Power distribution network energy storage-photovoltaic active / reactive cooperative voltage optimization method and related equipment

Abstract

The invention discloses a power distribution network energy storage-photovoltaic active / reactive cooperative voltage optimization method and related equipment, and the method comprises the steps: building a photovoltaic-energy storage combined voltage optimization model with the safe and stable operation of a power distribution network and equipment as a constraint and the minimum operation cost of the power distribution network and the minimum voltage deviation penalty as a target; decomposing the photovoltaic-energy storage combined voltage optimization model into energy storage-photovoltaic two-layer active / reactive cooperative voltage optimization models of different time scales according to resource characteristics; the energy storage-photovoltaic two-layer active / reactive cooperative voltage optimization model is converted into an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem by constructing an augmented Lagrange function method; and solving an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem to obtain a power distribution network energy storage-photovoltaic active / reactive cooperative voltage optimization scheme. The objective of the invention is to give consideration to power quality improvement and economic operation of the power distribution network, avoid voltage out-of-limit, better utilize various adjustment resource characteristics, and realize accurate and efficient treatment by adopting a hierarchical cooperative control method.

Description

Technical Field

[0001] The present invention belongs to the field of energy system and distribution system planning, and specifically relates to a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method and related equipment. Background Art

[0002] In traditional distribution networks, voltage regulation mainly relies on equipment such as 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 regulation will reduce the life of the equipment; shunt capacitor banks can only perform discrete reactive power compensation, and cannot accurately track changes in the system's reactive power demand, making it difficult to cope with complex and changeable load characteristics. Especially in modern distribution networks, the peak-to-valley difference in loads continues to increase, and traditional regulation methods are difficult to meet the requirements of fast and accurate voltage control.

[0003] In recent years, distributed photovoltaic power generation has been widely used in distribution networks due to its advantages of cleanliness and renewable energy. However, photovoltaic output has obvious intermittent and volatile characteristics, and is greatly affected by natural factors such as light intensity and temperature. When there is sufficient sunlight, photovoltaic power generation may cause excessive voltage at local nodes; when there is insufficient sunlight or at night, photovoltaic output may drop sharply or even reach zero, which may cause voltage drop. At the same time, distributed photovoltaic power generation mostly adopts the maximum power point tracking (MPPT) control strategy, which only focuses on maximizing active power output and does not participate in reactive power regulation, further exacerbating the voltage fluctuation of the distribution network. Energy storage systems provide a new way to solve the voltage problem of distribution networks because they can flexibly store and release electrical energy. By reasonably controlling the charging and discharging of energy storage, power fluctuations can be effectively smoothed and node voltages can be stabilized. However, there is a lack of effective coordinated control mechanism between energy storage and distributed photovoltaic power generation, and the comprehensive optimization of active and reactive power cannot be achieved, resulting in unsatisfactory voltage regulation. In addition, previous collaborative optimization methods optimized photovoltaics and energy storage on the same time scale. The longer time scale does not meet the volatility requirements of photovoltaic output, and the frequent output changes caused by the shorter time scale will significantly reduce the life of energy storage. Summary of the invention

[0004] In response to the problems existing in the prior art, the present invention provides a distribution network energy storage-photovoltaic active / reactive collaborative voltage optimization method and related equipment, which aims to take into account the improvement of power quality and economic operation of the distribution network, avoid voltage over-limit and better utilize the characteristics of various regulation resources, and adopt a hierarchical collaborative control method to achieve precise and efficient management.

[0005] In order to solve the above technical problems, the present invention is implemented by the following technical solutions:

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

[0007] With the safe and stable operation of the distribution network and equipment as constraints, and the goal of minimizing the distribution network operation cost and voltage deviation penalty, a photovoltaic-energy storage joint voltage optimization model is constructed;

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

[0009] The energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model is converted into an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem by constructing an augmented Lagrangian function method;

[0010] The unconstrained optimization problem of energy storage and the unconstrained optimization problem of photovoltaics are solved to obtain a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization solution.

