Distributed energy storage planning method and system considering energy storage and photovoltaic reactive output

Abstract

The present invention discloses a distributed energy storage planning method and system that considers energy storage and photovoltaic reactive power output, including: establishing an outer-layer energy storage optimization configuration model taking into account energy storage configuration costs and voltage quality improvement; performing voltage quality assessment through three-phase power flow calculations to initialize the outer-layer distributed energy storage access location and capacity; establishing an inner-layer model to optimize energy storage and photovoltaic operation strategies, taking into account the improvement in distribution network voltage quality caused by energy storage four-quadrant operation and distributed photovoltaic reactive power; and solving the inner and outer-layer nested models using a particle swarm algorithm with an improved initial population to obtain the optimal distributed energy storage configuration scheme. While obtaining the optimal energy storage installation location and capacity, the system effectively improves the distribution network voltage quality, fully utilizes distributed photovoltaic and distributed energy storage resources, and thus delays the subsequent distribution network voltage management cost investment.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy storage planning for distribution networks, and in particular to a distributed energy storage planning method and system that considers energy storage and photovoltaic reactive output. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Distributed photovoltaics occupy a crucial position in the existing clean energy landscape. Currently, most buildings are equipped with varying proportions of photovoltaic power sources, significantly increasing the penetration of distributed photovoltaics. The integration of large numbers of single-phase and three-phase distributed photovoltaics has significantly impacted the distribution network. Furthermore, the distribution network is closely linked to the load. On the one hand, the mismatch between the timing characteristics of residential loads and the output characteristics of distributed photovoltaics leads to the risk of undervoltage during peak demand periods and overvoltage during periods of high photovoltaic output. Furthermore, although single-phase loads are allocated based on a three-phase balance principle during initial load planning, the irregularity of residential electricity consumption and the varying usage times of a large number of single-phase electrical devices can easily lead to excessive three-phase imbalance. Furthermore, the frequent startup and shutdown of electrical equipment and short-term load fluctuations can easily cause excessive voltage fluctuations. Voltage quality issues in the distribution network further limit the integration of distributed photovoltaics.

[0004] The development of distributed energy storage systems offers a solution for integrating distributed photovoltaics into distribution networks. By rapidly storing and distributing electricity, these systems shift energy over time, enabling peak load shifting and valley filling, as well as efficient utilization of photovoltaic energy. Therefore, rationally planning the location and capacity of distributed energy storage is crucial.

[0005] Currently, most of the existing distributed energy storage planning methods for distribution networks consider cost, distributed energy consumption, network losses, and peak shaving and valley filling. A small number of them consider voltage deviation and voltage fluctuation in voltage quality. However, they often ignore the more serious three-phase imbalance problem in distribution networks. The existing methods for dealing with three-phase imbalance problems mainly focus on load cross-commutation, changing the network structure, and installing balancing devices.

[0006] On the other hand, due to the large resistance of the distribution network, the flow of active power and reactive power will affect the voltage quality. The existing distributed energy storage planning methods containing distributed photovoltaic power sources often only consider the active power output of the two and ignore the reactive power output. Summary of the Invention

[0007] To address the above issues, the present invention proposes a distributed energy storage planning method and system that considers energy storage and photovoltaic reactive output. Voltage quality is assessed using node voltage deviation, node voltage fluctuation, and three-phase imbalance as evaluation indicators. Outer-layer energy storage is planned based on the total energy storage configuration cost and voltage quality improvement. The inner-layer operating strategy is optimized based on the four-quadrant operating mode of distributed photovoltaic reactive output and distributed energy storage output power. This effectively improves the voltage quality problem of the distribution network while obtaining the optimal access location and access capacity.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a distributed energy storage planning method that takes into account energy storage and photovoltaic reactive output, comprising:

[0010] An outer energy storage optimization configuration model is constructed by considering the total configuration cost of distributed energy storage and the improvement in voltage quality. An inner energy storage and photovoltaic operation strategy optimization model is constructed by considering the improvement in distribution network voltage quality caused by the operation of distributed energy storage and the reactive output of distributed photovoltaics.

[0011] In the outer energy storage optimization configuration model, three-phase power flow calculations are used to obtain the voltage at each node in the distribution network. Voltage quality assessment indicators, including three-phase voltage imbalance, voltage deviation, and voltage fluctuation, are considered. After a weighted summation of these three indicators, the access location and access capacity of the distributed energy storage are initialized based on the voltage quality assessment results.

[0012] In the inner-layer energy storage and photovoltaic operation strategy optimization model, the objective function is to optimize voltage quality assessment, and the distributed energy storage operation constraints, distributed photovoltaic output constraints, existing reactive power compensation constraints, and power balance constraints are used as constraints. Under the current access location and access capacity of the distributed energy storage, the reactive power output of the distributed photovoltaics and the active and reactive power outputs of the distributed energy storage are optimized to determine the optimal operation strategy for the distributed energy storage and distributed photovoltaics.

[0013] In the outer energy storage optimization configuration model, the total configuration cost of distributed energy storage and the voltage quality improvement degree under the optimal operation strategy are determined. The voltage quality improvement cost-effectiveness is determined based on the total configuration cost of distributed energy storage and the voltage quality improvement degree. The optimal voltage quality improvement cost-effectiveness is used as the objective function to determine whether to update the optimal access location and access capacity of the distributed energy storage. The current access location and access capacity are updated with the access location constraints and access capacity constraints of the distributed energy storage as constraints. The inner and outer nested models are solved cyclically until the maximum number of iterations is reached, thereby obtaining the optimal distributed energy storage planning scheme.

