A comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification

Abstract

The application discloses a kind of based on multiple disturbance risk quantification integrated energy storage capacity optimization method, belong to integrated energy capacity optimization field, comprising: obtaining the operation data of integrated energy system, and obtain the parameter required for optimization;Multiple disturbance risk index is constructed, and normalization processing is carried out;Based on the multiple disturbance risk quantification value, capacity gap value is obtained;The capacity gap value is distributed to obtain capacity distribution value;The capacity distribution value is verified to obtain feasible capacity distribution value;Based on capacity optimization factor, the parameter required for optimization is optimized to obtain the optimized parameter of integrated energy storage system;Based on the optimized parameter, the integrated energy storage capacity risk bearing improvement rate is obtained.The problems that it is difficult to realize comprehensive quantification and dynamically correct risk quantification parameters and storage capacity configuration based on risk quantification results in the optimization of integrated energy system storage capacity in the prior art are solved.

Description

Technical Field

[0001] This application belongs to the field of integrated energy capacity optimization, and in particular relates to an integrated energy storage capacity optimization method based on the quantification of multiple disturbance risks. Background Technology

[0002] Integrated energy systems combine various energy devices such as photovoltaics, electrical energy storage, thermal energy storage, and heat pumps. As a core regulating resource within these systems, the capacity configuration of the energy storage system directly impacts the system's renewable energy absorption capacity, operational economics, and energy supply stability. However, integrated energy systems are susceptible to multiple disturbances during operation, including fluctuations in photovoltaic output, N-1 faults, and changes in electrothermal conversion efficiency. These disturbances can lead to increased curtailment rates, higher risk of load shedding, and more pronounced fluctuations in operating costs. Particularly under the combined effects of these multiple disturbances, insufficient energy storage capacity can easily reduce the system's regulating capacity, further affecting the stable operation of the integrated energy system.

[0003] Existing methods for optimizing energy storage capacity in integrated energy systems are mostly based on static capacity configuration under fixed operating scenarios, typically considering only a single disturbance factor and lacking comprehensive quantitative analysis of photovoltaic fluctuations, fault disturbances, and changes in electrothermal conversion characteristics. Furthermore, most existing energy storage capacity optimization methods employ complex optimization models, resulting in high computational complexity, insufficient flexibility in capacity adjustment, and poor engineering adaptability. They struggle to dynamically adjust energy storage capacity according to system operating conditions, leading to configuration results that are ill-suited to multi-disturbance operating scenarios. Moreover, multiple disturbances can expand the range of system net load fluctuations and place higher demands on the regulation capabilities of energy storage systems. Existing methods lack an effective description of the correlation between disturbance risk and energy storage capacity demand, making it difficult to dynamically optimize and adjust risk quantification parameters and energy storage capacity based on system operating conditions. Therefore, there is an urgent need to propose an integrated energy storage capacity optimization method that can comprehensively quantify multiple disturbance risks and dynamically adjust risk quantification parameters and energy storage capacity configuration based on the risk quantification results. Summary of the Invention

[0004] To address the challenges in existing technologies regarding the optimization of integrated energy storage capacity in comprehensive energy systems, particularly the difficulties in achieving comprehensive quantification and dynamic adjustment of risk quantification parameters and energy storage capacity configuration based on risk quantification results, this application discloses a comprehensive energy storage capacity optimization method based on multi-disturbance risk quantification. Specifically:

[0005] A comprehensive energy storage capacity optimization method based on multiple perturbation risk quantification, the optimization method comprising:

[0006] Obtain operational data from the integrated energy system and acquire the parameters required for optimization;

[0007] Based on the parameters required for optimization, a multi-disturbance risk index is constructed and normalized to obtain a quantitative value of the multi-disturbance risk.

[0008] Based on the quantified values ​​of the multiple disturbance risks, the capacity gap value of the integrated energy storage system is obtained.

[0009] The capacity gap value is allocated to obtain the capacity allocation value;

[0010] The feasibility of the capacity allocation value is verified to obtain a feasible capacity allocation value;

[0011] Based on the capacity optimization factor, the parameters required for optimization are optimized to obtain the optimized parameters of the integrated energy storage system.

