A method and system for robust optimization operation of integrated light storage and charging resources participating in the electricity market considering source load uncertainty

Abstract

This invention discloses a robust optimization operation method and system for integrated photovoltaic-storage-charging resources participating in the power market, considering source-load uncertainty. The method includes: acquiring input data of the integrated photovoltaic-storage-charging resource system; standardizing and organizing the calculation data of the integrated photovoltaic-storage-charging resource system's robust optimization model based on the input data, obtaining the system's result data; wherein, the construction of the integrated photovoltaic-storage-charging resource system's robust optimization model includes: constructing a resource robust optimization model and a security domain; constructing a robust optimization operation security domain; setting robust finite-dimensional deterministic constraints; configuring a two-stage robust optimization objective function; and constructing a power market ancillary service market benefit model. This invention effectively improves the operational robustness, safety, and economy of integrated photovoltaic-storage-charging resources in the power market, mitigating the risks of source-load uncertainty.

Description

Technical Field

[0001] This invention relates to a robust optimization operation method and system for integrated photovoltaic, energy storage, and charging resources participating in the power market, taking into account the uncertainty of source and load, and belongs to the technical field of market participants participating in the optimized operation of the power market. Background Technology

[0002] With the deepening of power market reform and the construction of new power systems, integrated photovoltaic, energy storage, and charging resources, as typical flexible and adjustable resources, play an important role in participating in power market ancillary services, improving the absorption of new energy sources, ensuring the efficient operation of the power market, and enhancing grid regulation capabilities. However, the intermittency of photovoltaic output and the randomness of charging pile load constitute significant uncertainties on both the source and load sides, posing a severe challenge to the optimized operation and regulation of integrated photovoltaic, energy storage, and charging resources in the power market. Robust optimization, because it can handle uncertainty issues, is gradually being applied to the operation optimization of power energy.

[0003] Currently, the industry has conducted research on various optimization scheduling models for photovoltaic-storage-charging systems. Some studies have attempted to combine optimization algorithms to achieve resource scheduling and operation, and some studies have also explored the application of robust optimization to the management and control of single photovoltaic, energy storage, or charging pile resources. However, most of these studies focus on energy management under single resource or uncertain scenarios, and a comprehensive robust optimization system for integrated photovoltaic-storage-charging resources has not yet been formed. There is little discussion on the two-stage robust optimization of integrated photovoltaic-storage-charging resources in the entire process of "declaration-clearing execution" in the power market. Furthermore, there is insufficient refined modeling of ancillary service compensation mechanisms, adjustment direction indicators, tiered compensation, and assessment penalties.

[0004] The existing technology has the following shortcomings: (1) It lacks a collaborative robust modeling of the deviation between photovoltaic output and charging pile load prediction, and fails to consider the impact of the deviation coefficient on the power feasible domain at the same time, which makes it easy for the optimization results to have power imbalance or insufficient reserve in actual operation; (2) It lacks a complete safety domain for photovoltaic, energy storage and charging resources, such as the dynamic boundary of energy storage energy state, mutual exclusion of charging and discharging, and the coupling relationship between the adjustable time period of charging pile load and the willingness tolerance coefficient, which makes it difficult to ensure the safe operation of the system in uncertain scenarios; (3) It lacks the collaborative optimization of the two-stage objective function of the pre-registration stage and the post-clearing execution stage of participating in the power market, and often only optimizes the single-stage benefit, ignoring the clearing amount, effective compensation adjustment amount and the auxiliary service benefit model of tiered compensation, which cannot accurately quantify the nonlinear relationship between the effective compensation adjustment amount and the compensation benefit and penalty cost, resulting in the resource optimization operation response strategy deviating from the market incentive signal, poor economy and insufficient robustness. Summary of the Invention

[0005] The technical problem to be solved by this invention is the lack of system operation safety, economy and robustness in the prior art due to the lack of deviation collaborative robust modeling, complete security domain construction and two-stage collaborative optimization of the power market.

[0006] To address the aforementioned technical problems, this invention presents a robust optimization operation method and system for integrated photovoltaic, energy storage, and charging resources participating in the power market, taking into account the uncertainty of source and load.

[0007] In a first aspect, the present invention provides a robust optimization operation method for integrated photovoltaic, energy storage, and charging resources participating in the electricity market, considering source-load uncertainty, including:

[0008] Acquire input data from the integrated photovoltaic, energy storage, and charging resource system;

[0009] Based on the input data of the integrated photovoltaic, energy storage and charging resource system, and based on the pre-built robust optimization model of the integrated photovoltaic, energy storage and charging resource system, the calculation data of the robust optimization model of the integrated photovoltaic, energy storage and charging resource system are standardized and organized to obtain the result data of the integrated photovoltaic, energy storage and charging resource system.

[0010] The construction of the robust optimization model for the integrated photovoltaic-storage-charging resource system includes:

[0011] A robust resource optimization model and security domain are constructed to characterize the input data of the integrated photovoltaic-storage-charging resource system, thereby obtaining the resource's operating characteristics, operating performance, and characterization data.

[0012] A robust and optimized operational safety domain is constructed to characterize the operational characteristics, performance, and operational data of the resources, thereby obtaining data on the system's coupling topology, balance relationship, and node power purchase and sale mutual exclusion relationship.

[0013] By setting robust finite-dimensional deterministic constraints, the coupling topology, equilibrium relationship, and node power purchase and sale mutual exclusion relationship data of the system are characterized to obtain the robustness data of the system.

[0014] A two-stage robust optimization objective function is configured to characterize the robustness data of the system and obtain the optimal calculation result data of the robust optimization objective of the system.

[0015] A market benefit model for ancillary services in the power market is constructed to characterize the optimal calculation results of the robust optimization objective of the system, thereby obtaining the calculation results for the pre-participation declaration stage and the post-participation execution stage of the power market.

[0016] This invention constructs a robust optimization model for an integrated photovoltaic, energy storage, and charging resource system, directly embedding the prediction deviation coefficient into the safety domain. This enables the optimized photovoltaic planned output and charging pile usage load to adapt to actual fluctuations, scientifically defining the operating boundaries of each resource. This significantly enhances the system's robustness in dealing with source-load uncertainties and avoids system power imbalance caused by prediction deviations.

[0017] Furthermore, it also includes: judging based on the result data of the integrated photovoltaic, energy storage and charging resource system, and if it is necessary to adjust the scene operation mode or reset the operation data, then updating the data, optimizing the calculation and re-inputting it into the robust optimization model of the integrated photovoltaic, energy storage and charging resource system.

[0018] Furthermore, the construction of the resource robust optimization model and security domain includes:

[0019] A robust optimization model and safety region for photovoltaic systems are constructed. The mathematical model expression is as follows:

[0020] ;

[0021] in, For time period The upper limit of the actual output range of the photovoltaic box-type system. For photovoltaic installed capacity, For time period The photovoltaic power output is predicted. For time period The positive deviation coefficient of the photovoltaic power prediction. For time period The lower limit of the actual output range of photovoltaic boxes. For time period The negative deviation coefficient of the photovoltaic power prediction. For time period Robust optimization of photovoltaic program output, To robustly optimize the time section set;

[0022] A robust optimization model and safety domain for energy storage are constructed, and the mathematical model expression is as follows:

[0023] ;

[0024] in, For time period Stored electrical energy For energy storage self-discharge rate, For time period Stored electrical energy For energy storage charging efficiency, For energy storage and discharge efficiency, For time period Energy storage charging power, For time period Energy storage charging power, For time period The energy storage discharge power, For time period The energy storage discharge power, To robustly optimize the runtime time segment step size, The last period of the scheduling cycle The remaining electrical energy stored The last period of the scheduling cycle Energy storage charging power, The last period of the scheduling cycle The energy storage discharge power, To store the initial electrical energy, This refers to the remaining electrical energy stored during the first time period of the scheduling cycle. For maximum charge and discharge power of energy storage, For time period The energy storage charging status, Indicates charging. Indicates no charging. For time period Energy storage discharge state, Indicates discharge. Indicates no discharge. The maximum state of charge factor for energy storage. The minimum state of charge coefficient for energy storage. For the rated capacity of energy storage, For time period The net output power of energy storage charging and discharging;

[0025] A robust optimization model and safety domain for charging piles are constructed, and the mathematical model expression is as follows:

[0026] ;

[0027] in, For time period The robust optimization plan for charging stations utilizes charging load. For time period The predicted load of electricity consumption for charging piles For time period The load on charging stations is being adjusted to increase. For time period Adjustment amount for reducing the load transfer of charging piles. For time period The tolerance coefficient for adjusting the load transfer of charging piles. For time period Tolerance coefficient for willingness to adjust charging pile load transfer. The start time for adjusting the load on the charging piles is now available. This is the end of the period when the charging pile load can be adjusted. For time period The maximum adjustable power for charging pile load transfer For time period The maximum adjustable power output of the charging pile load transfer For the installed capacity of charging piles, For time period The upper limit of the actual output range of the charging pile load. For time period Positive deviation coefficient of predicted load for charging piles. For time period The lower limit of the actual output range of the charging pile load. For time period Negative deviation coefficient of predicted load for charging piles For time period Net adjustment of charging pile load.

