Method and device for participating in active and reactive power joint clearing of power distribution network by energy storage under uncertainty

Abstract

The invention discloses a method and a device for participating in active and reactive power joint clearing of a power distribution network by energy storage under uncertainty, and relates to the technical field of power distribution network dispatching and energy system optimization, and the method comprises the steps: obtaining a topological structure, line parameters, distributed resource parameters and source load historical data of the power distribution network; inputting source load historical data into the fuzzy set model, and inputting a power distribution network topological structure and line parameters into power distribution network physical constraints based on a linearization power flow model; embedding the node net load power uncertainty represented by the fuzzy set model into the physical constraint of the power distribution network by introducing a distributed robust opportunity constraint to obtain an opportunity constraint, and converting the opportunity constraint into a deterministic constraint; solving the active-reactive joint clearing model to obtain active and reactive scheduling plans of each node of the power distribution network by taking the minimization of the total cost of system operation as a target; power distribution node marginal electricity price considering uncertainty is obtained through calculation on the basis of dual variables of the active-reactive joint clearing model, and active electric energy and reactive support service in the market are settled on the basis of the uncertainty power distribution node marginal electricity price and active and reactive scheduling plans of all nodes of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution network dispatching and energy system optimization technology, specifically to a method and apparatus for energy storage to participate in the joint clearing of active and reactive power in power distribution networks under uncertainty. Background Technology

[0002] With the large-scale integration of new energy sources and new loads, the uncertainty on both the source and load sides of the distribution network has intensified, leading to a significant increase in the risk of voltage exceeding limits. Due to the close coupling between active and reactive power in the distribution network, traditional transmission network market mechanisms based on DC power flow models cannot be directly applied; instead, complex AC power flow models must be used for regulation. However, existing technologies often struggle to balance computational efficiency with accurate quantification of uncertainty risks when dealing with AC power flow constraints. At the electricity market level, reactive power has long been treated as an ancillary service, typically using fixed compensation or a single pricing model, lacking a market-based pricing mechanism that reflects spatiotemporal value differences and uncertainty risks. This makes it difficult to effectively guide resources to participate in voltage management through economic means in scenarios with a high proportion of new energy integration. Although energy storage systems possess "four-quadrant" operation capabilities and can independently regulate active and reactive power, the current distribution market mainly focuses on their active value, such as peak shaving and valley filling, lacking pricing and settlement schemes for their reactive power support capabilities. Even when existing optimization methods consider uncertainty, they fail to generate nodal price signals that are aligned with market mechanisms, resulting in insufficient incentive for energy storage to participate in distribution network voltage security support and a waste of flexibility resources. Summary of the Invention

[0003] To address the shortcomings mentioned in the background section, the present invention aims to provide a method and apparatus for energy storage to participate in the joint clearing of active and reactive power in the distribution network under uncertainty.

[0004] Firstly, the objective of this invention can be achieved through the following technical solution: a method for energy storage to participate in the joint clearing of active and reactive power in a distribution network under uncertainty, the method comprising the following steps: The system acquires the distribution network topology, line parameters, distributed resource parameters, and historical source-load data; it inputs the historical source-load data into a pre-established fuzzy set model to obtain the node net load power uncertainty represented by the fuzzy set model; and it inputs the distribution network topology and line parameters into a pre-established linearized power flow model to output the distribution network physical constraints. The physical constraints of the distribution network include the node power balance constraints, voltage constraints, and line capacity constraints of the distribution network. The uncertainty of node net load power represented by the fuzzy set model is embedded into the physical constraints of the distribution network by introducing distributed robust chance constraints, thus obtaining chance constraints and transforming chance constraints into deterministic constraints. With the goal of minimizing the total operating cost of the system, a pre-established active and reactive power joint clearing model is solved to obtain the active and reactive power scheduling plans for each node of the distribution network; wherein, the active and reactive power joint clearing model is constructed based on the deterministic operating constraints and deterministic constraints of the energy storage system, distributed generators and main grid substations; The marginal electricity price of distribution nodes taking into account uncertainty is calculated based on the dual variables of the active-reactive joint clearing model. Based on the marginal electricity price of distribution nodes with uncertainty and the active and reactive power dispatch plans of each node of the distribution network, the active power and reactive power support services in the market are settled.