[0014] As an optional implementation, the three-phase voltage imbalance is:

[0015]

[0016] Where: T is the number of time periods in a day; are the voltage vectors of phase a, phase b, and phase c of node i at time t; α is the rotation factor, and its value is e j120° ;

[0017] The voltage deviation is:

[0018]

[0019] Where: Φ is the a, b, and c phases of the three-phase circuit; is the node i at time t Phase voltage amplitude; V* is the reference voltage amplitude;

[0020] The voltage fluctuation is:

[0021]

[0022] Where: is the phase voltage of node i at time t; For node i Daily average value of phase voltage;

[0023] The three-phase voltage unbalance, voltage deviation and voltage fluctuation are assigned the following weights:

[0024]

[0025] Where: w1', w'2 and w'3 represent the weights of three-phase voltage unbalance, voltage deviation and voltage fluctuation respectively; w1, w2 and w3 represent the decision maker's preference for the three indicators respectively; f 1,max 、f 2,max and f 3,max They represent the maximum three-phase voltage unbalance, maximum voltage deviation and maximum voltage fluctuation specified by national standards respectively;

[0026] The voltage quality assessment results of each node are:

[0027] f=w1'f1+w'2f2+w'3f3

[0028] The voltage quality assessment results of each node before the distributed energy storage is connected are sorted, and the access position and access capacity of the distributed energy storage are initialized according to the sorting results.

[0029] As an optional implementation, the voltage quality improvement cost performance is the ratio of the voltage quality improvement degree to the total configuration cost of the distributed energy storage.

[0030] As an optional implementation, the voltage quality improvement degree is the difference between the voltage quality evaluation result when the distributed energy storage is not connected and the voltage quality evaluation result when the distributed energy storage is connected.

[0031] As an optional implementation, the total configuration cost of the distributed energy storage includes the distributed energy storage construction cost, the distributed energy storage operation and maintenance cost, and the daily electricity purchase cost of the distribution network.

[0032] As an optional implementation, in the inner-layer energy storage and photovoltaic operation strategy optimization model, the optimal voltage quality assessment is used as the objective function. Specifically, the voltage quality assessment is performed on each node, and the arithmetic average of the voltage quality assessment results of all nodes in the distribution network is used to construct the objective function.

[0033] As an optional implementation, the distributed energy storage operation constraints include: distributed energy storage charging and discharging output constraints and energy storage energy capacity constraints, specifically:

[0034]

[0035]

[0036] Where: and They are respectively at node i at time t The single-phase energy storage charging and discharging active power and reactive power of the phase; is the apparent power of distributed energy storage; and They are 0 / 1 variables of the charge and discharge status of the energy storage during period t; and They are the single-phase energy storage charging and discharging efficiency; is the node i at time t Phase single-phase energy storage capacity; is the maximum capacity of energy storage at node i;

[0037] The distributed photovoltaic output constraints include: distributed photovoltaic power factor, active output and reactive output constraints; wherein the reactive output constraint is:

[0038]

[0039]

[0040] Where: is the node i at time t The maximum reactive power that a phase photovoltaic power source can output. The minimum reactive power is expressed by the negative maximum value. is the maximum apparent power of the single-phase photovoltaic power source; is the node i at time t Active power output by the phase photovoltaic power source; express Reactive power output by the phase photovoltaic power source;

[0041] The existing reactive power compensation constraints include: constraints on the compensation power of reactive power compensation equipment;

[0042] The power balance constraints include: constraints on the active power and reactive power injected by the branch into the node, constraints on the active power and reactive power injected by the node into the branch, constraints on the active power and reactive power output of the conventional power supply, constraints on energy storage and photovoltaic output, constraints on reactive power regulation by reactive compensation, and constraints on active power load and reactive power load.

[0043] As an optional implementation, the access location constraint and access capacity constraint of the distributed energy storage include: constraints on the number of distributed energy storage configurations, apparent power, and energy storage capacity.

[0044] In a second aspect, the present invention provides a distributed energy storage planning system that takes into account energy storage and photovoltaic reactive output, including:

[0045] The model building module is configured to construct an outer energy storage optimization configuration model by considering the total configuration cost of distributed energy storage and the improvement in voltage quality; and to construct an inner energy storage and photovoltaic operation strategy optimization model by considering the improvement in distribution network voltage quality caused by the operation of distributed energy storage and the reactive output of distributed photovoltaics;

[0046] The initialization module is configured to use three-phase power flow calculation to obtain the voltage of each node in the distribution network in the outer energy storage optimization configuration model, consider voltage quality assessment indicators including three-phase voltage imbalance, voltage deviation, and voltage fluctuation, perform a weighted sum of the three indicators, and initialize the access location and access capacity of the distributed energy storage based on the voltage quality assessment results;

[0047] The inner-layer operation optimization module is configured to use the optimal voltage quality assessment as the objective function in the inner-layer energy storage and photovoltaic operation strategy optimization model, and the distributed energy storage operation constraints, distributed photovoltaic output constraints, existing reactive power compensation constraints, and power balance constraints as constraints. Under the current access location and access capacity of the distributed energy storage, the module optimizes the reactive power output of the distributed photovoltaics and the active and reactive power outputs of the distributed energy storage, thereby determining the optimal operation strategy for the distributed energy storage and distributed photovoltaics.

