Distribution Network Energy Storage - Photovoltaic Active / Reactive Power Co-operation Voltage Optimization Method and Related Equipment

Abstract

This invention discloses a method and related equipment for coordinated active / reactive power voltage optimization of distribution network energy storage-photovoltaic systems. It constructs a photovoltaic-energy storage joint voltage optimization model, constrained by the safe and stable operation of the distribution network and equipment, and aiming to minimize the operating cost of the distribution network and the voltage deviation penalty. This model is then decomposed into two-layer coordinated active / reactive power voltage optimization models at different time scales based on resource characteristics. The augmented Lagrangian function method is used to transform the two-layer coordinated active / reactive power voltage optimization models into unconstrained optimization problems for energy storage and photovoltaics. Solving these problems yields a coordinated active / reactive power voltage optimization scheme for the distribution network. The purpose of this invention is to balance power quality improvement and economical operation of the distribution network, avoid voltage exceedances, better utilize the characteristics of various regulatory resources, and achieve precise and efficient management through a hierarchical coordinated control method.

Description

Technical Field

[0001] This invention belongs to the field of energy system and power distribution system planning, specifically relating to a method and related equipment for voltage optimization of power distribution network energy storage-photovoltaic active / reactive power coordination. Background Technology

[0002] In traditional distribution networks, voltage regulation mainly relies on on-load tap-changing transformers (OLTCs) and shunt capacitor banks. However, these traditional methods have many limitations. For example, OLTC regulation has time delays, and frequent adjustments reduce equipment lifespan; shunt capacitor banks can only perform discrete reactive power compensation, unable to accurately track changes in system reactive power demand, and struggle to cope with complex and variable load characteristics. Especially in modern distribution networks, where the peak-to-valley load difference is constantly increasing, traditional regulation methods are insufficient to meet the requirements for fast and accurate voltage control.

[0003] In recent years, distributed photovoltaic (PV) power generation has been widely used in power distribution networks due to its advantages such as being clean and renewable. However, PV output exhibits significant intermittency and fluctuation, and is greatly affected by natural factors such as sunlight intensity and temperature. When sunlight is abundant, PV generates a large amount of power, which may lead to excessively high voltage at local nodes; conversely, when sunlight is insufficient or at night, PV output drops sharply or even to zero, which may cause voltage dips. Furthermore, distributed PV often employs a maximum power point tracking (MPPT) control strategy, focusing only on maximizing active power output and neglecting reactive power regulation, further exacerbating voltage fluctuations in the power distribution network. Energy storage systems, due to their ability to flexibly store and release electrical energy, offer a new approach to solving power distribution network voltage problems. By rationally controlling the charging and discharging of energy storage, power fluctuations can be effectively mitigated, and node voltages can be stabilized. However, the lack of an effective coordinated control mechanism between energy storage and distributed PV prevents the comprehensive optimization of active and reactive power, resulting in unsatisfactory voltage regulation. Furthermore, previous collaborative optimization methods optimized photovoltaics and energy storage on the same time scale. A longer time scale does not meet the requirements for the volatility of photovoltaic output, while a shorter time scale leads to frequent output changes, which will significantly reduce the lifespan of energy storage. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method and related equipment for voltage optimization of distribution network energy storage-photovoltaic active / reactive power coordination. Its purpose is to balance the improvement of power quality and economic operation of distribution network, avoid voltage over-limit, and better utilize the characteristics of various regulation resources. It adopts a hierarchical collaborative control method to achieve precise and efficient management.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0006] According to a first aspect of the present invention, a method for coordinated voltage optimization of power distribution network energy storage-photovoltaic active / reactive power is provided, comprising:

[0007] A photovoltaic-energy storage joint voltage optimization model is constructed, with the constraints of safe and stable operation of distribution network and equipment, and the objectives of minimizing distribution network operating cost and voltage deviation penalty.

[0008] The photovoltaic-energy storage joint voltage optimization model is decomposed into two-layer active / reactive power coordinated voltage optimization models of energy storage and photovoltaic at different time scales according to resource characteristics. In the two-layer active / reactive power coordinated voltage optimization model of energy storage and photovoltaic, the energy storage device is used as the upper-layer optimization model and the photovoltaic inverter is used as the lower-layer optimization model. The upper-layer optimization model seeks the target optimal value of the distribution network by changing the active / reactive power output of the energy storage. The output of the upper-layer optimization model is used as the input of the lower-layer optimization model. After optimization, the active / reactive power output of the photovoltaic inverter is returned to the upper-layer optimization model, realizing the alternating optimization of the two-layer active / reactive power coordinated voltage optimization model of energy storage and photovoltaic.

[0009] The energy storage-photovoltaic two-layer active / reactive power coordinated voltage optimization model is transformed into an unconstrained optimization problem for energy storage and an unconstrained optimization problem for photovoltaic by constructing an augmented Lagrange function method.

[0010] Solving the unconstrained optimization problems of energy storage and photovoltaics, we obtain a voltage optimization scheme for coordinated active and reactive power of energy storage and photovoltaics in the distribution network.

[0011] In one possible implementation of the first aspect, the objective function of the photovoltaic-energy storage joint voltage optimization model is:

[0012]

[0013] In the formula, T represents the time series; This refers to the number of nodes in the distribution network.

