Dynamic optimization regulation and control method and device for distributed photovoltaic access power distribution area

Abstract

The invention relates to the technical field of power distribution area safe operation control, and particularly provides a dynamic optimization regulation and control method and device for distributed photovoltaic access to a power distribution area, and the method comprises the steps: dynamically selecting a model from a plurality of models as an optimization control model based on the operation condition of the power distribution area, and solving the optimization control model, an optimization result is obtained; based on the optimization result, obtaining an optimization control scheme of the power distribution area; wherein the plurality of models comprise a flexible scheduling model and a rigid control model. According to the technical scheme provided by the invention, the purpose of multi-aspect optimization of safe operation of the power distribution area and reliable power generation of the multi-distributed photovoltaic main body is achieved by combining the area source network load storage flexible coordinated scheduling and the photovoltaic rigid switching control strategy, so that the safe operation of the power distribution area can be ensured, the distributed photovoltaic power generation loss can be reduced, and the power generation efficiency is improved. Distributed photovoltaic absorption is promoted, and distributed photovoltaic large-scale development is supported.

Description

Technical Field

[0001] The present invention relates to the technical field of safe operation control of power distribution stations, and in particular to a dynamic optimization control method and device for accessing a distributed photovoltaic power distribution station. Background Art

[0002] With the large-scale development of distributed photovoltaics, the volatility and randomness of high-proportion distributed photovoltaic output have significantly increased, the grid load fluctuations have increased, showing a "duck curve" feature, and the difficulty of matching source and load supply and demand and balancing on-site has increased, resulting in prominent problems such as voltage / power exceeding the limit of the distribution network. The low-voltage network has "high voltage during the day and low voltage at night", and the voltage regulation is more difficult, which brings great challenges to the dispatching and control of the distribution network.

[0003] In order to ensure the safe operation of distribution substations and reduce the loss of distributed photovoltaic power generation, there is an urgent need for a dynamic optimization and control method that can promote the consumption of distributed photovoltaics, support the large-scale development of distributed photovoltaics and connect distributed photovoltaics to distribution substations. Summary of the invention

[0004] In order to overcome the above-mentioned defects, the present invention proposes a method and device for dynamically optimizing and controlling distributed photovoltaic access to a distribution station area.

[0005] In a first aspect, a method for dynamically optimizing and controlling a distributed photovoltaic access distribution substation is provided, wherein the method comprises:

[0006] Dynamically selecting a model from a plurality of models as an optimization control model based on the operation status of the distribution station area, and solving the optimization control model to obtain an optimization result;

[0007] Based on the optimization results, an optimization control scheme for the distribution station area is obtained;

[0008] Among them, the multiple models include: a flexible scheduling model and a rigid control model.

[0009] Preferably, the dynamically selecting the optimization control model based on the operation status of the distribution station area includes:

[0010] When the operating condition of the distribution station area is that the voltage exceeds the limit and is overloaded, the flexible scheduling model is selected as the optimization control model. When the operating condition of the distribution station area is that the voltage exceeds the limit and is seriously overloaded, the rigid control model is selected as the optimization control model.

[0011] Furthermore, when the flexible scheduling model is selected, the optimization results include: interruption time of interruptible load, transfer power of transferable load, output power of energy storage unit, and scheduling power of energy storage unit; when the rigid control model is selected, the optimization results include: grid-connected electricity price of distributed photovoltaics, controlled active power of distributed photovoltaics, and execution control coefficient of distributed photovoltaics.

[0012] Furthermore, when the voltage per unit value of the distribution station area exceeds the first preset value and does not exceed the second preset value, or is lower than the third preset value and not lower than the fourth preset value, the operation condition of the distribution station area is voltage over-limit; when the voltage per unit value of the distribution station area exceeds the second preset value or is lower than the fourth preset value, the operation condition of the distribution station area is severe voltage over-limit; when the load rate of the distribution station area exceeds 100% and lasts for more than a preset period of time, the operation condition of the distribution station area is overload operation; when the load rate of the distribution station area exceeds 120% and lasts for more than a preset period of time, the operation condition of the distribution station area is severe overload operation.

[0013] Preferably, the flexible scheduling model includes:

[0014] The first objective function aims at minimizing the total loss and the first constraint condition for configuring the dynamic optimization control of the distributed photovoltaic access distribution station area.

[0015] Furthermore, the mathematical model of the first objective function is as follows:

[0016]

[0017] In the above formula, O is the total loss target value, C es_run (t) is the energy storage operation loss at time t, C es_dis (t) is the energy storage dispatch loss at time t, C load_com (t) is the interruptible load compensation cost at time t, C load_trans (t) is the transferable load compensation cost at time t, C gird (t) is the cost of interacting with the grid at time t, and T is the optimization period.

[0018] Furthermore, the first constraint condition includes: energy storage constraint, interruptible load constraint, transferable load constraint, and power balance constraint.

