP net (t) represents the net load deviation value corresponding to the residential distribution station area at the current time t.

[0122] In one possible implementation, the energy storage power control value determination module 140 includes:

[0123] When ΔP net (t)<0, the nth period belongs to the peak period and soc(t)≤SOC min , then p ESS (t) = min{P ESS (n),0};

[0124] When ΔP net (t)<0, the nth period belongs to the peak period and SOC min <soc(t)<SOC max , then p ESS (t) = P ESS (n);

[0125] When ΔP net (t)<0, the nth period belongs to the peak period and soc(t)≥SOC max , then p ESS (t) = max{P ESS (n),0};

[0126] If ΔP net(t) < 0, the nth period belongs to the normal period, and soc(t) ≤ SOC min , then p ESS (t) = min{max[P ESS (n) + ΔP net (t), -P Cmax , 0};

[0127] If ΔP net (t) < 0, the nth period belongs to the normal period, and SOC min < soc(t) < SOC(t), then p ESS (t) = max{P ESS (n) + ΔP net (t), -P Cmax};

[0128] If ΔP net (t) < 0, the nth period belongs to the normal period, and SOC(t) < soc(t) < SOC max , then p ESS (t) = P ESS (n);

[0129] If ΔP net (t) < 0, the nth period belongs to the normal period, and soc(t) > SOC max , then p ESS (t) = max[P ESS (n), 0];

[0130] If ΔP net (t) < 0, the nth period belongs to the valley period and soc(t) ≤ SOC min , then p ESS (t) = min{max[P ESS (n) + ΔP net , -P Cmax , 0};

[0131] If ΔP net (t) < 0, the nth period belongs to the valley period and SOC min < soc(t) < SOC max , then p<所给内容中此标签有误,应为 ESS (t) = max[P ESS (n) + ΔP net , -P Cmax ;

[0132] If ΔP net (t) < 0, the nth period belongs to the valley period and soc(t) ≥ SOC max , then p ESS (t) = max{PESS (n)+ΔP net (t), 0};

[0133] If ΔP net (t) ≥ 0, the nth period belongs to the peak period, and soc(t) ≤ SOC min , then p ESS (t) = min{P ESS (n)+ΔP net (t), 0};

[0134] If ΔP net (t) ≥ 0, the nth period belongs to the peak period, and SOC min < soc(t) < SOC max , then p ESS (t) = min{P ESS (n)+ΔP net (t), P Dmax};

[0135] If ΔP net (t) ≥ 0, the nth period belongs to the peak period, and soc(t) ≥ SOC max , then p ESS (t) = max{P ESS (n)+ΔP net (t), 0};

[0136] If ΔP net (t) ≥ 0, the nth period belongs to the normal period and soc(t) ≤ SOC min , then p ESS (t) = min{P ESS (n), 0};

[0137] If ΔP net (t) ≥ 0, the nth period belongs to the normal period and SOC min < soc(t) < SOC(t), then p ESS (t) = P ESS (n);

[0138] If ΔP net (t) ≥ 0, the nth period belongs to the normal period and SOC(t) < soc(t) < SOC max , then p ESS (t) = min{P ESS (n)+ΔP net (t), P[[ID=B2]] Dmax};

[0139] If ΔP net (t) ≥ 0, the nth period belongs to the normal period and soc(t) ≥ SOCmax , then p ESS (t) = max{min[P ESS (n)+ΔP net (t), P Dmax ],0};

[0140] If ΔP net (t)≥0, the nth period belongs to the valley period, and soc(t)≤SOC min , then p ESS (t) = min{P ESS (n),0};

[0141] If ΔP net (t)≥0, the nth period belongs to the valley period, and SOC min <soc(t)<SOC max , then p ESS (t) = P ESS (n);

[0142] If ΔP net (t)≥0, the nth period belongs to the valley period, and soc(t)≥SOC max , then p ESS (t) = max{P ESS (n),0};

[0143] Where ΔP net (t) represents the net load deviation value corresponding to the residential distribution station area at time t, soc(t) represents the SOC value of the energy storage power at time t; SOC max Indicates the SOC upper limit of the energy storage device; SOC min Indicates the lower limit of SOC of the energy storage device; p ESS (t) represents the energy storage power dispatch value corresponding to the energy storage device at time t; p ESS (n) represents the energy storage power plan value corresponding to the energy storage device in the nth time period; P Dmax Indicates the upper limit of the discharge power of the energy storage device; P Cmax Indicates the upper limit of the charging power of the energy storage device.