[0005] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the construction of the pre-established fuzzy set model as follows: Combine load power and renewable energy power, and define a random variable for node net load power. : First moment of node net load power With second moment for: The fuzzy set of node net load power is: In the formula, and Let the load power and renewable energy power be random variables at time t. and Let be the first moments of the load power and the renewable energy power at time t, respectively. and These are the second moments of the load power and the renewable energy power at time t, respectively. and The first-order moment components of the net active and reactive power at time t are represented. , , and The second-order moment components of the net active and reactive power at time t after the merger are represented. The constraint parameter for the radius of the ellipsoidal fuzzy set. The constraint parameter is a semi-fixed cone fuzzy set.

[0006] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the pre-established node power balance equations based on the linearized power flow model are as follows: In the formula, Let Ch(n) represent the active and reactive power flow at time t on branch nm; Ch(n) represents the set of child nodes of node n, and Pa(n) represents the set of parent nodes of node n. The controllable power output at node n at time t; To provide power to the new energy source at time t at node n; Let represent the active and reactive power demand of node n at time t.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the deterministic equivalent form of the distributed robust chance constraint is: After conversion, we get In the formula, Let x be a random variable and x be a decision variable. For fuzzy sets, Let the decision variable be a linear function. Here, c represents the constant vector coefficients, and c represents the upper limit of the constraint. For confidence level, It is the inverse function of the standard normal cumulative distribution.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the objective function of the pre-established active-reactive joint clearing model is as follows: In the formula, , This represents the active power price and reactive power price of the power grid at time t. , , , This represents the active and reactive power quotes for node n distributed generators and the active and reactive power quotes for energy storage systems.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the operating model of the energy storage system is as follows: Satisfy the mutual exclusion constraint of charge and discharge states: Satisfy the reactive power factor constraint: Satisfy reactive and apparent power constraints: Satisfy the state of charge constraints: In the formula, Let n be a binary variable representing the charging and discharging state of node n at time t. When the energy storage device is in the charging state... , When in a discharge state , ; Let be the charging active power and discharging active power of node n at time t; Let be the reactive power of node n at time t; The maximum power factor angle allowed for energy storage at node n; The maximum apparent power of node n; Let n be the state of charge at time t. The maximum discharge depth for energy storage at node n. Let n be the rated capacity of the battery at node n.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the marginal electricity price at the distribution node taking into account uncertainties includes a balance component, a congestion component, a network loss component, and a voltage component, as follows: In the formula, for UDLMP at node n at time t, the equilibrium component and Reflecting the impact of node power balance on system marginal cost, blocking component and The component of network loss reflects the marginal cost resulting from line capacity constraints. and The marginal compensation cost reflecting network loss, voltage component and This reflects the marginal impact of node-injected power on voltage constraints.

[0011] Secondly, in order to achieve the above objectives, this invention discloses a device for energy storage to participate in the joint clearing of active and reactive power in a distribution network under uncertainty, comprising: The constraint integration module is used to acquire the distribution network topology, line parameters, distributed resource parameters, and historical source-load data; input the historical source-load data into a pre-established fuzzy set model to obtain the node net load power uncertainty represented by the fuzzy set model; input the distribution network topology and line parameters into a pre-established linearized power flow model to output the distribution network physical constraints. The physical constraints of the distribution network include the node power balance constraints, voltage constraints, and line capacity constraints of the distribution network. The constraint processing module is used to embed the uncertainty of node net load power represented by the fuzzy set model into the physical constraints of the distribution network by introducing distributed robust chance constraints, thereby obtaining chance constraints and transforming chance constraints into deterministic constraints. The model solving module is used to solve a pre-established active and reactive power joint clearing model with the goal of minimizing the total system operating cost, so as to obtain the active and reactive power scheduling plans for each node of the distribution network; wherein, the active and reactive power joint clearing model is constructed based on the deterministic operating constraints and deterministic constraints of the energy storage system, distributed generators and main grid substations; The scheduling module is used to calculate the marginal electricity price of distribution nodes taking into account uncertainty based on the dual variables of the active-reactive joint clearing model, and to settle the active power and reactive power support services in the market based on the marginal electricity price of distribution nodes taking into account uncertainty and the active and reactive power scheduling plans of each node of the distribution network.