[0028] Furthermore, the construction of the robust optimized runtime security domain includes:

[0029] The system power balance and net load balance constraints are constructed, and the data model expression is as follows:

[0030] ;

[0031] in, For time period Robust optimization of photovoltaic program output, For time period The energy storage discharge power, For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the power grid during the power purchase process. For time period The robust optimization plan for charging stations utilizes charging load. For time period Energy storage charging power; For time period The power output of integrated photovoltaic, energy storage, and charging resources sold to the grid. For time period Robust optimization of net load for integrated resource nodes To robustly optimize the time section set;

[0032] The data model expression for constructing the mutual exclusion constraint conditions for the purchase and sale of electricity at integrated resource nodes is as follows:

[0033] ;

[0034] in, This represents the maximum power that the integrated photovoltaic, energy storage, and charging resources can purchase from the grid. For time period Integrated photovoltaic, energy storage, and charging resources are purchased from the power grid. Indicates the purchase of electricity. Indicates that they will not purchase electricity. This refers to the maximum power output when the integrated photovoltaic, energy storage, and charging resources are sold to the grid. For time period The status of integrated photovoltaic, energy storage, and charging resources selling electricity to the grid. Indicates electricity sales. Indicates that they do not sell electricity. For whether electricity sales are permitted, This indicates permission. This indicates that it is not allowed.

[0035] This invention constructs a full-dimensional security domain consisting of a resource robust optimization security domain and a system robust optimization operation security domain, ensuring that the integrated photovoltaic storage and charging resources can meet power balance, backup capacity, and equipment constraints under any feasible uncertain scenario, thereby improving the system's operational security, reliability, and robustness.

[0036] Furthermore, the setting of robust finite-dimensional deterministic constraints includes:

[0037] The data model expression for setting robust feasibility conditions for increasing reserve capacity is as follows:

[0038] ;

[0039] in, For time period Energy storage can utilize discharge power. For time period The energy storage discharge power, This represents the maximum power that the integrated photovoltaic, energy storage, and charging resources can purchase from the grid. For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the power grid during the power purchase process. For time period Energy storage charging power, For time period The power output of integrated photovoltaic, energy storage, and charging resources sold to the grid. For time period Positive deviation coefficient of predicted load for charging piles. For time period The predicted load of electricity consumption for charging piles For time period Negative deviation coefficient of photovoltaic power forecast. For time period The photovoltaic power output is predicted. For maximum charge and discharge power of energy storage, For time period Stored electrical energy The minimum state of charge coefficient for energy storage. For the rated capacity of energy storage, To robustly optimize the runtime time segment step size, To robustly optimize the time section set, For energy storage discharge efficiency;

[0040] The data model expression for setting robust feasibility for reducing reserve capacity is as follows:

[0041] ;

[0042] in, For time period Energy storage can utilize charging power. This refers to the maximum power output when the integrated photovoltaic, energy storage, and charging resources are sold to the grid. For time period The energy storage discharge power, For time period Negative deviation coefficient of predicted load for charging piles For time period Positive deviation coefficient of photovoltaic power forecast. For maximum charge and discharge power of energy storage, This represents the maximum state of charge coefficient of the energy storage.

[0043] Furthermore, the configured two-stage robust optimization objective function includes:

[0044] The robust optimization objective function for the pre-participation declaration stage in the electricity market is configured, and its mathematical model expression is as follows:

[0045] ;

[0046] ;

[0047] in, To robustly optimize net income during the pre-registration phase for participation in the electricity market, To robustly optimize operational benefits during the pre-registration phase for participation in the electricity market, For the revenue generated from the regular operation of resources, "subject to" indicates a constraint. The price for surplus photovoltaic power sold to the grid. For time period The power output of integrated photovoltaic, energy storage, and charging resources sold to the grid. For time period The electricity price for charging services at charging stations For time period The robust optimization plan for charging stations utilizes charging load. For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the grid at the electricity price. For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the power grid during the power purchase process. The aging cost coefficient per unit energy during energy storage charging and discharging. For time period Energy storage charging power, For time period The energy storage discharge power, To robustly optimize the total compensation revenue during the pre-registration phase for participation in the electricity market, To robustly optimize the time section set, For time period The net load of integrated photovoltaic, energy storage, and charging resources is the baseline. For time period The net load of integrated photovoltaic, energy storage, and charging resources during normal operation. For time period The electricity price for charging services at charging stations For time period The predicted load of electricity consumption for charging piles To robustly optimize the runtime time segment step size, Input data values ​​for a given baseline load over time period t. Automatically generate flag bits for the baseline. 1 indicates that it is automatically generated based on prediction. 0 indicates reading or writing the given input data;

[0048] The mathematical model expression for the robust optimization objective function in the execution phase after the electricity market clearing is as follows:

[0049] ;

[0050] ;

[0051] in, To participate in the net profit of the robust optimization phase during the implementation phase after the electricity market clearing, To optimize operational benefits during the implementation phase after the electricity market clearing, The price for surplus photovoltaic power sold to the grid. For time period The net load of integrated photovoltaic, energy storage, and charging resources is the baseline. To compensate for the total revenue of robust optimization response during the implementation phase after the electricity market clearing, To optimize the total cost of assessment and penalties during the implementation phase after the power market clearing.

[0052] This invention employs a two-stage robust optimization objective function. In the application stage, it optimizes the ancillary service adjustment amount and compensation revenue. In the execution stage, it dynamically adjusts the output of photovoltaic, energy storage and charging resources and the purchase and sale of electricity based on the clearing results, thereby maximizing the net revenue of the entire application-clearing and execution process.

[0053] Furthermore, the construction of the market benefit model for electricity market ancillary services includes:

[0054] A robust optimization model for regulation and response compensation benefits during the pre-participation declaration stage of the electricity market is constructed. The numerical model expression is as follows:

[0055] ;

[0056] in, For time period Robust optimization of regulation volume during the pre-registration stage before participating in the electricity market. This serves as a marker for the direction of integrated photovoltaic, energy storage, and charging resources participating in the ancillary services regulation of the electricity market. =1 indicates the direction of filling the valley. =-1 indicates the peak clipping direction. For time period Robust optimization of net load during the pre-registration phase for participation in the electricity market. For time period The baseline net load of integrated photovoltaic, energy storage, and charging resources The starting period for the participation of integrated photovoltaic, energy storage, and charging resources in the ancillary services regulation of the electricity market. When the integrated photovoltaic, energy storage, and charging resources cease participating in the ancillary services regulation of the electricity market. For time period Robust optimization of compensation benefits during the pre-registration stage for participation in the electricity market. For time period Robust optimization of ancillary service compensation prices during the pre-participation declaration stage of the electricity market. To robustly optimize the runtime time segment step size, To robustly optimize the total compensation revenue during the pre-registration phase for participation in the electricity market;

[0057] A robust optimization regulation and effective compensation regulation model is constructed for the implementation phase after the electricity market clearing. The numerical model expression is as follows:

[0058] ;

[0059] in, For time period The robust optimization plan for the implementation of regulation quantities after the electricity market clearing phase. For time period Robust optimization of net load during the implementation phase after participation in the electricity market clearing process. For time period The net load of integrated photovoltaic, energy storage, and charging resources is the baseline. For time period Whether or not the clearing adjustment has been successfully completed is a key indicator status. =1 indicates that the successful bidder has exited the bidding process and is participating in the execution. =0 indicates that the winning bidder has cleared the market and does not need to participate in the execution. For time period The adjustment amount of the winning bid clearing, For time period After the electricity market clearing process, the robust optimization of effective compensation and regulation during the implementation phase will be crucial. , All of these are different parameters for the effective compensation adjustment amount and the tiered compensation revenue of ancillary services in the electricity market.

[0060] A robust optimization model for compensation benefits and performance penalty costs during the implementation phase after the electricity market clearing is constructed. The numerical model expression is as follows:

[0061] ;

[0062] in, For time period The robust optimization response adjustment compensation benefits during the implementation phase after the electricity market clearing. It is a time period During the implementation phase after the electricity market clearing, the actual response and regulation power provided... For time period After the electricity market clearing process, robust optimization of ancillary services and clearing compensation electricity prices will be implemented during the execution phase. To achieve robust optimization of total compensation revenue during the implementation phase after the electricity market clearing, For time period After the electricity market clearing process, robust optimization assessment and penalty costs will be implemented during the execution phase. The cost factor for performance evaluation and penalties of ancillary services in the electricity market. To optimize the total cost of assessment and penalties during the implementation phase after the power market clearing.

[0063] This invention introduces a tiered compensation revenue model and an effective compensation adjustment calculation, which accurately reflects the incentive and assessment penalty rules of ancillary services in the power market. This enables photovoltaic, energy storage, and charging resources to adaptively adjust the direction flag and winning bid signal, thereby reasonably avoiding penalty costs, maximizing the overall benefits of resources participating in the power market, and improving their market-oriented operation efficiency.