[0012] Based on the optimized parameters, the risk tolerance improvement rate of comprehensive energy storage capacity is obtained.

[0013] Optionally, acquiring the operating data of the integrated energy system and obtaining the parameters required for optimization includes:

[0014] Real-time acquisition of operational data from integrated energy systems based on power sensors;

[0015] The operational data of the integrated energy system includes both theoretical and actual data for all operations, which are recorded to obtain the parameters required for optimization.

[0016] Optionally, the step of constructing a multiple disturbance risk index based on the parameters required for optimization and performing normalization processing to obtain a quantitative value of the multiple disturbance risk includes:

[0017] A perturbation risk index is constructed for all the parameters required for optimization, and then normalized. The normalized index equation is as follows:

[0018] ,

[0019] in, This represents the normalized photovoltaic power output fluctuation risk. The normalized N-1 fault disturbance risk quantity; This represents the risk of disturbance to the normalized electrothermal conversion efficiency. For the first Actual photovoltaic output during the specified time period; For the first Forecasted photovoltaic output for the specified time period; For the first Solar power curtailment during the specified time period; For the first Electrical load power during each time period; For the first Thermal load power during each time period; For the first Electrical load power during each time period; For the first Heat load power for each time period; This is the thermal power conversion factor; The reference electrothermal conversion efficiency for the heat pump; For the first Actual electrothermal conversion efficiency of time-limited heat pumps; To optimize the total number of time periods in the cycle; Number the time period; This represents the maximum permissible value for the risk of fluctuations in photovoltaic power output. The maximum permissible value for fault disturbance risk is N-1; This represents the maximum permissible risk level for electrothermal conversion. To prevent extremely small positive numbers with a denominator of zero; T represents the upper limit of the time period.

[0020] Weights are assigned to all normalized disturbance risk indicators, and they are then merged to obtain a quantified value for multiple disturbance risks. The equation for the quantified value of multiple disturbance risks is as follows:

[0021] ,

[0022] in, , and These represent the normalized weights for photovoltaic power output fluctuation risk, normalized weights for N-1 fault disturbance risk, and normalized weights for electrothermal conversion efficiency disturbance risk, respectively. ; To optimize the quantification value of multiple disturbance risks within the cycle.

[0023] Optionally, obtaining the capacity gap value of the integrated energy storage system based on the quantified value of the multiple disturbance risks includes:

[0024] Based on the capacity gap determination equation, the capacity gap value of the integrated energy storage system under the influence of multiple disturbance risk quantification values ​​is obtained. The capacity gap determination equation is as follows:

[0025] ,

[0026] in, This represents the energy storage capacity risk gap value. This is a correction coefficient for photovoltaic volatility risk. N-1 is the fault risk correction coefficient; This is a correction factor for the risk of electrothermal conversion; Configure capacity for electrical energy storage; Configure capacity for thermal energy storage.

[0027] Optionally, allocating the capacity gap value to obtain a capacity allocation value includes:

[0028] The capacity gap value is allocated and determined, including the energy storage capacity allocation value and the thermal energy storage capacity allocation value;

[0029] The equation for determining the energy storage capacity allocation value is as follows:

[0030] in, This indicates the allocated energy storage capacity. This indicates the risk of fluctuations in photovoltaic power output; The fault disturbance risk is N-1. This represents the risk factor for disturbances in electrothermal conversion efficiency.

[0031] The equation for determining the thermal energy storage capacity allocation value is as follows:

[0032] in, This indicates the allocated value of thermal energy storage capacity.

[0033] Optionally, the feasibility verification of the capacity allocation value to obtain a feasible capacity allocation value includes:

[0034] Based on the feasibility verification equation for capacity allocation, the feasibility index of energy storage capacity is obtained. The feasibility verification equation is as follows:

[0035] ,

[0036] in, Weighting for energy storage capacity margin; Weighting for thermal energy storage capacity margin; For the state-of-charge margin weight;

[0037] Compare the feasibility indicators of energy storage capacity with the threshold values ​​of the feasibility indicators. ≥ At that time, the feasibility indicators of energy storage capacity were verified; when At that time, the feasibility indicators of energy storage capacity failed to pass the verification, and the risk quantification parameters needed to be optimized.

