Receiving-end urban power grid energy storage resource optimal configuration method oriented to low-carbon transformation

Abstract

The invention relates to a low-carbon transformation-oriented receiving-end urban power grid energy storage resource optimal configuration method, which comprises the following steps of: calculating node carbon potential based on carbon emission flow or marginal emission; the node carbon potential and the electricity market price are jointly incorporated into a configuration target; and under the set constraint, solving and outputting an energy storage installation configuration scheme by adopting a two-stage stochastic programming mode. Compared with the prior art, the method has the advantages that node carbon potential and electricity market price are coupled and incorporated into an energy storage optimization target, expected total cost and risk minimization are taken as a target, two-stage stochastic programming is combined to process uncertainty, collaborative optimization of energy storage capacity, position and investment / operation can be realized, and energy storage efficiency is improved. And an energy storage configuration scheme which simultaneously meets the safety, economy and low-carbon targets of the power grid is obtained.

Description

Technical Field

[0001] This invention relates to the field of energy storage regulation technology, and in particular to a method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation. Background Technology

[0002] In recent years, the rapid development of new energy sources, characterized by strong randomness, volatility, and intermittency, has limited the output of traditional power units, such as coal-fired power plants. Therefore, the use of energy storage resources—which possess low-carbon properties, a wide adjustment range, fast adjustment speed, and long duration—to participate in power system balancing has become an inevitable trend. Regarding the optimization and allocation strategies for energy storage resources in receiving-end urban power grids, energy storage resources can effectively mitigate wind and solar power fluctuations, promote the consumption of new energy sources, and exhibit significant low-carbon characteristics.

[0003] To reduce carbon emissions from the power system, existing research on low-carbon economic optimization methods that take into account carbon trading costs mostly focuses on multiple dimensions such as economic costs, carbon emissions, and the absorption of volatile new energy sources. However, existing research rarely considers the optimization of energy storage resources based on system voltage-frequency support and composite carbon regulations, and its consideration of the coupling characteristics between the security, economy, and low-carbon goals of the receiving-end urban power grid is relatively superficial.

[0004] However, as the penetration rate of renewable energy in urban power grids increases, energy storage configuration decisions are no longer simply about pursuing economic profits from electricity. Instead, there is an urgent need to balance grid security (voltage / frequency support), economic efficiency (revenue from the electricity / ancillary services market), and low-carbon goals (carbon tax / carbon trading / green certificates). Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for optimizing the allocation of energy storage resources in urban power grids for low-carbon transformation, which can obtain an energy storage configuration scheme that simultaneously meets the goals of grid security, economy and low carbon.

[0006] The objective of this invention can be achieved through the following technical solution: a method for optimizing the allocation of energy storage resources in urban power grids for low-carbon transformation, comprising the following steps: S1. Calculate the nodal carbon potential based on carbon emission streams or marginal emissions; S2. Incorporate nodal carbon potential and electricity market prices into the allocation target; S3. Under set constraints, a two-stage stochastic programming approach is used to solve for and output the energy storage configuration scheme.

[0007] Furthermore, step S1 specifically involves constructing a node-level carbon responsibility sharing model based on carbon emission flow theory or the average emission factor substitution marginal method, which is used to calculate the node carbon potential.

[0008] Furthermore, the node-level carbon responsibility sharing model is specifically designed for each time period. t Scene s The additional load on nodes is calculated through power flow sensitivity or scheduling simulation. The resulting increase in system carbon emissions The node carbon potential is obtained as follows: in, For nodes i In time t Scene s The node carbon potential below.

[0009] Furthermore, the electricity market price in step S2 includes scenarios. s Electricity price Ancillary service prices Carbon prices Green certificate price .

[0010] Furthermore, the configuration objective in step S2 is specifically to minimize the total cost and risk: in, For nodes k The rated power capacity of energy storage, For nodes k The rated energy capacity of energy storage, This is a binary addressing variable, indicating whether it is at the node. k Build energy storage units; 1 indicates construction, 0 indicates no construction. For time t Scene s The energy storage power output is below. For time t Scene s Next node i Power purchased from the power grid For the investment cost function of energy storage construction, For the energy storage operating cost function, For time t Scene s The system's carbon emissions, For time t Scene s The number of green certificates obtained. For risk aversion coefficient, Conditional Value at Risk (VaR) represents the value at a given confidence level. α The expected loss risk item below.