[0014] in:

[0015] Active power regulation cost of node i connected to photovoltaic inverter for:

[0016]

[0017] In the formula: The curtailment penalty coefficient for solar power output; This represents the maximum active power output of the photovoltaic inverter in MPPT mode. Contributing to photovoltaic power generation;

[0018] Charging and discharging loss cost of node i connected to energy storage for:

[0019]

[0020] In the formula: The cost per kilowatt-hour for energy storage charging and discharging; This refers to the energy storage discharge power; The charging power for energy storage; the transaction cost C0(t) between the distribution network and the upstream grid is:

[0021] C0(t)=ρ 0,t P 0,t

[0022] In the formula: ρ 0,t The electricity trading price for the distribution network and the upstream power grid; P 0,t To exchange active power between the distribution network and the upstream power grid;

[0023] Voltage deviation penalty at node i for:

[0024]

[0025] In the formula: V is the penalty coefficient for voltage deviation; i (t) represents the voltage amplitude; V ref This is the voltage reference value; It is a quadratic norm.

[0026] In one possible implementation of the first aspect, the constraints of the photovoltaic-energy storage joint voltage optimization model include energy storage constraints, photovoltaic constraints, power flow constraints, voltage constraints, branch capacity constraints, and transformer safety constraints.

[0027] The energy storage constraints are specifically:

[0028]

[0029] In the formula: To inject active power into the power grid for energy storage; To inject the lower limit of active power into energy storage; To inject the upper limit of active power into energy storage; Reactive power is injected into the grid for energy storage; To inject the lower limit of reactive power into energy storage; The upper limit of reactive power injection for energy storage; It is in a state of energy storage charge; This is the limit of the energy storage state of charge; η is the upper limit of the energy storage state of charge; η is the energy storage charge / discharge efficiency; E i For energy storage capacity;

[0030] The photovoltaic constraint is specifically as follows:

[0031]

[0032] In the formula: It is the reactive power of photovoltaic i at time t; This represents the lower limit of reactive power output of a photovoltaic inverter. This is the upper limit of the reactive power output of the photovoltaic inverter. It is the inverter capacity of photovoltaic i; It is the active power of photovoltaic i under MPPT mode at time t;

[0033] The power flow constraints are specifically as follows:

[0034]

[0035] In the formula: P i,t The injected active power for node i; Q i,t Injecting reactive power to node i; V i,t V represents the voltage magnitude at node i. j,t Y represents the voltage magnitude at node j. ij Let be the admittance between nodes i and j; symbol (·). * Indicates conjugation; Re is the real part; Im is the imaginary part;

[0036] Among them, considering photovoltaic inverters and energy storage devices, the power injection P i,t and Q i,t It can be represented as:

[0037]

[0038] In the formula: The active power consumed by the load at node i; The reactive power consumed by the load at node i;

[0039] The voltage constraint is specifically as follows:

[0040]

[0041] In the formula: V min and V max These are the upper and lower limits of the voltage amplitude;

[0042] The branch capacity constraint is specifically as follows:

[0043]

[0044] In the formula: Let be the active power of branch ij; Let be the line capacity of branch ij; ε be the set of distribution network branches;

[0045] The specific safety constraints on the transformer are as follows:

[0046]

[0047] In the formula: P 0,min and P 0,max The upper and lower limits of the active power allowed to be exchanged for the transformer.

[0048] In one possible implementation of the first aspect, the energy storage-photovoltaic two-layer active / reactive power coordinated voltage optimization model is as follows:

[0049] Upper-level optimization model:

[0050]

[0051] Lower-level optimization model:

[0052] minf2(P PV Q PV )

[0053]

[0054] In the formula: P PV Q represents the active power of the photovoltaic inverter. PV The reactive power of the photovoltaic inverter; The charging power for energy storage devices; Q represents the discharge power of the energy storage device. es The reactive power of the energy storage device; For local equality constraints of energy storage devices; m es The number of local equality constraints for the energy storage device; For local equality constraints of photovoltaic inverters; m pv The number of local equality constraints for the photovoltaic inverter; Global equality constraints for the coupling of photovoltaic inverters and energy storage devices; m global The number of global equality constraints for the coupling of photovoltaic inverters and energy storage devices; For local inequality constraints of energy storage devices; n es The number of local inequality constraints for the energy storage device; For local inequality constraints of photovoltaic inverters; n pv Let be the number of local inequality constraints for the photovoltaic inverter; Global inequality constraints for the coupling of photovoltaic inverters and energy storage devices; n global The number of global inequality constraints for the coupling of photovoltaic inverters and energy storage devices.

[0055] In one possible implementation of the first aspect, the transformation of the energy storage-photovoltaic two-layer active / reactive power coordinated voltage optimization model into an unconstrained optimization problem for energy storage and an unconstrained optimization problem for photovoltaics by constructing an augmented Lagrangian function method specifically involves:

[0056] For the energy storage optimization problem, introducing slack variables yields the equivalent form of the problem:

[0057]

[0058] s i ≥0i=1,2,...,n

[0059] m = m es +m global

[0060] n = n es +n global

[0061] In the formula: s i Slack variables introduced for inequality constraints in energy storage optimization problems; h i For the equality constraints of the energy storage optimization problem; g i denoted by inequality constraints for the energy storage optimization problem; m represents the number of equality constraints for the energy storage optimization problem; n represents the number of inequality constraints for the energy storage optimization problem.

