An optimization method for stabilizing photovoltaic power generation fluctuations based on scenario sets and distributed energy storage

Abstract

An optimization method for stabilizing photovoltaic (PV) power generation fluctuations using distributed energy storage based on scenario sets is proposed. This method establishes a dispatchable capacity model for distributed energy storage based on its physical characteristics; establishes node voltage constraints and branch transmission capacity constraints for the distribution network; introduces reactive power regulation equipment to establish a dispatchable reactive power support range; and maintains power and voltage stability through distributed energy storage and various reactive power regulation devices while considering branch transmission capacity constraints. Finally, a coordinated optimization model for distributed energy storage based on PV power generation prediction scenarios is established. This invention prioritizes the basic safety of the distribution network and can be well applied to cities with abundant solar resources. By equipping distributed energy storage and certain reactive power regulation devices, it alleviates power and voltage fluctuations caused by intermittent PV power generation, thereby improving grid operational stability, reducing economic costs, and conserving resources.

Description

Technical Field

[0001] This invention belongs to the field of distributed energy storage technology, specifically involving a collaborative optimization method for stabilizing photovoltaic power generation fluctuations based on scenario sets. Background Technology

[0002] In recent years, clean power generation resources, represented by photovoltaic (PV) power generation, have been continuously developing in my country. PV power generation has many advantages, such as cleanliness, safety, and long service life, and has become an irreplaceable and important component of the new power system. However, the characteristics of PV power generation differ from those of conventional coal-fired power generating units. It is greatly affected by changes in solar radiation, exhibiting significant intermittency and uncertainty. Consequently, its output power is also unstable, easily causing power quality fluctuations such as voltage fluctuations in the distribution network, posing challenges to the safe operation of the power system.

[0003] To mitigate the impact of photovoltaic (PV) power generation instability on the power grid, distributed energy storage technology is an effective approach. Unlike centralized energy storage, distributed energy storage involves distributing multiple energy storage units across various power supply areas of the distribution network. Through coordination, it achieves a dispatchability exceeding that of centralized energy storage. Distributed energy storage is widely used to alleviate line congestion. Through a unified dispatch mechanism, distributed energy storage across the distribution network is coordinated, providing power support in areas with high loads to prevent transmission line overload caused by increased load. Regarding the power quality degradation caused by PV power generation, distributed energy storage, installed near PV power plants, can smooth out fluctuating PV output. Summary of the Invention

[0004] The technical problem solved by this invention is to provide a collaborative optimization method for power and voltage fluctuations in the distribution network caused by photovoltaic power generation with strong uncertainty, based on photovoltaic prediction scenario sets and utilizing distributed energy storage and other devices.

[0005] Technical Solution: To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] An optimization method for stabilizing photovoltaic power generation fluctuations using distributed energy storage based on scenario sets, characterized by the following steps:

[0007] S1: Based on the physical characteristics of distributed energy storage, establish a dispatchable capability model for distributed energy storage;

[0008] S2: Establish node voltage constraints and branch transmission capacity constraints for the distribution network;

[0009] S3: Introduce reactive power regulation equipment to establish a dispatchable reactive power support range;

[0010] S4: While considering the branch transmission capacity constraints, maintain power and voltage stability through distributed energy storage and various reactive power regulation devices;

[0011] S5: Establish a coordinated optimization model for distributed energy storage based on photovoltaic power generation prediction scenarios.

[0012] Further, in step S1, characteristic parameters of distributed energy storage are collected, and the adjustable capability range of each distributed energy storage unit is established; assuming the distributed energy storage operates at a unity power factor and the reactive power output of the energy storage is 0, the following steps are taken: and Let n represent the sets of branches and nodes in the distribution network, respectively. Therefore, the adjustable power range of distributed energy storage connected to node n is expressed by the following formula:

[0013]

[0014] In the formula: φ represents the phase, and a, b, c represent each phase in the three-phase transmission, i.e., φ∈{a, b, c};

[0015] This represents the active power output of the distributed energy storage connected to phase φ at node n.