[0019] Furthermore, the mathematical model of the energy storage constraint is as follows:

[0020] M i (t) = M i (t-1)-P i es (t-1)δ(t)

[0021] 0.1M max≤M i (t)≤0.9M max

[0022] -P es_max ≤P i es (t)≤P es_max

[0023] -P es_max ≤P i es_dis (t)≤P es_max

[0024] The mathematical model of the interruptible load constraint is as follows:

[0025]

[0026]

[0027]

[0028] The mathematical model of the transferable load constraint is as follows:

[0029]

[0030]

[0031] The mathematical model of the power balance constraint is as follows:

[0032]

[0033] In the above formula, M i (t) is the capacity state of the i-th group of energy storage at time t, M i (t-1) is the capacity state of the i-th group of energy storage at time t-1, P i es (t-1) is the output power of the i-th energy storage group at time t-1, δ(t) is the energy storage continuous control time, M max is the maximum capacity of energy storage, P es_max is the maximum charge and discharge power of energy storage, P i es (t) is the output power of the i-th group of energy storage at time t, P i es_dis (t) is the dispatching power of the i-th group of energy storage at time t, is the maximum interruptible duration of the kth interruptible load, u load_com,k (m) is the interruption coefficient of the kth interruptible load at time m. If the interruptible load is interrupted, it takes 1, otherwise it takes 0. is the minimum interruptible duration of the kth interruptible load, is the minimum output power of the kth interruptible load at time t, P load_com,k (t) is the output power of the kth interruptible load at time t, is the maximum output power of the kth interruptible load at time t, is the minimum transfer duration of the jth transferable load, u load_trans,j (m) is the transfer coefficient of the jth transferable load at time m. If the transferable load is transferred, it takes 1, otherwise it takes 0. is the minimum output power of the jth transferable load at time t, P load_trans,j (t) is the output power of the jth transferable load at time t, is the maximum output power of the jth transferable load at time t, P gird (t) is the interaction power with the grid at time t, P PV (t) is the photovoltaic power generation at time t, N es is the total number of energy storage, N load_com is the total number of interruptible loads, N load_trans is the total number of transferable loads, P load_fixed (t) is the fixed load power at time t, and t is the current time.

[0034] Preferably, the rigid control model includes:

[0035] The second objective function, the third objective function and the fourth objective function respectively aim at minimizing the impact on distributed photovoltaic power generation income, minimizing the total active control power of distributed photovoltaics, and minimizing the number of controlled distributed photovoltaics, and the second constraint condition, the third constraint condition and the fourth constraint condition configured for the second objective function, the third objective function and the fourth objective function.

[0036] Further, solving the optimization control model includes:

[0037] Solve the optimization model composed of the second objective function and the second constraint condition to obtain the optimization result of the on-grid electricity price of distributed photovoltaics and the first optimization result of the controlled active power of distributed photovoltaics;

[0038] Substituting the distributed photovoltaic grid-connected electricity price optimization result and the first optimization result of the distributed photovoltaic controlled active power into the optimization model composed of the third objective function and the third constraint condition and solving them to obtain the second optimization result of the distributed photovoltaic controlled active power;

[0039] Substituting the second optimization result of the controlled active power of the distributed photovoltaic into the optimization model composed of the fourth objective function and the fourth constraint condition and solving it to obtain the optimization result of the execution control coefficient of the distributed photovoltaic;

[0040] The optimization result of the distributed photovoltaic grid-connected electricity price, the second optimization result of the controlled active power of the distributed photovoltaic, and the optimization result of the execution control coefficient of the distributed photovoltaic are taken as the final optimization result;

[0041] The third constraint condition is composed of the second constraint condition and the first additional condition, and the fourth constraint condition is composed of the third constraint condition and the second additional condition.

[0042] Furthermore, the mathematical model of the second objective function is as follows:

[0043]

[0044] The mathematical model of the third objective function is as follows:

[0045]

[0046] The mathematical model of the fourth objective function is as follows:

[0047]

[0048] In the above formula, N PV is the total number of distributed photovoltaics, is the grid-connected electricity price of the nth distributed photovoltaic power station, is the controlled active power of the nth photovoltaic, is the active power execution control coefficient of the nth distributed photovoltaic. If the nth distributed photovoltaic executes control at time t, it takes 1, otherwise it takes 0.

[0049] Furthermore, the mathematical model of the second constraint condition is as follows:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] The mathematical model of the first additional condition is as follows:

[0057]

[0058] The mathematical model of the second additional condition is as follows:

[0059]

[0060] In the above formula, is the controlled active power of distributed photovoltaic at grid node x, is the active power execution control coefficient of distributed photovoltaic at grid node x, is the real-time active power of distributed photovoltaic at grid node x, is the real-time active power of the load at the grid node x, V x is the voltage of grid node x, N x is the set of nodes connected to node x, V y is the voltage of grid node y, G xy is the conductivity of the line from node x to node y, δ xy is the phase angle difference between node x and node y, B xy is the susceptance of the line from node x to node y, is the real-time reactive power of distributed photovoltaic at grid node x, is the controlled reactive power of distributed photovoltaic at grid node x, is the real-time reactive power of the load at the grid node x, is the reactive power execution control coefficient of distributed photovoltaic at grid node x, for, is the voltage lower limit of the grid node x, is the voltage upper limit of the grid node x, P xy is the active power from node x to node y, Q xy is the reactive power from node x to node y, is the maximum allowable value of the current from node x to node y, I rd is the objective function value of the second objective function, p rd is the objective function value of the third objective function.

[0061] In a second aspect, a dynamic optimization control device for accessing a distributed photovoltaic power distribution station area is provided, wherein the dynamic optimization control device for accessing a distributed photovoltaic power distribution station area comprises:

[0062] A first analysis module is used to dynamically select a model from multiple models as an optimization control model based on the operation status of the distribution station area, and solve the optimization control model to obtain an optimization result;

[0063] A second analysis module is used to obtain an optimization control scheme for the distribution station area based on the optimization result;

[0064] Among them, the multiple models include: a flexible scheduling model and a rigid control model.

[0065] In a third aspect, a computer device is provided, comprising: one or more processors;

[0066] The processor is used to store one or more programs;

[0067] When the one or more programs are executed by the one or more processors, the method for dynamically optimizing and controlling the distributed photovoltaic access to the power distribution station area is implemented.