[0038] Optionally, the optimization of the parameters required for optimization based on the capacity optimization factor to obtain the optimized parameters of the integrated energy storage system includes:

[0039] Based on the capacity optimization factor update equation, the capacity optimization factor is obtained. The capacity optimization factor update equation is as follows:

[0040] ,

[0041] in, For the first Capacity optimization factor in the next iteration; For the first Capacity optimization factor in the next iteration; Optimize the adjustment coefficient for capacity;

[0042] Based on the capacity optimization factor, the parameters required for optimization are optimized to obtain the optimized parameters of the integrated energy storage system. The optimization equations for the required parameters are as follows:

[0043] ,

[0044] in, To optimize the actual photovoltaic output in time period t; To optimize the photovoltaic curtailment power in time period t; To optimize the electrical load power in the t-th time period; The optimized thermal load power for the t-th time period; To optimize the electrical load power in the t-th time period; The optimized heat load power for the t-th time period; To optimize the actual electrothermal conversion efficiency of the heat pump in time period t.

[0045] Optionally, obtaining the comprehensive energy storage capacity risk bearing improvement rate based on the optimized parameters includes:

[0046] The energy storage risk carrying capacity value of the integrated energy system before optimization is obtained. The equation for the energy storage risk carrying capacity value before optimization is as follows:

[0047] ,

[0048] Obtain the optimized energy storage risk carrying capacity value of the integrated energy system. The equation for the optimized energy storage risk carrying capacity value is as follows:

[0049] ,

[0050] in, Configure the optimized energy storage capacity; Configure the optimized thermal energy storage capacity; The optimized energy storage capacity allocation value; The optimized thermal energy storage capacity allocation value; Optimize weights for energy storage capacity; Optimize weights for thermal energy storage capacity; G s To optimize the risk-bearing capacity value of front-end energy storage; To optimize the risk-bearing capacity value of front-end energy storage;

[0051] Based on the energy storage risk carrying capacity values ​​before and after optimization, the energy storage risk carrying capacity improvement rate is obtained. The equation for determining the energy storage risk carrying capacity improvement rate is as follows:

[0052] ,

[0053] in, This indicates the improvement rate of energy storage risk tolerance.

[0054] The beneficial effects of this application are as follows: By constructing a multi-disturbance risk quantification model, the impact of photovoltaic fluctuations, N-1 fault disturbances, and electrothermal conversion characteristics on the operating status of the integrated energy system is comprehensively considered, and the system's operating risk level is dynamically characterized using the multi-disturbance risk quantification results. Through energy storage capacity risk gap calculation, energy storage capacity optimization feasibility assessment, and dynamic correction of capacity optimization factors, cyclical optimization of risk quantification parameters and energy storage configuration capacity is achieved. Compared to traditional energy storage capacity configuration methods based on fixed operating scenarios, this invention can dynamically correct risk quantification parameters according to the system operating status and adaptively adjust the electrical energy storage configuration capacity and thermal energy storage configuration capacity in conjunction with the energy storage capacity optimization results, thereby improving the adaptability of the energy storage capacity configuration results to multi-disturbance operating scenarios and the rationality of engineering applications. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the embodiments of this application or the prior art will be briefly introduced below. Obviously, the following description is only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings are used to provide a further understanding of this disclosure and constitute a part of the specification. They are used together with the following detailed description to explain this disclosure, but do not constitute a limitation of this disclosure. In the drawings:

[0056] Figure 1 A flowchart illustrating a comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification provided in this application embodiment. Detailed Implementation

[0057] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Furthermore, in the embodiments of this application, "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0058] The invention described in this application will now be explained in detail, as follows: Figure 1 The diagram shown is a flowchart of a comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification provided in an embodiment of this application. Specifically:

[0059] Obtain operational data from the integrated energy system and acquire the parameters required for optimization;

[0060] Based on the parameters required for optimization, a multi-disturbance risk index is constructed and normalized to obtain a quantitative value of the multi-disturbance risk.

[0061] Based on the quantified values ​​of the multiple disturbance risks, the capacity gap value of the integrated energy storage system is obtained.

[0062] The capacity gap value is allocated to obtain the capacity allocation value;

[0063] The feasibility of the capacity allocation value is verified to obtain a feasible capacity allocation value;

[0064] Based on the capacity optimization factor, the parameters required for optimization are optimized to obtain the optimized parameters of the integrated energy storage system.