[0062] The augmented Lagrangian function is constructed as follows:

[0063]

[0064] In the formula: λ i For the energy storage optimization problem, the equal-form constraint multiplier coefficients; μ i σ is the multiplier coefficient of the inequality constraint in the energy storage optimization problem; σ is the penalty coefficient; p(x,s) is the quadratic penalty function of the inequality constraint in the energy storage optimization problem;

[0065] Eliminating s, we obtain an optimization problem that concerns only x, i.e., an unconstrained optimization problem for energy storage:

[0066]

[0067] In the formula: λ k and μ k c represents the multiplier coefficients in the k-th solution. i (x) is the obstacle function in the energy storage optimization problem;

[0068] For the photovoltaic optimization problem, introducing slack variables yields an equivalent form of the problem:

[0069] minf2(P pv Q pv )

[0070] sth j (P pv Q pv )=0j=1,2,...,r

[0071] g j (P pv Q pv )+s j =0j=1,2,...,l

[0072] s j ≥0j=1,2,...,n

[0073] r = m pv +m global

[0074] l=n pv +n global

[0075] In the formula: s j Relaxed variables introduced for inequality constraints in photovoltaic optimization problems; h j For the equality constraints of the photovoltaic optimization problem; g j Let r be the inequality constraints for the photovoltaic optimization problem; r be the number of equality constraints for the photovoltaic optimization problem; l be the number of inequality constraints for the photovoltaic optimization problem.

[0076] The augmented Lagrangian function is constructed as follows:

[0077]

[0078] y = (P pv Q pv )

[0079] In the formula: λ j For the photovoltaic optimization problem, the equal-form constraint multiplier coefficients; μ j denoted as , where is the multiplier coefficient of the inequality constraint in the photovoltaic optimization problem; p(y,s) is the quadratic penalty function of the inequality constraint in the iso-photovoltaic optimization problem;

[0080] Eliminating s, we obtain an optimization problem that concerns only y, i.e., an unconstrained photovoltaic optimization problem:

[0081]

[0082] In the formula: c j (y) is the obstacle function in the photovoltaic optimization problem.

[0083] In one possible implementation of the first aspect, solving the unconstrained optimization problem of energy storage and the unconstrained optimization problem of photovoltaics specifically involves:

[0084] The Hippo optimization algorithm is used to solve the unconstrained optimization problems of energy storage and photovoltaics.

[0085] In one possible implementation of the first aspect, the use of the Hippo optimization algorithm to solve the unconstrained optimization problems of energy storage and photovoltaics specifically involves:

[0086] The optimization problem is the process of finding the optimal location for a hippopotamus population.

[0087] The objective function to be optimized is the fitness of the hippopotamus population;

[0088] The optimal solution set is the hippopotamus population;

[0089] The optimal solution is an individual hippopotamus;

[0090] The optimization solution is updated iteratively based on the location update and biological behavior of the hippopotamus population.

[0091] According to a second aspect of the present invention, a distribution network energy storage-photovoltaic active / reactive power coordinated voltage optimization device is provided, comprising:

[0092] A module is built to construct a photovoltaic-energy storage joint voltage optimization model with constraints on the safe and stable operation of the distribution network and equipment, and with the objectives of minimizing the operating cost of the distribution network and minimizing the voltage deviation penalty.

[0093] The decomposition module is used to decompose the photovoltaic-energy storage joint voltage optimization model into two-layer active / reactive power coordinated voltage optimization models of energy storage and photovoltaic at different time scales according to resource characteristics. In the two-layer active / reactive power coordinated voltage optimization model of energy storage and photovoltaic, the energy storage device is used as the upper-layer optimization model and the photovoltaic inverter is used as the lower-layer optimization model. The upper-layer optimization model seeks the optimal value of the distribution network target by changing the active / reactive power output of the energy storage. The output of the upper-layer optimization model is used as the input of the lower-layer optimization model. After optimization, the active / reactive power output of the photovoltaic inverter is returned to the upper-layer optimization model, realizing the alternating optimization of the two-layer active / reactive power coordinated voltage optimization model of energy storage and photovoltaic.

[0094] The conversion module is used to convert the energy storage-photovoltaic two-layer active / reactive power coordinated voltage optimization model into an unconstrained optimization problem for energy storage and an unconstrained optimization problem for photovoltaic by constructing an augmented Lagrangian function method.

[0095] The solution module is used to solve the unconstrained optimization problem of energy storage and the unconstrained optimization problem of photovoltaics, and obtain the active / reactive power coordinated voltage optimization scheme of energy storage-photovoltaic in the distribution network.

[0096] According to a third aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aforementioned method for coordinated voltage optimization of power distribution network energy storage-photovoltaic active / reactive power.

[0097] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for coordinated voltage optimization of power distribution network energy storage-photovoltaic active / reactive power.

[0098] Compared with the prior art, the present invention has at least the following beneficial effects:

[0099] This invention provides a novel active / reactive power coordinated voltage optimization method for distribution networks using energy storage and photovoltaic (PV) power. Based on a PV-energy storage joint voltage optimization model that considers power quality improvement and economic operation, and addressing the heterogeneous regulation characteristics of energy storage and PV, a two-layer active / reactive power coordinated voltage optimization model suitable for new distribution networks is proposed. The energy storage device is used as the upper-layer optimization model, and the PV inverter as the lower-layer optimization model. Joint optimization of energy storage and PV is achieved at different time scales. Long-term energy storage optimization ensures stable power output, while short-term PV optimization enables flexible regulation. Therefore, this invention balances power quality improvement and economic operation of the distribution network, avoids voltage exceedances, and better utilizes the characteristics of various regulation resources. The hierarchical coordinated control method achieves precise and efficient governance.