[0064] Furthermore, the input data of the integrated photovoltaic, energy storage, and charging resource system includes: system static parameter data information and system time-series dynamic variable data information;

[0065] The system static parameter data information includes:

[0066] System operating parameters: robust optimization time section set, robust optimization running time section step size, total number of robust optimization time section sets, photovoltaic surplus electricity grid connection price, and energy storage charging and discharging unit energy aging cost coefficient;

[0067] Energy storage resource operation parameters: energy storage charging efficiency, energy storage discharging efficiency, energy storage self-discharge rate, energy storage maximum state of charge coefficient, energy storage minimum state of charge coefficient, energy storage rated capacity, energy storage initial electrical energy, and energy storage maximum charging and discharging power.

[0068] Grid-connected line operating parameters: Maximum power purchased from the grid by the integrated photovoltaic-storage-charging resource, and maximum power sold to the grid by the integrated photovoltaic-storage-charging resource.

[0069] Photovoltaic resource operating parameters: Photovoltaic installed capacity:

[0070] Charging pile resource operation parameters: Charging pile installed capacity:

[0071] Electricity market ancillary service parameters: Start time of integrated photovoltaic-storage-charging resources participating in electricity market ancillary service regulation; End time of integrated photovoltaic-storage-charging resources participating in electricity market ancillary service regulation; Direction flag for integrated photovoltaic-storage-charging resources participating in electricity market ancillary service regulation; Whether electricity sales are permitted.

[0072] Charging pile resource load adjustment period parameters: the start time of charging pile load adjustment and the end time of charging pile load adjustment;

[0073] The system's time-series dynamic variable data information includes: the electricity purchase price of the integrated photovoltaic, energy storage, and charging resources when purchasing electricity from the grid; the electricity sales price of charging pile charging services; the compensation price for robust optimization ancillary services during the pre-participation application stage of the electricity market; the compensation price for robust optimization ancillary services during the execution stage after participating in the electricity market clearing; the photovoltaic predicted output; the predicted load of charging piles; the positive deviation coefficient of the photovoltaic predicted output; the negative deviation coefficient of the photovoltaic predicted output; the positive deviation coefficient of the predicted load of charging piles; the negative deviation coefficient of the predicted load of charging piles; the tolerance coefficient for the willingness to adjust the load transfer into the charging piles; the tolerance coefficient for the willingness to adjust the load transfer out of the charging piles; the given baseline load input data value; the net load of the integrated photovoltaic, energy storage, and charging resources during normal operation; and the adjustment amount during the bidding and clearing process.

[0074] Furthermore, the result data of the integrated photovoltaic, energy storage, and charging resource system includes:

[0075] The robust optimization results data information for power variables include: robustly optimized photovoltaic planned output, robustly optimized charging load of charging piles, power purchased from the grid by integrated photovoltaic-storage-charging resources, power sold to the grid by integrated photovoltaic-storage-charging resources, energy storage charging power, energy storage discharging power, net energy storage charging and discharging output power, adjustment amount for increased charging pile load transfer, and adjustment amount for decreased charging pile load transfer.

[0076] The robust optimization result data information of state variables includes: the status of photovoltaic-storage-charging integrated resources purchasing electricity from the grid, the status of photovoltaic-storage-charging integrated resources selling electricity to the grid, the energy storage charging status, the energy storage discharging status, and the status of whether the bid clearing adjustment has been won.

[0077] Robust optimization results data information for electrical energy variables, including: stored electrical energy;

[0078] Robust optimization results data for net load variables include: robust optimized net load for integrated resource nodes, robust optimized net load during the pre-participation declaration stage, and robust optimized net load during the post-participation clearing execution stage.

[0079] Robust optimization results data for regulation variables include: robust optimized regulation quantities during the pre-participation electricity market declaration phase, robust optimized planned execution regulation quantities during the post-participation electricity market clearing execution phase, and robust optimized effective compensation regulation quantities during the post-participation electricity market clearing execution phase.

[0080] The robust optimization results data information for the variables of benefits and assessment penalties include: robust optimization compensation benefits during the pre-participation electricity market application stage, robust optimization response adjustment compensation benefits during the post-participation electricity market clearing and execution stage, robust optimization assessment penalty costs during the post-participation electricity market clearing and execution stage, total robust optimization compensation benefits during the pre-participation electricity market application stage, total robust optimization response compensation benefits during the post-participation electricity market clearing and execution stage, and total robust optimization assessment penalty costs during the post-participation electricity market clearing and execution stage.

[0081] Secondly, this invention provides a robust optimized operation system for integrated photovoltaic, energy storage, and charging resources participating in the power market, considering source-load uncertainty, comprising:

[0082] The data acquisition module is used to acquire input data from the integrated photovoltaic, energy storage, and charging resource system.

[0083] The robust optimization module for the integrated photovoltaic, energy storage, and charging resource system is used to obtain the result data of the integrated photovoltaic, energy storage, and charging resource system based on the input data of the integrated photovoltaic, energy storage, and charging resource system and a pre-built robust optimization model of the integrated photovoltaic, energy storage, and charging resource system.

[0084] The construction of the robust optimization model for the integrated photovoltaic-storage-charging resource system includes:

[0085] A robust resource optimization model and security domain are constructed to characterize the input data of the integrated photovoltaic-storage-charging resource system, thereby obtaining the resource's operating characteristics, operating performance, and characterization data.

[0086] A robust and optimized operational safety domain is constructed to characterize the operational characteristics, performance, and operational data of the resources, thereby obtaining data on the system's coupling topology, balance relationship, and node power purchase and sale mutual exclusion relationship.

[0087] By setting robust finite-dimensional deterministic constraints, the coupling topology, equilibrium relationship, and node power purchase and sale mutual exclusion relationship data of the system are characterized to obtain the robustness data of the system.

[0088] A two-stage robust optimization objective function is configured to characterize the robustness data of the system and obtain the optimal calculation result data of the robust optimization objective of the system.

[0089] A market benefit model for ancillary services in the power market is constructed to characterize the optimal calculation results of the robust optimization objective of the system, thereby obtaining the calculation results for the pre-participation declaration stage and the post-participation execution stage of the power market.

[0090] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention effectively improves the operational robustness, safety and economy of integrated photovoltaic, energy storage and charging resources in the power market through box-type robust modeling of source-load uncertainty, two-stage collaborative robust optimization and tiered compensation benefit model, and is suitable for market participants to participate in the optimized operation and regulation of the power market. Attached Figure Description

[0091] Figure 1 This is a schematic diagram of the robust optimization operation method for integrated photovoltaic, energy storage, and charging resources participating in the power market, considering source-load uncertainty, as shown in Embodiment 1 of the present invention.

[0092] Figure 2 This is a schematic diagram of the time-series dynamic variable data of electricity prices as shown in Embodiment 2 of the present invention;

[0093] Figure 3 This is a schematic diagram of the time-series dynamic variable data of photovoltaic predicted output, charging pile electricity predicted load, and the net load of the photovoltaic-storage-charging integrated resource under normal operation as shown in Embodiment 2 of the present invention.

[0094] Figure 4 This is a schematic diagram of the time-series dynamic variable data of the upper and lower limits of the actual output range of the photovoltaic box shown in Embodiment 2 of the present invention;

[0095] Figure 5This is a schematic diagram of the time-series dynamic variable data of the upper and lower limits of the actual output range of the charging pile load shown in Embodiment 2 of the present invention.

[0096] Figure 6 This is a schematic diagram of the power balance result of the integrated photovoltaic, energy storage and charging resource system shown in Embodiment 2 of the present invention;

[0097] Figure 7 This is a schematic diagram illustrating the net load of the integrated photovoltaic, energy storage, and charging resource during normal operation, the robustly optimized net load during the pre-participation application stage, and the robustly optimized adjustment quantity during the pre-participation application stage, as shown in Embodiment 2 of the present invention.

[0098] Figure 8 This is a schematic diagram of the baseline net load, the adjustment amount after winning the bid clearing, the robust optimized net load in the execution phase after participating in the power market clearing, the robust optimized planned execution adjustment amount in the execution phase after participating in the power market clearing, and the robust optimized effective compensation adjustment amount in the execution phase after participating in the power market clearing, as shown in Embodiment 2 of the present invention.

[0099] Figure 9 This is a schematic diagram showing the robust optimization results of the stored electrical energy of the energy storage resource shown in Embodiment 2 of the present invention during the application stage before participating in the electricity market and the robust optimization results during the execution stage after participating in the electricity market clearing. Detailed Implementation

[0100] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0101] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0102] Example 1

[0103] This embodiment introduces a robust optimization operation method for integrated photovoltaic, energy storage, and charging resources participating in the electricity market, considering source-load uncertainty, including the following steps:

[0104] Step 1: Obtain input data from the integrated photovoltaic, energy storage, and charging resource system.