[0012] In another aspect of the present invention, in order to achieve the above-mentioned objective, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it employs the method described above for energy storage to participate in the active and reactive power joint clearing of the distribution network under uncertainty.

[0013] In another aspect of the present invention, in order to achieve the above-mentioned objective, a computer-readable storage medium is disclosed, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is loaded and executed by a processor, the method for joint clearing of active and reactive power in the distribution network under uncertainty, as described above, is adopted.

[0014] The beneficial effects of this invention are: This invention, by introducing fuzzy set theory and distributed robust chance constraints, maps the high proportion of new energy sources and load uncertainty into a deterministic equivalent form of substation capacity, node voltage, and line capacity constraints. This reduces system operating costs while ensuring distribution network voltage safety and preventing line overruns, achieving a balance between distribution network operation safety and economy. The constructed active-reactive power joint market mechanism fully utilizes the active-reactive power four-quadrant regulation capability of energy storage devices. Compared to traditional methods that only involve energy storage in active power regulation, this further reduces the total system operating cost, with a more significant cost improvement effect in operating scenarios with high uncertainty levels. Furthermore, this invention… Based on a joint clearing model, this paper derives marginal electricity prices for active and reactive power nodes, providing price signals that reflect the voltage security value of distributed resources such as energy storage. This ensures that their reactive power output receives reasonable compensation, thus forming a beneficial supplement to the existing electricity market at the distribution level. It achieves an organic connection between the active power market and reactive voltage support services, improving the flexibility and robustness of the distribution system. Through the active-reactive joint clearing mechanism, it realizes the functional expansion of the energy storage system to "one machine for multiple uses" and optimizes the power flow distribution of the distribution network spatially, reducing the dependence on high-cost backup resources. This significantly improves the overall allocation efficiency and economic value of flexible resources on the distribution side. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the workflow of the present invention; Figure 3 This is a schematic diagram of the four-quadrant operation mode of energy storage in this invention; Figure 4 This is a schematic diagram of the improved IEEE 33-node system and resource distribution according to the present invention; Figure 5 This is a schematic diagram illustrating the uncertainty of load and new energy sources in this invention; Figure 6 This is a schematic diagram of the node voltage characteristics of the present invention; Figure 7 This is a schematic diagram of the UDLMP electricity price comparison analysis of the energy storage node of this invention; Figure 8 This is a schematic diagram of the device structure of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: like Figure 1 As shown, a method for energy storage to participate in the joint clearing of active and reactive power in the distribution network under uncertainty includes the following steps: S101: Obtain the distribution network topology, line parameters, distributed resource parameters, and historical source-load data; input the historical source-load data into a pre-established fuzzy set model to obtain the node net load power uncertainty represented by the fuzzy set model; input the distribution network topology and line parameters into a pre-established linearized power flow model to output the distribution network physical constraints. The physical constraints of the distribution network include the node power balance constraints, voltage constraints, and line capacity constraints of the distribution network. The pre-established fuzzy set model is constructed as follows: Combine load power and renewable energy power, and define a random variable for node net load power. : First moment of node net load power With second moment for: The fuzzy set of node net load power is: In the formula, and Let the load power and renewable energy power be random variables at time t. and Let be the first moments of the load power and the renewable energy power at time t, respectively. and These are the second moments of the load power and the renewable energy power at time t, respectively. and The first-order moment components of the net active and reactive power at time t are represented. , , and The second-order moment components of the net active and reactive power at time t after the merger are represented. The constraint parameter for the radius of the ellipsoidal fuzzy set. The constraint parameter is a semi-fixed cone fuzzy set.

[0018] Based on a linearized LinDistFlow model, power flow constraint equations for the distribution network are constructed to determine the power balance at each node and the power-voltage relationship of each branch. Constraints on node voltage, current, and power are established, including that the voltage amplitude at each node must be maintained within a specified range, and the current or power of each branch must not exceed its rated capacity. This provides a physical constraint basis for joint clearing of the power grid. Therefore, the net active power injected into a node should be equal to the sum of the active power outflows from all branches of that node, and the same applies to net reactive power balance. The pre-established nodal power balance equations based on the linearized power flow model are as follows: In the formula, Let Ch(n) represent the active and reactive power flow at time t on branch nm; Ch(n) represents the set of child nodes of node n, and Pa(n) represents the set of parent nodes of node n. The controllable power output at node n at time t; To provide power to the new energy source at time t at node n; Let represent the active and reactive power demand of node n at time t.