[0100] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0101] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the drawings used in the description of the specific embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0102] Figure 1 This is a flowchart illustrating a novel distribution network energy storage-photovoltaic active / reactive power coordinated voltage optimization method according to an embodiment of the present invention;

[0103] Figure 2 This is a schematic diagram illustrating the breakdown of photovoltaic energy storage issues in an embodiment of the present invention;

[0104] Figure 3 This is a schematic diagram of the active / reactive power coordinated voltage optimization model of the energy storage-photovoltaic two-layer system according to an embodiment of the present invention;

[0105] Figure 4 This is a schematic diagram illustrating the correspondence between the optimization problem and the Hippo algorithm in an embodiment of the present invention;

[0106] Figure 5 This is a schematic diagram of the IEEE 13-node topology connection according to an embodiment of the present invention;

[0107] Figure 6 This is a schematic diagram of the photovoltaic curtailment curve in an embodiment of the present invention;

[0108] Figure 7 This is a schematic diagram of the energy storage SOC curve according to an embodiment of the present invention;

[0109] Figure 8 This is a schematic diagram of the node voltage curve in an embodiment of the present invention. Detailed Implementation

[0110] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0111] like Figures 1 to 4 As shown, this invention provides a method for optimizing the active / reactive power voltage of a distribution network using energy storage-photovoltaic co-operation, specifically including the following steps:

[0112] S1. With the constraints of safe and stable operation of the distribution network and equipment, and with the objectives of minimizing the operating cost of the distribution network and minimizing the voltage deviation penalty, a photovoltaic-energy storage joint voltage optimization model is constructed.

[0113] In one possible implementation, the objective function of the photovoltaic-energy storage joint voltage optimization model is:

[0114]

[0115] In the formula, T represents the time series; This refers to the number of nodes in the distribution network.

[0116] in:

[0117] Active power regulation cost of node i connected to photovoltaic inverter for:

[0118]

[0119] In the formula: The curtailment penalty coefficient for solar power output; This represents the maximum active power output of the photovoltaic inverter in MPPT mode. Contributing to photovoltaic power generation;

[0120] Charging and discharging loss cost of node i connected to energy storage for:

[0121]

[0122] In the formula: The cost per kilowatt-hour for energy storage charging and discharging; This refers to the energy storage discharge power; The charging power for energy storage; the transaction cost C0(t) between the distribution network and the upstream grid is:

[0123] C0(t)=ρ 0,t P 0,t

[0124] In the formula: ρ 0,t The electricity trading price for the distribution network and the upstream power grid; P 0,t To exchange active power between the distribution network and the upstream power grid;

[0125] Voltage deviation penalty at node i for:

[0126]

[0127] In the formula: V is the penalty coefficient for voltage deviation; i (t) represents the voltage amplitude; V ref This is the voltage reference value; It is a quadratic norm.

[0128] In one possible implementation, the constraints of the photovoltaic-energy storage joint voltage optimization model include energy storage constraints, photovoltaic constraints, power flow constraints, voltage constraints, branch capacity constraints, and transformer safety constraints.

[0129] The energy storage constraints are specifically:

[0130]

[0131] In the formula: To inject active power into the power grid for energy storage; To inject the lower limit of active power into energy storage; To inject the upper limit of active power into energy storage; Reactive power is injected into the grid for energy storage; To inject the lower limit of reactive power into energy storage; The upper limit of reactive power injection for energy storage; It is in a state of energy storage charge; This is the limit of the energy storage state of charge; η is the upper limit of the energy storage state of charge; η is the energy storage charge / discharge efficiency; E i For energy storage capacity.

[0132] The photovoltaic constraint is specifically as follows:

[0133]

[0134] In the formula: It is the reactive power of photovoltaic i at time t; This represents the lower limit of reactive power output of a photovoltaic inverter. This is the upper limit of the reactive power output of the photovoltaic inverter. It is the inverter capacity of photovoltaic i; It is the active power of photovoltaic i under MPPT mode at time t.

[0135] The power flow constraints are specifically as follows:

[0136]

[0137] In the formula: P i,t The injected active power for node i; Q i,t Injecting reactive power to node i; V i,t V represents the voltage magnitude at node i. j,t Y represents the voltage magnitude at node j. ij Let be the admittance between nodes i and j; symbol (·). * Re denotes conjugation; Re is the real part; Im is the imaginary part.

[0138] Among them, considering photovoltaic inverters and energy storage devices, the power injection P i,t and Q i,t It can be represented as:

[0139]

[0140] In the formula: The active power consumed by the load at node i; The reactive power consumed by the load at node i.

[0141] The voltage constraint is specifically as follows:

[0142]

[0143] In the formula: V min and V max These are the upper and lower limits of the voltage amplitude.

[0144] The branch capacity constraint is specifically as follows:

[0145]

[0146] In the formula: Let be the active power of branch ij; Let ε be the line capacity of branch ij; ε is the set of distribution network branches.