[0105] Specifically, the input data of the integrated photovoltaic, energy storage and charging resource system includes: system static parameter data information and system time-series dynamic variable data information;

[0106] System static parameter data includes:

[0107] System operating parameters: robust optimization time section set, robust optimization running time section step size, total number of robust optimization time section sets, photovoltaic surplus electricity grid connection price, and energy storage charging and discharging unit energy aging cost coefficient;

[0108] Energy storage resource operation parameters: energy storage charging efficiency, energy storage discharging efficiency, energy storage self-discharge rate, energy storage maximum state of charge coefficient, energy storage minimum state of charge coefficient, energy storage rated capacity, energy storage initial electrical energy, and energy storage maximum charging and discharging power.

[0109] Grid-connected line operating parameters: Maximum power purchased from the grid by the integrated photovoltaic-storage-charging resource, and maximum power sold to the grid by the integrated photovoltaic-storage-charging resource.

[0110] Photovoltaic resource operating parameters: Photovoltaic installed capacity:

[0111] Charging pile resource operation parameters: Charging pile installed capacity:

[0112] Electricity market ancillary service parameters: Start time of integrated photovoltaic-storage-charging resources participating in electricity market ancillary service regulation; End time of integrated photovoltaic-storage-charging resources participating in electricity market ancillary service regulation; Direction flag for integrated photovoltaic-storage-charging resources participating in electricity market ancillary service regulation; Whether electricity sales are permitted.

[0113] Charging pile resource load adjustment period parameters: the start time of charging pile load adjustment and the end time of charging pile load adjustment;

[0114] The system's time-series dynamic variable data information includes: the electricity purchase price of the integrated photovoltaic, energy storage, and charging resources when purchasing electricity from the grid; the electricity sales price of charging pile charging services; the compensation price for robust optimization ancillary services during the pre-participation application stage of the electricity market; the compensation price for robust optimization ancillary services during the execution stage after participating in the electricity market clearing; the photovoltaic predicted output; the predicted load of charging piles; the positive deviation coefficient of the photovoltaic predicted output; the negative deviation coefficient of the photovoltaic predicted output; the positive deviation coefficient of the predicted load of charging piles; the negative deviation coefficient of the predicted load of charging piles; the tolerance coefficient for the willingness to adjust the load transfer into the charging piles; the tolerance coefficient for the willingness to adjust the load transfer out of the charging piles; the given baseline load input data value; the net load of the integrated photovoltaic, energy storage, and charging resources during normal operation; and the adjustment amount during the bidding and clearing process.

[0115] Step 2, as follows Figure 1 As shown, based on the input data of the integrated photovoltaic, energy storage and charging resource system, and based on the pre-constructed robust optimization model of the integrated photovoltaic, energy storage and charging resource system, the calculation data of the robust optimization model of the integrated photovoltaic, energy storage and charging resource system are standardized and organized to obtain the result data of the integrated photovoltaic, energy storage and charging resource system.

[0116] The construction of the robust optimization model for the integrated photovoltaic-storage-charging resource system includes:

[0117] A robust resource optimization model and security domain are constructed to characterize the input data of the integrated photovoltaic-storage-charging resource system, thereby obtaining the resource's operating characteristics, operating performance, and characterization data.

[0118] A robust and optimized operational safety domain is constructed to characterize the operational characteristics, performance, and data of resources, thereby obtaining data on the system's coupling topology, balance relationship, and mutual exclusion relationship between node power purchase and sale.

[0119] By setting robust finite-dimensional deterministic constraints, the coupling topology, equilibrium relationship, and node power purchase and sale mutual exclusion relationship of the system are characterized, and the robustness data of the system is obtained.

[0120] A two-stage robust optimization objective function is configured to characterize the robustness data of the system and obtain the optimal calculation result data of the robust optimization objective of the system.

[0121] A market benefit model for ancillary services in the power market is constructed to characterize the optimal calculation results of the system's robust optimization objective, thereby obtaining calculation results for the system's pre-participation declaration stage and post-participation execution stage in the power market clearing process.

[0122] Step 2.1, constructing a resource robust optimization model and security domains includes:

[0123] A robust optimization model and safety region for photovoltaic systems are constructed. The mathematical model expression is as follows:

[0124] ;

[0125] in, For time period The upper limit of the actual output range of the photovoltaic box-type system. For photovoltaic installed capacity, For time period The photovoltaic power output is predicted. For time period The positive deviation coefficient of the photovoltaic power prediction. For time period The lower limit of the actual output range of photovoltaic boxes. For time period The negative deviation coefficient of the photovoltaic power prediction. For time period Robust optimization of photovoltaic program output, To robustly optimize the time section set;

[0126] A robust optimization model and safety domain for energy storage are constructed, and the mathematical model expression is as follows:

[0127] ;

[0128] in, For time period Stored electrical energy For energy storage self-discharge rate, For time period Stored electrical energy For energy storage charging efficiency, For energy storage and discharge efficiency, For time period Energy storage charging power, For time period Energy storage charging power, For time period The energy storage discharge power, For time period The energy storage discharge power, To robustly optimize the runtime time segment step size, The last period of the scheduling cycle The remaining electrical energy stored The last period of the scheduling cycle Energy storage charging power, The last period of the scheduling cycle The energy storage discharge power, To store the initial electrical energy, This refers to the remaining electrical energy stored during the first time period of the scheduling cycle. For maximum charge and discharge power of energy storage, For time period The energy storage charging status, Indicates charging. Indicates no charging. For time period Energy storage discharge state, Indicates discharge. Indicates no discharge. The maximum state of charge factor for energy storage. The minimum state of charge coefficient for energy storage. For the rated capacity of energy storage, For time period The net output power of energy storage charging and discharging;

[0129] A robust optimization model and safety domain for charging piles are constructed, and the mathematical model expression is as follows:

[0130] ;

[0131] in, For time period The robust optimization plan for charging stations utilizes charging load. For time period The predicted load of electricity consumption for charging piles For time period The load on charging stations is being adjusted to increase. For time period Adjustment amount for reducing the load transfer of charging piles. For time period The tolerance coefficient for adjusting the load transfer of charging piles. For time period Tolerance coefficient for willingness to adjust charging pile load transfer. The start time for adjusting the load on the charging piles is now available. This is the end of the period when the charging pile load can be adjusted. For time period The maximum adjustable power for charging pile load transfer For time period The maximum adjustable power output of the charging pile load transfer For the installed capacity of charging piles, For time period The upper limit of the actual output range of the charging pile load. For time period Positive deviation coefficient of predicted load for charging piles. For time period The lower limit of the actual output range of the charging pile load. For time period Negative deviation coefficient of predicted load for charging piles For time period Net adjustment of charging pile load.

[0132] Step 2.2, constructing a robust and optimized operational security domain includes:

[0133] The system power balance and net load balance constraints are constructed, and the data model expression is as follows:

[0134] ;

[0135] in, For time period Robust optimization of photovoltaic program output, For time period The energy storage discharge power, For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the power grid during the power purchase process. For time period The robust optimization plan for charging stations utilizes charging load. For time period Energy storage charging power; For time period The power output of integrated photovoltaic, energy storage, and charging resources sold to the grid. For time period Robust optimization of net load for integrated resource nodes To robustly optimize the time section set;

[0136] The data model expression for constructing the mutual exclusion constraint conditions for the purchase and sale of electricity at integrated resource nodes is as follows:

[0137] ;

[0138] in, This represents the maximum power that the integrated photovoltaic, energy storage, and charging resources can purchase from the grid. For time period Integrated photovoltaic, energy storage, and charging resources are purchased from the power grid. Indicates the purchase of electricity. Indicates that they will not purchase electricity. This refers to the maximum power output when the integrated photovoltaic, energy storage, and charging resources are sold to the grid. For time period The status of integrated photovoltaic, energy storage, and charging resources selling electricity to the grid. Indicates electricity sales. Indicates that they do not sell electricity. For whether electricity sales are permitted, This indicates permission. This indicates that it is not allowed.

[0139] Step 2.3, setting robust finite-dimensional deterministic constraints includes:

[0140] The data model expression for setting robust feasibility conditions for increasing reserve capacity is as follows:

[0141] ;

[0142] in, For time period Energy storage can utilize discharge power. For time period The energy storage discharge power, This represents the maximum power that the integrated photovoltaic, energy storage, and charging resources can purchase from the grid. For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the power grid during the power purchase process. For time period Energy storage charging power, For time period The power output of integrated photovoltaic, energy storage, and charging resources sold to the grid. For time period Positive deviation coefficient of predicted load for charging piles. For time period The predicted load of electricity consumption for charging piles For time period Negative deviation coefficient of photovoltaic power forecast. For time period The photovoltaic power output is predicted. For maximum charge and discharge power of energy storage, For time period Stored electrical energy The minimum state of charge coefficient for energy storage. For the rated capacity of energy storage, To robustly optimize the runtime time segment step size, To robustly optimize the time section set, For energy storage discharge efficiency;

[0143] The data model expression for setting robust feasibility for reducing reserve capacity is as follows:

[0144] ;

[0145] in, For time period Energy storage can utilize charging power. This refers to the maximum power output when the integrated photovoltaic, energy storage, and charging resources are sold to the grid. For time period The energy storage discharge power, For time period Negative deviation coefficient of predicted load for charging piles For time period Positive deviation coefficient of photovoltaic power forecast. For maximum charge and discharge power of energy storage, This represents the maximum state of charge coefficient of the energy storage.