[0065] Based on the optimized parameters, the risk tolerance improvement rate of comprehensive energy storage capacity is obtained.

[0066] The acquisition of operational data from the integrated energy system and the obtaining of parameters required for optimization include:

[0067] Real-time acquisition of operational data from integrated energy systems based on power sensors;

[0068] The operational data of the integrated energy system includes both theoretical and actual data for all operations, which are recorded to obtain the parameters required for optimization.

[0069] The process of constructing a multiple disturbance risk index based on the parameters required for optimization, and performing normalization processing to obtain a quantitative value for the multiple disturbance risk, includes:

[0070] A perturbation risk index is constructed for all the parameters required for optimization, and then normalized. The normalized index equation is as follows:

[0071] ,

[0072] in, This represents the normalized photovoltaic power output fluctuation risk. The normalized N-1 fault disturbance risk quantity; This represents the risk of disturbance to the normalized electrothermal conversion efficiency. For the first Actual photovoltaic output during the specified time period; For the first Forecasted photovoltaic output for the specified time period; For the first Solar power curtailment during the specified time period; For the first Electrical load power during each time period; For the first Thermal load power during each time period; For the first Electrical load power during each time period; For the first Heat load power for each time period; This is the thermal power conversion factor; The reference electrothermal conversion efficiency for the heat pump; For the first Actual electrothermal conversion efficiency of time-limited heat pumps; To optimize the total number of time periods in the cycle; Number the time period; This represents the maximum permissible value for the risk of fluctuations in photovoltaic power output. The maximum permissible value for fault disturbance risk is N-1; This represents the maximum permissible risk level for electrothermal conversion. To prevent extremely small positive numbers with a denominator of zero; T represents the upper limit of the time period.

[0073] Weights are assigned to all normalized disturbance risk indicators, and they are then merged to obtain a quantified value for multiple disturbance risks. The equation for the quantified value of multiple disturbance risks is as follows:

[0074] ,

[0075] in, , and These represent the normalized weights for photovoltaic power output fluctuation risk, normalized weights for N-1 fault disturbance risk, and normalized weights for electrothermal conversion efficiency disturbance risk, respectively. ; To optimize the quantification value of multiple disturbance risks within the cycle.

[0076] The process of obtaining the capacity gap value of the integrated energy storage system based on the quantified value of the multiple disturbance risks includes:

[0077] Based on the capacity gap determination equation, the capacity gap value of the integrated energy storage system under the influence of multiple disturbance risk quantification values ​​is obtained. The capacity gap determination equation is as follows:

[0078] ,

[0079] in, This represents the energy storage capacity risk gap value. This is a correction coefficient for photovoltaic volatility risk. N-1 is the fault risk correction coefficient; This is a correction factor for the risk of electrothermal conversion; Configure capacity for electrical energy storage; Configure capacity for thermal energy storage.

[0080] The allocation of the capacity gap value to obtain the capacity allocation value includes:

[0081] The capacity gap value is allocated and determined, including the energy storage capacity allocation value and the thermal energy storage capacity allocation value;

[0082] The equation for determining the energy storage capacity allocation value is as follows:

[0083] in, This indicates the allocated energy storage capacity. This indicates the risk of fluctuations in photovoltaic power output; The fault disturbance risk is N-1. This represents the risk factor for disturbances in electrothermal conversion efficiency.

[0084] The equation for determining the thermal energy storage capacity allocation value is as follows:

[0085] in, This indicates the allocated value of thermal energy storage capacity.

[0086] The feasibility verification of the capacity allocation value to obtain a feasible capacity allocation value includes:

[0087] Based on the feasibility verification equation for capacity allocation, the feasibility index of energy storage capacity is obtained. The feasibility verification equation is as follows:

[0088] ,

[0089] in, Weighting for energy storage capacity margin; Weighting for thermal energy storage capacity margin; For the state-of-charge margin weight;

[0090] Compare the feasibility indicators of energy storage capacity with the threshold values ​​of the feasibility indicators. ≥ At that time, the feasibility indicators of energy storage capacity were verified; when At that time, the feasibility indicators of energy storage capacity failed to pass the verification, and the risk quantification parameters needed to be optimized.