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

[0012]

[0013] In the formula, T is the time series; is the number of distribution network nodes;

[0014] in:

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

[0016]

[0017] Where: is the penalty coefficient for abandonment of photovoltaic active power; It is the maximum active output of the photovoltaic inverter in MPPT mode; Provide active power for photovoltaics;

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

[0019]

[0020] Where: The cost per kilowatt-hour of charging and discharging energy storage; is the energy storage discharge power; is the energy storage charging power; the transaction cost C0(t) of the distribution network and the upstream power grid is:

[0021] C0(t)=ρ 0,t P 0,t

[0022] Where: 0,t is the electricity transaction price between the distribution network and the upstream power grid; P 0,t Exchange active power between the distribution network and the upstream power grid;

[0023] Voltage deviation penalty at node i for:

[0024]

[0025] Where: is the penalty coefficient of voltage deviation; V i (t) is the voltage amplitude; V ref is the voltage reference value; is the quadratic norm.

[0026] In a possible implementation of the first aspect, the constraints of the photovoltaic-energy storage combined 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] Where: Injecting active power into the grid for energy storage; Inject active power lower limit for energy storage; Inject active power cap for energy storage; Inject reactive power into the grid for energy storage; Inject reactive power lower limit for energy storage; injecting reactive power caps into energy storage; It is the energy storage charge state; The lower limit of the energy storage charge state; is the upper limit of the energy storage state of charge; η is the energy storage charging and discharging efficiency; E i is the energy storage capacity;

[0030] The photovoltaic constraints are specifically:

[0031]

[0032] Where: is the reactive power of PV i at time t; It is the lower limit of reactive power output of photovoltaic inverter; It is the upper limit of reactive power output of the photovoltaic inverter; is the inverter capacity of PV i; is the active power of PV i in MPPT mode at time t;

[0033] The power flow constraints are specifically:

[0034]

[0035] Where: P i,t is the injected active power of node i; Q i,t is the injected reactive power of node i; V i,t is the voltage amplitude of node i; V j,t is the voltage amplitude of node j; Y ij is the admittance between nodes i and j; symbol (·) * Re represents the real part; Im represents the imaginary part;

[0036] Among them, considering the photovoltaic inverter and energy storage device, the power injection P i,t and Q i,t It can be expressed as:

[0037]

[0038] Where: is the active power consumed by the load at node i; is the reactive power consumed by the load at node i;

[0039] The voltage constraint is specifically:

[0040]

[0041] Where: V min and V max is the upper and lower limits of the voltage amplitude;

[0042] The branch capacity constraint is specifically:

[0043]

[0044] Where: is the active power of branch ij; is the line capacity of branch ij; ε is the set of distribution network branches;

[0045] The transformer safety constraints are specifically:

[0046]

[0047] Where: P 0,min and P 0,max Allows the transformer to swap the upper and lower active power limits.

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

[0049] Upper optimization model:

[0050]

[0051] Lower layer optimization model:

[0052] minf2(P PV ,Q PV )

[0053]

[0054] Where: P PV is the active power of the photovoltaic inverter; Q PV is the reactive power of the photovoltaic inverter; is the charging power of the energy storage device; is the discharge power of the energy storage device; Q es is the reactive power of the energy storage device; is the local equality constraint of the energy storage device; m es is the number of local equality constraints for the energy storage device; is the local equality constraint of the PV inverter; m pv is the number of local equality constraints of the PV inverter; is the coupling global equality constraint of the PV inverter and the energy storage device; m global is the number of coupled global equality constraints for the PV inverter and the energy storage device; is the local inequality constraint of the energy storage device; n es is the number of local inequality constraints of the energy storage device; is the local inequality constraint of the photovoltaic inverter; n pv is the number of local inequality constraints of the PV inverter; is the coupled global inequality constraint between the PV inverter and the energy storage device; n global is the number of coupled global inequality constraints for the PV inverter and the energy storage device.