[0146] Step 2.4, configuring the two-stage robust optimization objective function includes:

[0147] The robust optimization objective function for the pre-participation declaration stage in the electricity market is configured, and its mathematical model expression is as follows:

[0148] ;

[0149] ;

[0150] in, To robustly optimize net income during the pre-registration phase for participation in the electricity market, To robustly optimize operational benefits during the pre-registration phase for participation in the electricity market, For the revenue generated from the regular operation of resources, "subject to" indicates a constraint. The price for surplus photovoltaic power sold to the grid. For time period The power output of integrated photovoltaic, energy storage, and charging resources sold to the grid. For time period The electricity price for charging services at charging stations For time period The robust optimization plan for charging stations utilizes charging load. For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the grid at the electricity price. For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the power grid during the power purchase process. The aging cost coefficient per unit energy during energy storage charging and discharging. For time period Energy storage charging power, For time period The energy storage discharge power, To robustly optimize the total compensation revenue during the pre-registration phase for participation in the electricity market, To robustly optimize the time section set, For time period The net load of integrated photovoltaic, energy storage, and charging resources is the baseline. For time period The net load of integrated photovoltaic, energy storage, and charging resources during normal operation. For time period The electricity price for charging services at charging stations For time period The predicted load of electricity consumption for charging piles To robustly optimize the runtime time segment step size, Input data values ​​for a given baseline load over time period t. Automatically generate flag bits for the baseline. 1 indicates that it is automatically generated based on prediction. 0 indicates reading or writing the given input data;

[0151] The mathematical model expression for the robust optimization objective function in the execution phase after the electricity market clearing is as follows:

[0152] ;

[0153] ;

[0154] in, To participate in the net profit of the robust optimization phase during the implementation phase after the electricity market clearing, To optimize operational benefits during the implementation phase after the electricity market clearing, The price for surplus photovoltaic power sold to the grid. For time period The net load of integrated photovoltaic, energy storage, and charging resources is the baseline. To compensate for the total revenue of robust optimization response during the implementation phase after the electricity market clearing, To optimize the total cost of assessment and penalties during the implementation phase after the power market clearing.

[0155] Step 2.5, Constructing a market benefit model for ancillary services in the electricity market, includes:

[0156] A robust optimization model for regulation and response compensation benefits during the pre-participation declaration stage of the electricity market is constructed. The numerical model expression is as follows:

[0157] ;

[0158] in, For time period Robust optimization of regulation volume during the pre-registration stage before participating in the electricity market. This serves as a marker for the direction of integrated photovoltaic, energy storage, and charging resources participating in the ancillary services regulation of the electricity market. =1 indicates the direction of filling the valley. =-1 indicates the peak clipping direction. For time period Robust optimization of net load during the pre-registration phase for participation in the electricity market. For time period The baseline net load of integrated photovoltaic, energy storage, and charging resources The starting period for the participation of integrated photovoltaic, energy storage, and charging resources in the ancillary services regulation of the electricity market. When the integrated photovoltaic, energy storage, and charging resources cease participating in the ancillary services regulation of the electricity market. For time period Robust optimization of compensation benefits during the pre-registration stage for participation in the electricity market. For time period Robust optimization of ancillary service compensation prices during the pre-participation declaration stage of the electricity market. To robustly optimize the runtime time segment step size, To robustly optimize the total compensation revenue during the pre-registration phase for participation in the electricity market;

[0159] A robust optimization regulation and effective compensation regulation model is constructed for the implementation phase after the electricity market clearing. The numerical model expression is as follows:

[0160] ;

[0161] in, For time period The robust optimization plan for the implementation of regulation quantities after the electricity market clearing phase. For time period Robust optimization of net load during the implementation phase after participation in the electricity market clearing process. For time period The net load of integrated photovoltaic, energy storage, and charging resources is the baseline. For time period Whether or not the clearing adjustment has been successfully completed is a key indicator status. =1 indicates that the successful bidder has exited the bidding process and is participating in the execution. =0 indicates that the winning bidder has cleared the market and does not need to participate in the execution. For time period The adjustment amount of the winning bid clearing, For time period After the electricity market clearing process, the robust optimization of effective compensation and regulation during the implementation phase will be crucial. , All of these are different parameters for the effective compensation adjustment amount and the tiered compensation revenue of ancillary services in the electricity market.

[0162] A robust optimization model for compensation benefits and performance penalty costs during the implementation phase after the electricity market clearing is constructed. The numerical model expression is as follows:

[0163] ;

[0164] in, For time period The robust optimization response adjustment compensation benefits during the implementation phase after the electricity market clearing. It is a time period During the implementation phase after the electricity market clearing, the actual response and regulation power provided... For time period After the electricity market clearing process, robust optimization of ancillary services and clearing compensation electricity prices will be implemented during the execution phase. For time period After the electricity market clearing process, robust optimization assessment and penalty costs will be implemented during the execution phase. For time period After the electricity market clearing process, robust optimization assessment and penalty costs will be implemented during the execution phase. The cost factor for performance evaluation and penalties of ancillary services in the electricity market. To optimize the total cost of assessment and penalties during the implementation phase after the power market clearing.

[0165] Specifically, the results data of the integrated photovoltaic, energy storage, and charging resource system include:

[0166] The robust optimization results data information for power variables include: robustly optimized photovoltaic planned output, robustly optimized charging load of charging piles, power purchased from the grid by integrated photovoltaic-storage-charging resources, power sold to the grid by integrated photovoltaic-storage-charging resources, energy storage charging power, energy storage discharging power, net energy storage charging and discharging output power, adjustment amount for increased charging pile load transfer, and adjustment amount for decreased charging pile load transfer.

[0167] The robust optimization result data information of state variables includes: the status of photovoltaic-storage-charging integrated resources purchasing electricity from the grid, the status of photovoltaic-storage-charging integrated resources selling electricity to the grid, the energy storage charging status, the energy storage discharging status, and the status of whether the bid clearing adjustment has been won.

[0168] Robust optimization results data information for electrical energy variables, including: stored electrical energy;

[0169] Robust optimization results data for net load variables include: robust optimized net load for integrated resource nodes, robust optimized net load during the pre-participation declaration stage, and robust optimized net load during the post-participation clearing execution stage.

[0170] Robust optimization results data for regulation variables include: robust optimized regulation quantities during the pre-participation electricity market declaration phase, robust optimized planned execution regulation quantities during the post-participation electricity market clearing execution phase, and robust optimized effective compensation regulation quantities during the post-participation electricity market clearing execution phase.

[0171] The robust optimization results data information for the variables of benefits and assessment penalties include: robust optimization compensation benefits during the pre-participation electricity market application stage, robust optimization response adjustment compensation benefits during the post-participation electricity market clearing and execution stage, robust optimization assessment penalty costs during the post-participation electricity market clearing and execution stage, total robust optimization compensation benefits during the pre-participation electricity market application stage, total robust optimization response compensation benefits during the post-participation electricity market clearing and execution stage, and total robust optimization assessment penalty costs during the post-participation electricity market clearing and execution stage.

[0172] Step 3: Based on the results data of the integrated photovoltaic, energy storage and charging resource system, make a judgment. If it is necessary to adjust the scenario operation mode or reset the operation data, update the data, optimize the calculation, and re-enter the robust optimization model of the integrated photovoltaic, energy storage and charging resource system.

[0173] Example 2

[0174] In this embodiment, the robust optimization operation method for integrated photovoltaic, energy storage and charging resources participating in the power market, which considers the uncertainty of source and load, is adopted in Example 1 to participate in the power market for two consecutive hours on a certain day.

[0175] The main input data for the integrated photovoltaic-storage-charging resource system includes: a robust optimization time segment set of 1 day, a robust optimization operating time segment step size of 15 minutes, a total number of robust optimization time segment sets of 96, an energy aging cost coefficient per unit energy for energy storage charging and discharging of 0.01 CNY / kWh, and a photovoltaic surplus electricity grid connection price of 0.3912 CNY / kWh; energy storage charging efficiency of 0.98, energy storage discharging efficiency of 0.98, energy storage self-discharge rate of 0.3%, maximum energy state factor of 0.9, minimum energy state factor of 0.1, rated energy storage capacity of 1000 kWh, initial energy storage capacity of 200 kWh, and maximum charging and discharging power of 500 kW; photovoltaic-storage... The maximum power purchased by the integrated photovoltaic-storage-charging resource from the grid is 1300kW, and the maximum power sold to the grid by the integrated photovoltaic-storage-charging resource is 1800kW; the photovoltaic installed capacity is 1800kW; the charging pile installed capacity is 1400kW; the start time for the integrated photovoltaic-storage-charging resource to participate in the power market ancillary service regulation is 11:00, the end time is 13:00, the direction flag for the integrated photovoltaic-storage-charging resource to participate in the power market ancillary service regulation is 1, and the power sales permission flag is 1; the charging pile resource load adjustment time parameters are: the start time for the adjustable charging pile load is 8:00 AM, and the end time is 4:45 PM.