[0016] This represents the limit value of the active power output of the distributed energy storage system connected to node n in phase φ.

[0017] and This represents the initial storage capacity and maximum allowable storage capacity of the distributed energy storage of phase φ connected to node n;

[0018] τ represents a time period within the research scenario, and t represents time;

[0019] ΔT represents the charging and discharging time.

[0020] Furthermore, in step S2, the node voltage constraints are as follows: Assume there are N+1 nodes in the distribution network, numbered 0, ..., N, where node 0 is the common coupling point; the three voltage equations of the distribution network are expressed as follows:

[0021] v=Rp+Xq+O (2)

[0022] In the formula: v = [v1, ..., v N ] T Composed of the squares of the node voltages in a three-phase distribution network, the three-phase voltage of each node is expressed as: Similarly, p = [p1, ..., p] N ] T and q = [q1, ..., q N ]T Represent the active and reactive power injected into each node of the three-phase distribution network, respectively; R = 2Fdiag(r)F T X = 2Fdiag(x)F T and O = v01 N Let F be the corresponding coefficient matrix, where A is the node-branch incidence matrix, and F = -A. -1 r is a vector composed of branch resistances, x is a vector composed of branch reactances, 1 N Let v0 be an N-dimensional column vector of all 1s, where v0 represents the square of the three-phase voltage amplitude at the point of common coupling (PCC) between the virtual power plant and the upstream power grid.

[0023] For each node, the square of its voltage magnitude must satisfy the following equation:

[0024]

[0025] In the formula: v max and v min These represent the squares of the maximum and minimum node voltage magnitude limits, respectively; It represents the square of the voltage amplitude of phase φ at the nth node.

[0026] Furthermore, in step S2, the distribution network must ensure that the transmission power does not exceed the transmission limit of the branch, which includes active power, reactive power, and apparent power. The branch transmission capacity constraint is approximately expressed as follows:

[0027]

[0028] Among them, S l This represents the transmission limit of branch l. and Let φ represent the active and reactive power of phase φ at the nth node, respectively.

[0029] Furthermore, in step S3, for the parallel capacitor connected to node n, its output reactive power... The limits depend on the node voltage, and assuming it is continuously controllable within the following limits, the reactive power support range of the parallel capacitor is expressed as follows:

[0030]

[0031] In the formula: This indicates the reactive power output limit of the parallel capacitor, where CAP is the capacitor capacity.

[0032] Furthermore, reactive power support can also be provided by adjusting the turns ratio of the voltage regulator or transformer. Assuming that their internal resistance is not affected by the ratio change and the effect of voltage drop on internal resistance is ignored, the voltage regulator and transformer should meet the following constraints shown in (6):

[0033]

[0034] In the formula, i and j represent the node numbers at both ends of the voltage regulator or transformer. This indicates the tap ratio of a voltage regulator or transformer. and These represent the lower and upper limits of the tap, respectively. kind Let represent the squares of the voltage amplitudes of phase φ at nodes j and i, respectively.

[0035] Furthermore, in step S4, assuming the power factor for load reduction is constant, the active and reactive load reductions are expressed as follows:

[0036]

[0037] here, and This represents the active and reactive load reduction of phase φ at node n, where pf is the power factor of the load reduction. and Let φ be the initial active and reactive power of the load at node n.

[0038] Furthermore, in step S4, assuming there are NS scenarios, denoted as iS∈{1, ...,NS}, and the probabilities of these scenarios are known, the objective is to minimize the weighted load reduction under all scenarios, as follows:

[0039]

[0040] Here, pr0b iS Let i be the probability corresponding to scenario iS. The load reduction weights for the load connected to node n.