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

[0056] For the energy storage optimization problem, slack variables are introduced to obtain 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] Where: s i The slack variable introduced for the inequality constraint in the energy storage optimization problem; h i is the equality constraint of the energy storage optimization problem; g i is the inequality constraint of the energy storage optimization problem; m is the number of equality constraints of the energy storage optimization problem; n is the number of inequality constraints of the energy storage optimization problem;

[0062] The augmented Lagrangian function is constructed as:

[0063]

[0064] Where: i is the multiplier coefficient of the equation constraint in the energy storage optimization problem; μ i 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 get the optimization problem only about x, that is, the unconstrained optimization problem of energy storage:

[0066]

[0067] Where: k and μ k is the multiplier coefficient for the kth solution; c i (x) is the barrier function in the energy storage optimization problem;

[0068] For the photovoltaic optimization problem, the slack variables are introduced to obtain the 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] Where: s j The slack variable introduced for the inequality constraint in the photovoltaic optimization problem; h j is the equality constraint of the photovoltaic optimization problem; g j is the inequality constraint of the photovoltaic optimization problem; r is the number of equality constraints of the photovoltaic optimization problem; l is the number of inequality constraints of the photovoltaic optimization problem;

[0076] The augmented Lagrangian function is constructed as:

[0077]

[0078] y=(P pv ,Q pv )

[0079] Where: j is the multiplier coefficient of the equational constraint in the photovoltaic optimization problem; μ j 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 photovoltaic optimization problem;

[0080] Eliminating s, we get the optimization problem only about y, that is, the photovoltaic unconstrained optimization problem:

[0081]

[0082] Where: c j (y) is the barrier function in the photovoltaic optimization problem.

[0083] In a possible implementation manner of the first aspect, solving the energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem is specifically:

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

[0085] In a possible implementation manner of the first aspect, the Hippo optimization algorithm is used to solve the energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem, specifically:

[0086] The optimization problem is the process of finding the optimal location of the hippo population;

[0087] The optimization objective function is the fitness of the hippo population;

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

[0089] The optimal solution is the hippopotamus individual;

[0090] The update iteration of the optimization solution is the position update and biological behavior of the hippo population.

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

[0092] A construction module is used to construct a photovoltaic-energy storage joint voltage optimization model with the safe and stable operation of the distribution network and equipment as constraints, and the minimum distribution network operation cost and voltage deviation penalty as goals;

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

[0094] A conversion module, used for converting the energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model into an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem by constructing an augmented Lagrangian function method;

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

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

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

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

[0099] The present invention provides a novel distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method. Based on the photovoltaic-energy storage joint voltage optimization model considering power quality improvement and economic operation, a two-layer energy storage-photovoltaic active / reactive coordinated voltage optimization model suitable for the novel distribution network is proposed for the heterogeneous regulation characteristics of energy storage and photovoltaics. The energy storage device is used as the upper optimization model, and the photovoltaic inverter is used as the lower optimization model. The joint optimization of energy storage and photovoltaics is realized from different time scales. The energy storage optimization is performed on a long time scale to ensure stable output, and the photovoltaic optimization is performed on a short time scale to achieve flexible regulation. Therefore, the present invention takes into account the improvement of power quality and economic operation of the distribution network, avoids voltage over-limit and better utilizes the characteristics of multiple regulation resources, and adopts a hierarchical collaborative control method to achieve precise and efficient management.

[0100] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] In order to more clearly illustrate the technical solutions in the specific implementation modes of the present invention, the drawings required for use in the description of the specific implementation modes will be briefly introduced below. Obviously, the drawings described below are some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

[0103] Figure 2 This is a schematic diagram of decomposing the photovoltaic energy storage problem according to an embodiment of the present invention;

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

[0105] Figure 4 A schematic diagram of the corresponding relationship between the optimization problem of an embodiment of the present invention and the Hippo algorithm;

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

[0107] Figure 6 Schematic diagram of photovoltaic abandonment curve according to an embodiment of the present invention;

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

[0109] Figure 8 Schematic diagram of node voltage curve according to an embodiment of the present invention. DETAILED DESCRIPTION

[0110] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. 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.

[0111] like Figures 1 to 4 As shown, an embodiment of the present invention provides a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method, which specifically includes the following steps:

[0112] S1. With the safe and stable operation of the distribution network and equipment as constraints, and the minimum distribution network operation cost and voltage deviation penalty as goals, a photovoltaic-energy storage combined voltage optimization model is constructed.