[0091] The optimization of the parameters required for optimization based on the capacity optimization factor to obtain the optimized parameters of the integrated energy storage system includes:

[0092] Based on the capacity optimization factor update equation, the capacity optimization factor is obtained. The capacity optimization factor update equation is as follows:

[0093] ,

[0094] in, For the first Capacity optimization factor in the next iteration; For the first Capacity optimization factor in the next iteration; Optimize the adjustment coefficient for capacity;

[0095] Based on the capacity optimization factor, the parameters required for optimization are optimized to obtain the optimized parameters of the integrated energy storage system. The optimization equations for the required parameters are as follows:

[0096] ,

[0097] in, To optimize the actual photovoltaic output in time period t; To optimize the photovoltaic curtailment power in time period t; To optimize the electrical load power in the t-th time period; The optimized thermal load power for the t-th time period; To optimize the electrical load power in the t-th time period; The optimized heat load power for the t-th time period; To optimize the actual electrothermal conversion efficiency of the heat pump in time period t.

[0098] The step of obtaining the comprehensive energy storage capacity risk bearing improvement rate based on the optimized parameters includes:

[0099] The energy storage risk carrying capacity value of the integrated energy system before optimization is obtained. The equation for the energy storage risk carrying capacity value before optimization is as follows:

[0100] ,

[0101] Obtain the optimized energy storage risk carrying capacity value of the integrated energy system. The equation for the optimized energy storage risk carrying capacity value is as follows:

[0102] ,

[0103] in, Configure the optimized energy storage capacity; Configure the optimized thermal energy storage capacity; The optimized energy storage capacity allocation value; The optimized thermal energy storage capacity allocation value; Optimize weights for energy storage capacity; Optimize weights for thermal energy storage capacity; G s To optimize the risk-bearing capacity value of front-end energy storage; To optimize the risk-bearing capacity value of front-end energy storage;

[0104] Based on the energy storage risk carrying capacity values ​​before and after optimization, the energy storage risk carrying capacity improvement rate is obtained. The equation for determining the energy storage risk carrying capacity improvement rate is as follows:

[0105] ,

[0106] in, This indicates the improvement rate of energy storage risk tolerance.

[0107] The following will provide step-by-step instructions for all the above technical aspects:

[0108] Step 1: Collect parameters required for integrated energy system capacity optimization:

[0109] Photovoltaic power generation forecast Actual output of photovoltaic power Photovoltaic curtailment power Electrical load power Heat load power State of charge of electrical energy storage Energy storage configuration capacity Thermal energy storage configuration capacity Heat pump electrothermal conversion efficiency Reference electrothermal conversion efficiency of heat pump N-1 fault electrical load power N-1 fault thermal loss load power Upper limit of state of charge of electrical energy storage State of charge limit of electrical energy storage Upper limit of electrical energy storage capacity Lower limit of energy storage capacity thermal energy storage capacity limit Lower limit of thermal energy storage capacity Maximum allowable value for photovoltaic power output fluctuation risk Maximum permissible value of N-1 fault disturbance risk Maximum permissible value of electrothermal conversion risk Energy storage capacity feasibility threshold .

[0110] Step 2: Construct multiple disturbance risk indicators and perform normalization processing:

[0111] ,

[0112] In the formula, This represents the normalized photovoltaic power output fluctuation risk. The normalized N-1 fault disturbance risk quantity; This represents the risk of disturbance to the normalized electrothermal conversion efficiency. For the first Actual photovoltaic output during the specified time period; For the first Forecasted photovoltaic output for the specified time period; For the first Solar power curtailment during the specified time period; For the first Electrical load power during each time period; For the first Thermal load power during each time period; For the first Electrical load power during each time period; For the first Heat load power for each time period; This is the thermal power conversion factor; The reference electrothermal conversion efficiency for the heat pump; For the first Actual electrothermal conversion efficiency of time-limited heat pumps; To optimize the total number of time periods in the cycle; Number the time period; This represents the maximum permissible value for the risk of fluctuations in photovoltaic power output. The maximum permissible value for fault disturbance risk is N-1; This represents the maximum permissible risk level for electrothermal conversion. To prevent extremely small positive numbers with a denominator of zero.