[0147] The specific safety constraints on the transformer are as follows:

[0148]

[0149] In the formula: P 0,min and P 0,max The upper and lower limits of the active power allowed to be exchanged for the transformer.

[0150] S2. The photovoltaic-energy storage joint voltage optimization model is decomposed into two-layer active / reactive power coordinated voltage optimization models of energy storage and photovoltaic at different time scales according to resource characteristics. In the two-layer active / reactive power coordinated voltage optimization model of energy storage and photovoltaic, the energy storage device is used as the upper-layer optimization model and the photovoltaic inverter is used as the lower-layer optimization model. The upper-layer optimization model seeks the optimal value of the distribution network by changing the active / reactive power output of the energy storage. The output of the upper-layer optimization model is used as the input of the lower-layer optimization model. After optimization, the active / reactive power output of the photovoltaic inverter is returned to the upper-layer optimization model, realizing the alternating optimization of the two-layer active / reactive power coordinated voltage optimization model of energy storage and photovoltaic.

[0151] Specifically, the energy storage-photovoltaic two-layer active / reactive power coordinated voltage optimization model is as follows:

[0152] Upper-level optimization model:

[0153]

[0154] Lower-level optimization model:

[0155] minf2(P PV Q PV )

[0156]

[0157] In the formula: P PV Q represents the active power of the photovoltaic inverter. PV The reactive power of the photovoltaic inverter; The charging power for energy storage devices; Q represents the discharge power of the energy storage device. es The reactive power of the energy storage device; For local equality constraints of energy storage devices; m es The number of local equality constraints for the energy storage device; For local equality constraints of photovoltaic inverters; m pv The number of local equality constraints for the photovoltaic inverter; Global equality constraints for the coupling of photovoltaic inverters and energy storage devices; m global The number of global equality constraints for the coupling of photovoltaic inverters and energy storage devices; For local inequality constraints of energy storage devices; n esThe number of local inequality constraints for the energy storage device; For local inequality constraints of photovoltaic inverters; n pv Let be the number of local inequality constraints for the photovoltaic inverter; Global inequality constraints for the coupling of photovoltaic inverters and energy storage devices; n global The number of global inequality constraints for the coupling of photovoltaic inverters and energy storage devices.

[0158] S3. By constructing the augmented Lagrangian function method, the energy storage-photovoltaic two-layer active / reactive power coordinated voltage optimization model is transformed into an unconstrained optimization problem for energy storage and an unconstrained optimization problem for photovoltaic.

[0159] In one feasible approach, for the energy storage optimization problem, introducing slack variables yields an equivalent form of the problem:

[0160]

[0161] s i ≥0i=1,2,...,n

[0162] m = m es +m global

[0163] n = n es +n global

[0164] In the formula: s i Slack variables introduced for inequality constraints in energy storage optimization problems; h i For the equality constraints of the energy storage optimization problem; g i denoted by inequality constraints for the energy storage optimization problem; m represents the number of equality constraints for the energy storage optimization problem; n represents the number of inequality constraints for the energy storage optimization problem.

[0165] The augmented Lagrangian function is constructed as follows:

[0166]

[0167] In the formula: λ i For the energy storage optimization problem, the equal-form constraint multiplier coefficients; μ i σ is the multiplier coefficient of the inequality constraint in the energy storage optimization problem; σ is the penalty coefficient; p(x,s) is the quadratic penalty function of the inequality constraint in the energy storage optimization problem;

[0168] Eliminating s, we obtain an optimization problem that concerns only x, i.e., an unconstrained optimization problem for energy storage:

[0169]

[0170] In the formula: λ k and μk c represents the multiplier coefficients in the k-th solution. i (x) is the obstacle function in the energy storage optimization problem.

[0171] For the photovoltaic optimization problem, introducing slack variables yields an equivalent form of the problem:

[0172] minf2(P pv Q pv )

[0173] sth j (P pv Q pv )=0j=1,2,...,r

[0174] g j (P pv Q pv )+s j =0j=1,2,...,l

[0175] s j ≥0j=1,2,...,n

[0176] r = m pv +m global

[0177] l=n pv +n global

[0178] In the formula: s j Relaxed variables introduced for inequality constraints in photovoltaic optimization problems; h j For the equality constraints of the photovoltaic optimization problem; g j Let r be the inequality constraints for the photovoltaic optimization problem; r be the number of equality constraints for the photovoltaic optimization problem; l be the number of inequality constraints for the photovoltaic optimization problem.

[0179] The augmented Lagrangian function is constructed as follows:

[0180]

[0181] y = (P pv Q pv )

[0182] In the formula: λ j For the photovoltaic optimization problem, the equal-form constraint multiplier coefficients; μ j denoted as , where is the multiplier coefficient of the inequality constraint in the photovoltaic optimization problem; p(y,s) is the quadratic penalty function of the inequality constraint in the iso-photovoltaic optimization problem;

[0183] Eliminating s, we obtain an optimization problem that concerns only y, i.e., an unconstrained photovoltaic optimization problem:

[0184]

[0185] In the formula: c j (y) is the obstacle function in the photovoltaic optimization problem.

[0186] S4. Solve the unconstrained optimization problems of energy storage and photovoltaics to obtain the voltage optimization scheme of energy storage-photovoltaic active / reactive power coordination in the distribution network.

[0187] In one feasible approach, the Hippo optimization algorithm is used to solve the unconstrained optimization problems of energy storage and photovoltaics, specifically as follows:

[0188] The optimization problem is the process of finding the optimal location for a hippopotamus population.