[0144] In one possible implementation, the light charging control module 150 includes:

[0145] If the net load deviation value is less than zero and the grid power supply power of the residential distribution station area at the current moment is less than the power supply power lower limit, then reduce the energy storage power control value at the current moment until the grid power supply power of the residential distribution station area at the current moment is not less than the power supply power lower limit;

[0146] If the energy storage power control value of the energy storage device at the current moment is reduced to the first target threshold or the SOC value at the current moment is greater than or equal to the SOC upper limit of the energy storage device, and the grid power supply power of the residential distribution station area at the current moment is still less than the power supply power lower limit, then the photovoltaic power generation power control value at the current moment is reduced until the grid power supply power of the residential distribution station area at the current moment is not less than the power supply power lower limit; the first target threshold is the inverse of the charging power upper limit of the energy storage device;

[0147] If the net load deviation value is not less than zero and the grid power supply power of the residential distribution station area at the current moment is greater than the power supply power upper limit, then increase the energy storage power control value at the current moment until the grid power supply power of the residential distribution station area at the current moment is no greater than the power supply power upper limit;

[0148] If the energy storage power control value of the energy storage device at the current moment is reduced to the discharge power upper limit of the energy storage device or the SOC value at the current moment is less than or equal to the SOC lower limit of the energy storage device, and the grid power supply power of the residential distribution station area at the current moment is still greater than the power supply power upper limit, then the electric vehicle charging power control value at the current moment is reduced until the grid power supply power of the residential distribution station area at the current moment is no greater than the power supply power upper limit.

[0149] In one possible implementation, the day-ahead scheduling plan data determination module 150 includes:

[0150] Taking the minimization of the cost of purchasing electricity from the upper distribution network and the operating cost of the energy storage device as the optimization objectives, a day-ahead optimal scheduling model for residential distribution stations that includes distributed photovoltaic systems, energy storage devices, and electric vehicle charging devices is constructed.

[0151] The day-ahead optimization scheduling model is solved based on the particle swarm optimization algorithm to obtain the optimal day-ahead scheduling plan data for the residential power distribution station area.

[0152] In one possible implementation, the objective function of the day-ahead optimization scheduling model is:

[0153]

[0154] Among them, F Grid F represents the electricity purchase and sales costs of residential distribution stations; ESS Represents the energy storage operating cost; C Buy (n) represents the electricity purchase price of the residential distribution station area in the nth period, P Buy (n) represents the purchased power in the nth period, C Sell (n) represents the electricity price of the residential distribution area in the nth period, P Sell(n) represents the electricity sales power of the residential distribution station area in the nth period; P C (n) represents the charging power of the energy storage device in the nth period; P D (n) represents the discharge power of the energy storage device in the nth period; C ESS It represents the unit operating cost of the energy storage device during the charging and discharging process; N represents the number of time periods in a single cycle.

[0155] In one possible implementation, the constraints of the day-ahead optimization scheduling model include power balance constraints for residential distribution stations, power supply constraints, energy storage charging and discharging power constraints, energy storage SOC transfer relationship constraints, energy storage SOC state upper and lower limit constraints, energy storage SOC state balance constraints at the beginning and end of the scheduling period, photovoltaic output constraints, and electric vehicle charging power constraints.

[0156] The power balance constraint of the residential distribution station area is:

[0157] P Grid (n)+P PV (n)+P ESS (n) = P EV (n)+P Load (n)

[0158] Among them, P Grid (n) represents the planned power supply value of the power grid in the residential distribution area in the nth period; P PV (n) represents the planned photovoltaic power generation value of the residential distribution station area in the nth period; P ESS (n) represents the planned energy storage power value for the nth period; P EV (n) represents the planned charging power value of the electric vehicle in the nth period, P Load (n) represents the planned power value of conventional load in the nth period;

[0159] The power supply constraints are:

[0160] P Gmin ≤P Grid (n)≤P Gmax

[0161] Among them, P Gmin Indicates the lower limit of the power supply, P Gmax Indicates the upper limit of power supply;

[0162] The energy storage charging and discharging power constraints are:

[0163]

[0164] Among them, P c (n) represents the planned charging power value of the energy storage device in the nth time period; Pc (n) represents the planned discharge power value of the energy storage device in the nth time period; P Cmax Indicates the upper limit of the charging power of the energy storage device, P Dmax Indicates the upper limit of the discharge power of the energy storage device;

[0165] The energy storage SOC transfer relationship constraint is:

[0166]