[0019] S102: The uncertainty of node net load power represented by the fuzzy set model is embedded into the physical constraints of the distribution network by introducing distributed robust chance constraints to obtain chance constraints, and the chance constraints are transformed into deterministic constraints. By introducing distributed robust chance constraints, the impact of uncertain variables on operational safety constraints is limited by a pre-set confidence level. This transforms the original safety constraint of "the probability of satisfying the operating state is not lower than a given confidence level" into an equivalent deterministic constraint and incorporates it into the market clearing model. Through this step, even considering the most unfavorable value of uncertainty, the distribution network can be guaranteed to meet operational requirements such as voltage and safety within the constructed uncertainty set. Combining this with the aforementioned fuzzy set modeling, for the three types of linear inequality constraints—substation capacity, voltage amplitude, and line capacity—when rewritten as distributed robust chance constraints, their respective upper and lower bound constraints are split into two unilateral constraints, which can be unified as follows: In the formula, Let x be a random variable and x be a decision variable. For fuzzy sets, Let the decision variable be a linear function. is the constant vector coefficient, and c is the upper limit of the constraint.

[0020] assumed If it follows a normal distribution, then They still follow a normal distribution, with mean and variance as follows: Therefore, the above equation can be initially transformed into: Further rewriting: In the formula, Let x be a random variable and x be a decision variable. For fuzzy sets, Let the decision variable be a linear function. Here, c represents the constant vector coefficients, and c represents the upper limit of the constraint. For confidence level, It is the inverse function of the standard normal cumulative distribution.

[0021] S103: With the goal of minimizing the total operating cost of the system, the pre-established active and reactive power joint clearing model is solved to obtain the active and reactive power scheduling plans for each node of the distribution network; wherein, the active and reactive power joint clearing model is constructed based on the deterministic operating constraints and deterministic constraints of the energy storage system, distributed generators and main grid substations. The objective function of the pre-established active-reactive joint clearing model is as follows: In the formula, , This represents the active power price and reactive power price of the power grid at time t. , , , This represents the active and reactive power quotes for node n distributed generators and the active and reactive power quotes for energy storage systems.

[0022] The joint clearing model incorporates different types of market players, including main distribution network points (substations), distributed generators (DG), and energy storage systems (ES). Energy Storage System (ES): For energy storage systems, a constraint model considering four-quadrant operation characteristics is established, specifically including charge / discharge mutual exclusion constraints, apparent power constraints, reactive power regulation capability constraints, and state of charge constraints. (1) Charge and discharge mutual exclusion constraint: In the formula, and Let n represent the charging and discharging states of the energy stored at node n at time t, respectively. , When it is time to indicate energy storage charging, and vice versa. , Indicates discharge; and Let these represent the charging power and discharging power of the energy stored at node n at time t, respectively. The active power output at time t for storing energy at node n The maximum charge and discharge power for energy storage at node n.

[0023] (2) Reactive power regulation capability constraint: In the formula, The reactive power output at time t for storing energy at node n. The maximum reactive power output capacity for storing energy at node n. The apparent power capacity for energy storage at node n.

[0024] (3) Reactive power factor constraint: In the formula, The maximum power factor angle allowed for energy storage at node n.

[0025] (4) Charge state transition constraints: In the formula, Store the remaining energy at time t for node n. and The maximum and minimum energy storage capacity for node n. These are the energy storage charging and discharging efficiencies, For time intervals; The maximum discharge depth for energy storage at node n. Let n be the rated capacity of the battery at node n.

[0026] Substation (Main Grid Power Supply Point): The active power output of the substation access node (default is node 1) is set by the main grid power supply capacity or contract limit. and doing nothing The upper limit. Substations are generally not allowed to feed power back to the upper-level power grid, therefore there are constraints. In the market, substations can be viewed as marginal power sources that provide unrestricted power but at a higher price, used to balance the power deficit in the distribution network.

[0027] Distributed Generators (DG): Each DG unit (such as gas turbine, micro gas turbine, CHP, biomass energy, small hydropower, etc.) typically provides active power according to its pricing curve, and can also provide some reactive power support when necessary, specifically including: In the formula, and This represents the active and reactive power output of the DG unit at time t at node n. and This represents the upper limit of active power output and the upper limit of reactive power output of the DG unit at node n.