[0113] Quantitative value of multiple disturbance risks The calculation formula is:

[0114] ,

[0115] in:

[0116] ,

[0117] In the formula, To optimize the quantification value of multiple disturbance risks within the cycle; The risk weighting is set at the volatility level. The fault disturbance risk weight is N-1; This represents the risk weight for electrothermal conversion.

[0118] Step 3: Calculate the energy storage capacity risk gap value

[0119] The comprehensive disturbance risk quantification value can only reflect the degree of operational risk of the integrated energy system under multiple disturbance scenarios, and cannot directly reflect the current energy storage capacity required by the system. Therefore, it is necessary to further calculate the energy storage capacity demand by combining the photovoltaic curtailment state, the load shedding state, and the electrothermal conversion state, so as to obtain the energy storage capacity risk gap value of the integrated energy system under the current operating state. The formula for calculating the energy storage capacity risk gap value is:

[0120] ,

[0121] In the formula, This represents the energy storage capacity risk gap value. This is a correction coefficient for photovoltaic volatility risk. N-1 is the fault risk correction coefficient; This is a correction factor for the risk of electrothermal conversion; Configure capacity for electrical energy storage; Configure capacity for thermal energy storage.

[0122] Step 4: Allocate the energy storage capacity risk gap

[0123] Calculate the energy storage capacity allocation value:

[0124] ,

[0125] Calculate the thermal energy storage capacity allocation value:

[0126] ,

[0127] Step 5: Assess the feasibility of energy storage capacity optimization

[0128] Feasibility calculations were performed on the allocation values ​​of electrical energy storage capacity and thermal energy storage capacity to obtain energy storage capacity feasibility indicators:

[0129] ,

[0130] In the formula, Weighting for energy storage capacity margin; Weighting for thermal energy storage capacity margin; This represents the weighting of the state of charge margin.

[0131] Feasibility indicators Feasibility threshold When comparing, At that time, there is no need to optimize the risk quantification parameters in the system; when At this time, it is necessary to optimize the risk quantification parameters in the system.

[0132] Step 6: Optimize the risk quantification parameters using a capacity optimization factor.

[0133] Since the energy storage capacity optimization requirements differ under different disturbance scenarios, to avoid unreasonable adjustments to the risk quantification parameters, a capacity optimization factor is introduced to optimize the actual photovoltaic output. Photovoltaic curtailment power N-1 fault electrical load power N-1 fault thermal loss load power Electrical load power Heat load power Heat pump electrothermal conversion efficiency Optimize.

[0134] The formula for updating the capacity optimization factor is:

[0135] ,

[0136] In the formula, For the first Capacity optimization factor in the next iteration; For the first Capacity optimization factor in the next iteration; Optimize the adjustment coefficient for capacity.

[0137] The formula for parameter optimization is as follows:

[0138] ,

[0139] in, To optimize the actual photovoltaic output in time period t; To optimize the photovoltaic curtailment power in time period t; To optimize the electrical load power in the t-th time period; The optimized thermal load power for the t-th time period; To optimize the electrical load power in the t-th time period; The optimized heat load power for the t-th time period; To optimize the actual electrothermal conversion efficiency of the heat pump in time period t.

[0140] Step 7: Calculate the risk tolerance improvement rate of energy storage capacity

[0141] The formula for the energy storage risk carrying capacity value before optimization is:

[0142] ,

[0143] The optimized formula for the energy storage risk carrying capacity value is:

[0144] ,

[0145] In the formula, Configure the optimized energy storage capacity; Configure the optimized thermal energy storage capacity; The optimized energy storage capacity allocation value; The optimized thermal energy storage capacity allocation value; Optimize weights for energy storage capacity; Optimize the weights for thermal energy storage capacity.

[0146] The formula for improving the risk tolerance of energy storage is:

[0147] ,

[0148] To better understand this invention, the following embodiments further illustrate its content; however, the scope of this invention is not limited to the embodiments described below. Those skilled in the art can make various modifications or alterations to this invention, and these equivalent forms are also within the scope defined by the claims listed in this application.