[0011] Furthermore, the time t Scene s The specific carbon emissions of the system are as follows: in, For time t Scene s Next node i The net power increment.

[0012] Furthermore, in step S3, the set constraints include energy storage dynamic constraints, SOC limit constraints, power upper and lower limit constraints, voltage constraints, and voltage-frequency support constraints.

[0013] Furthermore, the set constraint specifically refers to: Energy storage dynamics: ; SOC Limit: 0≤ ≤ ; Power upper and lower limits: , ; Power flow / voltage constraints: ; Voltage-frequency support constraints: Meeting the support index in critical scenarios and The threshold.

[0014] Furthermore, the two-stage stochastic programming method in step S3 includes a first-stage scale investment decision and a second-stage scenario scheduling decision.

[0015] Furthermore, the scale investment decision specifically involves: for each candidate point k Select installed capacity With energy capacity And determine the binary addressing variables; The scenario scheduling decision specifically involves: determining the time. t Scene s The corresponding active power, reactive power, and state of charge during charging and discharging.

[0016] Compared with the prior art, the present invention has the following advantages: This invention first calculates nodal carbon potential based on carbon emission flows or marginal emissions; then, it incorporates nodal carbon potential and electricity market prices into the allocation objective; finally, under ensemble constraints, it uses a two-stage stochastic programming approach to solve for and output an energy storage capacity configuration scheme. This incorporates nodal-level carbon responsibility (nodal carbon potential) into the energy storage capacity and location decision-making process, achieving a "low-carbon and economical" energy storage resource allocation, and obtaining an energy storage configuration scheme that simultaneously meets the goals of grid security, economy, and low carbon emissions.

[0017] This invention constructs a node-level carbon responsibility allocation model based on carbon emission flow theory or the average emission factor substitution marginal method to calculate node carbon potential. It also incorporates node carbon potential and electricity market price into the configuration objective of minimizing expected total cost and risk, which can achieve accurate carbon responsibility allocation, avoid the problem of ambiguity in carbon cost accounting, and make energy storage configuration directly related to carbon emission reduction targets, providing a reliable quantitative basis for subsequent energy storage investment decisions.

[0018] This invention constructs a first-stage investment cost, a second-stage expected operating cost, and a risk penalty term in the configuration target, which can take into account both long-term investment costs and short-term operating costs, thereby effectively reducing the overall cost. Furthermore, by quantifying and controlling extreme risks (such as sudden increases in carbon prices, sudden increases in load, and fluctuations in external power input) through the risk penalty term, it can cope with various uncertainties and improve the robustness of decision-making.

[0019] This invention adopts a two-stage stochastic programming framework: the first stage optimizes the binary variables of energy storage capacity, location, and site selection (investment decision); the second stage optimizes the active / reactive power and SOC of charging and discharging under different scenarios (scheduling decision). This can break the traditional model of separating investment and scheduling, achieve the synergistic optimization of energy storage capacity, location, and operation strategy, and avoid resource waste caused by investment decisions not considering the operation scenario. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0021] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0023] Example 1 like Figure 1 As shown, a method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation includes the following steps: S1. Calculate the nodal carbon potential based on carbon emission streams or marginal emissions; Specifically, based on carbon emission flow theory or the average emission factor substitution marginal method, a nodal-level carbon responsibility allocation model is constructed to calculate the nodal carbon potential. This nodal-level carbon responsibility allocation model is designed for each time period. t Scene s The additional load on nodes is calculated through power flow sensitivity or scheduling simulation. The resulting increase in system carbon emissions The node carbon potential is obtained as follows: In the formula, For nodes i In time t Scene s The next node carbon potential S2. Integrating node carbon potential with electricity market prices (including scenarios) s Electricity price Ancillary service prices Carbon prices Green certificate price Jointly included in the allocation target: The configuration objective is to minimize the expected total cost and risk. In the formula, For nodes k The rated power capacity of energy storage, For nodes k The rated energy capacity of energy storage, This is a binary addressing variable, indicating whether it is at the node. k Build energy storage units; 1 indicates construction, 0 indicates no construction. For time t Scene s The energy storage power output is below. For time t Scene s Next node i Power purchased from the power grid For the investment cost function of energy storage construction, For the energy storage operating cost function, For time t Scene s The system's carbon emissions, For time t Scene s The number of green certificates obtained. For risk aversion coefficient, Conditional Value at Risk (VaR) represents the value at a given confidence level. α The expected loss risk item below.