[0028] S104: The marginal electricity price of the distribution node taking into account uncertainty is calculated based on the dual variables of the active-reactive joint clearing model. Based on the marginal electricity price of the distribution node with uncertainty and the active and reactive power dispatch plans of each node of the distribution network, the active power energy and reactive power support services in the market are settled.

[0029] Consider including The power distribution system of each node, during the dispatch period (e.g., 24 hours discretely divided into...) (Each time period), the active and reactive power trading clearing volume for each node in each time period is determined through joint clearing optimization. The optimization objective is to minimize the total system operating cost, including the generation cost of each distributed energy source and the opportunity cost of energy storage devices providing reactive power support. The minimization objective function consists of distributed energy bids and grid purchase prices: In the formula, , This represents the active power price and reactive power price of the power grid at time t. , , , This represents the active and reactive power quotes for node n distributed generators and the active and reactive power quotes for energy storage systems.

[0030] Node pricing calculation and pricing mechanism: Based on the active-reactive joint clearing optimization model of the uncertain distribution market after deterministic equivalence transformation, a Lagrangian function is constructed. Based on the KKT optimality condition, the decomposition formula of the uncertain distribution locational marginal pricing (UDLMP) is obtained as follows: In the formula, for UDLMP at node n at time t, the equilibrium component and Reflecting the impact of node power balance on system marginal cost, blocking component and The component of network loss reflects the marginal cost resulting from line capacity constraints. and The marginal compensation cost reflecting network loss, voltage component and This reflects the marginal impact of node-injected power on voltage constraints.

[0031] Based on the Lagrange function, the equilibrium component of uncertain nodal electricity prices can be expressed as: The blocking component can be represented as: The voltage component can be expressed as: In the formula, These are the sensitivity coefficients of power flow at line ij and the square of voltage at node i to active and reactive power at node n, respectively.

[0032] Specifically, the present invention will be further illustrated below through embodiments: The test system topology used in this embodiment is as follows: Figure 4 As shown, the system comprises 33 nodes with a voltage level of 12.66kV. Distributed photovoltaic power is connected at nodes 2, 11, 12, and 19; distributed gas turbines are connected at nodes 17, 21, and 28; and distributed energy storage systems are connected at nodes 4, 27, and 33. The system's base capacity is 10MVA, with an optimized scheduling cycle of 24 hours and a time step of 15 minutes.

[0033] The specific execution flow of this embodiment is as follows: (corresponding to) Figure 2 process): Step 1: Parameter Initialization and Model Building. Read historical load and photovoltaic output data, and set the technical parameters of the energy storage system (e.g., maximum charge / discharge power 0.06MW, capacity 0.3MWh, charge / discharge efficiency 95%, etc.). Establish four-quadrant operating constraints for the energy storage in the mathematical model. By introducing apparent power constraints, ensure that the energy storage converter can generate or absorb reactive power using its remaining capacity while outputting active power, thus participating in voltage regulation.

[0034] Step 2: Source Load Uncertainty Handling. Based on historical data statistics, a fuzzy set representing source load fluctuations is constructed. To verify the effectiveness of the proposed method under different uncertainty levels, this embodiment selects a typical fluctuation scenario for analysis. For example, the relative standard deviation (RSD) of load fluctuations is set to 5%, and the RSD of photovoltaic fluctuations is set to 10%. The first moment (mean) and second moment (covariance) of the node net load are calculated under this scenario, and the corresponding fuzzy set is constructed.

[0035] Step 3: Deterministic Transformation of Uncertainty Constraints. To ensure system voltage safety under renewable energy fluctuations, a system safety confidence level needs to be set. This embodiment demonstrates this using a 95% confidence level (corresponding to a standard normal distribution quantile of approximately 1.645) as an example, transforming the node voltage constraints affected by uncertainty into deterministic constraints. During day-ahead scheduling, the system will automatically calculate and reserve the corresponding voltage safety margin based on the source-load prediction error magnitude (i.e., the second moment corresponding to the RSD) set in Step 2, thereby compressing the feasible region to avoid real-time operational risks. Figure 6 This demonstrates the system voltage fluctuation range and safety margin provisions under these parameter settings.