[0048] The outer configuration optimization module is configured to determine the total configuration cost of distributed energy storage and the voltage quality improvement degree under the optimal operation strategy in the outer energy storage optimization configuration model, determine the voltage quality improvement cost-effectiveness based on the total configuration cost of distributed energy storage and the voltage quality improvement degree, and use the optimal voltage quality improvement cost-effectiveness as the objective function to determine whether to update the optimal access location and access capacity of the distributed energy storage; use the access location constraints and access capacity constraints of the distributed energy storage as constraints to update the current access location and access capacity, and cyclically solve the inner and outer nested models until the maximum number of iterations is reached, thereby obtaining the optimal distributed energy storage planning scheme.

[0049] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] In response to the planning problem of using distributed energy storage to improve the voltage quality of the distribution network, the present invention proposes a distributed energy storage planning method and system that takes into account energy storage and photovoltaic reactive output. Voltage quality is evaluated using node voltage deviation, node voltage fluctuation, and three-phase imbalance as evaluation indicators. Outer-layer energy storage is planned based on the total energy storage configuration cost and voltage quality improvement. The inner-layer photovoltaic and energy storage operation strategies are optimized based on the four-quadrant operation mode of distributed photovoltaic reactive output and distributed energy storage output power. While obtaining the optimal access location and access capacity, the voltage quality problem existing in the distribution network is effectively improved, distributed photovoltaic and distributed energy storage resources are fully utilized, and the subsequent distribution network voltage management cost investment is delayed.

[0053] Compared with conventional voltage regulation and reactive power compensation methods, which have limitations such as delayed action or high investment and maintenance costs, the present invention considers the improvement of distribution network voltage quality through the four-quadrant operation of distributed energy storage output power and the reactive power regulation capability of distributed photovoltaics during the planning stage. It also considers voltage quality issues including three-phase imbalance. Compared with traditional distributed energy storage planning methods, while taking into account both the investment cost of the energy storage system and the peak-shaving and valley-filling effect, it improves the safety margin of the distribution network voltage quality, thereby reducing the subsequent investment cost of voltage management of the distribution network.

[0054] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0056] Figure 1 A flow chart of a distributed energy storage planning method considering energy storage and photovoltaic reactive output provided in Example 1 of the present invention;

[0057] Figure 2 This is a diagram of the improved IEEE 33-node distribution network model provided in Example 1 of the present invention;

[0058] Figure 3 The distributed photovoltaic reactive power output diagram provided in Example 1 of the present invention;

[0059] Figure 4 This is a diagram of the distributed energy storage 1 operation strategy provided in Example 1 of the present invention;

[0060] Figure 5 This is the distributed energy storage 2 operation strategy diagram provided in Example 1 of the present invention;

[0061] Figure 6 This is a diagram of the distributed energy storage 3 operation strategy provided in Example 1 of the present invention;

[0062] Figure 7 This is a diagram of the distributed energy storage 4 operation strategy provided in Example 1 of the present invention;

[0063] Figure 8 This is the distributed energy storage 5 operation strategy diagram provided in Example 1 of the present invention;

[0064] Figure 9 A voltage comparison diagram of nodes 1-18 at 4:00, 14:00, and 20:00 before and after access to distributed energy storage provided in Example 1 of the present invention;

[0065] Figure 10 A voltage comparison diagram of nodes 19-33 at 4:00, 14:00, and 20:00 before and after access to distributed energy storage provided in Example 1 of the present invention;

[0066] Figure 11 A comparison diagram of the three-phase voltages of nodes 1-18 at 10:00 before and after access to distributed energy storage provided in Example 1 of the present invention;

[0067] Figure 12 A comparison diagram of the three-phase voltages of nodes 19-33 at 10:00 before and after access to distributed energy storage provided in Example 1 of the present invention;

[0068] Figure 13This is a comparison diagram of the phase b voltage before and after node 17 provided in Example 1 of the present invention is connected to the distributed energy storage. DETAILED DESCRIPTION

[0069] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0070] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0071] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0072] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0073] Example 1

[0074] like Figure 1 As shown, this embodiment proposes a distributed energy storage planning method that considers energy storage four-quadrant operation and photovoltaic reactive output, specifically including:

[0075] An outer energy storage optimization configuration model is constructed by considering the total configuration cost of distributed energy storage and the improvement in voltage quality. An inner energy storage and photovoltaic operation strategy optimization model is constructed by considering the improvement in distribution network voltage quality caused by the operation of distributed energy storage and the reactive output of distributed photovoltaics.

[0076] In the outer energy storage optimization configuration model, three-phase power flow calculations are used to obtain the voltage at each node in the distribution network. Voltage quality assessment indicators, including three-phase voltage imbalance, voltage deviation, and voltage fluctuation, are considered. After a weighted summation of these three indicators, the access location and access capacity of the distributed energy storage are initialized based on the voltage quality assessment results.

[0077] In the inner-layer energy storage and photovoltaic operation strategy optimization model, the objective function is to optimize voltage quality assessment, and the distributed energy storage operation constraints, distributed photovoltaic output constraints, existing reactive power compensation constraints, and power balance constraints are used as constraints. Under the current access location and access capacity of the distributed energy storage, the reactive power output of the distributed photovoltaics and the active and reactive power outputs of the distributed energy storage are optimized to determine the optimal operation strategy for the distributed energy storage and distributed photovoltaics.