[0024] time t Scene s The specific carbon emissions of the system are as follows: In the formula, For time t Scene s Next node i The net power increment; S3. Under set constraints, a two-stage stochastic programming approach is used to solve for and output the energy storage configuration scheme. The set of constraints includes dynamic constraints on energy storage, SOC limits, upper and lower power limits, voltage constraints, and voltage-frequency support constraints. Energy storage dynamics: ; SOC Limit: 0≤ ≤ ; Power upper and lower limits: , ; Power flow / voltage constraints: ; Voltage-frequency support constraints: Meeting the support index in critical scenarios and The threshold; The two-stage stochastic programming approach includes a first-stage decision on scale investment and a second-stage decision on scenario scheduling.

[0025] The specific investment decision-making process for each candidate point is as follows: k Select installed capacity With energy capacity And determine the binary addressing variables; The specific decision-making process for scene scheduling is as follows: determining the time. t Scene s The corresponding active power, reactive power, and state of charge during charging and discharging.

[0026] It should be noted that in the practical application of the above method, an electronic device including a central processing unit (CPU) can be used. This CPU can execute various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) or loaded from storage units into random access memory (RAM). RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0027] Multiple components in the device are connected to an I / O interface, including: input units such as a keyboard, mouse, etc.; output units such as various types of displays, speakers, etc.; storage units such as disks, optical disks, etc.; and communication units such as network interface cards, modems, wireless transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks. The processing unit performs the various methods and processes described above, such as the method of the present invention. For example, in some embodiments, the method of the present invention may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method of the present invention described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the method of the present invention by any other suitable means (e.g., by means of firmware).

[0028] The functions described above in this invention can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0029] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0030] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0031] Example 2 This embodiment applies the technical solution from Embodiment 1, and its main contents include: Step 1: Basic Data Collection and Uncertainty Scenario Construction Data collection: Acquire core basic data of the receiving-end urban power grid, including: Power grid topology data (network node set ϰ, candidate energy storage location set) k ⊑ϰ); Operating data (external power input, user load curve, historical voltage / frequency data); Market and policy data (electricity market prices, ancillary service prices, carbon tax / carbon trading prices, green certificate prices); Energy storage equipment parameters (charge and discharge efficiency, maximum installed capacity / energy capacity limit, initial SOC value and degradation characteristics).

[0032] Scenario generation: Based on historical data, the Sample Average Approximation (SAA) method is used to generate a probabilistic scenario set SS covering external power input fluctuations, load changes, market price fluctuations, and carbon price fluctuations, which will be used for subsequent uncertainty processing.

[0033] Step 2: Define concepts and clarify variables Clearly define the sets, decision variables, and key parameters involved in the optimization process to lay the foundation for subsequent calculations: Core sets: network node set ϰ, candidate energy storage location set k ( k ⊑ϰ), scene set S, time dimension set T.

[0034] Decision variables: Phase 1 (Investment Decision Variables): Candidate Points k Installed capacity (kW), energy capacity (kWh), and binary decision-making A binary variable representing (whether to select an address); Phase Two (Scheduling Decision Variables, by Scenario s∈S, Time t∈T): Active Power of Energy Storage Charging and Discharging (Positive discharge), reactive power State of charge .

[0035] Key parameters: Market price parameters: Electricity price Ancillary service prices Carbon prices Green certificate price ; Node carbon potential ( ):time t Scene s Next, node i The marginal carbon emissions (kgCO2 / MWh) resulting from an increase of 1MW of load need to be calculated separately.

[0036] Step 3: Calculation of nodal carbon potential (carbon responsibility allocation) This is the core step in achieving the coupling of carbon cost and grid optimization, as follows: Carbon emission increment calculation: for each time point t Scene s Using the "carbon emission flow method" or the "marginal emission factor method," and through tidal flow sensitivity analysis or scheduling simulation, the "node" is calculated. i The increase in total system carbon emissions caused by "additional increase in unit load" ( ).