[0068] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed, the dynamic optimization and control method of the distributed photovoltaic access distribution station area is implemented.

[0069] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0070] The present invention provides a method and device for dynamic optimization and control of distributed photovoltaic access to a distribution station area, including: dynamically selecting a model from multiple models as an optimization control model based on the operation status of the distribution station area, and solving the optimization control model to obtain an optimization result; based on the optimization result, obtaining an optimization control scheme for the distribution station area; wherein the multiple models include: a flexible scheduling model and a rigid control model. The technical scheme provided by the present invention combines the flexible coordinated scheduling of the source, grid, load and storage in the substation area with the photovoltaic rigid switching control strategy to achieve the multi-faceted optimization goals of safe operation of the distribution station area and reliable power generation of multiple distributed photovoltaic entities. It can not only ensure the safe operation of the distribution station area, but also reduce the loss of distributed photovoltaic power generation, promote the consumption of distributed photovoltaics, and support the large-scale development of distributed photovoltaics.

[0071] Specifically, the flexible dispatch model can achieve optimal dispatch of energy storage and load, effectively guarantee the safe operation of the distribution area, and promote the consumption of distributed photovoltaic power. The rigid control model can not only guarantee the safe operation of the distribution area, but also minimize the loss of distributed photovoltaic power generation, so as to ensure the power generation and consumption of distributed photovoltaic power and support the large-scale development of distributed photovoltaic power. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a schematic flow chart of the main steps of the dynamic optimization control method for accessing a distributed photovoltaic power distribution area according to an embodiment of the present invention;

[0073] Figure 2 It is a schematic diagram of the principle of a dynamic optimization control method for accessing a distributed photovoltaic power distribution area according to an embodiment of the present invention;

[0074] Figure 3 It is a main structural block diagram of a dynamic optimization control device for a distributed photovoltaic access distribution station area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0076] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] As disclosed in the background technology, with the large-scale development of distributed photovoltaics, the volatility and randomness of high-proportion distributed photovoltaic output have significantly increased, the grid load fluctuations have increased, showing a "duck curve" feature, and the difficulty of matching source and load supply and demand and balancing on-site has increased, resulting in prominent problems such as voltage / power exceeding the limit of the distribution network. The low-voltage network has "high voltage during the day and low voltage at night", and the voltage regulation is more difficult, which brings great challenges to the dispatching and control of the distribution network.

[0078] In order to ensure the safe operation of distribution substations and reduce the loss of distributed photovoltaic power generation, there is an urgent need for a dynamic optimization and control method that can promote the consumption of distributed photovoltaics, support the large-scale development of distributed photovoltaics and connect distributed photovoltaics to distribution substations.

[0079] In order to improve the above-mentioned problems, the present invention provides a method and device for dynamic optimization and control of distributed photovoltaic access to distribution stations, including: dynamically selecting a model from multiple models as an optimization control model based on the operation status of the distribution station, and solving the optimization control model to obtain optimization results; based on the optimization results, obtaining an optimization control scheme for the distribution station; wherein the multiple models include: a flexible scheduling model and a rigid control model. The technical solution provided by the present invention combines the flexible coordinated scheduling of the source, grid, load and storage in the substation with the photovoltaic rigid switching control strategy to achieve the multi-faceted optimization goals of safe operation of the distribution station and reliable power generation of multiple distributed photovoltaic entities. It can not only ensure the safe operation of the distribution station, but also reduce the loss of distributed photovoltaic power generation, promote the consumption of distributed photovoltaics, and support the large-scale development of distributed photovoltaics.

[0080] Specifically, the flexible dispatch model can achieve optimal dispatch of energy storage and load, effectively guarantee the safe operation of the distribution area, and promote the consumption of distributed photovoltaic power. The rigid control model can not only guarantee the safe operation of the distribution area, but also minimize the loss of distributed photovoltaic power generation, so as to ensure the power generation and consumption of distributed photovoltaic power and support the large-scale development of distributed photovoltaic power. The above scheme is elaborated in detail below.

[0081] Example 1

[0082] See attached Figure 1 , Figure 1 1 is a schematic flow chart of the main steps of a method for dynamically optimizing and controlling a distributed photovoltaic access distribution area according to an embodiment of the present invention. Figure 1 As shown, the dynamic optimization control method for accessing the distributed photovoltaic power distribution area in the embodiment of the present invention mainly includes the following steps:

[0083] Step S101: dynamically selecting a model from a plurality of models as an optimization control model based on the operation status of the distribution station area, and solving the optimization control model to obtain an optimization result;

[0084] Step S102: based on the optimization result, obtaining an optimization control scheme for the distribution station area;

[0085] Among them, the multiple models include: a flexible scheduling model and a rigid control model.

[0086] Specifically, Figure 2 As shown, the flexible scheduling model is triggered and executed when the voltage in the substation exceeds the limit or is overloaded. Through flexible coordinated scheduling of source, grid, load and storage, the safe operation of the substation is guaranteed and the consumption of distributed photovoltaic is promoted. The objects of coordinated scheduling are mainly the adjustable energy storage and load resources within the distribution station area.

[0087] The rigid control model is triggered and executed when the voltage in the substation seriously exceeds the limit or is seriously overloaded. Through the switching control of distributed photovoltaics, the safe operation of the substation is guaranteed and the loss of distributed photovoltaic power generation is minimized. The object of rigid control is the distributed photovoltaics that can be interrupted within the distribution station area.