[0167] Where SOC(n) represents the planned SOC value of the energy storage device at the end of the nth period; η C represents the charging efficiency of the energy storage device, η D represents the discharge efficiency of the energy storage device; ΔT represents the scheduling time interval; E N Indicates the rated capacity of the energy storage device;

[0168] The upper and lower limits of the energy storage SOC state are:

[0169]

[0170] Among them, SOC(0) represents the SOC planned value of the energy storage device at the beginning of the first period; SOC ini Indicates the initial SOC value of the energy storage device for day-ahead scheduling; SOC max Indicates the SOC upper limit of the energy storage device; SOC min Indicates the lower limit of the SOC of the energy storage device;

[0171] The energy storage SOC state balance constraint at the beginning and end of the scheduling period is:

[0172] SOC(0)=SOC(N)

[0173] The photovoltaic output constraint is:

[0174] 0≤P PV (n)≤P PVmax (n)

[0175] Among them, P PVmax (n) represents the estimated value of photovoltaic power generation in the residential distribution area in the nth period;

[0176] The electric vehicle charging power constraint is:

[0177] 0≤P EV (n)≤P EVmax (n)

[0178] Among them, P EVmax (n) represents the upper limit of the charging power of electric vehicles in the residential distribution station area in the nth period.

[0179] The PV-storage-charging power coordination optimization control device for residential distribution stations provided in this embodiment first obtains historical data on the total power supplied to the station by the power grid, or the total load power (i.e., the sum of conventional load and charging pile load), and the historical data on photovoltaic power generation, stored in the station's intelligent terminal. Then, using the total load data for the previous weekday or weekend and the photovoltaic power generation data for the previous day, a day-ahead optimization scheduling model for PV-storage-charging is constructed, with the goal of minimizing operating costs (power purchase costs + energy storage charging and discharging costs) and constraints such as ensuring that the distribution transformer is not overloaded. This model determines the optimal day-ahead output plan for energy storage. Finally, a day-ahead optimization control strategy for PV-storage-charging is developed that takes into account time-of-use electricity prices. Based on the day-ahead scheduling plan, the PV-storage-charging power, charging pile charging power, and photovoltaic power generation power are optimized and adjusted in real time according to the real-time power of the station's photovoltaic power generation, conventional load, and charging pile load. The intelligent terminal then issues adjustment commands to the PV-storage-charging equipment. This device can be independently implemented by the intelligent terminal and effectively improves the economic efficiency and reliability of PV-storage-charging residential distribution station operations without the support of a higher-level master station.

[0180] Figure 3 Schematic diagram of a terminal provided by an embodiment of the present invention. Figure 3 As shown, the terminal 3 of this embodiment includes: a processor 30 and a memory 31. The memory 31 is used to store a computer program 32, and the processor 30 is used to call and run the computer program 32 stored in the memory 31 to perform the steps in the embodiment of the method for coordinated optimization and control of photovoltaic storage and charging power in each residential distribution station area, such as Figure 1 Alternatively, the processor 30 is used to call and run the computer program 32 stored in the memory 31 to implement the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of the modules 110 to 50 are shown.

[0181] Exemplarily, the computer program 32 may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 32 in the terminal 3. For example, the computer program 32 may be divided into Figure 2 Modules 110 to 150 are shown.

[0182] The terminal 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that Figure 3It is only an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0183] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0184] The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the terminal 3. Furthermore, the memory 31 may include both an internal storage unit of the terminal 3 and an external storage device. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is about to be output.

[0185] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0186] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0187] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0188] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, and can be electrical, mechanical, or other forms.

[0189] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0190] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0191] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the embodiment of the above-mentioned method for coordinated optimization and control of photovoltaic storage and charging power in each residential distribution station area. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0192] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution areas, characterized in that: include: Obtaining optimal day-ahead scheduling data for a residential distribution area containing a distributed photovoltaic system, an energy storage device, and an electric vehicle charging device, the optimal day-ahead scheduling data including a planned photovoltaic power generation value, a planned energy storage power value, a planned electric vehicle charging power value, and a planned state of charge (SOC) value of the energy storage device at the end of each time period; Determine the planned SOC value at the current moment based on the planned SOC values ​​of the energy storage device at the end of the previous and next time periods; Based on the planned photovoltaic power generation value and energy storage power value for the current period, determine the net load deviation value corresponding to the residential distribution station area at the current moment; Determine the energy storage power control value of the energy storage device at the current moment based on the power time period, the net load deviation value, and the SOC plan value; Based on the net load deviation value of the current period, the actual power supply power of the power grid, the energy storage power control value, the photovoltaic power generation power plan value and the electric vehicle charging power plan value, the photovoltaic power generation power control value and the electric vehicle charging power control value at the current moment are determined.