[0037] Nodal carbon potential definition: Based on the increment of carbon emissions, the nodal carbon potential is defined as follows: (Unit: kgCO2 / MWh), characterizing nodes i Carbon responsibility intensity.

[0038] Carbon cost conversion: Incorporating carbon costs into node operating costs, the formula is: Node carbon cost = Carbon tax / Carbon allowance price ×node carbon potential × Energy storage discharge power This enables direct coupling between carbon costs and energy storage scheduling.

[0039] Step 4: Construct a two-stage stochastic programming objective function With the goal of minimizing overall cost and controlling risk, a joint objective function for the first stage (investment) and the second stage (scheduling) is constructed.

[0040] Overall objective = Minimize "Phase 1 energy storage investment cost + Phase 2 expected operating costs under various scenarios + risk control items" The mathematical expression is: in: The expected operating costs (including electricity market purchase and sale costs, ancillary service costs, and carbon costs) for all scenarios. As a risk control item, (Conditional Value at Risk) Considers the “worst 1−α ratio high cost scenario” to limit extreme risks (such as cost overruns caused by a sudden increase in carbon prices or a sudden increase in load).

[0041] Step 5: Establish a multi-dimensional constraint set Ensure that energy storage configuration and operation meet the constraints of "grid security, energy storage characteristics, and low-carbon goals," specifically including three core constraints: (1) Constraints of energy storage equipment itself Energy dynamic balance constraint: SOC_t = SOC_{t-1} + energy storage charging power × efficiency - energy storage discharging power / efficiency (time step needs to be considered); SOC limit constraint: 0≤SOC_t≤Energy storage capacity Ecap (to avoid overcharging and over-discharging); Power upper and lower limit constraints: -Pcap≤P_t≤Pcap (Pcap is the maximum charging and discharging power of energy storage, negative for charging); reactive power is similar: -Qcap≤Q_t≤Qcap.

[0042] (2) Power grid security constraints Voltage constraint: Vmin≤V_t≤Vmax (V_t is the voltage at node t, which can be converted into a linear / convex constraint using the "steady-state LinDistFlow" or "SOCP relaxation" method to adapt the solver). Power flow constraints: Depending on the power grid topology, either AC power flow or linearized power flow model can be selected to ensure the safety of power flow distribution in the power grid.

[0043] (3) Voltage-frequency support constraint Incorporate it in the form of "soft constraints" or "penalties": In critical scenarios (such as a sudden drop in renewable energy output or large load access), energy storage must meet the preset thresholds of "voltage support index" and "frequency support index" to ensure the safety and stability of the power grid.

[0044] Step 6: Select an uncertainty resolution strategy For two-stage integer programming problems involving discrete addressing variables, efficient solution algorithms can be selected in practical applications, as follows: If the Benders decomposition algorithm is used, the optimization problem is broken down into a "main problem" and "subproblems": Main problem: Solve the first-stage energy storage investment decision (capacity, location) and output a preliminary investment plan; Sub-problem: For each scenario s, verify the feasibility of the main problem solution, calculate the scenario operation cost, and feed back the "feasibility cut" or "optimality cut" to the main problem, iteratively optimize until convergence.

[0045] If the Progressive Hedging algorithm is selected, parallel processing of multi-scenario scheduling problems is adopted to improve the solution efficiency (suitable for scenarios with a large number of scenarios).

[0046] Finally, solver adaptation is performed: This embodiment selects commonly used engineering solvers (Gurobi / CPLEX / Ipopt), which are compatible with linear / convex programming models to ensure solution speed and accuracy.

[0047] Step 7: Perform optimization solution Based on the algorithm selected in step 6 (such as Benders decomposition), call the solver, input the basic data from step 1, the nodal carbon potentials from step 3, the objective function from step 4, and the constraint set from step 5, and perform iterative solution. The output includes: the first stage "optimal energy storage configuration scheme" (candidate points) k The installed capacity, energy capacity, and site selection), as well as the second phase "optimal energy storage scheduling strategy in various scenarios" (charge and discharge active / reactive power, SOC curve).