[0088] In this embodiment, the selecting of the optimization control model based on the operation status of the distribution station area includes:

[0089] When the operating condition of the distribution station area is that the voltage exceeds the limit and is overloaded, the flexible scheduling model is selected as the optimization control model. When the operating condition of the distribution station area is that the voltage exceeds the limit and is seriously overloaded, the rigid control model is selected as the optimization control model.

[0090] Among them, when the flexible scheduling model is selected, the optimization results include: the interruption time of the interruptible load, the transfer power of the transferable load, the output power of the energy storage unit, and the scheduling power of the energy storage unit; when the rigid control model is selected, the optimization results include: the on-grid electricity price of distributed photovoltaics, the controlled active power of distributed photovoltaics, and the execution control coefficient of distributed photovoltaics.

[0091] Among them, when the voltage per unit value of the distribution station area exceeds the first preset value and does not exceed the second preset value, or is lower than the third preset value and not lower than the fourth preset value, the operation condition of the distribution station area is voltage over-limit; when the voltage per unit value of the distribution station area exceeds the second preset value or is lower than the fourth preset value, the operation condition of the distribution station area is severe voltage over-limit; when the load rate of the distribution station area exceeds 100% and lasts for more than the preset period of time, the operation condition of the distribution station area is overload operation; when the load rate of the distribution station area exceeds 120% and lasts for more than the preset period of time, the operation condition of the distribution station area is severe overload operation.

[0092] Among them, the first preset value may be 1.03, the second preset value may be 1.07, the third preset value may be 0.97, the fourth preset value may be 0.93, and the preset period may be 2 hours.

[0093] In this embodiment, the flexible scheduling model includes:

[0094] The first objective function aims at minimizing the total loss and the first constraint condition for configuring the dynamic optimization control of the distributed photovoltaic access distribution station area.

[0095] In one embodiment, the mathematical model of the first objective function is as follows:

[0096]

[0097] In the above formula, O is the total loss target value, C es_run (t) is the energy storage operation loss at time t, C es_dis (t) is the energy storage dispatch loss at time t, C load_com (t) is the interruptible load compensation cost at time t, C load_trans (t) is the transferable load compensation cost at time t, C gird (t) is the cost of interacting with the grid at time t, and T is the optimization period.

[0098] The mathematical model of energy storage operation loss at time t is as follows:

[0099]

[0100] In the formula, C es_run (t) represents the operating loss cost of energy storage at time t; N es Indicates the total number of energy storage; P i es (t) represents the output power of the i-th energy storage group at time t; τ es Indicates the energy storage life loss coefficient; Represents the energy storage cost of a single group; u es,i(t) is a 0, 1 variable indicating whether the i-th group of energy storage is dispatched. It takes 0 when the energy storage is in dispatched operation and takes 1 when it is not in dispatched operation.

[0101] The mathematical model of energy storage dispatch loss at time t is as follows:

[0102]

[0103] In the formula, C es_dis (t) represents the dispatching cost of energy storage at time t; P i es_dis (t) represents the dispatching power of the i-th group of energy storage at time t.

[0104] The mathematical model of the interruptible load compensation cost at time t is as follows:

[0105]

[0106] In the formula, C load_com (t) represents the interruptible load compensation cost at time t; N load_com Indicates the total number of interruptible loads; represents the interruption compensation cost of the i-th group of interruptible loads; u load_com,i A 0, 1 variable indicating whether the i-th group of interruptible loads is interrupted. It takes 1 when the load is interrupted and 0 otherwise.

[0107] The mathematical model of the transferable load compensation cost at time t is as follows:

[0108]

[0109] In the formula, C load_trans (t) represents the compensation cost of the transferable load at time t; N load_trans Indicates the total number of transferable loads; represents the unit price of transfer compensation for the i-th group of transferable loads; represents the transfer power of the i-th transferable load; u load_trans,i (t) is a 0, 1 variable indicating whether the i-th group of transferable loads is transferred. It takes 1 when the load is transferred and 0 otherwise.

[0110] The mathematical model of the cost of interacting with the power grid at time t is as follows:

[0111] C gird (t) = λ gird (t)P gird (t)u gird (t)δ(t)

[0112] In the formula, C gird (t) represents the cost of interacting with the power grid, including the cost of purchasing electricity and the cost of selling electricity;gird (t) represents the electricity price at time t; P gird (t) represents the interaction power with the power grid at time t; u gird (t) represents a 1, -1 variable that interacts with the large power grid. It takes 1 when purchasing electricity from the large power grid and takes -1 when selling electricity.

[0113] Furthermore, the first constraint condition includes: energy storage constraint, interruptible load constraint, transferable load constraint, and power balance constraint.

[0114] In one embodiment, the mathematical model of the energy storage constraint is as follows:

[0115] M i (t) = M i (t-1)-P i es (t-1)δ(t)

[0116] 0.1M max ≤M i (t)≤0.9M max

[0117] -P es_max ≤P i es (t)≤P es_max

[0118] -P es_max ≤P i es_dis (t)≤P es_max

[0119] The mathematical model of the interruptible load constraint is as follows:

[0120]

[0121]

[0122]

[0123] The mathematical model of the transferable load constraint is as follows:

[0124]

[0125]

[0126] The mathematical model of the power balance constraint is as follows:

[0127]