2. The method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution area according to claim 1 is characterized in that: The determining of the planned SOC value at the current moment based on the planned SOC values ​​of the energy storage device at the end of the previous and next time periods includes: Based on the formula Determine the SOC value at the current moment; Wherein, ceil() represents a round-up function; t represents the t-th moment of the current cycle, n represents the n-th period of the current cycle, SOC(n) represents the planned SOC value at the end of the n-th period; SOC(n-1) represents the planned SOC value at the end of the n-1-th period.

3. The method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution area according to claim 1 is characterized in that: The method of determining the net load deviation value corresponding to the residential distribution area at the current moment based on the planned photovoltaic power generation value and the planned energy storage power value in the current period includes: Based on the formula p net (t) = p Grid (t)-p ESS-f (t) Determine the actual value of the net load of the residential distribution station area at the current moment; Based on the formula P net (n) = P Grid (n)-P ESS (n) Determine the planned net load value of the residential distribution area during the current period; Based on the formula ΔP net (t) = p net (t)-P net (n) Determine the net load deviation value corresponding to the residential distribution station area at the current moment; Among them, p Grid (t) represents the actual power supply of the power grid at the current time t, p ESS-f (t) represents the actual energy storage power at the current time t; P Grid (n) represents the planned value of the power supply power of the power grid in the residential distribution area in the current period; P ESS (n) represents the planned energy storage power value of the residential distribution station area in the current period; ΔP net (t) represents the net load deviation value corresponding to the residential distribution station area at the current time t.

4. The method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution area according to claim 1 is characterized in that: The determining of the energy storage power control value of the energy storage device at the current moment based on the power time period, the net load deviation value, and the SOC plan value at the current moment includes: When ΔP net (t)<0, the nth period belongs to the peak period and soc(t)≤SOC min , then p ESS (t) = min{P ESS (n),0}; When ΔP net (t)<0, the nth period belongs to the peak period and SOC min <soc(t)<SOC max , then p ESS (t) = P ESS (n); When ΔP net (t)<0, the nth period belongs to the peak period and soc(t)≥SOC max , then p ESS (t) = max{P ESS (n),0}; If ΔP net (t)<0, the nth period belongs to the normal period, and soc(t)≤SOC min , then p ESS (t) = min{max[P ESS (n)+ΔP net (t), -P Cmax ],0}; If ΔP net (t) < 0, the nth period belongs to the normal period, and SOC min < soc(t) < SOC(t), then p ESS (t) = max{P ESS (n) + ΔP net (t), -P Cmax}; If ΔP net (t)<0, the nth period belongs to the normal period, and SOC(t) <soc(t)<SOC max , then p ESS (t) = P ESS (n); If ΔP net (t)<0, the nth period belongs to the normal period, and soc(t)>SOC max , then p ESS (t) = max[P ESS (n),0]; If ΔP net (t)<0, the nth period belongs to the valley period and soc(t)≤SOC min , then p ESS (t) = min{max[P ESS (n)+ΔP net , -P Cmax ],0}; If ΔP net (t)<0, the nth period belongs to the valley period and SOC min <soc(t)<SOC max , then p ESS (t) = max[P ESS (n)+ΔP net , -P Cmax ]; If ΔP net (t)<0, the nth period belongs to the valley period and soc(t)≥SOC max , then p ESS (t) = max{P ESS (n)+ΔP net (t), 0}; If ΔP net (t)≥0, the nth period belongs to the peak period, and soc(t)≤SOC min , then p ESS (t) = min{P ESS (n)+ΔP net (t), 0}; If ΔP net (t)≥0, the nth period belongs to the peak period, and SOC min <soc(t)<SOC max , then p ESS (t) = min{P ESS (n)+ΔP net (t), P Dmax }; If ΔP net (t)≥0, the nth period belongs to the peak period, and soc(t)≥SOC max , then p ESS (t) = max{P ESS (n)+ΔP net (t), 0}; If ΔP net (t)≥0, the nth period belongs to the normal period and soc(t)≤SOC min , then p ESS (t) = min{P ESS (n),0}; If ΔP net (t) ≥ 0, the nth period belongs to the normal period and SOC min < soc(t) < SOC(t), then p ESS (t) = P ESS (n); If ΔP net (t)≥0, the nth period belongs to the normal period and SOC(t) <soc(t)<SOC max , then p ESS (t) = min{P ESS (n)+ΔP net (t), P Dmax }; If ΔP net (t)≥0, the nth period belongs to the normal period and soc(t)≥SOC max , then p ESS (t) = max{min[P ESS (n)+ΔP net (t), P Dmax ],0}; If ΔP net (t)≥0, the nth period belongs to the valley period, and soc(t)≤SOC min , then p ESS (t) = min{P ESS (n),0}; If ΔP net (t)≥0, the nth period belongs to the valley period, and SOC min <soc(t)<SOC max , then p ESS (t) = P ESS (n); If ΔP net (t)≥0, the nth period belongs to the valley period, and soc(t)≥SOC max , then p ESS (t) = max{P ESS (n),0}; Where ΔP net (t) represents the net load deviation value corresponding to the residential distribution station area at time t, soc(t) represents the SOC value of the energy storage power at time t; SOC max Indicates the SOC upper limit of the energy storage device; SOC min Indicates the lower limit of SOC of the energy storage device; p ESS (t) represents the energy storage power dispatch value corresponding to the energy storage device at time t; p ESS (n) represents the energy storage power plan value corresponding to the energy storage device in the nth time period; P Dmax Indicates the upper limit of the discharge power of the energy storage device; P Cmax Indicates the upper limit of the charging power of the energy storage device.