[0048] Step 8: Verify and output the solution. 1. Robustness verification: Substitute the optimal solution into different uncertainty scenarios (such as a 20% increase in carbon prices and a 15% increase in load) to verify whether the solution still meets the "voltage / frequency support constraints", "cost control targets", and "low-carbon targets", ensuring the solution's adaptability to fluctuations; 2. Final Output: Output "Receiving-End City Power Grid Energy Storage Optimization Configuration Report", including: Optimal energy storage location, installed capacity, and energy capacity list; Energy storage operation strategies (charge and discharge plans, SOC control range) for different times / scenarios; The results of the assessment of the scheme's economics (investment payback period, market returns), low carbon emissions (carbon emission reduction, carbon cost savings), and safety (voltage / frequency support capability).

[0049] In summary, this scheme couples nodal-level carbon responsibility (carbon potential) with electricity market prices and incorporates it into the energy storage optimization objective. It also quantifies risks and meets grid constraints, and aims for "safety (voltage / frequency support) + economy (electricity / ancillary services market) + low carbon (carbon tax / carbon trading)". By combining two-stage stochastic programming to handle uncertainties, it provides an engineering-feasible energy storage configuration scheme that can reliably achieve the optimal synergy between energy storage capacity, location, and investment / operation.

Claims

1. A method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation, characterized in that, Includes the following steps: S1. Calculate the nodal carbon potential based on carbon emission streams or marginal emissions; S2. Incorporate nodal carbon potential and electricity market prices into the allocation target; S3. Under set constraints, a two-stage stochastic programming approach is used to solve for and output the energy storage configuration scheme.

2. The method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation according to claim 1, characterized in that, Specifically, step S1 involves constructing a node-level carbon responsibility sharing model based on carbon emission flow theory or the average emission factor substitution marginal method, which is used to calculate the node carbon potential.

3. The method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation according to claim 2, characterized in that, The node-level carbon responsibility sharing model is specifically designed for each time period. t Scene s The additional load on nodes is calculated through power flow sensitivity or scheduling simulation. The resulting increase in system carbon emissions The node carbon potential is obtained as follows: in, For nodes i In time t Scene s The node carbon potential below.

4. The method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation according to claim 3, characterized in that, The electricity market price in step S2 includes scenarios. s Electricity price Ancillary service prices Carbon prices Green certificate price .

5. A method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation, as described in claim 4, is characterized in that... The specific objective in step S2 is to minimize the total cost and risk. in, For nodes k The rated power capacity of energy storage, For nodes k The rated energy capacity of energy storage, This is a binary addressing variable, indicating whether it is at the node. k Build energy storage units; 1 indicates construction, 0 indicates no construction. For time t Scene s The energy storage power output is below. For time t Scene s Next node i Power purchased from the power grid For the investment cost function of energy storage construction, For the energy storage operating cost function, For time t Scene s The system's carbon emissions, For time t Scene s The number of green certificates obtained. For risk aversion coefficient, Conditional Value at Risk (VaR) represents the value at a given confidence level. α The expected loss risk item below.

6. The method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation according to claim 5, characterized in that, The time t Scene s The specific carbon emissions of the system are as follows: in, For time t Scene s Next node i The net power increment.

7. The method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation according to claim 1, characterized in that, In step S3, the set of constraints includes energy storage dynamic constraints, SOC limit constraints, power upper and lower limit constraints, voltage constraints, and voltage-frequency support constraints.

8. A method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation, as described in claim 7, is characterized in that... The set constraints are specifically as follows: Energy storage dynamics: ; SOC Limit: 0≤ ≤ ; Power upper and lower limits: , ; Power flow / voltage constraints: ; Voltage-frequency support constraints: Meeting the support index in critical scenarios and The threshold.

9. A method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation according to claim 1, characterized in that, The two-stage stochastic programming method in step S3 includes the first stage of scale investment decision and the second stage of scenario scheduling decision.

10. A method for optimizing the allocation of energy storage resources in receiving-end urban power grids for low-carbon transformation, as described in claim 9, is characterized in that... The specific investment decision-making process for each candidate point is as follows: k Select installed capacity With energy capacity And determine the binary addressing variables; The scenario scheduling decision specifically involves: determining the time. t Scene s The corresponding active power, reactive power, and state of charge during charging and discharging.

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