[0078] In the outer energy storage optimization configuration model, the total configuration cost of distributed energy storage and the voltage quality improvement degree under the optimal operation strategy are determined. The voltage quality improvement cost-effectiveness is determined based on the total configuration cost of distributed energy storage and the voltage quality improvement degree. The optimal voltage quality improvement cost-effectiveness is used as the objective function to determine whether to update the optimal access location and access capacity of the distributed energy storage. The current access location and access capacity are updated with the access location constraints and access capacity constraints of the distributed energy storage as constraints. The inner and outer nested models are solved cyclically until the maximum number of iterations is reached, thereby obtaining the optimal distributed energy storage planning scheme.

[0079] In this embodiment, an outer energy storage optimization configuration model is constructed by considering the total configuration cost of distributed energy storage and the improvement degree of voltage quality. An inner energy storage and photovoltaic operation strategy optimization model is established by considering the cost-effectiveness of the improvement of the distribution network voltage quality by the four-quadrant operation of distributed energy storage and the reactive output of distributed photovoltaics. The inner and outer nested models are solved by using a particle swarm algorithm with an improved initial population to obtain the optimal distributed energy storage configuration solution.

[0080] In this embodiment, typical daily scenarios for each quarter of the year are generated based on the load forecast data and photovoltaic output forecast data within the planning period. Load parameters, network topology parameters, distributed photovoltaic power supply related parameters, distributed energy storage system related parameters, system reference power and reference voltage, voltage deviation limit, voltage fluctuation limit, and three-phase imbalance limit are preset to solve the inner and outer nested models. Single-phase systems are considered for both distributed photovoltaic power supplies and distributed energy storage to reduce voltage fluctuations in each phase and three-phase voltage imbalance.

[0081] In this embodiment, the voltage quality of each node in the initial distribution network is evaluated through three-phase power flow calculation to initialize the access location and access capacity of the distributed energy storage;

[0082] The voltage quality evaluation indicators include: three-phase voltage imbalance, voltage deviation and voltage fluctuation; based on the voltage quality evaluation results, locations with poor voltage quality are selected to connect to distributed energy storage, generate an outer initial population, and set the population size, maximum number of iterations, acceleration weight coefficient, acceleration constant, inertia weight, and maximum particle speed of the inner and outer layers respectively.

[0083] The voltage quality evaluation index is specifically:

[0084] (1) In this embodiment, the negative sequence voltage is used to measure the three-phase voltage imbalance. The three-phase voltage imbalance of each node is:

[0085]

[0086] Where: n is the number of network nodes; T is the number of time periods in a day; are the voltage vectors of the three phases of node i at time t; α is the rotation factor, whose value is e j120° .

[0087] (2) The voltage deviation of each node is:

[0088]

[0089] Where: Φ is the a, b, and c phases of the three-phase circuit; is the node i at time t Phase voltage amplitude; V* is the reference voltage amplitude.

[0090] (3) The voltage fluctuation of each node is:

[0091]

[0092] Where: is the phase voltage of node i at time t; For node i Daily average value of phase voltage.

[0093] In this embodiment, the above three indicators are assigned the following weights, taking into account national standards and decision maker preferences:

[0094]

[0095] Where: w1', w'2 and w'3 represent the weights of three-phase voltage unbalance, voltage deviation and voltage fluctuation respectively; w1, w2 and w3 represent the decision maker's preference for the three indicators respectively; f 1,max 、f 2,max and f 3,max They represent the maximum three-phase voltage unbalance, maximum voltage deviation and maximum voltage fluctuation specified by national standards respectively;

[0096] The voltage quality assessment results of each node are:

[0097] f=w1'f1+w'2f2+w'3f3 (5)

[0098] Before connecting distributed energy storage to the distribution network, the voltage quality assessment results of each node are sorted from worst to best, and the initial connection position of the distributed energy storage in the initial population is determined according to the sorting results.

[0099] In this embodiment, after determining the initial population of distributed energy storage access locations and access capacity, the inner energy storage and photovoltaic operation strategies are optimized, taking into account the constraints on energy storage power and access capacity, that is, the output power of each distributed energy storage and the reactive output of each distributed photovoltaic at each time;

[0100] Specifically, the fitness of each particle in the inner model is calculated to determine the individual and global optimality. The particle fitness function is the inverse of the voltage quality assessment objective function. The voltage quality assessment objective function adopts the voltage quality of the entire distribution network. The distribution network voltage quality assessment result is the average value of the sum of the voltage quality assessment results of each node. Therefore, the following transformations need to be made to equations (1)-(3):

[0101] (1) The three-phase voltage imbalance of the distribution network is:

[0102]

[0103] (2) The voltage deviation of the distribution network is:

[0104]

[0105] (3) The voltage fluctuation of the distribution network is:

[0106]

[0107] Substituting the transformed evaluation index into Equation (4) and Equation (5) yields the voltage quality evaluation objective function.