[0128] In the above formula, M i (t) is the capacity state of the i-th group of energy storage at time t, Mi (t-1) is the capacity state of the i-th group of energy storage at time t-1, P i es (t-1) is the output power of the i-th energy storage group at time t-1, δ(t) is the energy storage continuous control time, M max is the maximum capacity of energy storage, P es_max is the maximum charge and discharge power of energy storage, P i es (t) is the output power of the i-th group of energy storage at time t, P i es_dis (t) is the dispatching power of the i-th group of energy storage at time t, is the maximum interruptible duration of the kth interruptible load, u load_com,k (m) is the interruption coefficient of the kth interruptible load at time m. If the interruptible load is interrupted, it takes 1, otherwise it takes 0. is the minimum interruptible duration of the kth interruptible load, is the minimum output power of the kth interruptible load at time t, P load_com,k (t) is the output power of the kth interruptible load at time t, is the maximum output power of the kth interruptible load at time t, is the minimum transfer duration of the jth transferable load, u load_trans,j (m) is the transfer coefficient of the jth transferable load at time m. If the transferable load is transferred, it takes 1, otherwise it takes 0. is the minimum output power of the jth transferable load at time t, P load_trans,j (t) is the output power of the jth transferable load at time t, is the maximum output power of the jth transferable load at time t, P gird (t) is the interaction power with the grid at time t, P PV (t) is the photovoltaic power generation at time t, N es is the total number of energy storage, N load_com is the total number of interruptible loads, N load_trans is the total number of transferable loads, P load_fixed (t) is the fixed load power at time t, and t is the current time.

[0129] In this embodiment, the rigid control model includes:

[0130] The second objective function, the third objective function and the fourth objective function respectively aim at minimizing the impact on distributed photovoltaic power generation income, minimizing the total active control power of distributed photovoltaics, and minimizing the number of controlled distributed photovoltaics, and the second constraint condition, the third constraint condition and the fourth constraint condition configured for the second objective function, the third objective function and the fourth objective function.

[0131] In one embodiment, solving the optimization control model includes:

[0132] Solve the optimization model composed of the second objective function and the second constraint condition to obtain the optimization result of the on-grid electricity price of distributed photovoltaics and the first optimization result of the controlled active power of distributed photovoltaics;

[0133] Substituting the distributed photovoltaic grid-connected electricity price optimization result and the first optimization result of the distributed photovoltaic controlled active power into the optimization model composed of the third objective function and the third constraint condition and solving them to obtain the second optimization result of the distributed photovoltaic controlled active power;

[0134] Substituting the second optimization result of the controlled active power of the distributed photovoltaic into the optimization model composed of the fourth objective function and the fourth constraint condition and solving it to obtain the optimization result of the execution control coefficient of the distributed photovoltaic;

[0135] The optimization result of the distributed photovoltaic grid-connected electricity price, the second optimization result of the controlled active power of the distributed photovoltaic, and the optimization result of the execution control coefficient of the distributed photovoltaic are taken as the final optimization result;

[0136] The third constraint condition is composed of the second constraint condition and the first additional condition, and the fourth constraint condition is composed of the third constraint condition and the second additional condition.

[0137] In one embodiment, the mathematical model of the second objective function is as follows:

[0138]

[0139] The mathematical model of the third objective function is as follows:

[0140]

[0141] The mathematical model of the fourth objective function is as follows:

[0142]

[0143] In the above formula, N PV is the total number of distributed photovoltaics, is the grid-connected electricity price of the nth distributed photovoltaic power station, is the controlled active power of the nth photovoltaic, is the active power execution control coefficient of the nth distributed photovoltaic. If the nth distributed photovoltaic executes control at time t, it takes 1, otherwise it takes 0.

[0144] In one embodiment, the mathematical model of the second constraint is as follows:

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] The mathematical model of the first additional condition is as follows:

[0152]

[0153] The mathematical model of the second additional condition is as follows:

[0154]

[0155] In the above formula, is the controlled active power of distributed photovoltaic at grid node x, is the active power execution control coefficient of distributed photovoltaic at grid node x, is the real-time active power of distributed photovoltaic at grid node x, is the real-time active power of the load at the grid node x, V x is the voltage of grid node x, N x is the set of nodes connected to node x, V y is the voltage of grid node y, G xy is the conductivity of the line from node x to node y, δ xy is the phase angle difference between node x and node y, B xy is the susceptance of the line from node x to node y, is the real-time reactive power of distributed photovoltaic at grid node x, is the controlled reactive power of distributed photovoltaic at grid node x, is the real-time reactive power of the load at the grid node x, is the reactive power execution control coefficient of distributed photovoltaic at grid node x, for, is the voltage lower limit of the grid node x, is the voltage upper limit of the grid node x, P xy is the active power from node x to node y, Q xy is the reactive power from node x to node y, is the maximum allowable value of the current from node x to node y, I rdis the objective function value of the second objective function, p rd is the objective function value of the third objective function.

[0156] In one implementation, a particle swarm algorithm may be used to solve the optimization control model, as follows:

[0157] The output results of each unit in each period of the next 24 hours are solved through an iterative optimization algorithm.

[0158] Randomly initialize the start / stop status and output results of each unit in each period.

[0159] The particle swarm algorithm is used to update the start and stop status and output results of each unit in each period.

[0160] Update individual optimal values ​​and global optimal values.

[0161] The optimization loop iterates until convergence.

[0162] Output the best result.

[0163] Example 2

[0164] Based on the same inventive concept, the present invention also provides a dynamic optimization control device for accessing a distributed photovoltaic power distribution area, such as Figure 3 As shown, the dynamic optimization control method device of the distributed photovoltaic access distribution station area includes:

[0165] A first analysis module is used to dynamically select a model from multiple models as an optimization control model based on the operation status of the distribution station area, and solve the optimization control model to obtain an optimization result;

[0166] A second analysis module is used to obtain an optimization control scheme for the distribution station area based on the optimization result;

[0167] Among them, the multiple models include: a flexible scheduling model and a rigid control model.