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

[0114]

[0115] In the formula, T is the time series; is the number of distribution network nodes;

[0116] in:

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

[0118]

[0119] Where: is the penalty coefficient for abandonment of photovoltaic active power; It is the maximum active output of the photovoltaic inverter in MPPT mode; Provide active power for photovoltaics;

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

[0121]

[0122] Where: The cost per kilowatt-hour of charging and discharging energy storage; is the energy storage discharge power; is the energy storage charging power; the transaction cost C0(t) of the distribution network and the upstream power grid is:

[0123] C0(t)=ρ 0,t P 0,t

[0124] Where: 0,t is the electricity transaction price between the distribution network and the upstream power grid; P 0,t Exchange active power between the distribution network and the upstream power grid;

[0125] Voltage deviation penalty at node i for:

[0126]

[0127] Where: is the penalty coefficient of voltage deviation; V i (t) is the voltage amplitude; V ref is the voltage reference value; is the quadratic norm.

[0128] In one achievable manner, the constraints of the photovoltaic-energy storage combined 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] Where: Injecting active power into the grid for energy storage; Inject active power lower limit for energy storage; Inject active power cap for energy storage; Inject reactive power into the grid for energy storage; Inject reactive power lower limit for energy storage; injecting reactive power caps into energy storage; It is the energy storage charge state; The lower limit of the energy storage charge state; is the upper limit of the energy storage state of charge; η is the energy storage charging and discharging efficiency; E i For the energy storage capacity.

[0132] The photovoltaic constraints are specifically:

[0133]

[0134] Where: is the reactive power of PV i at time t; It is the lower limit of reactive power output of photovoltaic inverter; It is the upper limit of reactive power output of the photovoltaic inverter; is the inverter capacity of PV i; is the active power of photovoltaic i in MPPT mode at time t.

[0135] The power flow constraints are specifically:

[0136]

[0137] Where: P i,t is the injected active power of node i; Q i,t is the injected reactive power of node i; V i,t is the voltage amplitude of node i; V j,t is the voltage amplitude of node j; Y ij is the admittance between nodes i and j; symbol (·) * represents conjugate; Re is the real part; Im is the imaginary part.

[0138] Among them, considering the photovoltaic inverter and energy storage device, the power injection P i,t and Q i,t It can be expressed as:

[0139]

[0140] Where: is the active power consumed by the load at node i; is the reactive power consumed by the load at node i.

[0141] The voltage constraint is specifically:

[0142]

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

[0144] The branch capacity constraint is specifically:

[0145]

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

[0147] The transformer safety constraints are specifically:

[0148]

[0149] Where: P 0,min and P 0,max Allows the transformer to swap the upper and lower active power limits.

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

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

[0152] Upper optimization model:

[0153]

[0154] Lower layer optimization model:

[0155] minf2(P PV ,Q PV )

[0156]

[0157] Where: P PV is the active power of the photovoltaic inverter; Q PV is the reactive power of the photovoltaic inverter; is the charging power of the energy storage device; is the discharge power of the energy storage device; Q es is the reactive power of the energy storage device; is the local equality constraint of the energy storage device; m es is the number of local equality constraints for the energy storage device; is the local equality constraint of the PV inverter; m pv is the number of local equality constraints of the PV inverter; is the coupling global equality constraint of the PV inverter and the energy storage device; m global is the number of coupled global equality constraints for the PV inverter and the energy storage device; is the local inequality constraint of the energy storage device; n esis the number of local inequality constraints of the energy storage device; is the local inequality constraint of the photovoltaic inverter; n pv is the number of local inequality constraints of the PV inverter; is the coupled global inequality constraint between the PV inverter and the energy storage device; n global is the number of coupled global inequality constraints for the PV inverter and the energy storage device.

[0158] S3. The energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model is converted into an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem by constructing an augmented Lagrangian function method.

[0159] In one possible implementation, for the energy storage optimization problem, slack variables are introduced to obtain 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] Where: s i The slack variable introduced for the inequality constraint in the energy storage optimization problem; h i is the equality constraint of the energy storage optimization problem; g i is the inequality constraint of the energy storage optimization problem; m is the number of equality constraints of the energy storage optimization problem; n is the number of inequality constraints of the energy storage optimization problem;

[0165] The augmented Lagrangian function is constructed as:

[0166]

[0167] Where: i is the multiplier coefficient of the equation constraint in the energy storage optimization problem; μ i 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 get the optimization problem only about x, that is, the unconstrained optimization problem of energy storage:

[0169]

[0170] Where: k and μk is the multiplier coefficient for the kth solution; c i (x) is the barrier function in the energy storage optimization problem.