[0036] Step 4: Market Clearing Solution. Collect price information from various market participants (e.g., active power price of distributed gas turbines $500 / MWh, active power price of energy storage $200 / MWh, reactive power price of $40 / Mvarh, etc.). Construct an optimization objective function to minimize the overall grid's electricity purchase cost, distributed power source dispatch cost, and energy storage dispatch cost. Use a commercial solver (such as Gurobi or COPT) to solve the above mixed-integer second-order cone programming (MISOCP) model to obtain the active and reactive power dispatch instructions for each node in the next 24 hours.

[0037] Step 5: Node Price Calculation and Settlement After obtaining the optimal scheduling solution, the dual variables of each constraint in the model are extracted to calculate the Uncertainty Node Marginal Price (UDLMP). Taking the selected large-scale photovoltaic (PV) scenario as an example, when PV fluctuations increase the risk of node voltage exceeding limits, according to the pricing formula, if the energy storage system absorbs reactive power (inductive reactive power) and helps reduce voltage, then its reactive power settlement price will include a significant voltage component incentive. Figure 7 It is evident that under this implementation method, energy storage operators can proactively adjust their charging and discharging strategies and reactive power output based on price signals that include uncertain risk values. This allows them to alleviate grid voltage congestion while obtaining reasonable economic benefits, thus achieving synergy between safe operation and market-oriented operation of the distribution network.

[0038] Table 1. Multi-scenario System Operating Cost Analysis Table 1 illustrates the impact of different load RSDs and renewable energy RSDs on system operating costs under various scenarios. A horizontal comparison in Table 1 shows that the system operating cost decreases after energy storage participates in reactive power regulation compared to when energy storage does not participate, indicating that the proposed mechanism has a clear cost-reduction benefit. A vertical comparison in Table 1 shows that under different levels of source-load uncertainty, the change in total system operating cost is reduced after energy storage participates in reactive power regulation, indicating that the reactive power support capability of energy storage can mitigate the impact of uncertainty on costs. Further observation reveals a significant positive correlation between the reduction in system operating costs before and after energy storage participates in reactive power regulation and the RSDs of the load and renewable energy sources. This reflects the increasing demand for flexibility in high-penetration renewable energy scenarios. Energy storage, relying on rapid response and four-quadrant operation, provides a more effective voltage and power balance through active-reactive power synergy, thereby achieving more significant cost optimization. Analysis of the system cost structure reveals that the reduction in operating costs mainly stems from the reduction in DG costs. The physical mechanism is as follows: under the premise of meeting voltage over-limit constraints, the reactive power support capability of energy storage reduces the reactive power flow and voltage drop of the line, thereby increasing the active power output of energy storage and other equipment with lower marginal costs to replace part of the DG output under a given voltage margin. In high uncertainty scenarios, this substitution effect is more significant, thereby further reducing the total system cost.

[0039] Table 2. Revenue Analysis for Multi-Scenario Energy Storage Operators Table 2 shows the active and reactive power revenue composition of energy storage aggregators in multiple scenarios. The results show that energy storage participation in reactive power regulation can bring positive revenue improvement. Moreover, as the load RSD and renewable energy RSD increase, this revenue increment generally shows an upward trend. However, under extremely high uncertainty and extremely high security confidence requirements (such as scenario 6), the revenue may actually decrease. This is because under the active and reactive power coupling constraint, energy storage operators do not consider the opportunity cost of giving up active power output, resulting in the abandonment of more reactive power to increase active power output, which disrupts the incremental growth of active power revenue. Therefore, it is necessary to formulate a strategic active and reactive power joint pricing strategy for system voltage regulation needs.

[0040] Table 3 Equipment Price Coefficient Table 3 shows the economic attribute parameters of each market participant in the embodiments of the present invention. This table provides a cost calculation benchmark for the model by quantifying the active and reactive power quotations of distributed generators and energy storage power stations at different access nodes, so that the system can prioritize the use of low-cost resources based on the principle of economy and under the premise of meeting voltage safety.