[0108] In this embodiment, the particle swarm algorithm is iterated on the inner model to optimize the active and reactive output of energy storage and the reactive output of photovoltaics, thereby obtaining the distributed energy storage optimization operation strategy and the distributed photovoltaic optimization operation strategy. The constraints are as follows:

[0109] (1) Distributed energy storage operation constraints include distributed energy storage charging and discharging output constraints and energy storage capacity constraints, specifically:

[0110]

[0111]

[0112]

[0113]

[0114] Where: and They are respectively at node i at time t The single-phase energy storage charging and discharging active power and reactive power of the phase; is the apparent power of distributed energy storage; and are 0 / 1 variables of the charge and discharge state of the energy storage in period t; η 1c and They are the single-phase energy storage charging and discharging efficiency; is the node i at time t Phase single-phase energy storage capacity; is the maximum capacity of energy storage at node i; is the node i at time t+1 Phase single-phase energy storage capacity; D 1 is the maximum discharge depth of single-phase energy storage; formula (11) represents the relationship between the output power and power of the energy storage battery; formula (12) indicates that the daily charge and discharge capacity of the energy storage device is 0.

[0115] (2) Distributed photovoltaic output constraints include the power factor, active output and reactive output constraints of distributed photovoltaics; specifically:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] Where: is the node i at time t The maximum reactive power that a phase photovoltaic power source can output, and its minimum reactive output value is expressed as the negative maximum value; is the maximum apparent power of the single-phase photovoltaic power source; is the node i at time t Active power output by the phase photovoltaic power source; Represents the node i at time t Power factor of the phase photovoltaic power source; and Respectively represent the minimum and maximum values allowed for the power factor of a single-phase photovoltaic power source; Represents the node i at time t The maximum active power that can be output by the photovoltaic power source; Indicates the maximum power curtailment rate allowed for single-phase photovoltaics; express The reactive power output by the phase photovoltaic power source; Equation (17) represents the relationship between the power factor of the energy storage device and the active power and reactive power.

[0122] (3) Existing reactive power compensation constraints include constraints on the compensation power of reactive power compensation equipment; specifically:

[0123]

[0124] Where: Represents the node i at time t Compensation power of phase reactive power compensation equipment; Q q,i,max and Q q,i,min Respectively represent the maximum and minimum values of the compensation power of the reactive compensation equipment; A 0 / 1 variable indicating whether node i is configured with a reactive compensation device.

[0125] (4) Power balance constraints, including constraints on active power and reactive power injected from branches to nodes, constraints on active power and reactive power injected from nodes to branches, constraints on active power and reactive power output from conventional power sources, constraints on energy storage and photovoltaic output, constraints on reactive power regulation by reactive power compensation, and constraints on active power load and reactive power load. Specifically:

[0126]

[0127] Where: and They represent the active and reactive power injected into node i by the branch respectively; and They represent the active and reactive power injected by node i into the branch respectively; and They represent the active power and reactive power injected into node i by the conventional power source at time t; and Respectively represent the photovoltaic active and reactive output; and Respectively represent the active and reactive output of energy storage; and denote the active power load and reactive power load at node i respectively; Indicates the reactive output of reactive compensation.

[0128] In this embodiment, after the inner-layer model is solved, outer-layer energy storage planning is performed based on the inner-layer optimization. The voltage quality improvement cost-effectiveness is determined by the total configuration cost of the distributed energy storage and the voltage quality improvement degree. It is then determined whether the voltage quality improvement cost-effectiveness is optimal. If so, the access location and access capacity of the distributed energy storage are updated.

[0129] Specifically, the particle fitness function in the outer energy storage optimization configuration model is to optimize the cost-effectiveness of voltage quality improvement. The total cost of distributed energy storage configuration, including energy storage construction cost, energy storage operation and maintenance cost, and electricity purchase cost from the upper-level power grid, is specifically:

[0130]

[0131] Where: is the annual value coefficient of single-phase energy storage; The construction cost of single-phase distributed energy storage; is the operation and maintenance cost of single-phase distributed energy storage; f ele It is the daily cost of electricity purchased by the distribution network from the upper-level power grid.

[0132] Among them, the construction cost of single-phase distributed energy storage is:

[0133]

[0134]

[0135] Where: A 0 / 1 variable representing the energy storage configuration of node i; is the apparent power capacity and energy capacity of the single-phase distributed energy storage. If the reactive power of the energy storage is not considered, the apparent power is equal to the active power. The unit price of power capacity and energy capacity for single-phase energy storage; is the discount rate of energy storage; y 1 The life of the energy storage in years.

[0136] The operation and maintenance cost of distributed energy storage is:

[0137]

[0138] Where: It is the annual operation and maintenance cost of single-phase energy storage.

[0139] The daily electricity purchase cost of the distribution network from the upper grid is:

[0140]

[0141] Where: is the power flow from the upper power grid to the distribution network at time t Active power transmitted by each phase; k t It is the time-of-use electricity price that the distribution network purchases electricity from the upper power grid at time t; Δt is the duration of a time period.

[0142] The voltage quality improvement is the difference between the voltage quality assessment results before and after energy storage is connected:

[0143] Δf=f0-f (25)

[0144] Where: f0 represents the voltage quality assessment result when distributed energy storage is not connected; f represents the voltage quality assessment result when distributed energy storage is connected.

[0145] The outer optimization configuration objective function is calculated based on the total cost of energy storage configuration and the voltage quality improvement. The cost-effectiveness of voltage quality improvement is:

[0146]

[0147] Where: λ represents the degree of improvement in voltage quality under unit energy storage configuration cost.