[0176] Specifically, the integrated photovoltaic, energy storage, and charging resources utilize dynamic data from the electricity purchase price from the grid, the electricity sales price for charging services at charging piles, and the time-series dynamic variables of the robust optimization ancillary service compensation price during the pre-participation application stage in the electricity market, such as... Figure 2 As shown, the compensation price for robust optimization ancillary services during the implementation phase after participating in the electricity market clearing is the same as the compensation price for robust optimization ancillary services during the declaration phase before participating in the electricity market.

[0177] Specifically, the data includes the time-series dynamic variables of photovoltaic power output forecast, charging pile power load forecast, and the net load of integrated photovoltaic-storage-charging resources during normal operation, such as... Figure 3 As shown.

[0178] Specifically, based on parameters such as the positive deviation coefficient of the photovoltaic (PV) predicted output, the negative deviation coefficient of the PV predicted output, and the PV installed capacity, the upper limit boundary and the lower limit boundary of the actual PV output range can be calculated. The time-series dynamic variable data information for the upper and lower limits of the actual PV output range is then obtained, such as... Figure 4 As shown.

[0179] Specifically, based on parameters such as the positive deviation coefficient of the predicted load of the charging pile, the negative deviation coefficient of the predicted load of the charging pile, the tolerance coefficient for the willingness to adjust the load transfer into the charging pile, and the tolerance coefficient for the willingness to adjust the load transfer out of the charging pile, the upper limit boundary and the lower limit boundary of the actual output range of the charging pile load can be calculated. The time-series dynamic variable data information of the upper limit boundary and the lower limit boundary of the actual output range of the charging pile load can then be obtained, such as... Figure 5 As shown.

[0180] In this embodiment, the robust optimization result of the system during the pre-participation declaration stage in the electricity market is as follows: based on the robustly optimized photovoltaic plan output, the power purchased from the grid by the integrated photovoltaic-storage-charging resources, the energy storage discharge power, the charging load used by the charging piles in the robust optimization plan, the energy storage charging power, and the power sold to the grid by the integrated photovoltaic-storage-charging resources, the system power balance result is characterized as follows: Figure 6 As shown, according to Figure 6 In this system, photovoltaic resources, energy storage resources, and charging pile resources are coupled and interact collaboratively, and the system power is always in balance. Under a robust optimization operation method that considers the uncertainty of source and load, the integrated photovoltaic, energy storage, and charging resources achieve global optimal operation.

[0181] Specifically, robust optimization during the pre-market application phase can yield the system's regulation capacity. The robust optimization results for the integrated photovoltaic-storage-charging resource's normal operating net load, the robustly optimized net load during the pre-market application phase, and the robustly optimized regulation capacity during the pre-market application phase are as follows: Figure 7 As shown, according to Figure 7 The robust optimization operation method optimizes the net load during the pre-market application stage of the system, thereby obtaining the robust optimization regulation during the pre-market application stage, enabling the photovoltaic-storage-charging integrated resource system to participate in the electricity market and generate profits.

[0182] In this embodiment, the robust optimization results of the system during the execution phase after participating in the power market clearing are as follows: When the adjustment amount after the bidding clearing is 1000kW, the robust optimization results of the integrated photovoltaic-storage-charging resource baseline net load, the adjustment amount after the bidding clearing, the robust optimization net load during the execution phase after participating in the power market clearing, the robust optimization planned execution adjustment amount during the execution phase after participating in the power market clearing, and the robust optimization effective compensation adjustment amount during the execution phase after participating in the power market clearing are as follows: Figure 8 As shown, according to Figure 8Although the regulation capacity after the power market clearing is 1000kW, the regulation capacity of the robust optimization plan execution in the power market clearing stage is 1200kW. This is because the robust optimization regulation capacity and effective compensation regulation capacity model and the robust optimization compensation benefit and assessment penalty cost model constructed in this invention play a guiding role. Robust optimization optimizes the planned execution regulation capacity so that the robust optimization objective function in the power market clearing stage reaches the global optimum. At this time, the effective compensation regulation capacity in the power market clearing stage is also 1200kW. The system's regulation capacity and the net benefit of robust optimization in the power market clearing stage both reach the optimum.

[0183] Specifically, the robust optimization results of the stored electrical energy of energy storage resources during the pre-participation application stage and the robust optimization results during the implementation stage after participation in the electricity market clearing are as follows: Figure 9 As shown, according to Figure 9 The results of robust optimization of energy storage in the two stages are quite similar. The robust optimization results of the application stage before participating in the electricity market and the execution stage after participating in the electricity market clearing mutually confirm that the overall energy storage operation strategy has reached the optimal level. The amount of regulation in the bidding and clearing will affect the energy storage operation curve. The flexible operation and fine adjustment of energy storage in the execution stage after participating in the electricity market clearing maximizes the release of energy storage regulation capacity.

[0184] In this embodiment, the robust optimization results of the system's net benefit, operating benefit, and compensation benefit during the application stage before participating in the electricity market and the robust optimization results during the execution stage after participating in the electricity market clearing are shown in Table 1.

[0185] Table 1: Net Income, Operating Income, and Compensation Income of Robust Optimization

[0186]

[0187] According to Table 1, the values ​​of robust optimization net benefit, operating benefit, and compensation benefit in the pre-participation application stage of the electricity market are all greater than the values ​​of the corresponding indicators in the post-participation implementation stage. This is because in the pre-participation application stage, the benefits are evaluated and calculated based on the maximum regulation of the system. However, the actual declared regulation and the regulation of the winning bid cleared will be lower than the maximum regulation of the system, and the system's regulation capacity is not fully released. At this time, the robust optimization net benefit, operating benefit, and compensation benefit in the post-participation implementation stage will also be smaller.

[0188] Example 3

[0189] Based on the same inventive concept as Embodiment 1, this embodiment introduces a robust optimized operation system for integrated photovoltaic, energy storage, and charging resources participating in the power market, considering source-load uncertainty, including:

[0190] The data acquisition module is used to acquire input data from the integrated photovoltaic, energy storage, and charging resource system.

[0191] The robust optimization module for the integrated photovoltaic, energy storage, and charging resource system is used to obtain the result data of the integrated photovoltaic, energy storage, and charging resource system based on the input data of the integrated photovoltaic, energy storage, and charging resource system and a pre-built robust optimization model of the integrated photovoltaic, energy storage, and charging resource system.

[0192] The construction of the robust optimization model for the integrated photovoltaic-storage-charging resource system includes:

[0193] A robust resource optimization model and security domain are constructed to characterize the input data of the integrated photovoltaic-storage-charging resource system, thereby obtaining the resource's operating characteristics, operating performance, and characterization data.

[0194] A robust and optimized operational safety domain is constructed to characterize the operational characteristics, performance, and data of resources, thereby obtaining data on the system's coupling topology, balance relationship, and mutual exclusion relationship between node power purchase and sale.

[0195] By setting robust finite-dimensional deterministic constraints, the coupling topology, equilibrium relationship, and node power purchase and sale mutual exclusion relationship of the system are characterized, and the robustness data of the system is obtained.

[0196] A two-stage robust optimization objective function is configured to characterize the robustness data of the system and obtain the optimal calculation result data of the robust optimization objective of the system.

[0197] A market benefit model for ancillary services in the power market is constructed to characterize the optimal calculation results of the system's robust optimization objective, thereby obtaining calculation results for the system's pre-participation declaration stage and post-participation execution stage in the power market clearing process.

[0198] In summary, the present invention effectively improves the operational robustness, safety, and economy of integrated photovoltaic, energy storage, and charging resources in the power market, and can be developed, integrated, manufactured, and implemented in engineering.