[0189] The objective function to be optimized is the fitness of the hippopotamus population;

[0190] The optimal solution set is the hippopotamus population;

[0191] The optimal solution is an individual hippopotamus;

[0192] The optimization solution is updated iteratively based on the location update and biological behavior of the hippopotamus population.

[0193] The present invention will now be described with reference to the embodiments.

[0194] This embodiment studies a typical IEEE 13-node power distribution network, with the topology diagram as follows: Figure 5 Given the day-ahead forecast data and mid-day ultra-short-term forecast data for photovoltaic and load, photovoltaic data is incorporated into five nodes on a large scale. The node types are shown in Table 1, Table 2 is the line data, and Tables 3 and 4 are the overhead line data and transformer data, respectively.

[0195] Table 1. Node types in IEEE 13-node distribution networks

[0196]

[0197] Table 2 Line Data

[0198]

[0199]

[0200] Table 3 Data on Overhead Lines

[0201]

[0202] By substituting the line data, photovoltaic data, and load data into the energy storage-photovoltaic two-layer active / reactive power coordinated voltage optimization model, the control commands for energy storage and photovoltaics and the node voltages can be obtained, such as... Figures 6 to 8 As shown. From Figure 6 As can be seen, the amount of solar power curtailment is relatively small, which ensures the absorption of new energy and improves economic efficiency; Figure 7 The SOC curve of the energy storage is displayed, indicating that the energy storage is operating well; Figure 8 The optimized node voltage curves are shown, with no voltage exceedances at any node. In summary, this invention achieves a balance between improving power quality and economical operation of the distribution network, preventing voltage exceedances, better utilizing the characteristics of various regulatory resources, and employing a hierarchical collaborative control method to achieve precise and efficient governance.

[0203] This invention provides a distribution network energy storage-photovoltaic active / reactive power coordinated voltage optimization device, used to implement the aforementioned distribution network energy storage-photovoltaic active / reactive power coordinated voltage optimization method, specifically including the following modules:

[0204] A module is built to construct a photovoltaic-energy storage joint voltage optimization model with constraints on the safe and stable operation of the distribution network and equipment, and with the objectives of minimizing the operating cost of the distribution network and minimizing the voltage deviation penalty.

[0205] The decomposition module is used to decompose the photovoltaic-energy storage joint voltage optimization model into two-layer active / reactive power coordinated voltage optimization models of energy storage and photovoltaic at different time scales according to resource characteristics. In the two-layer active / reactive power coordinated voltage optimization model of energy storage and photovoltaic, the energy storage device is used as the upper-layer optimization model and the photovoltaic inverter is used as the lower-layer optimization model. The upper-layer optimization model seeks the target optimal value of the distribution network by changing the active / reactive power output of the energy storage. The output of the upper-layer optimization model is used as the input of the lower-layer optimization model. After optimization, the active / reactive power output of the photovoltaic inverter is returned to the upper-layer optimization model, realizing the alternating optimization of the two-layer active / reactive power coordinated voltage optimization model of energy storage and photovoltaic.

[0206] The conversion module is used to convert the energy storage-photovoltaic two-layer active / reactive power coordinated voltage optimization model into an unconstrained optimization problem for energy storage and an unconstrained optimization problem for photovoltaic by constructing an augmented Lagrangian function method.

[0207] The solution module is used to solve the unconstrained optimization problem of energy storage and the unconstrained optimization problem of photovoltaics, and obtain the active / reactive power coordinated voltage optimization scheme of energy storage-photovoltaic in the distribution network.

[0208] All relevant content of each step involved in the aforementioned embodiment of the distribution network energy storage-photovoltaic active / reactive power coordinated voltage optimization method can be referenced from the functional description of the corresponding functional module of the distribution network energy storage-photovoltaic active / reactive power coordinated voltage optimization device in the embodiments of the present invention, and will not be repeated here. The module division in the embodiments of the present invention is illustrative and is only a logical functional division. In actual implementation, there may be other division methods. In addition, the functional modules in the various embodiments of the present invention can be integrated into a processor, exist separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of software functional modules.

[0209] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes 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 (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a power distribution network energy storage-photovoltaic active / reactive power coordinated voltage optimization method.

[0210] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). 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 storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the distribution network energy storage-photovoltaic active / reactive power coordinated voltage optimization method in the above embodiments.

[0211] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0212] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0213] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0214] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0215] This invention also provides a computer program product, which is used to execute any of the above-described methods for coordinating active and reactive power voltage optimization in distribution network energy storage-photovoltaic systems. Since the computer program product provided by this invention belongs to the same inventive concept as the above-described method for coordinating active and reactive power voltage optimization in distribution network energy storage-photovoltaic systems, it possesses all the advantages of the above-described method. Therefore, the beneficial effects of the computer program product provided by this invention will not be elaborated upon here.