[0168] Preferably, the first analysis module is specifically used for:

[0169] When the operating condition of the distribution station area is that the voltage exceeds the limit and is overloaded, the flexible scheduling model is selected as the optimization control model. When the operating condition of the distribution station area is that the voltage exceeds the limit and is seriously overloaded, the rigid control model is selected as the optimization control model.

[0170] Furthermore, when the flexible scheduling model is selected, the optimization results include: interruption time of interruptible load, transfer power of transferable load, output power of energy storage unit, and scheduling power of energy storage unit; when the rigid control model is selected, the optimization results include: grid-connected electricity price of distributed photovoltaics, controlled active power of distributed photovoltaics, and execution control coefficient of distributed photovoltaics.

[0171] Furthermore, when the voltage per unit value of the distribution station area exceeds the first preset value and does not exceed the second preset value, or is lower than the third preset value and not lower than the fourth preset value, the operation condition of the distribution station area is voltage over-limit; when the voltage per unit value of the distribution station area exceeds the second preset value or is lower than the fourth preset value, the operation condition of the distribution station area is severe voltage over-limit; when the load rate of the distribution station area exceeds 100% and lasts for more than a preset period of time, the operation condition of the distribution station area is overload operation; when the load rate of the distribution station area exceeds 120% and lasts for more than a preset period of time, the operation condition of the distribution station area is severe overload operation.

[0172] Preferably, the flexible scheduling model includes:

[0173] The first objective function aims at minimizing the total loss and the first constraint condition for configuring the dynamic optimization control of the distributed photovoltaic access distribution station area.

[0174] Furthermore, the mathematical model of the first objective function is as follows:

[0175]

[0176] In the above formula, O is the total loss target value, C es_run (t) is the energy storage operation loss at time t, C es_dis (t) is the energy storage dispatch loss at time t, C load_com (t) is the interruptible load compensation cost at time t, C load_trans (t) is the transferable load compensation cost at time t, C gird (t) is the cost of interacting with the grid at time t, and T is the optimization period.

[0177] Furthermore, the first constraint condition includes: energy storage constraint, interruptible load constraint, transferable load constraint, and power balance constraint.

[0178] Furthermore, the mathematical model of the energy storage constraint is as follows:

[0179] M i (t) = M i (t-1)-P i es (t-1)δ(t)

[0180] 0.1M max≤M i (t)≤0.9M max

[0181] -P es_max ≤P i es (t)≤P es_max

[0182] -P es_max ≤P i es_dis (t)≤P es_max

[0183] The mathematical model of the interruptible load constraint is as follows:

[0184]

[0185]

[0186]

[0187] The mathematical model of the transferable load constraint is as follows:

[0188]

[0189]

[0190] The mathematical model of the power balance constraint is as follows:

[0191]

[0192] In the above formula, M i (t) is the capacity state of the i-th group of energy storage at time t, M i (t-1) is the capacity state of the i-th group of energy storage at time t-1, P i es (t-1) is the output power of the i-th energy storage group at time t-1, δ(t) is the energy storage continuous control time, M max is the maximum capacity of energy storage, P es_max is the maximum charge and discharge power of energy storage, P i es (t) is the output power of the i-th group of energy storage at time t, P i es_dis (t) is the dispatching power of the i-th group of energy storage at time t, is the maximum interruptible duration of the kth interruptible load, u load_com,k (m) is the interruption coefficient of the kth interruptible load at time m. If the interruptible load is interrupted, it takes 1, otherwise it takes 0. is the minimum interruptible duration of the kth interruptible load, is the minimum output power of the kth interruptible load at time t, P load_com,k (t) is the output power of the kth interruptible load at time t, is the maximum output power of the kth interruptible load at time t, is the minimum transfer duration of the jth transferable load, u load_trans,j (m) is the transfer coefficient of the jth transferable load at time m. If the transferable load is transferred, it takes 1, otherwise it takes 0. is the minimum output power of the jth transferable load at time t, P load_trans,j (t) is the output power of the jth transferable load at time t, is the maximum output power of the jth transferable load at time t, P gird (t) is the interaction power with the grid at time t, P PV (t) is the photovoltaic power generation at time t, N es is the total number of energy storage, N load_com is the total number of interruptible loads, N load_trans is the total number of transferable loads, P load_fixed (t) is the fixed load power at time t, and t is the current time.

[0193] Preferably, the rigid control model includes:

[0194] The second objective function, the third objective function and the fourth objective function respectively aim at minimizing the impact on distributed photovoltaic power generation income, minimizing the total active control power of distributed photovoltaics, and minimizing the number of controlled distributed photovoltaics, and the second constraint condition, the third constraint condition and the fourth constraint condition configured for the second objective function, the third objective function and the fourth objective function.

[0195] Further, solving the optimization control model includes:

[0196] Solve the optimization model composed of the second objective function and the second constraint condition to obtain the optimization result of the on-grid electricity price of distributed photovoltaics and the first optimization result of the controlled active power of distributed photovoltaics;

[0197] Substituting the distributed photovoltaic grid-connected electricity price optimization result and the first optimization result of the distributed photovoltaic controlled active power into the optimization model composed of the third objective function and the third constraint condition and solving them to obtain the second optimization result of the distributed photovoltaic controlled active power;

[0198] Substituting the second optimization result of the controlled active power of the distributed photovoltaic into the optimization model composed of the fourth objective function and the fourth constraint condition and solving it to obtain the optimization result of the execution control coefficient of the distributed photovoltaic;

[0199] The optimization result of the distributed photovoltaic grid-connected electricity price, the second optimization result of the controlled active power of the distributed photovoltaic, and the optimization result of the execution control coefficient of the distributed photovoltaic are taken as the final optimization result;

[0200] The third constraint condition is composed of the second constraint condition and the first additional condition, and the fourth constraint condition is composed of the third constraint condition and the second additional condition.