[0199] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0200] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0201] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0202] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0203] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A robust optimization operation method for integrated photovoltaic-storage-charging resources participating in the electricity market, considering source-load uncertainty, characterized in that, include: Acquire input data from the integrated photovoltaic, energy storage, and charging resource system; Based on the input data of the integrated photovoltaic, energy storage and charging resource system, and based on the pre-built robust optimization model of the integrated photovoltaic, energy storage and charging resource system, the calculation data of the robust optimization model of the integrated photovoltaic, energy storage and charging resource system are standardized and organized to obtain the result data of the integrated photovoltaic, energy storage and charging resource system. The construction of the robust optimization model for the integrated photovoltaic-storage-charging resource system includes: A robust resource optimization model and security domain are constructed to characterize the input data of the integrated photovoltaic-storage-charging resource system, thereby obtaining the resource's operating characteristics, operating performance, and characterization data. A robust and optimized operational safety domain is constructed to characterize the operational characteristics, performance, and operational data of the resources, thereby obtaining data on the system's coupling topology, balance relationship, and node power purchase and sale mutual exclusion relationship. By setting robust finite-dimensional deterministic constraints, the coupling topology, equilibrium relationship, and node power purchase and sale mutual exclusion relationship data of the system are characterized to obtain the robustness data of the system. A two-stage robust optimization objective function is configured to characterize the robustness data of the system and obtain the optimal calculation result data of the robust optimization objective of the system. A market benefit model for ancillary services in the power market is constructed to characterize the optimal calculation results of the robust optimization objective of the system, thereby obtaining the calculation results for the pre-participation declaration stage and the post-participation execution stage of the power market.

2. The robust optimization operation method for integrated photovoltaic-storage-charging resources participating in the power market considering source-load uncertainty, as described in claim 1, is characterized in that... Also includes: Based on the results data of the integrated photovoltaic, energy storage, and charging resource system, if it is necessary to adjust the scenario operation mode or reset the operation data, then update the data, optimize the calculation, and re-enter the robust optimization model of the integrated photovoltaic, energy storage, and charging resource system.

3. The robust optimization operation method for integrated photovoltaic-storage-charging resources participating in the power market considering source-load uncertainty, as described in claim 1, is characterized in that... The construction of the resource robust optimization model and security domain includes: A robust optimization model and safety region for photovoltaic systems are constructed. The mathematical model expression is as follows: ； in, For time period The upper limit of the actual output range of the photovoltaic box-type system. For photovoltaic installed capacity, For time period The photovoltaic power output is predicted. For time period The positive deviation coefficient of the photovoltaic power prediction. For time period The lower limit of the actual output range of photovoltaic boxes. For time period The negative deviation coefficient of the photovoltaic power prediction. For time period Robust optimization of photovoltaic program output, To robustly optimize the time section set; A robust optimization model and safety domain for energy storage are constructed, and the mathematical model expression is as follows: ； in, For time period Stored electrical energy For energy storage self-discharge rate, For time period Stored electrical energy For energy storage charging efficiency, For energy storage and discharge efficiency, For time period Energy storage charging power, For time period Energy storage charging power, For time period The energy storage discharge power, For time period The energy storage discharge power, To robustly optimize the runtime time segment step size, The last period of the scheduling cycle The remaining electrical energy stored The last period of the scheduling cycle Energy storage charging power, The last period of the scheduling cycle The energy storage discharge power, To store the initial electrical energy, This refers to the remaining electrical energy stored during the first time period of the scheduling cycle. For maximum charge and discharge power of energy storage, For time period The energy storage charging status, Indicates charging. Indicates no charging. For time period Energy storage discharge state, Indicates discharge. Indicates no discharge. The maximum state of charge factor for energy storage. The minimum state of charge coefficient for energy storage. For the rated capacity of energy storage, For time period The net output power of energy storage charging and discharging; A robust optimization model and safety domain for charging piles are constructed, and the mathematical model expression is as follows: ； in, For time period The robust optimization plan for charging stations utilizes charging load. For time period The predicted load of electricity consumption for charging piles For time period The load on charging stations is being adjusted to increase. For time period Adjustment amount for reducing the load transfer of charging piles. For time period The tolerance coefficient for adjusting the load transfer of charging piles. For time period Tolerance coefficient for willingness to adjust charging pile load transfer. The start time for adjusting the load on the charging piles is now available. This is the end of the period when the charging pile load can be adjusted. For time period The maximum adjustable power for charging pile load transfer For time period The maximum adjustable power output of the charging pile load transfer For the installed capacity of charging piles, For time period The upper limit of the actual output range of the charging pile load. For time period Positive deviation coefficient of predicted load for charging piles. For time period The lower limit of the actual output range of the charging pile load. For time period Negative deviation coefficient of predicted load for charging piles For time period Net adjustment of charging pile load.

4. The robust optimization operation method for integrated photovoltaic-storage-charging resources participating in the power market considering source-load uncertainty, as described in claim 1, is characterized in that... The construction of the robust and optimized operational security domain includes: The system power balance and net load balance constraints are constructed, and the data model expression is as follows: ； in, For time period Robust optimization of photovoltaic program output, For time period The energy storage discharge power, For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the power grid during the power purchase process. For time period The robust optimization plan for charging stations utilizes charging load. For time period Energy storage charging power; For time period The power output of integrated photovoltaic, energy storage, and charging resources sold to the grid. For time period Robust optimization of net load for integrated resource nodes To robustly optimize the time section set; The data model expression for constructing the mutual exclusion constraint conditions for the purchase and sale of electricity at integrated resource nodes is as follows: ； in, This represents the maximum power that the integrated photovoltaic, energy storage, and charging resources can purchase from the grid. For time period Integrated photovoltaic, energy storage, and charging resources are purchased from the power grid. Indicates the purchase of electricity. Indicates that they will not purchase electricity. This refers to the maximum power output when the integrated photovoltaic, energy storage, and charging resources are sold to the grid. For time period The status of integrated photovoltaic, energy storage, and charging resources selling electricity to the grid. Indicates electricity sales. Indicates that they do not sell electricity. For whether electricity sales are permitted, This indicates permission. This indicates that it is not allowed.

5. The robust optimization operation method for integrated photovoltaic-storage-charging resources participating in the power market considering source-load uncertainty, as described in claim 1, is characterized in that... The robust finite-dimensional deterministic constraints include: The data model expression for setting robust feasibility conditions for increasing reserve capacity is as follows: ； in, For time period Energy storage can utilize discharge power. For time period The energy storage discharge power, This represents the maximum power that the integrated photovoltaic, energy storage, and charging resources can purchase from the grid. For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the power grid during the power purchase process. For time period Energy storage charging power, For time period The power output of integrated photovoltaic, energy storage, and charging resources sold to the grid. For time period Positive deviation coefficient of predicted load for charging piles. For time period The predicted load of electricity consumption for charging piles For time period Negative deviation coefficient of photovoltaic power forecast. For time period The photovoltaic power output is predicted. For maximum charge and discharge power of energy storage, For time period Stored electrical energy The minimum state of charge coefficient for energy storage. For the rated capacity of energy storage, To robustly optimize the runtime time segment step size, To robustly optimize the time section set, For energy storage discharge efficiency; The data model expression for setting robust feasibility for reducing reserve capacity is as follows: ； in, For time period Energy storage can utilize charging power. This refers to the maximum power output when the integrated photovoltaic, energy storage, and charging resources are sold to the grid. For time period Negative deviation coefficient of predicted load for charging piles For time period Positive deviation coefficient of photovoltaic power forecast. This represents the maximum state of charge coefficient of the energy storage.

6. The robust optimization operation method for integrated photovoltaic-storage-charging resources participating in the power market considering source-load uncertainty, as described in claim 1, is characterized in that... The configured two-stage robust optimization objective function includes: The robust optimization objective function for the pre-participation declaration stage in the electricity market is configured, and its mathematical model expression is as follows: ； ； in, To robustly optimize net income during the pre-registration phase for participation in the electricity market, To robustly optimize operational benefits during the pre-registration phase for participation in the electricity market, For the revenue generated from the regular operation of resources, Indicates constraints. The price for surplus photovoltaic power sold to the grid. For time period The power output of integrated photovoltaic, energy storage, and charging resources sold to the grid. For time period The electricity price for charging services at charging stations For time period The robust optimization plan for charging stations utilizes charging load. For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the grid at the electricity price. For time period The integrated photovoltaic, energy storage, and charging resources are purchased from the power grid during the power purchase process. The aging cost coefficient per unit energy during energy storage charging and discharging. For time period Energy storage charging power, For time period The energy storage discharge power, To robustly optimize the total compensation revenue during the pre-registration phase for participation in the electricity market, To robustly optimize the time section set, For time period The net load of integrated photovoltaic, energy storage, and charging resources is the baseline. For time period The net load of integrated photovoltaic, energy storage, and charging resources during normal operation. For time period The electricity price for charging services at charging stations For time period The predicted load of electricity consumption for charging piles To robustly optimize the runtime time segment step size, Input data values ​​for a given baseline load over time period t. Automatically generate flag bits for the baseline. 1 indicates that it is automatically generated based on prediction. 0 indicates reading or writing the given input data; The mathematical model expression for the robust optimization objective function in the execution phase after the electricity market clearing is as follows: ； ； in, To participate in the net profit of the robust optimization phase during the implementation phase after the electricity market clearing, To optimize operational benefits during the implementation phase after the electricity market clearing, The price for surplus photovoltaic power sold to the grid. To compensate for the total revenue of robust optimization response during the implementation phase after the electricity market clearing, To optimize the total cost of assessment and penalties during the implementation phase after the power market clearing.