[0041] Furthermore, during the optimization process, the distribution network considers the uncertainty of photovoltaic output. The distribution network must minimize the expected load shedding under all scenarios within the considered NT time period in order to minimize the additional overhead incurred in maintaining the power and voltage stability of photovoltaics. Therefore, the goal is to minimize load shedding under all circumstances. For each scenario and each time t, the following constraints should be met: the node voltage constraints and branch transmission capacity constraints of the distribution network, the adjustable power range of distributed energy storage connected to node n, the reactive power support range of parallel capacitors, and the regulation range constraints of voltage regulators and transformers, as well as the relationship constraints between active and reactive load shedding.

[0042] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0043] This invention presents an optimization method for stabilizing photovoltaic (PV) power generation fluctuations based on scenario sets and distributed energy storage. It coordinates the scheduling of distributed energy storage and reactive power regulation equipment to stabilize power and voltage fluctuations caused by PV power generation. To avoid large-scale accidents in the distribution network, the optimization process requires node voltage and branch power flow to be maintained within specified ranges. By predicting a set of anticipated PV output scenarios, and using a weighted method, the method minimizes load reduction caused by PV power generation under each scenario. This approach maintains power system security while minimizing grid losses, demonstrating high practical value.

[0044] In the process of coordination and optimization, this invention considers the node voltage constraints and branch transmission capacity constraints of the distribution network, prioritizing the basic safety of the distribution network. It utilizes distributed energy storage and reactive power regulation equipment to provide active and reactive power support for distribution networks containing photovoltaic power generation. Based on a predicted photovoltaic scenario set, and while prioritizing ensuring that the voltage does not exceed the specified range, it minimizes the load shedding caused by photovoltaic fluctuations, thereby minimizing the impact of unstable photovoltaics on the distribution network and the resulting economic losses. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method of the present invention;

[0046] Figure 2 It is a linear approximation of the tributary transmission capacity constraint;

[0047] Figure 3 This is a schematic diagram of a voltage regulator and a transformer. Detailed Implementation

[0048] The present invention will be further illustrated below with reference to specific embodiments. These embodiments are implemented based on the technical solutions of the present invention, and it should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0049] The present invention provides a scenario-based collaborative optimization method for distributed energy storage to stabilize photovoltaic power generation fluctuations. First, based on the physical characteristics of distributed energy storage, a dispatchable capacity model is established. Then, to ensure the safe operation of the distribution network, node voltage constraints and branch transmission capacity constraints are established. Reactive power regulation equipment such as capacitors is introduced to establish a dispatchable reactive power support range. Finally, while considering branch transmission capacity constraints, the distributed energy storage and various reactive power regulation equipment are coordinated to prioritize ensuring node voltage indicators remain within specified ranges and minimize load shedding, thus establishing a distributed energy storage coordinated optimization model based on photovoltaic power generation prediction scenarios. Specifically, the method includes the following steps:

[0050] Step 1: First, collect the characteristic parameters of distributed energy storage and establish the adjustable capacity range of each distributed energy storage.

[0051] Assume that distributed energy storage operates at a unity power factor, meaning the reactive power output of the energy storage is zero. and Let n and n represent the sets of branches and nodes in the distribution network, respectively. Therefore, the connection to node n... The adjustable power range of distributed energy storage can be expressed by the following formula:

[0052]

[0053] In the formula: φ represents the phase, and a, b, c represent each phase in the three-phase transmission, i.e., φ∈{a, b, c};

[0054] This represents the active power output of the distributed energy storage connected to phase φ at node n.

[0055] This represents the limit value of the active power output of the distributed energy storage system connected to node n in phase φ.

[0056] and This represents the initial storage capacity and maximum allowable storage capacity of the distributed energy storage of phase φ connected to node n;

[0057] τ represents a time period within the research scenario, and t represents time;

[0058] ΔT represents the charging and discharging time.

[0059] Step 2: In order to ensure the safe operation of the distribution network, it is necessary to consider the node voltage constraints and branch transmission capacity constraints of the distribution network.