[0201] Furthermore, the mathematical model of the second objective function is as follows:

[0202]

[0203] The mathematical model of the third objective function is as follows:

[0204]

[0205] The mathematical model of the fourth objective function is as follows:

[0206]

[0207] In the above formula, N PV is the total number of distributed photovoltaics, is the grid-connected electricity price of the nth distributed photovoltaic power station, is the controlled active power of the nth photovoltaic, is the active power execution control coefficient of the nth distributed photovoltaic. If the nth distributed photovoltaic executes control at time t, it takes 1, otherwise it takes 0.

[0208] Furthermore, the mathematical model of the second constraint condition is as follows:

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215] The mathematical model of the first additional condition is as follows:

[0216]

[0217] The mathematical model of the second additional condition is as follows:

[0218]

[0219] In the above formula, is the controlled active power of distributed photovoltaic at grid node x, is the active power execution control coefficient of distributed photovoltaic at grid node x, is the real-time active power of distributed photovoltaic at grid node x, is the real-time active power of the load at the grid node x, V x is the voltage of grid node x, N x is the set of nodes connected to node x, V y is the voltage of grid node y, G xy is the conductivity of the line from node x to node y, δ xy is the phase angle difference between node x and node y, B xy is the susceptance of the line from node x to node y, is the real-time reactive power of distributed photovoltaic at grid node x, is the controlled reactive power of distributed photovoltaic at grid node x, is the real-time reactive power of the load at the grid node x, is the reactive power execution control coefficient of distributed photovoltaic at grid node x, for, is the voltage lower limit of the grid node x, is the voltage upper limit of the grid node x, P xy is the active power from node x to node y, Q xy is the reactive power from node x to node y, is the maximum allowable value of the current from node x to node y, I rd is the objective function value of the second objective function, p rd is the objective function value of the third objective function.

[0220] Example 3

[0221] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor 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 gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a dynamic optimization control method for a distributed photovoltaic access distribution station area in the above embodiment.

[0222] Example 4

[0223] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a dynamic optimization control method for a distributed photovoltaic access distribution station area in the above embodiment.

[0224] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0225] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0226] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0227] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0228] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A dynamic optimization control method for distributed photovoltaic access to a distribution station area, characterized in that: The method comprises: Dynamically selecting a model from a plurality of models as an optimization control model based on the operation status of the distribution station area, and solving the optimization control model to obtain an optimization result; Based on the optimization results, an optimization control scheme for the distribution station area is obtained; Among them, the multiple models include: a flexible scheduling model and a rigid control model.

2. The method according to claim 1, characterized in that The dynamically selecting the optimization control model based on the operation status of the distribution station area includes: When the operating condition of the distribution station area is that the voltage exceeds the limit and is overloaded, the flexible scheduling model is selected as the optimization control model. When the operating condition of the distribution station area is that the voltage exceeds the limit and is seriously overloaded, the rigid control model is selected as the optimization control model.

3. The method according to claim 2, characterized in that When the flexible scheduling model is selected, the optimization results include: interruption time of interruptible load, transfer power of transferable load, output power of energy storage unit, and scheduling power of energy storage unit. When the rigid control model is selected, the optimization results include: grid-connected electricity price of distributed photovoltaics, controlled active power of distributed photovoltaics, and execution control coefficient of distributed photovoltaics.

4. The method according to claim 2, characterized in that When the voltage per unit value of the distribution station area exceeds the first preset value and does not exceed the second preset value, or is lower than the third preset value and not lower than the fourth preset value, the operation condition of the distribution station area is voltage over-limit; when the voltage per unit value of the distribution station area exceeds the second preset value or is lower than the fourth preset value, the operation condition of the distribution station area is severe voltage over-limit; when the load rate of the distribution station area exceeds 100% and lasts for more than the preset period of time, the operation condition of the distribution station area is overload operation; when the load rate of the distribution station area exceeds 120% and lasts for more than the preset period of time, the operation condition of the distribution station area is severe overload operation.

5. The method according to claim 1, characterized in that The flexible scheduling model includes: The first objective function aims at minimizing the total loss and the first constraint condition for configuring the dynamic optimization control of the distributed photovoltaic access distribution station area.

6. The method according to claim 5, characterized in that The mathematical model of the first objective function is as follows: In the above formula, O is the total loss target value, C es_run (t) is the energy storage operation loss at time t, C es_dis (t) is the energy storage dispatch loss at time t, C load_com (t) is the interruptible load compensation cost at time t, C load_trans (t) is the transferable load compensation cost at time t, C gird (t) is the cost of interacting with the grid at time t, and T is the optimization period.

7. The method according to claim 5, characterized in that The first constraint conditions include: energy storage constraint, interruptible load constraint, transferable load constraint, and power balance constraint.