7. The robust optimization operation method for integrated photovoltaic-storage-charging resources participating in the power market considering source-load uncertainty, as described in claim 1, is characterized in that... The construction of the market benefit model for ancillary services in the electricity market includes: A robust optimization model for regulation and response compensation benefits during the pre-participation declaration stage of the electricity market is constructed. The numerical model expression is as follows: ； in, For time period Robust optimization of regulation volume during the pre-registration stage before participating in the electricity market. This serves as a marker for the direction of integrated photovoltaic, energy storage, and charging resources participating in the ancillary services regulation of the electricity market. =1 indicates the direction of filling the valley. =-1 indicates the direction of peak clipping. For time period Robust optimization of net load during the pre-registration phase for participation in the electricity market. For time period The net load of the photovoltaic-storage-charging integrated resource baseline The starting period for the participation of integrated photovoltaic, energy storage, and charging resources in the ancillary services regulation of the electricity market. When the integrated photovoltaic, energy storage, and charging resources cease participating in the ancillary services regulation of the electricity market. For time period Robust optimization of compensation benefits during the pre-registration stage for participation in the electricity market. For time period Robust optimization of ancillary service compensation prices during the pre-participation declaration stage of the electricity market. To robustly optimize the runtime time segment step size, To robustly optimize the total compensation revenue during the pre-registration phase for participation in the electricity market; A robust optimization regulation and effective compensation regulation model is constructed for the implementation phase after the electricity market clearing. The numerical model expression is as follows: ； in, For time period The robust optimization plan for the implementation of regulation quantities after the electricity market clearing phase. For time period Robust optimization of net load during the implementation phase after participation in the electricity market clearing process. For time period The net load of integrated photovoltaic, energy storage, and charging resources is the baseline. For time period Whether or not the clearing adjustment has been successfully completed is a key indicator status. =1 indicates that the successful bidder has exited the bidding process and is participating in the execution. =0 indicates that the winning bidder has cleared the market and does not need to participate in the execution. For time period The adjustment amount of the winning bid clearing, For time period After the electricity market clearing process, the robust optimization of effective compensation and regulation during the implementation phase will be crucial. , All parameters are tiered compensation revenue parameters for effective compensation adjustment of ancillary services in the electricity market. A robust optimization model for compensation benefits and performance penalty costs during the implementation phase after the electricity market clearing is constructed. The numerical model expression is as follows: ； in, For time period The robust optimization response adjustment compensation benefits during the implementation phase after the electricity market clearing. It is a time period The actual response and regulation power provided during the implementation phase after the electricity market clearing. For time period After the electricity market clearing process, robust optimization of ancillary services and clearing compensation electricity prices will be implemented during the execution phase. To achieve robust optimization of total compensation revenue during the implementation phase after the electricity market clearing, For time period After the electricity market clearing process, robust optimization assessment and penalty costs will be implemented during the execution phase. The cost factor for performance evaluation and penalties of ancillary services in the electricity market. To optimize the total cost of assessment and penalties during the implementation phase after the power market clearing.

8. The robust optimization operation method for integrated photovoltaic-storage-charging resources participating in the power market considering source-load uncertainty, as described in claim 1, is characterized in that... The input data of the integrated photovoltaic, energy storage and charging resource system includes: system static parameter data information and system time-series dynamic variable data information; The system static parameter data information includes: System operating parameters: robust optimization time section set, robust optimization time section step size, total number of robust optimization time section sets, photovoltaic surplus electricity grid connection price, and energy storage charging and discharging unit energy aging cost coefficient; Energy storage resource operation parameters: energy storage charging efficiency, energy storage discharging efficiency, energy storage self-discharge rate, energy storage maximum state of charge coefficient, energy storage minimum state of charge coefficient, energy storage rated capacity, energy storage initial electrical energy, and energy storage maximum charging and discharging power. Grid-connected line operating parameters: Maximum power purchased from the grid by the integrated photovoltaic-storage-charging resource, and maximum power sold to the grid by the integrated photovoltaic-storage-charging resource. Photovoltaic resource operating parameters: Photovoltaic installed capacity: Charging pile resource operation parameters: Charging pile installed capacity: Electricity market ancillary service parameters: Start time of integrated photovoltaic-storage-charging resources participating in electricity market ancillary service regulation; End time of integrated photovoltaic-storage-charging resources participating in electricity market ancillary service regulation; Direction flag for integrated photovoltaic-storage-charging resources participating in electricity market ancillary service regulation; Whether electricity sales are permitted. Charging pile resource load adjustment period parameters: the start time of charging pile load adjustment and the end time of charging pile load adjustment; The system's time-series dynamic variable data information includes: the electricity purchase price of the integrated photovoltaic, energy storage, and charging resources when purchasing electricity from the grid; the electricity sales price of charging pile charging services; the compensation price for robust optimization ancillary services during the pre-participation application stage of the electricity market; the compensation price for robust optimization ancillary services during the execution stage after participating in the electricity market clearing; the photovoltaic predicted output; the predicted load of charging piles; the positive deviation coefficient of the photovoltaic predicted output; the negative deviation coefficient of the photovoltaic predicted output; the positive deviation coefficient of the predicted load of charging piles; the negative deviation coefficient of the predicted load of charging piles; the tolerance coefficient for the willingness to adjust the load transfer into the charging piles; the tolerance coefficient for the willingness to adjust the load transfer out of the charging piles; the given baseline load input data value; the net load of the integrated photovoltaic, energy storage, and charging resources during normal operation; and the adjustment amount during the bidding and clearing process.

9. The robust optimization operation method for integrated photovoltaic-storage-charging resources participating in the power market considering source-load uncertainty, as described in claim 1, is characterized in that... The results data of the integrated photovoltaic, energy storage, and charging resource system include: The robust optimization results data information for power variables include: robustly optimized photovoltaic planned output, robustly optimized charging load of charging piles, power purchased from the grid by integrated photovoltaic-storage-charging resources, power sold to the grid by integrated photovoltaic-storage-charging resources, energy storage charging power, energy storage discharging power, net energy storage charging and discharging output power, adjustment amount for increased charging pile load transfer, and adjustment amount for decreased charging pile load transfer. The robust optimization result data information of state variables includes: the status of photovoltaic-storage-charging integrated resources purchasing electricity from the grid, the status of photovoltaic-storage-charging integrated resources selling electricity to the grid, the energy storage charging status, the energy storage discharging status, and the status of whether the bid clearing adjustment has been won; Robust optimization results data information for electrical energy variables, including: stored electrical energy; Robust optimization results data for net load variables include: robust optimized net load for integrated resource nodes, robust optimized net load during the pre-participation declaration stage, and robust optimized net load during the post-participation clearing execution stage. Robust optimization results data for regulation variables include: robust optimized regulation quantities during the pre-participation electricity market declaration phase, robust optimized planned execution regulation quantities during the post-participation electricity market clearing execution phase, and robust optimized effective compensation regulation quantities during the post-participation electricity market clearing execution phase. The robust optimization results data information for the variables of benefits and assessment penalties include: robust optimization compensation benefits during the pre-participation electricity market application stage, robust optimization response adjustment compensation benefits during the post-participation electricity market clearing and execution stage, robust optimization assessment penalty costs during the post-participation electricity market clearing and execution stage, total robust optimization compensation benefits during the pre-participation electricity market application stage, total robust optimization response compensation benefits during the post-participation electricity market clearing and execution stage, and total robust optimization assessment penalty costs during the post-participation electricity market clearing and execution stage.

10. A robust optimized operation system for integrated photovoltaic, energy storage, and charging resources participating in the electricity market, considering source-load uncertainty, characterized in that... include: The data acquisition module is used to acquire input data from the integrated photovoltaic, energy storage, and charging resource system. The robust optimization module for the integrated photovoltaic, energy storage, and charging resource system is used to obtain the result data of the integrated photovoltaic, energy storage, and charging resource system based on the input data of the integrated photovoltaic, energy storage, and charging resource system and a pre-built robust optimization model of the integrated photovoltaic, energy storage, and charging resource system. The construction of the robust optimization model for the integrated photovoltaic-storage-charging resource system includes: A robust resource optimization model and security domain are constructed to characterize the input data of the integrated photovoltaic-storage-charging resource system, thereby obtaining the resource's operating characteristics, operating performance, and characterization data. A robust and optimized operational safety domain is constructed to characterize the operational characteristics, performance, and operational data of the resources, thereby obtaining data on the system's coupling topology, balance relationship, and node power purchase and sale mutual exclusion relationship. By setting robust finite-dimensional deterministic constraints, the coupling topology, equilibrium relationship, and node power purchase and sale mutual exclusion relationship data of the system are characterized to obtain the robustness data of the system. A two-stage robust optimization objective function is configured to characterize the robustness data of the system and obtain the optimal calculation result data of the robust optimization objective of the system. A market benefit model for ancillary services in the power market is constructed to characterize the optimal calculation results of the robust optimization objective of the system, thereby obtaining the calculation results for the pre-participation declaration stage and the post-participation execution stage of the power market.

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