[0041] Table 4 Resource Parameter Settings Table 4 details the boundary constraints of each physical device in the method of this invention during actual operation, ensuring the physical feasibility of the model clearing results. Specifically, the maximum apparent power and maximum power factor angle of the energy storage system jointly define the physical limits of its four-quadrant regulation, enabling the algorithm to strictly adhere to the converter capacity limitations when calculating reactive power output. By clearly defining the output limits and SOC operating ranges of substations, distributed generators, and energy storage, this device achieves accurate simulation of the operating states of multiple flexible resources under complex distribution network topologies, providing necessary hardware boundary references for evaluating the effectiveness of distributed robust chance constraints in extreme scenarios.

[0042] Example 2: To achieve the above objective, such as Figure 8 As shown, based on Embodiment 1, this invention discloses a combined active and reactive power clearing device for distribution networks under uncertainty risk constraints, comprising: The constraint integration module 11 is used to acquire the distribution network topology, line parameters, distributed resource parameters, and historical source load data; input the historical source load data into a pre-established fuzzy set model to obtain the node net load power uncertainty represented by the fuzzy set model; input the distribution network topology and line parameters into a pre-established linearized power flow model to output the distribution network physical constraints. The physical constraints of the distribution network include the node power balance constraints, voltage constraints, and line capacity constraints of the distribution network. The constraint processing module 12 is used to embed the uncertainty of node net load power represented by the fuzzy set model into the physical constraints of the distribution network by introducing distributed robust chance constraints, thereby obtaining chance constraints and transforming chance constraints into deterministic constraints. The model solving module 13 is used to solve the pre-established active and reactive power joint clearing model with the goal of minimizing the total system operating cost, so as to obtain the active and reactive power scheduling plans for each node of the distribution network; wherein, the active and reactive power joint clearing model is constructed based on the deterministic operating constraints and deterministic constraints of the energy storage system, distributed generators and main grid substations; The scheduling module 14 is used to calculate the marginal electricity price of the distribution node taking into account uncertainty based on the dual variables of the active-reactive joint clearing model, and to settle the active power and reactive power support services in the market based on the marginal electricity price of the distribution node taking into account uncertainty and the active and reactive power scheduling plans of each node of the distribution network.

[0043] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0044] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0045] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0046] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A method for energy storage to participate in the joint clearing of active and reactive power in distribution networks under uncertainty, characterized in that, The method includes the following steps: The system acquires the distribution network topology, line parameters, distributed resource parameters, and historical source-load data; it inputs the historical source-load data into a pre-established fuzzy set model to obtain the node net load power uncertainty represented by the fuzzy set model; and it inputs the distribution network topology and line parameters into a pre-established linearized power flow model to output the distribution network physical constraints. The physical constraints of the distribution network include the node power balance constraints, voltage constraints, and line capacity constraints of the distribution network. The uncertainty of node net load power represented by the fuzzy set model is embedded into the physical constraints of the distribution network by introducing distributed robust chance constraints, thus obtaining chance constraints and transforming chance constraints into deterministic constraints. With the goal of minimizing the total operating cost of the system, a pre-established active and reactive power joint clearing model is solved to obtain the active and reactive power scheduling plans for each node of the distribution network; wherein, the active and reactive power joint clearing model is constructed based on the deterministic operating constraints and deterministic constraints of the energy storage system, distributed generators and main grid substations; The marginal electricity price of distribution nodes, taking into account uncertainty, is calculated based on the dual variables of the active-reactive joint clearing model. Based on the marginal electricity price of distribution nodes with uncertainty and the active and reactive power dispatch plans of each node in the distribution network, the active power and reactive power support services in the market are settled.

2. The method for joint active and reactive power clearing of energy storage in distribution networks under uncertainty as described in claim 1, characterized in that, The pre-established fuzzy set model is constructed as follows: Combine load power and renewable energy power, and define a random variable for node net load power. : First moment of node net load power With second moment for: The fuzzy set of node net load power is: In the formula, and Let the load power and renewable energy power be random variables at time t. and Let be the first moments of the load power and the renewable energy power at time t, respectively. and These are the second moments of the load power and the renewable energy power at time t, respectively. and The first-order moment components of the net active and reactive power at time t are represented. , , and The second-order moment components of the net active and reactive power at time t after the merger are represented. The constraint parameter for the radius of the ellipsoidal fuzzy set. The constraint parameter is a semi-fixed cone fuzzy set.