5. The method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution area according to claim 1 is characterized in that: The method of determining the photovoltaic power generation power control value and the electric vehicle charging power control value at the current moment based on the net load deviation value of the current period, the actual power supply power of the power grid, the energy storage power control value, the photovoltaic power generation power plan value and the electric vehicle charging power plan value includes: If the net load deviation value is less than zero and the grid power supply power of the residential distribution station area at the current moment is less than the power supply power lower limit, then reduce the energy storage power control value at the current moment until the grid power supply power of the residential distribution station area at the current moment is not less than the power supply power lower limit; If the energy storage power control value of the energy storage device at the current moment is reduced to the first target threshold or the SOC value at the current moment is greater than or equal to the SOC upper limit of the energy storage device, and the grid power supply power of the residential distribution station area at the current moment is still less than the power supply power lower limit, then the photovoltaic power generation power control value at the current moment is reduced until the grid power supply power of the residential distribution station area at the current moment is not less than the power supply power lower limit; the first target threshold is the inverse of the charging power upper limit of the energy storage device; If the net load deviation value is not less than zero and the grid power supply power of the residential distribution station area at the current moment is greater than the power supply power upper limit, then increase the energy storage power control value at the current moment until the grid power supply power of the residential distribution station area at the current moment is no greater than the power supply power upper limit; If the energy storage power control value of the energy storage device at the current moment is reduced to the discharge power upper limit of the energy storage device or the SOC value at the current moment is less than or equal to the SOC lower limit of the energy storage device, and the grid power supply power of the residential distribution station area at the current moment is still greater than the power supply power upper limit, then the electric vehicle charging power control value at the current moment is reduced until the grid power supply power of the residential distribution station area at the current moment is no greater than the power supply power upper limit.

6. The method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution area according to claim 1 is characterized in that: The acquisition of optimal day-ahead dispatch plan data for residential distribution areas containing distributed photovoltaic systems, energy storage devices, and electric vehicle charging devices includes: Taking the minimization of the cost of purchasing electricity from the upper distribution network and the operating cost of the energy storage device as the optimization objectives, a day-ahead optimal scheduling model for residential distribution stations that includes distributed photovoltaic systems, energy storage devices, and electric vehicle charging devices is constructed. The day-ahead optimization scheduling model is solved based on the particle swarm optimization algorithm to obtain the optimal day-ahead scheduling plan data for the residential power distribution station area.

7. The method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution area according to claim 6 is characterized in that: The objective function of the day-ahead optimization scheduling model is: Among them, F Grid F represents the electricity purchase and sales costs of residential distribution stations; ESS Represents the energy storage operating cost; C Buy (n) represents the electricity purchase price of the residential distribution station area in the nth period, P Buy (n) represents the purchased power in the nth period, C Sell (n) represents the electricity price of the residential distribution area in the nth period, P Sell (n) represents the electricity sales power of the residential distribution station area in the nth period; P C (n) represents the charging power of the energy storage device in the nth period; P D (n) represents the discharge power of the energy storage device in the nth period; C ESS It represents the unit operating cost of the energy storage device during the charging and discharging process; N represents the number of time periods in a single cycle.