[0148] In this embodiment, the access location constraints and access capacity constraints of distributed energy storage are:

[0149]

[0150]

[0151]

[0152] Where: Configure the 0 / 1 variable of energy storage for node i; Indicates the maximum configuration quantity of energy storage; and are the minimum and maximum values of the energy storage apparent power respectively; and are the minimum and maximum values of single-phase energy storage capacity respectively.

[0153] At this point, a complete outer layer nested inner layer model solution is completed, and the outer layer calculation termination condition is judged, that is, whether the maximum number of outer layer iterations is reached; if the outer layer termination condition is not met, the position and velocity of the outer layer particles are updated and the solution is continued until the outer layer termination condition is met, and the optimal configuration plan for distributed energy storage is obtained.

[0154] In this embodiment, a particle swarm algorithm is used for the solution, in which each element in the particle position matrix of the outer energy storage planning model represents the capacity of the energy storage connected to the three phases a, b, and c of the node; each element in the particle position matrix of the inner energy storage and photovoltaic power supply optimization operation model represents the charging and discharging power (including active power and reactive power) of the energy storage in each time period, as well as the reactive output of each photovoltaic power source.

[0155] This embodiment uses the improved IEEE33 node distribution network for verification. Figure 2As shown in the figure, 200kW single-phase photovoltaic power sources are connected to phase a of node 17, phase a of node 20, phase b of node 24, phase c of node 5, and phase c of node 32 respectively. For the convenience of calculation, load data of 12 moments evenly distributed within a typical day are used as the load basis for this example. The photovoltaic power source outputs power according to the typical daily photovoltaic output curve at each moment.

[0156] The three-phase power flow calculation based on forward-backward substitution method is used to obtain the node voltage and transmission power at each moment;

[0157] Voltage quality assessment index parameters: According to national standards, the maximum and minimum values of the three-phase voltage imbalance are set to 2% and 0 respectively; the maximum and minimum values of the voltage deviation are set to 7% and 0 respectively; the maximum and minimum values of the voltage fluctuation are set to 2% and 0 respectively.

[0158] Model parameter settings: the number of energy storage installations is 5, it can continuously charge and discharge for 2 hours, and the maximum installed power is 200kVA; the peak electricity price is set to 0.57 yuan / kWh, the duration is 10:00-20:00, and the rest of the time is the valley electricity price of 0.37 yuan / kWh.

[0159] Particle swarm algorithm parameter setting: This embodiment adopts the inner and outer nested particle swarm algorithm, the inner and outer layer population sizes are both 50, and the maximum number of iterations is both 100.

[0160] The distributed energy storage planning results obtained by the particle swarm optimization algorithm based on the improved initial population are shown in Table 1;

[0161] Table 1 Distributed energy storage planning results

[0162]

[0163] Distributed photovoltaic reactive power output Figure 3 As shown in the figure, negative values indicate that photovoltaics absorb reactive power; the distributed energy storage operation strategy is as follows Figure 4-Figure 8 As shown in the figure, analysis shows that when the load is heavy, the distributed photovoltaic system outputs reactive power to maintain the voltage deviation, and when the load is light, it absorbs reactive power to reduce voltage fluctuations. At the same time, due to the unbalanced three-phase load and the different locations and capacities of the photovoltaic system connected to each phase, phase b has a heavier load and a smaller photovoltaic capacity, so the energy storage system connected to phase b has a larger capacity. In addition, the five distributed energy storage systems perform power conversion for four-quadrant operation according to different connection locations and times.

[0164] After connecting to distributed energy storage, the distribution network voltage quality assessment results, total energy storage planning costs, and cost-effectiveness of voltage quality improvement are shown in Table 2.

[0165] Table 2 Improvement of distribution network voltage after connecting to distributed energy storage

[0166]

[0167] Figure 9 and Figure 10 A comparative analysis of the node voltages at valley load 4:00, peak load 20:00, and 14:00 before and after the access to distributed energy storage shows that due to the discharge of energy storage during peak hours, the node voltages at 14:00 and 20:00 both increase significantly, and the voltage deviation decreases. Due to the charging of energy storage during valley hours, the load at 4:00 increases compared to before the access to energy storage, the voltage decreases slightly, and the voltage deviation increases slightly.

[0168] Figure 11 and Figure 12 The voltage of each phase node at 10:00 before and after the energy storage was connected was unbalanced. Taking the load of phase A as the benchmark, the load of phase B was too heavy, while the load of phase C was too light, resulting in a large difference in the three-phase voltages, a large voltage deviation of phase B, and voltage exceeding the limit at the end node of the branch. After the distributed energy storage was connected, the three-phase voltages all increased to varying degrees, especially the voltage increase of phase B, which was the most obvious, and the imbalance of the three-phase voltage was effectively alleviated.

[0169] The voltage conditions of the node at each time within 17 days are as follows: Figure 13 As shown in the figure, analysis shows that before energy storage and photovoltaics are connected, the voltage difference during peak and valley load periods is large, and there are serious voltage over-limit and voltage fluctuations. After energy storage is connected, during peak load periods, distributed energy storage discharges and outputs active power, and photovoltaic output during the noon period during the day is higher than before energy storage is connected. This solves the voltage over-limit problem, greatly reduces voltage fluctuations, and plays a role in peak shaving and valley filling.

[0170] The above calculation examples show that this embodiment can effectively improve the voltage quality of the distribution network, reduce voltage deviation, minimize voltage fluctuation, and reduce three-phase imbalance while taking into account the economic efficiency of distributed energy storage planning.