[0171] For the photovoltaic optimization problem, the slack variables are introduced to obtain the 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] Where: s j The slack variable introduced for the inequality constraint in the photovoltaic optimization problem; h j is the equality constraint of the photovoltaic optimization problem; g j is the inequality constraint of the photovoltaic optimization problem; r is the number of equality constraints of the photovoltaic optimization problem; l is the number of inequality constraints of the photovoltaic optimization problem;

[0179] The augmented Lagrangian function is constructed as:

[0180]

[0181] y=(P pv ,Q pv )

[0182] Where: j is the multiplier coefficient of the equational constraint in the photovoltaic optimization problem; μ j 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 photovoltaic optimization problem;

[0183] Eliminating s, we get the optimization problem only about y, that is, the photovoltaic unconstrained optimization problem:

[0184]

[0185] Where: c j (y) is the barrier function in the photovoltaic optimization problem.

[0186] S4. Solve the energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem to obtain a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization solution.

[0187] In one possible implementation, the Hippo optimization algorithm is used to solve the energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem, specifically:

[0188] The optimization problem is the process of finding the optimal location of the hippo population;

[0189] The optimization objective function is the fitness of the hippo population;

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

[0191] The optimal solution is the hippopotamus individual;

[0192] The update iteration of the optimization solution is the position update and biological behavior of the hippo population.

[0193] The present invention is described below in conjunction with embodiments.

[0194] This embodiment studies a typical IEEE 13-node power distribution network, and the topology connection diagram is as follows: Figure 5 , the day-ahead forecast data and the day-to-day ultra-short-term forecast data of photovoltaic and load are known, and photovoltaic is integrated 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 of IEEE 13-node distribution network

[0196]

[0197] Table 2 Line data

[0198]

[0199]

[0200] Table 3 Overhead line data

[0201]

[0202] Substituting the line data, photovoltaic and load data into the energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model, the energy storage and photovoltaic control instructions and node voltages can be obtained, such as Figures 6 to 8 As shown. Figure 6 It can be seen that the amount of photovoltaic abandoned light is small, which ensures the consumption of new energy and improves economic benefits; Figure 7 The SOC curve of the energy storage is shown, and the energy storage is operating well; Figure 8 The optimized node voltage curve is shown, and the voltage of each node does not exceed the limit at all. In summary, the present invention can achieve the goal of taking into account the improvement of power quality and economic operation of the distribution network, avoiding voltage exceeding the limit while making better use of the characteristics of multiple regulation resources, and adopting a hierarchical collaborative control method to achieve the effect of accurate and efficient management.

[0203] The embodiment of the present invention provides a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization device, which is used to implement the above-mentioned distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method, and specifically includes the following modules:

[0204] The building module is used to construct a photovoltaic-energy storage joint voltage optimization model with the safe and stable operation of the distribution network and equipment as constraints, and the minimum distribution network operation cost and voltage deviation penalty as goals.

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

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

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

[0208] All relevant contents of each step involved in the aforementioned embodiment of a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method can be referred to the functional description of the functional module corresponding to a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization device in the embodiment of the present invention, and will not be repeated here. The division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention can be integrated into a processor, or it can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules.

[0209] In another embodiment of the present invention, a computer device is provided, the computer device including a processor and a memory, 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., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method.

[0210] In another embodiment of the present invention, 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 is understandable 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, which 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 corresponding steps of the above-mentioned embodiment in a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method.

[0211] 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.

[0212] 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.

[0213] 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 comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0214] 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 The steps for the functions specified in one or more boxes.

[0215] The present invention also provides a computer program product, which is used to execute any of the above-mentioned distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization methods. Since the computer program product provided by the present invention and the above-mentioned distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method belong to the same inventive concept, the computer program product provided by the present invention has all the advantages of the above-mentioned distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method, so the beneficial effects of the computer program product provided by the present invention will not be described one by one here.