[0060] Suppose there are N+1 nodes in the distribution network, numbered 0, ..., N respectively. Node 0 is the common coupling point. Based on the extended DistFlow model, the voltage equations of the three-phase distribution network can be expressed as follows:

[0061] v=Rp+Xq+O (2)

[0062] In the formula: v = [v1, ..., v N ] T Composed of the squares of the node voltages in a three-phase distribution network, the three-phase voltage of each node is expressed as: Similarly, p = [p1, ..., p] N ] T and q = [q1, ..., q N ] T These represent the active and reactive power injected into each node of the three-phase distribution network, respectively. R = 2Fdiag(r)F T X = 2Fdiag(x)F T and O = v01 N Let F be the corresponding coefficient matrix, where A is the node-branch incidence matrix, and F = -A. -1 r is a vector composed of branch resistances, x is a vector composed of branch reactances, 1 N Let v0 be an N-dimensional column vector of all 1s, where v0 represents the square of the three-phase voltage amplitude at the point of common coupling (PCC) between the virtual power plant and the upstream power grid.

[0063] For each node, the square of its voltage magnitude must satisfy the following equation:

[0064]

[0065] In the formula: v max and v min These represent the squares of the maximum and minimum node voltage magnitude limits, respectively; It represents the square of the voltage amplitude of phase φ at the nth node.

[0066] In addition to node voltage constraints, the distribution network must also ensure that the transmitted power does not exceed the transmission limits of the branches. This includes active, reactive, and apparent power, and its linear approximation constraints are as follows: Figure 2 As shown.

[0067] The tributary transmission capacity constraint can be approximated as follows:

[0068]

[0069] Among them, S l This represents the transmission limit of branch l. and Let φ represent the active and reactive power of phase φ at the nth node, respectively.

[0070] Step 3: To provide voltage support to the distribution network during photovoltaic fluctuations, reactive power regulation equipment needs to be introduced. This will be illustrated using parallel capacitors as an example.

[0071] For a parallel capacitor connected to node n, its output reactive power The limits depend on the node voltage, and assuming it is continuously controllable within the following limits, the reactive power support range of the parallel capacitor can be expressed as follows:

[0072]

[0073] In the formula: This indicates the reactive power output limit of the parallel capacitor, where CAP is the capacitor capacity.

[0074] Besides parallel capacitors, reactive power support can also be provided by adjusting the turns ratio of voltage regulators or transformers. For the voltage regulator and transformer ratios, it is assumed that their internal resistance is unaffected by ratio changes, and the effect of voltage drop on internal resistance is ignored. Schematic diagrams of voltage regulators and transformers are shown below. Figure 3 As shown, the following constraints shown in (6) should be satisfied:

[0075]

[0076] In the formula, i and j represent the node numbers at both ends of the voltage regulator or transformer. This indicates the tap ratio of a voltage regulator or transformer. and These represent the lower and upper limits of the tap, respectively. and Let represent the squares of the voltage amplitudes of phase φ at nodes j and i, respectively.

[0077] Step 4: When photovoltaic (PV) output fluctuates, maintain power and voltage stability as much as possible by scheduling distributed energy storage and reactive power regulation equipment. Voltage stability is treated as a mandatory indicator, which may be accompanied by a certain degree of load shedding: when load shedding is 0, it indicates that the fluctuations caused by PV have been completely absorbed through the coordination of various distributed resources; when load shedding is not 0, it indicates that some load output must be reduced to maintain power and voltage stability. Assuming the power factor of the load shedding is constant, the active and reactive load shedding is represented as follows:

[0078]

[0079] here, and This represents the active and reactive load reduction of phase φ at node n, where pf is the power factor of the load reduction. and Let φ be the initial active and reactive power of the load at node n.

[0080] In practical applications, the potential output of photovoltaic power can be predicted within a certain range, and a set of uncertain scenarios for photovoltaic output over a future period can be established based on the predicted information. We assume there are NS scenarios, denoted as iS∈{1,...,NS}, and the probabilities of these scenarios are known. The objective is to minimize the weighted load reduction under all scenarios, as follows:

[0081]

[0082] Here, prob iS Let i be the probability corresponding to scenario iS. The load reduction weights for the load connected to node n.