8. The method for coordinated optimization and control of photovoltaic storage and charging power in residential distribution area according to claim 6 is characterized in that: The constraints of the day-ahead optimization scheduling model include power balance constraints for residential distribution stations, power supply constraints, energy storage charging and discharging power constraints, energy storage SOC transfer relationship constraints, energy storage SOC state upper and lower limit constraints, energy storage SOC state balance constraints at the beginning and end of the scheduling period, photovoltaic output constraints, and electric vehicle charging power constraints. The power balance constraint of the residential distribution station area is: P Grid (n)+P PV (n)+P ESS (n)=P EV (n)+P Load (n) Among them, P Grid (n) represents the planned power supply value of the power grid in the residential distribution area in the nth period; P PV (n) represents the planned photovoltaic power generation value of the residential distribution station area in the nth period; P ESS (n) represents the planned energy storage power value for the nth period; P EV (n) represents the planned charging power value of the electric vehicle in the nth period, P Load (n) represents the planned power value of conventional load in the nth period; The power supply constraint is: P Gmin ≤P Grid (n)≤P Gmax Among them, P Gmin Indicates the lower limit of the power supply, P Gmax Indicates the upper limit of power supply; The energy storage charging and discharging power constraints are: Among them, P c (n) represents the planned charging power value of the energy storage device in the nth time period; P c (n) represents the planned discharge power value of the energy storage device in the nth time period; P Cmax Indicates the upper limit of the charging power of the energy storage device, P Dmax Indicates the upper limit of the discharge power of the energy storage device; The energy storage SOC transfer relationship constraint is: Where SOC(n) represents the planned SOC value of the energy storage device at the end of the nth period; η C represents the charging efficiency of the energy storage device, η D Indicates the discharge efficiency of the energy storage device; E N represents the rated capacity of the energy storage device; ΔT represents the scheduling time interval; The upper and lower limits of the energy storage SOC state are: Among them, SOC(0) represents the SOC planned value of the energy storage device at the beginning of the first period; SOC ini Indicates the initial SOC value of the energy storage device for day-ahead scheduling; SOC max Indicates the SOC upper limit of the energy storage device; SOC min Indicates the lower limit of the SOC of the energy storage device; The energy storage SOC state balance constraint at the beginning and end of the scheduling period is: SOC(0)=SOC(N) The photovoltaic output constraint is: 0≤P PV (n)≤P PVmax (n) Among them, P PVmax (n) represents the estimated value of photovoltaic power generation in the residential distribution area in the nth period; The electric vehicle charging power constraint is: 0≤P EV (n)≤P EVmax (n) Among them, P EVmax (n) represents the upper limit of the charging power of electric vehicles in the residential distribution station area in the nth period.

9. A device for coordinating and optimizing the photovoltaic storage and charging power in a residential distribution area, characterized in that: include: A day-ahead scheduling plan data determination module is used to obtain the optimal day-ahead scheduling plan data for a residential distribution station area containing a distributed photovoltaic system, an energy storage device, and an electric vehicle charging device. The optimal day-ahead scheduling plan data includes the planned photovoltaic power generation power value, the planned energy storage power value, the planned electric vehicle charging power value, and the planned state of charge (SOC) value of the energy storage device at the end of each time period; The energy storage SOC control module is used to determine the planned SOC value at the current moment based on the planned SOC values ​​of the energy storage device at the end of the previous and next time periods; The net load deviation value calculation module is used to determine the net load deviation value corresponding to the residential distribution station area at the current moment based on the photovoltaic power generation power plan value and the energy storage power plan value of the current period; An energy storage power control value determination module is used to determine the energy storage power control value of the energy storage device at the current moment based on the power time period, net load deviation value and SOC plan value at the current moment; The photovoltaic charging control module is used to determine the photovoltaic power generation power control value and electric vehicle charging power control value at the current moment based on the net load deviation value of the current period, the actual power supply power of the power grid, the energy storage power control value, the photovoltaic power generation power plan value and the electric vehicle charging power plan value.

10. The device for coordinating and optimizing the photovoltaic storage and charging power in residential distribution area according to claim 9 is characterized in that: The energy storage SOC control module includes: Based on the formula Determine the SOC value at the current moment; Wherein, ceil() represents a round-up function; t represents the t-th moment of the current cycle, n represents the n-th period of the current cycle, SOC(n) represents the planned SOC value at the end of the n-th period; SOC(n-1) represents the planned SOC value at the end of the n-1-th period.

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