[0171] Example 2

[0172] This embodiment provides a distributed energy storage planning system that considers energy storage and photovoltaic reactive output, including:

[0173] The model building module is configured to construct an outer energy storage optimization configuration model by considering the total configuration cost of distributed energy storage and the improvement in voltage quality; and to construct an inner energy storage and photovoltaic operation strategy optimization model by considering the improvement in distribution network voltage quality caused by the operation of distributed energy storage and the reactive output of distributed photovoltaics;

[0174] The initialization module is configured to use three-phase power flow calculation to obtain the voltage of each node in the distribution network in the outer energy storage optimization configuration model, consider voltage quality assessment indicators including three-phase voltage imbalance, voltage deviation, and voltage fluctuation, perform a weighted sum of the three indicators, and initialize the access location and access capacity of the distributed energy storage based on the voltage quality assessment results;

[0175] The inner-layer operation optimization module is configured to use the optimal voltage quality assessment as the objective function in the inner-layer energy storage and photovoltaic operation strategy optimization model, and the distributed energy storage operation constraints, distributed photovoltaic output constraints, existing reactive power compensation constraints, and power balance constraints as constraints. Under the current access location and access capacity of the distributed energy storage, the module optimizes the reactive power output of the distributed photovoltaics and the active and reactive power outputs of the distributed energy storage, thereby determining the optimal operation strategy for the distributed energy storage and distributed photovoltaics.

[0176] The outer configuration optimization module is configured to determine the total configuration cost of distributed energy storage and the voltage quality improvement degree under the optimal operation strategy in the outer energy storage optimization configuration model, determine the voltage quality improvement cost-effectiveness based on the total configuration cost of distributed energy storage and the voltage quality improvement degree, and use the optimal voltage quality improvement cost-effectiveness as the objective function to determine whether to update the optimal access location and access capacity of the distributed energy storage; use the access location constraints and access capacity constraints of the distributed energy storage as constraints to update the current access location and access capacity, and cyclically solve the inner and outer nested models until the maximum number of iterations is reached, thereby obtaining the optimal distributed energy storage planning scheme.

[0177] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0178] In further embodiments, there is also provided:

[0179] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed by the processor, wherein when the computer instructions are executed by the processor, the method described in Example 1 is performed. For the sake of brevity, no further details are given here.

[0180] It should be understood that in this embodiment, the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0181] The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0182] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is performed.

[0183] The method in Example 1 can be directly implemented as a hardware processor, or can be implemented using a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, it will not be described in detail here.

[0184] Those skilled in the art will appreciate that the units, i.e., algorithm steps, of the various examples described in conjunction with this embodiment can be implemented using electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0185] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A distributed energy storage planning method considering energy storage and photovoltaic reactive output is characterized by: include: An outer energy storage optimization configuration model is constructed by considering the total configuration cost of distributed energy storage and the improvement in voltage quality. An inner energy storage and photovoltaic operation strategy optimization model is constructed by considering the improvement in distribution network voltage quality caused by the operation of distributed energy storage and the reactive output of distributed photovoltaics. In the outer energy storage optimization configuration model, three-phase power flow calculations are used to obtain the voltage at each node in the distribution network. Voltage quality assessment indicators, including three-phase voltage imbalance, voltage deviation, and voltage fluctuation, are considered. After a weighted summation of these three indicators, the access location and access capacity of the distributed energy storage are initialized based on the voltage quality assessment results. In the inner-layer energy storage and photovoltaic operation strategy optimization model, the objective function is to optimize voltage quality assessment, and the distributed energy storage operation constraints, distributed photovoltaic output constraints, existing reactive power compensation constraints, and power balance constraints are used as constraints. Under the current access location and access capacity of the distributed energy storage, the reactive power output of the distributed photovoltaics and the active and reactive power outputs of the distributed energy storage are optimized to determine the optimal operation strategy for the distributed energy storage and distributed photovoltaics. In the outer energy storage optimization configuration model, the total configuration cost of distributed energy storage and the voltage quality improvement degree under the optimal operation strategy are determined. The voltage quality improvement cost-effectiveness is determined based on the total configuration cost of distributed energy storage and the voltage quality improvement degree. The optimal voltage quality improvement cost-effectiveness is used as the objective function to determine whether to update the optimal access location and access capacity of distributed energy storage. Taking the access location constraint and access capacity constraint of distributed energy storage as constraints, the current access location and access capacity are updated, and the inner and outer nested models are solved cyclically until the maximum number of iterations is reached, thereby obtaining the optimal distributed energy storage planning scheme.

2. The distributed energy storage planning method considering energy storage and photovoltaic reactive output according to claim 1, characterized in that: The three-phase voltage unbalance degree is: Where: T is the number of time periods in a day; 、 、 They are t Time Node i The voltage vectors of phases a, b, and c; α is the rotation factor, whose value is e j120° ; The voltage deviation is: Where: For the three phases a, b, and c of the three-phase circuit; for t Time Node i of Phase voltage amplitude; is the reference voltage amplitude; The voltage fluctuation is: Where: For nodes i of Daily average value of phase voltage; The three-phase voltage unbalance, voltage deviation and voltage fluctuation are assigned the following weights: Where: 、 and Respectively represent the weights of three-phase voltage unbalance, voltage deviation and voltage fluctuation; 、 and Represent the decision maker’s preferences for the three indicators respectively; 、 and They represent the maximum three-phase voltage unbalance, maximum voltage deviation and maximum voltage fluctuation specified by national standards respectively; The voltage quality assessment results of each node are: The voltage quality assessment results of each node before the distributed energy storage is connected are sorted, and the access position and access capacity of the distributed energy storage are initialized according to the sorting results.