[0216] In this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0217] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A power distribution network energy storage-photovoltaic active / reactive power collaborative voltage optimization method, characterized in that, The application relates to a photovoltaic-accumulating energy joint voltage optimization model, and a method for optimizing voltage of a photovoltaic-accumulating energy joint system. The application relates to a photovoltaic-accumulating energy joint voltage optimization model, and a method for optimizing voltage of a photovoltaic-accumulating energy joint system. In the formula, T is a time series; is the number of distribution network nodes; MPPT Node i Cost of active regulation of access photovoltaic inverters To: wherein: is the penalty factor for PV active power curtailment of wasted light; is the maximum active power in the PV inverter The photovoltaic-accumulating energy joint voltage optimization model is decomposed into a two-layer energy storage-photovoltaic active / reactive power collaborative voltage optimization model of different time scales according to resource characteristics; in the two-layer energy storage-photovoltaic active / reactive power collaborative voltage optimization model, an energy storage device is used as an upper layer optimization model, and a photovoltaic inverter is used as a lower layer optimization model; the upper layer optimization model seeks optimal values of the power grid by changing energy storage active / reactive power output; the output of the upper layer optimization model is used as input of the lower layer optimization model; after optimization of the lower layer optimization model, the active / reactive power output of the photovoltaic inverter is returned to the upper layer optimization model, so that the two-layer energy storage-photovoltaic active / reactive power collaborative voltage optimization model is alternately optimized; mode; is the PV active power; Node i Cost of charge and discharge losses to access energy storage To: wherein: is the cost per kWh of energy storage charge and discharge; is the energy storage discharge power; is the energy storage charge power; Transaction costs for distribution networks and upstream grids To: In the formula: is the power trading price for the distribution grid and the upstream grid; is the active power exchanged by the distribution grid with the upstream grid; Node i of voltage deviation penalty is: wherein: is a penalty coefficient for voltage deviation; is a voltage amplitude; is a voltage reference value; is a quadratic norm; The two-layer energy storage-photovoltaic active / reactive power collaborative voltage optimization model is converted into an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem through a constructed augmented Lagrange function method; The energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem are solved, so that an energy storage-photovoltaic active / reactive power collaborative voltage optimization scheme of the power grid is obtained. The constraints of the photovoltaic-accumulating energy joint voltage optimization model include energy storage constraints, photovoltaic constraints, power flow constraints, voltage constraints, branch capacity constraints and transformer safety constraints; 2.The power grid energy storage-PV active / reactive power collaborative voltage optimization method according to claim 1, characterized in that, The energy storage constraints are specifically as follows: The photovoltaic constraints are specifically as follows: wherein: Pgrid is the active power injected by the energy storage to the grid; Pmin is the active power injection lower limit for the energy storage; Pmax is the active power injection upper limit for the energy storage; Qgrid is the reactive power injected by the energy storage to the grid; Qmin is the reactive power injection lower limit for the energy storage; Qmax is the reactive power injection upper limit for the energy storage; SoC is the state of charge of the energy storage; SoCmin is the state of charge lower limit for the energy storage; SoCmax is the state of charge upper limit for the energy storage; η is the charge-discharge efficiency of the energy storage; C is the capacity of the energy storage; The power flow constraints are specifically as follows: wherein: is the reactive power of the photovoltaic i at time t; is the lower limit of the reactive power output of the photovoltaic inverter; is the upper limit of the reactive power output of the photovoltaic inverter; is the inverter capacity of the photovoltaic i ; and is the maximum active power output of the photovoltaic inverter in MPPT mode. The voltage constraints are specifically as follows: In the formula: For nodes i The injected active power; For nodes i Injected reactive power; For nodes i The voltage amplitude; For nodes j The voltage amplitude; For nodes i , j Admittance between; symbol Indicates conjugation; Re is the real part; Im is the imaginary part; where, considering a photovoltaic inverter and an energy storage device, the power injection may be expressed as: In the formulae: is the active power consumed by the load at node i is the reactive power consumed by the load at node i is the reactive power consumed by the load at node​ The branch capacity constraints are specifically as follows: In the formulae: are upper and lower voltage amplitude limits; The transformer safety constraints are specifically as follows: wherein: is the active power of branch The two-layer energy storage-photovoltaic active / reactive power collaborative voltage optimization model is specifically as follows: ; is the line capacity of branch The upper layer optimization model is as follows: ; is the set of distribution network branches; The lower layer optimization model is as follows: In the formula: Pmax and Pmin are the upper and lower limits of the active power exchange allowed for the transformer. 3.The power grid energy storage-PV active / reactive power coordinated voltage optimization method of claim 2, wherein, The two-layer energy storage-photovoltaic active / reactive power collaborative voltage optimization model is converted into an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem through a constructed augmented Lagrange function method, and the conversion is specifically as follows: For the energy storage optimization problem, a slack variable is introduced, so that an equivalent form of the problem is obtained: An augmented Lagrange function is constructed as follows: where: Ppv is the active power of the photovoltaic inverter; Qpv is the reactive power of the photovoltaic inverter; Pch is the charging power of the energy storage device; Pdis is the discharging power of the energy storage device; Qch is the reactive power of the energy storage device; Ceq is the local equality constraint of the energy storage device; Ceq is the number of local equality constraints of the energy storage device; Ceq is the local equality constraint of the photovoltaic inverter; Ceq is the number of local equality constraints of the photovoltaic inverter; Ceq is the coupling global equality constraint of the photovoltaic inverter and the energy storage device; Ceq is the number of coupling global equality constraints of the photovoltaic inverter and the energy storage device; Cineq is the local inequality constraint of the energy storage device; Cineq is the number of local inequality constraints of the energy storage device; Cineq is the local inequality constraint of the photovoltaic inverter; Cineq is the number of local inequality constraints of the photovoltaic inverter; Cineq is the coupling global inequality constraint of the photovoltaic inverter and the energy storage device; Cineq is the number of coupling global inequality constraints of the photovoltaic inverter and the energy storage device.