[0216] In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0217] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method, characterized in that: include: With the safe and stable operation of the distribution network and equipment as constraints, and the goal of minimizing the distribution network operation cost and voltage deviation penalty, a photovoltaic-energy storage joint voltage optimization model is constructed; Decomposing the photovoltaic-energy storage joint voltage optimization model into energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization models of different time scales according to resource characteristics; in the energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model, the energy storage device is used as the upper optimization model, and the photovoltaic inverter is used as the lower optimization model. The upper optimization model seeks the optimal value of the distribution network target by changing the active / reactive output of the energy storage. The output of the upper optimization model is used as the input of the optimization of the lower optimization model. After the optimization of the lower optimization model, the active / reactive output of the photovoltaic inverter is returned to the upper optimization model, so as to realize the alternating optimization of the energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model; The energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model is converted into an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem by constructing an augmented Lagrangian function method; The unconstrained optimization problem of energy storage and the unconstrained optimization problem of photovoltaics are solved to obtain a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization solution.

2. A distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method according to claim 1, characterized in that: The objective function of the photovoltaic-energy storage combined voltage optimization model is: In the formula, T is the time series; is the number of distribution network nodes; in: Active power regulation cost of node i connected to photovoltaic inverter for: Where: is the penalty coefficient for abandonment of photovoltaic active power; It is the maximum active output of the photovoltaic inverter in MPPT mode; Provide active power for photovoltaics; Charging and discharging loss cost of node i connected to energy storage for: Where: The cost per kilowatt-hour of charging and discharging energy storage; is the energy storage discharge power; Charging power for energy storage; The transaction cost C0(t) between the distribution network and the upstream power grid is: C0(t)=ρ 0,t P 0,t Where: 0,t is the electricity transaction price between the distribution network and the upstream power grid; P 0,t Exchange active power between the distribution network and the upstream power grid; Voltage deviation penalty at node i for: Where: is the penalty coefficient of voltage deviation; V i (t) is the voltage amplitude; V ref is the voltage reference value; ||·|| 2 2 is the quadratic norm.

3. A distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method according to claim 2, characterized in that: The constraints of the photovoltaic-energy storage combined voltage optimization model include energy storage constraints, photovoltaic constraints, power flow constraints, voltage constraints, branch capacity constraints and transformer safety constraints; The energy storage constraints are specifically: Where: Injecting active power into the grid for energy storage; Injecting active power lower limit into energy storage; Inject active power cap for energy storage; Inject reactive power into the grid for energy storage; Inject reactive power lower limit for energy storage; injecting reactive power caps into energy storage; It is the energy storage charge state; The lower limit of the energy storage charge state; The upper limit of the energy storage state of charge; η is the energy storage charging and discharging efficiency; E i is the energy storage capacity; The photovoltaic constraints are specifically: Where: is the reactive power of PV i at time t; It is the lower limit of reactive power output of photovoltaic inverter; It is the upper limit of reactive power output of the photovoltaic inverter; is the inverter capacity of PV i; is the active power of PV i in MPPT mode at time t; The power flow constraints are specifically: Where: P i,t is the injected active power of node i; Q i,t is the injected reactive power of node i; V i,t is the voltage amplitude of node i; V j,t is the voltage amplitude of node j; Y ij is the admittance between nodes i and j; symbol (·) * Re represents the real part; Im represents the imaginary part; Among them, considering the photovoltaic inverter and energy storage device, the power injection P i,t and Q i,t It can be expressed as: Where: is the active power consumed by the load at node i; is the reactive power consumed by the load at node i; The voltage constraint is specifically: Where: V min and V max is the upper and lower limits of the voltage amplitude; The branch capacity constraint is specifically: Where: is the active power of branch ij; is the line capacity of branch ij; ε is the set of distribution network branches; The transformer safety constraints are specifically: Where: P 0,min and P 0,max Allows the transformer to swap the upper and lower active power limits.

4. A distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method according to claim 3, characterized in that: The energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model is as follows: Upper optimization model: Lower layer optimization model: minf2(P PV ,Q PV ) Where: P PV is the active power of the photovoltaic inverter; Q PV is the reactive power of the photovoltaic inverter; is the charging power of the energy storage device; is the discharge power of the energy storage device; Q es is the reactive power of the energy storage device; is the local equality constraint of the energy storage device; m es is the number of local equality constraints for the energy storage device; is the local equality constraint of the PV inverter; m pv is the number of local equality constraints of the PV inverter; is the coupling global equality constraint of the PV inverter and the energy storage device; m global is the number of coupled global equality constraints for the PV inverter and the energy storage device; is the local inequality constraint of the energy storage device; n es is the number of local inequality constraints of the energy storage device; is the local inequality constraint of the photovoltaic inverter; n pv is the number of local inequality constraints of the PV inverter; is the coupled global inequality constraint between the PV inverter and the energy storage device; n global is the number of coupled global inequality constraints for the PV inverter and the energy storage device.