3. The distributed energy storage planning method considering energy storage and photovoltaic reactive output according to claim 1, characterized in that: The voltage quality improvement cost performance ratio is the ratio of the voltage quality improvement degree to the total configuration cost of distributed energy storage; The voltage quality improvement degree is the difference between the voltage quality evaluation result when the distributed energy storage is not connected and the voltage quality evaluation result when the distributed energy storage is connected.

4. The distributed energy storage planning method considering energy storage and photovoltaic reactive output according to claim 1, characterized in that: The total configuration cost of distributed energy storage includes the construction cost of distributed energy storage, the operation and maintenance cost of distributed energy storage, and the daily electricity purchase cost of the distribution network.

5. The distributed energy storage planning method considering energy storage and photovoltaic reactive output according to claim 1, characterized in that: In the inner-layer energy storage and photovoltaic operation strategy optimization model, the optimal voltage quality assessment is used as the objective function. Specifically, the voltage quality of each node is evaluated, and the arithmetic average of the voltage quality assessment results of all nodes in the distribution network is used to construct the objective function.

6. The distributed energy storage planning method considering energy storage and photovoltaic reactive output according to claim 1, characterized in that: The distributed energy storage operation constraints include: distributed energy storage charging and discharging output constraints and energy storage energy capacity constraints, specifically: Where: and They are t Time Node i Department The single-phase energy storage charging and discharging active power and reactive power of the phase; is the apparent power of distributed energy storage; and Energy storage t 0 / 1 variables of the charge and discharge status of the time period; and They are the single-phase energy storage charging and discharging efficiency; for t Time Node i Department Phase single-phase energy storage capacity; For nodes i The maximum capacity of energy storage; D 1 is the maximum discharge depth of single-phase energy storage; The distributed photovoltaic output constraints include: distributed photovoltaic power factor, active output and reactive output constraints; wherein the reactive output constraint is: Where: for t Time Node i Department The maximum reactive power that a phase photovoltaic power source can output. The minimum reactive power is expressed by the negative maximum value. is the maximum apparent power of the single-phase photovoltaic power source; for t Time Node i Department Active power output by the phase photovoltaic power source; express Reactive power output by the phase photovoltaic power source; The existing reactive power compensation constraints include: constraints on the compensation power of reactive power compensation equipment; The power balance constraints include: constraints on the active power and reactive power injected by the branch into the node, constraints on the active power and reactive power injected by the node into the branch, constraints on the active power and reactive power output of the conventional power supply, constraints on energy storage and photovoltaic output, constraints on reactive power regulation by reactive compensation, and constraints on active power load and reactive power load.

7. The distributed energy storage planning method considering energy storage and photovoltaic reactive output according to claim 1, characterized in that: The access location constraints and access capacity constraints of the distributed energy storage include: constraints on the number of distributed energy storage configurations, apparent power, and energy storage capacity.

8. A distributed energy storage planning system considering energy storage and photovoltaic reactive output, characterized in that: include: The model building module is configured to construct an outer energy storage optimization configuration model by considering the total configuration cost of distributed energy storage and the improvement in voltage quality; and to construct an inner energy storage and photovoltaic operation strategy optimization model by considering the improvement in distribution network voltage quality caused by the operation of distributed energy storage and the reactive output of distributed photovoltaics; The initialization module is configured to use three-phase power flow calculation to obtain the voltage of each node in the distribution network in the outer energy storage optimization configuration model, consider voltage quality assessment indicators including three-phase voltage imbalance, voltage deviation, and voltage fluctuation, perform a weighted sum of the three indicators, and initialize the access location and access capacity of the distributed energy storage based on the voltage quality assessment results; The inner-layer operation optimization module is configured to use the optimal voltage quality assessment as the objective function in the inner-layer energy storage and photovoltaic operation strategy optimization model, and the distributed energy storage operation constraints, distributed photovoltaic output constraints, existing reactive power compensation constraints, and power balance constraints as constraints. Under the current access location and access capacity of the distributed energy storage, the module optimizes the reactive power output of the distributed photovoltaics and the active and reactive power outputs of the distributed energy storage, thereby determining the optimal operation strategy for the distributed energy storage and distributed photovoltaics. An outer configuration optimization module is configured to determine the total configuration cost of distributed energy storage and the voltage quality improvement degree under the optimal operation strategy in the outer energy storage optimization configuration model, determine the voltage quality improvement cost-effectiveness based on the total configuration cost of distributed energy storage and the voltage quality improvement degree, and use the optimal voltage quality improvement cost-effectiveness as the objective function to determine whether to update the optimal access location and access capacity of the distributed energy storage; Taking the access location constraint and access capacity constraint of distributed energy storage as constraints, the current access location and access capacity are updated, and the inner and outer nested models are solved cyclically until the maximum number of iterations is reached, thereby obtaining the optimal distributed energy storage planning scheme.

9. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 7 is completed.

10. A computer-readable storage medium, characterized in that Used to store computer instructions, which, when executed by a processor, complete the method according to any one of claims 1 to 7.

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