4. The active / reactive power coordinated voltage optimization method for energy storage-PV in a power distribution network according to claim 3, characterized in that, For the photovoltaic optimization problem, a slack variable is introduced, so that an equivalent form of the problem is obtained: An augmented Lagrange function is constructed as follows: wherein: is a slack variable introduced for inequality constraints in the energy storage optimization problem; is an equality constraint of the energy storage optimization problem; is an inequality constraint of the energy storage optimization problem; is the number of equality constraints of the energy storage optimization problem; is the number of inequality constraints of the energy storage optimization problem; The energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem are solved, and the solving is specifically as follows: wherein: is the coefficient of the equality constraint multiplier in the energy storage optimization problem; is the coefficient of the inequality constraint multiplier in the energy storage optimization problem; is the penalty coefficient; is the quadratic penalty function for the equality constraint in the energy storage optimization problem; Elimination , resulting in an optimization problem only about , i.e. the energy storage unconstrained optimization problem: In the formula: and is the multiplier coefficient of the nth iteration; k is the multiplier coefficient of the nth iteration; is the barrier function in the energy storage optimization problem; The energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem are solved by using a rhino optimization algorithm. wherein: is a slack variable introduced for inequality constraints in the photovoltaic optimization problem; is an equality constraint of the photovoltaic optimization problem; is an inequality constraint of the photovoltaic optimization problem; is the number of equality constraints of the photovoltaic optimization problem; is the number of inequality constraints of the photovoltaic optimization problem; The energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem are solved by using a rhino optimization algorithm, and the solving is specifically as follows: wherein: is the coefficient of the equality constraint multiplier in the photovoltaic optimization problem; is the coefficient of the inequality constraint multiplier in the photovoltaic optimization problem; is the quadratic penalty function for equality constraints in the photovoltaic optimization problem; Elimination , resulting in an optimization problem only about , i.e. the photovoltaic unconstrained optimization problem: In the formula: is the barrier function in the photovoltaic optimization problem.

5. The power distribution network energy storage-PV active / reactive power coordinated voltage optimization method according to claim 1, characterized in that, An optimization problem is a rhino population position optimization process; An optimization objective function is a rhino population fitness; 6. The power distribution network energy storage-PV active / reactive power coordinated voltage optimization method according to claim 5, characterized in that, An optimization solution set is a rhino population; An optimization solution is a rhino individual; An optimization solution update iteration is a rhino population position update and biological behavior. The application relates to a photovoltaic-accumulating energy joint voltage optimization model, and a method for optimizing voltage of a photovoltaic-accumulating energy joint system. The application relates to a photovoltaic-accumulating energy joint voltage optimization model, and a method for optimizing voltage of a photovoltaic-accumulating energy joint system. ​ 7. A power distribution network energy storage-photovoltaic active / reactive power collaborative voltage optimization device, characterized in that, ​ ​ In the formula, T is a time series; is the number of distribution network nodes; ​ Node i Cost of active regulation of access photovoltaic inverters For: In the formula: is a penalty factor for photovoltaic active power output for light rejection; is the maximum active power output of the photovoltaic inverter ​ in the mode; is the photovoltaic active power output; Node i Cost of charge and discharge losses to access energy storage For: In the formula: is the cost per kWh of energy storage charging and discharging; is the energy storage discharging power; is the energy storage charging power; Transaction costs for distribution networks and upstream grids To: In the formulae: is the power trading price for the distribution grid and the upstream grid; is the active power exchanged by the distribution grid with the upstream grid. Node i of voltage deviation penalty is: wherein: is a penalty coefficient for voltage deviation; is a voltage amplitude; is a voltage reference value; is a quadratic norm; The decomposition module is used for decomposing the photovoltaic- energy storage combined voltage optimization model into energy storage- photovoltaic two-layer active / reactive power collaborative voltage optimization models of different time scales according to resource characteristics; in the energy storage- photovoltaic two-layer active / reactive power collaborative voltage optimization models, the energy storage device is taken as an upper-layer optimization model, and the photovoltaic inverter is taken as a lower-layer optimization model; the upper-layer optimization model seeks optimal values of power grid targets by changing energy storage active / reactive power output; the output of the upper-layer optimization model is taken as input of the lower-layer optimization model; after optimization of the lower-layer optimization model, the active / reactive power output of the photovoltaic inverter is output to the upper-layer optimization model, so as to realize alternating optimization of the energy storage- photovoltaic two-layer active / reactive power collaborative voltage optimization models; The conversion module is used for converting the energy storage- photovoltaic two-layer active / reactive power collaborative voltage optimization models into energy storage unconstrained optimization problems and photovoltaic unconstrained optimization problems by constructing an augmented Lagrange function method; The solving module is used for solving the energy storage unconstrained optimization problems and the photovoltaic unconstrained optimization problems, so as to obtain an active / reactive power collaborative voltage optimization scheme of the power grid energy storage- photovoltaic.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the power grid energy storage- photovoltaic active / reactive power collaborative voltage optimization method in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to realize the power grid energy storage- photovoltaic active / reactive power collaborative voltage optimization method in any one of claims 1 to 6.

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