5. A distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method according to claim 4, characterized in that: The energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model is converted into an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem by constructing an augmented Lagrangian function method, specifically: For the energy storage optimization problem, slack variables are introduced to obtain the equivalent form of the problem: s i ≥0i=1,2,...,n m=m es +m global n=n es +n global Where: s i The slack variable introduced for the inequality constraint in the energy storage optimization problem; h i is the equality constraint of the energy storage optimization problem; g i is the inequality constraint of the energy storage optimization problem; m is the number of equality constraints of the energy storage optimization problem; n is the number of inequality constraints of the energy storage optimization problem; The augmented Lagrangian function is constructed as: Where: i is the multiplier coefficient of the equation constraint in the energy storage optimization problem; μ i 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; Eliminating s, we get the optimization problem only about x, that is, the unconstrained optimization problem of energy storage: Where: k and μ k is the multiplier coefficient for the kth solution; c i (x) is the barrier function in the energy storage optimization problem; For the photovoltaic optimization problem, the slack variables are introduced to obtain the equivalent form of the problem: minf2(P pv ,Q pv ) sth j (P pv ,Q pv )=0j=1,2,...,r g j (P pv ,Q pv )+s j =0j=1,2,...,l s j ≥0j=1,2,...,n r=m pv +m global l=n pv +n global Where: s j The slack variable introduced for the inequality constraint in the photovoltaic optimization problem; h j is the equality constraint of the photovoltaic optimization problem; g j is the inequality constraint of the photovoltaic optimization problem; r is the number of equality constraints of the photovoltaic optimization problem; l is the number of inequality constraints of the photovoltaic optimization problem; The augmented Lagrangian function is constructed as: and=(P pv ,Q pv ) Where: j is the multiplier coefficient of the equational constraint in the photovoltaic optimization problem; μ j 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 photovoltaic optimization problem; Eliminating s, we get the optimization problem only about y, that is, the photovoltaic unconstrained optimization problem: Where: c j (y) is the barrier function in the photovoltaic optimization problem.

6. A distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method according to claim 1, characterized in that: The solution of the energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem is specifically as follows: The Hippo optimization algorithm is used to solve the unconstrained optimization problem of energy storage and the unconstrained optimization problem of photovoltaics.

7. A distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method according to claim 6, characterized in that: The Hippo optimization algorithm is used to solve the energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem, specifically: The optimization problem is the process of finding the optimal location of the hippo population; The optimization objective function is the fitness of the hippo population; The optimal solution set is the hippo population; The optimal solution is the hippopotamus individual; The update iteration of the optimization solution is the position update and biological behavior of the hippo population.

8. A distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization device, characterized in that: include: A construction module is used to construct a photovoltaic-energy storage joint voltage optimization model with the safe and stable operation of the distribution network and equipment as constraints, and the minimum distribution network operation cost and voltage deviation penalty as goals; A decomposition module is used to decompose the photovoltaic-energy storage joint voltage optimization model into energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization models of different time scales according to resource characteristics; in the energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model, the energy storage device is used as the upper optimization model, and the photovoltaic inverter is used as the lower optimization model. The upper optimization model seeks the optimal value of the distribution network target by changing the active / reactive output of the energy storage, and the output of the upper optimization model is used as the input of the optimization of the lower optimization model. After the optimization of the lower optimization model, the active / reactive output of the photovoltaic inverter is returned to the upper optimization model, so as to realize the alternating optimization of the energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model; A conversion module, used for converting the energy storage-photovoltaic two-layer active / reactive coordinated voltage optimization model into an energy storage unconstrained optimization problem and a photovoltaic unconstrained optimization problem by constructing an augmented Lagrangian function method; The solution module is used to solve the energy storage unconstrained optimization problem and the photovoltaic unconstrained optimization problem to obtain a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization solution.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a distribution network energy storage-photovoltaic active / reactive coordinated voltage optimization method as described in any one of claims 1 to 7 is implemented.

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