[0083] Step 5: In summary, during the optimization process, the distribution network considers the uncertainty of photovoltaic output. The distribution network must minimize the expected load shedding under all scenarios within the considered NT time period to minimize the additional overhead incurred in maintaining the power and voltage stability of photovoltaics. Therefore, the objective is to minimize load shedding as much as possible under all scenarios shown in (8). For each scenario and each time t, the network constraints shown in (2)-(4), the constraints of each energy storage, parallel capacitor and voltage regulator shown in (1), (5)-(6), and the load shedding constraints shown in (7) should be satisfied.

[0084] This invention, in its coordination and optimization process, considers the node voltage constraints and branch transmission capacity constraints of the distribution network, prioritizing the basic safety of the distribution network. It utilizes distributed energy storage and reactive power regulation equipment to provide active and reactive power support for distribution networks containing photovoltaic (PV) power generation. Based on predicted PV scenario sets, while prioritizing ensuring that the voltage does not exceed the specified range, it minimizes load shedding caused by PV fluctuations, thereby minimizing the impact of unstable PV on the distribution network and the resulting economic losses. This invention can be well applied to cities with abundant solar resources. By equipping them with distributed energy storage and certain reactive power regulation equipment, it alleviates power and voltage fluctuations caused by intermittent PV, improving grid operational stability, reducing economic costs, and conserving resources.

[0085] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An optimization method for stabilizing photovoltaic power fluctuations based on a scenario set of distributed energy storage, characterized in that, Includes the following steps: S1: Based on the physical characteristics of distributed energy storage, establish a dispatchable capability model for distributed energy storage. S2: Establish node voltage constraints and branch transmission capacity constraints for the distribution network; S3: Introduce reactive power regulation equipment to establish a dispatchable reactive power support range; S4: While considering the branch transmission capacity constraints, power and voltage stability are maintained through distributed energy storage and various reactive power regulation devices. When the load shedding is 0, it indicates that the fluctuations caused by photovoltaic power have been completely absorbed through the coordination of various distributed resources. When the load shedding is not 0, it indicates that the output of some loads has to be reduced in order to maintain power and voltage stability. Assuming that the power factor of the load shedding is constant, the active and reactive load shedding is represented as follows: Here, and denote the active and reactive load curtailment of phase φ of node n, pf is the power factor of the load curtailment, and are the initial active and reactive power of the load of phase φ of node n. S5: Establish a coordinated optimization model for distributed energy storage based on photovoltaic power generation prediction scenarios; A certain range of predictions is made regarding the possible output of photovoltaic power. Based on the prediction information, a set of uncertain scenarios for photovoltaic power output in the future period is established. Assume there are NS scenarios, denoted as iS∈{1,…,NS}, and the probabilities of these scenarios are known. The objective is to minimize the weighted load reduction under all scenarios, as expressed below: Here, prob iS Let i be the probability corresponding to scenario iS. The load reduction weights for the load connected to node n.

2. The optimization method for stabilizing photovoltaic power generation fluctuations based on scenario sets for distributed energy storage according to claim 1, characterized in that: In step S1, characteristic parameters of distributed energy storage are collected to establish the adjustable capability range of each distributed energy storage unit; assuming the distributed energy storage operates at a unity power factor and the reactive power output of the energy storage is 0, the following steps are taken: and Let n represent the sets of branches and nodes in the distribution network, respectively. Therefore, the adjustable power range of distributed energy storage connected to node n is expressed by the following formula: In the formula: φ represents the phase, and a, b, c represent each phase in the three-phase transmission, i.e. φ∈{a,b,c}; This represents the active power output of the distributed energy storage connected to phase φ at node n. This represents the limit value of the active power output of the distributed energy storage system connected to node n in phase φ. and This represents the initial storage capacity and maximum allowable storage capacity of the distributed energy storage of phase φ connected to node n; τ represents a time period within the research scenario, and t represents time; ΔT represents the charging and discharging time.