3. The method for joint active and reactive power clearing of energy storage in distribution networks under uncertainty as described in claim 1, characterized in that, The pre-established nodal power balance equations based on the linearized power flow model are as follows: In the formula, Let Ch(n) represent the active and reactive power flow at time t on branch nm; Ch(n) represents the set of child nodes of node n, and Pa(n) represents the set of parent nodes of node n. The controllable power output at node n at time t; To provide power to the new energy source at time t at node n; Let represent the active and reactive power demand of node n at time t.

4. The method for joint active and reactive power clearing of energy storage in distribution networks under uncertainty as described in claim 1, characterized in that, The deterministic equivalent form of the distributed robust chance constraint is: After conversion, we get In the formula, Let x be a random variable and x be a decision variable. For fuzzy sets, Let the decision variable be a linear function. Here, c represents the constant vector coefficients, and c represents the upper limit of the constraint. For confidence level, It is the inverse function of the standard normal cumulative distribution.

5. The method for joint active and reactive power clearing of energy storage in distribution networks under uncertainty as described in claim 1, characterized in that, The objective function of the pre-established active-reactive power joint clearing model is as follows: In the formula, , This represents the active power price and reactive power price of the power grid at time t. , , , This represents the active and reactive power quotes for node n distributed generators and the active and reactive power quotes for energy storage systems.

6. The method for joint active and reactive power clearing of distribution networks under uncertainty according to claim 1, characterized in that, The operating model of the energy storage system is as follows: Satisfy the mutual exclusion constraint of charge and discharge states: Satisfy the reactive power factor constraint: Satisfy reactive and apparent power constraints: Satisfy the state of charge constraints: In the formula, Let n be a binary variable representing the charging and discharging state of node n at time t. When the energy storage device is in the charging state... , When in a discharge state , ; Let be the charging active power and discharging active power of node n at time t; Let be the reactive power of node n at time t; The maximum power factor angle allowed for energy storage at node n; The maximum apparent power of node n; Let n be the state of charge at time t. The maximum discharge depth for energy storage at node n. Let n be the rated capacity of the battery at node n.

7. The method for joint active and reactive power clearing of energy storage in distribution networks under uncertainty as described in claim 1, characterized in that, The marginal electricity price at the distribution node that takes into account uncertainty includes an energy component, a congestion component, and a voltage component, as follows: In the formula, for UDLMP at node n at time t, the electrical energy component and Reflecting the impact of node power balance on system marginal cost, blocking component and The component of network loss reflects the marginal cost resulting from line capacity constraints. and The marginal compensation cost reflecting network loss, voltage component and This reflects the marginal impact of node-injected power on voltage constraints.

8. A device for joint active and reactive power clearing of energy storage in distribution networks under uncertainty, employing the method for joint active and reactive power clearing of energy storage in distribution networks under uncertainty as described in any one of claims 1 to 7, characterized in that... include: The constraint integration module is used to acquire the distribution network topology, line parameters, distributed resource parameters, and historical source-load data. Historical source-load data are input into a pre-established fuzzy set model to obtain the node net load power uncertainty represented by the fuzzy set model; the distribution network topology and line parameters are input into a pre-established linearized power flow model to output the distribution network physical constraints. The physical constraints of the distribution network include the node power balance constraints, voltage constraints, and line capacity constraints of the distribution network. The constraint processing module is used to embed the uncertainty of node net load power represented by the fuzzy set model into the physical constraints of the distribution network by introducing distributed robust chance constraints, thereby obtaining chance constraints and transforming chance constraints into deterministic constraints. The model solving module is used to solve a pre-established active and reactive power joint clearing model with the goal of minimizing the total system operating cost, so as to obtain the active and reactive power scheduling plans for each node of the distribution network; wherein, the active and reactive power joint clearing model is constructed based on the deterministic operating constraints and deterministic constraints of the energy storage system, distributed generators and main grid substations; The scheduling module is used to calculate the marginal electricity price of distribution nodes taking into account uncertainty based on the dual variables of the active-reactive joint clearing model, and to settle the active power and reactive power support services in the market based on the marginal electricity price of distribution nodes taking into account uncertainty and the active and reactive power scheduling plans of each node of the distribution network.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on a processor. When the processor loads and executes the computer program, it employs the method for joint clearing of active and reactive power in the distribution network under uncertainty, as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it employs the method for joint clearing of active and reactive power in the distribution network under uncertainty as described in any one of claims 1 to 7.

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