8. The method according to claim 7, characterized in that The mathematical model of the energy storage constraint is as follows: M i (t)=M i (t-1)-P i es (t-1)δ(t) 0.1M max ≤M i (t)≤0.9M max -P es_max ≤P i es (t)≤P es_max -P es_max ≤P i es_dis (t)≤P es_max The mathematical model of the interruptible load constraint is as follows: The mathematical model of the transferable load constraint is as follows: The mathematical model of the power balance constraint is as follows: In the above formula, M i (t) is the capacity state of the i-th group of energy storage at time t, M i (t-1) is the capacity state of the i-th group of energy storage at time t-1, P i es (t-1) is the output power of the i-th energy storage group at time t-1, δ(t) is the energy storage continuous control time, M max is the maximum capacity of energy storage, P es_max is the maximum charge and discharge power of energy storage, P i es (t) is the output power of the i-th group of energy storage at time t, P i es_dis (t) is the dispatching power of the i-th group of energy storage at time t, is the maximum interruptible duration of the kth interruptible load, u load_com,k (m) is the interruption coefficient of the kth interruptible load at time m. If the interruptible load is interrupted, it takes 1, otherwise it takes 0. is the minimum interruptible duration of the kth interruptible load, is the minimum output power of the kth interruptible load at time t, P load_com,k (t) is the output power of the kth interruptible load at time t, is the maximum output power of the kth interruptible load at time t, is the minimum transfer duration of the jth transferable load, u load_trans,j (m) is the transfer coefficient of the jth transferable load at time m. If the transferable load is transferred, it takes 1, otherwise it takes 0. is the minimum output power of the jth transferable load at time t, P load_trans,j (t) is the output power of the jth transferable load at time t, is the maximum output power of the jth transferable load at time t, P gird (t) is the interaction power with the grid at time t, P PV (t) is the photovoltaic power generation at time t, N es is the total number of energy storage, N load_com is the total number of interruptible loads, N load_trans is the total number of transferable loads, P load_fixed (t) is the fixed load power at time t, and t is the current time.

9. The method according to claim 1, characterized in that The rigid control model includes: The second objective function, the third objective function and the fourth objective function respectively aim at minimizing the impact on distributed photovoltaic power generation income, minimizing the total active control power of distributed photovoltaics, and minimizing the number of controlled distributed photovoltaics, and the second constraint condition, the third constraint condition and the fourth constraint condition configured for the second objective function, the third objective function and the fourth objective function.

10. The method according to claim 9, characterized in that The solving of the optimization control model comprises: Solve the optimization model composed of the second objective function and the second constraint condition to obtain the optimization result of the on-grid electricity price of distributed photovoltaics and the first optimization result of the controlled active power of distributed photovoltaics; Substituting the distributed photovoltaic grid-connected electricity price optimization result and the first optimization result of the distributed photovoltaic controlled active power into the optimization model composed of the third objective function and the third constraint condition and solving them to obtain the second optimization result of the distributed photovoltaic controlled active power; Substituting the second optimization result of the controlled active power of the distributed photovoltaic into the optimization model composed of the fourth objective function and the fourth constraint condition and solving it to obtain the optimization result of the execution control coefficient of the distributed photovoltaic; The optimization result of the distributed photovoltaic grid-connected electricity price, the second optimization result of the controlled active power of the distributed photovoltaic, and the optimization result of the execution control coefficient of the distributed photovoltaic are taken as the final optimization result; The third constraint condition is composed of the second constraint condition and the first additional condition, and the fourth constraint condition is composed of the third constraint condition and the second additional condition.

11. The method according to claim 10, characterized in that The mathematical model of the second objective function is as follows: The mathematical model of the third objective function is as follows: The mathematical model of the fourth objective function is as follows: In the above formula, N PV is the total number of distributed photovoltaics, is the grid-connected electricity price of the nth distributed photovoltaic power station, is the controlled active power of the nth photovoltaic, is the active power execution control coefficient of the nth distributed photovoltaic. If the nth distributed photovoltaic executes control at time t, it takes 1, otherwise it takes 0.

12. The method according to claim 11, characterized in that The mathematical model of the second constraint is as follows: The mathematical model of the first additional condition is as follows: The mathematical model of the second additional condition is as follows: In the above formula, is the controlled active power of distributed photovoltaic at grid node x, is the active power execution control coefficient of distributed photovoltaic at grid node x, is the real-time active power of distributed photovoltaic at grid node x, is the real-time active power of the load at the grid node x, V x is the voltage of the grid node x, N x is the set of nodes connected to node x, V y is the voltage of grid node y, G xy is the conductivity of the line from node x to node y, δ xy is the phase angle difference between node x and node y, B xy is the susceptance of the line from node x to node y, is the real-time reactive power of distributed photovoltaic at grid node x, is the controlled reactive power of distributed photovoltaic at grid node x, is the real-time reactive power of the load at the grid node x, is the reactive power execution control coefficient of distributed photovoltaic at grid node x, for, is the voltage lower limit of the grid node x, is the voltage upper limit of the grid node x, P xy is the active power from node x to node y, Q xy is the reactive power from node x to node y, is the maximum allowable value of the current from node x to node y, I rd is the objective function value of the second objective function, p rd is the objective function value of the third objective function.

13. A dynamic optimization control device for distributed photovoltaic access to a distribution station area, characterized in that: The device comprises: A first analysis module is used to dynamically select a model from multiple models as an optimization control model based on the operation status of the distribution station area, and solve the optimization control model to obtain an optimization result; A second analysis module is used to obtain an optimization control scheme for the distribution station area based on the optimization result; Among them, the multiple models include: a flexible scheduling model and a rigid control model.

14. A computer device, characterized in that: include: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the dynamic optimization and control method for accessing a distributed photovoltaic power distribution station area as described in any one of claims 1 to 12 is implemented.

15. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the dynamic optimization and control method for accessing a distributed photovoltaic power distribution station area as described in any one of claims 1 to 12 is implemented.

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