3. The optimization method for stabilizing photovoltaic power generation fluctuations based on scenario sets for distributed energy storage according to claim 1, characterized in that: In step S2, the node voltage constraints are as follows: Assume there are N+1 nodes in the distribution network, numbered 0, ..., N, where node 0 is the common coupling point; the three voltage equations of the distribution network are expressed as follows: v=Rp+Xq+O (2) In the formula: v = [v1, ..., v N ] T It consists of the squares of the node voltages of a three-phase distribution network, and the three-phase voltage of each node is expressed as: Similarly, p = [p1, ..., p N ] T and q = [q1, ..., q N ] T Represent the active and reactive power injected into each node of the three-phase distribution network, respectively; R = 2Fdiag(r)F T X = 2Fdiag(x)F T and O = v01 N Let F be the corresponding coefficient matrix, where A is the node-branch incidence matrix, and F = -A. -1 r is a vector composed of branch resistances, x is a vector composed of branch reactances, 1 N It is an N-dimensional column vector of all 1s, where v0 represents the square of the three-phase voltage amplitude at the common connection point between the virtual power plant and the upper-level power grid. For each node, the square of its voltage magnitude must satisfy the following equation: In the formula: v max and V min These represent the squares of the maximum and minimum node voltage magnitude limits, respectively; It represents the square of the voltage amplitude of phase φ at the nth node.

4. The optimization method for stabilizing photovoltaic power generation fluctuations based on scenario sets for distributed energy storage according to claim 1, characterized in that: In step S2, the distribution network must ensure that the transmission power does not exceed the transmission limit of the branch, which includes active power, reactive power, and apparent power. The branch transmission capacity constraint is approximately expressed as follows: Among them, S l This represents the transmission limit of branch l. and These represent the active and reactive power of phase φ at the nth node, respectively.

5. The optimization method for stabilizing photovoltaic power generation fluctuations based on scenario sets for distributed energy storage according to claim 1, characterized in that: In step S3, for the parallel capacitor connected to node n, its output reactive power... The limits depend on the node voltage, and assuming it is continuously controllable within the following limits, the reactive power support range of the parallel capacitor is expressed as follows: In the formula: CAP represents the reactive power limit of the parallel capacitor, where CAP is the capacitor capacity. It represents the square of the voltage amplitude of phase φ at the nth node.

6. The optimization method for stabilizing photovoltaic power generation fluctuations based on scenario sets for distributed energy storage according to claim 1, characterized in that: Reactive power support can also be provided by adjusting the turns ratio of the voltage regulator or transformer. Assuming that their internal resistance is not affected by the change in the ratio and ignoring the effect of voltage drop on internal resistance, the voltage regulator and transformer should meet the following constraints shown in (6): In the formula, i and j represent the node numbers at both ends of the voltage regulator or transformer. This indicates the tap ratio of a voltage regulator or transformer. and These represent the lower and upper limits of the tap, respectively. and Let represent the squares of the voltage amplitudes of phase φ at nodes j and i, respectively.

7. The optimization method for stabilizing photovoltaic power generation fluctuations based on scenario sets for distributed energy storage according to claim 1, characterized in that: During the optimization process, the distribution network considers the uncertainty of photovoltaic output. The distribution network must minimize the expected load shedding under all scenarios within the considered NT time period in order to minimize the additional overhead incurred in maintaining the power and voltage stability of photovoltaics. Therefore, the goal is to minimize load shedding under all circumstances. For each scenario and each time t, the following three constraints should be met: the node voltage constraint and the branch transmission capacity constraint of the distribution network. In addition, the adjustable power range of distributed energy storage connected to node n, the reactive power support range of parallel capacitors, and the regulation range constraints of voltage regulators and transformers should also be met, as well as the relationship constraint between active and reactive load shedding.

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