[0149] Step 1: Collect parameters required for integrated energy system capacity optimization:

[0150] Taking a typical winter day as an example, the optimization period is selected as a typical day in winter. 24 hours; time period number The time interval is 1 hour, and the parameters required for data acquisition capacity optimization are shown in Table 1:

[0151] Table 1 Optimization Parameter Collection Table

[0152] ,

[0153] Step 2: Construct multiple disturbance risk indicators and perform normalization processing:

[0154] ,

[0155] In the formula, This represents the normalized photovoltaic power output fluctuation risk. The normalized N-1 fault disturbance risk quantity; This represents the risk of disturbance to the normalized electrothermal conversion efficiency. For the first Actual photovoltaic output during the specified time period; For the first Forecasted photovoltaic output for the specified time period; For the first Solar power curtailment during the specified time period; For the first Electrical load power during each time period; For the first Thermal load power during each time period; For the first Electrical load power during each time period; For the first Heat load power for each time period; The heat power conversion factor is set to 0.35; The reference electrothermal conversion efficiency for the heat pump is taken as 3.0; For the first Actual electrothermal conversion efficiency of time-limited heat pumps; To optimize the total number of time periods in the cycle; Number the time period; The maximum permissible value for the risk of fluctuations in photovoltaic power output is set at 0.30; The maximum permissible value for the N-1 fault disturbance risk is set at 0.08; The maximum permissible risk for electrothermal conversion is set at 0.45; To prevent extremely small positive numbers with a denominator of zero from being assigned the value of 1.

[0156] Quantitative value of multiple disturbance risks The calculation formula is:

[0157] ,

[0158] In the formula, To optimize the quantification value of multiple disturbance risks within the cycle; The volatility risk weight is set to 0.35; The risk weight for N-1 fault disturbances is set to 0.40; The risk weight for electrothermal conversion is set to 0.25.

[0159] Step 3: Calculate the energy storage capacity risk gap value

[0160] The comprehensive disturbance risk quantification value can only reflect the degree of operational risk of the integrated energy system under multiple disturbance scenarios, and cannot directly reflect the current energy storage capacity required by the system. Therefore, it is necessary to further calculate the energy storage capacity demand by combining the photovoltaic curtailment state, the load shedding state, and the electrothermal conversion state, so as to obtain the energy storage capacity risk gap value of the integrated energy system under the current operating state. The formula for calculating the energy storage capacity risk gap value is:

[0161] ,

[0162] In the formula, This represents the energy storage capacity risk gap value. The correction factor for photovoltaic volatility risk is set at 0.35; The correction factor for the N-1 fault risk is set to 0.40; The risk correction factor for electrothermal conversion is set to 0.25; The capacity for energy storage is set at 500 kWh; The capacity for thermal energy storage is set at 850 kWh.

[0163] Step 4: Allocate the energy storage capacity risk gap

[0164] Calculate the energy storage capacity allocation value:

[0165] ,

[0166] Calculate the thermal energy storage capacity allocation value:

[0167] ,

[0168] Step 5: Assess the feasibility of energy storage capacity optimization

[0169] Feasibility calculations were performed on the allocation values ​​of electrical energy storage capacity and thermal energy storage capacity to obtain energy storage capacity feasibility indicators:

[0170] ,

[0171] In the formula, The weight for the energy storage capacity margin is set to 0.35; The weight for thermal energy storage capacity margin is set to 0.35; The weight for the charge state margin is set to 0.3.

[0172] Feasibility indicators Feasibility threshold Comparison, The value is 0.5. Therefore, it is necessary to optimize the risk quantification parameters in the system.

[0173] Step 6: Optimize the risk quantification parameters using a capacity optimization factor.

[0174] The formula for updating the capacity optimization factor is:

[0175] ,

[0176] In the formula, The initial value of the optimization factor is set to 0.05; For the first Capacity optimization factor in the next iteration; For the first Capacity optimization factor in the next iteration; The capacity optimization adjustment coefficient is set to 0.8.

[0177] The formula for parameter optimization is as follows:

[0178] ,

[0179] Iterate risk quantification parameters and energy storage capacity feasibility indicators until... .

[0180] Step 7: Calculate the risk tolerance improvement rate of energy storage capacity

[0181] The formula for the energy storage risk carrying capacity value before optimization is:

[0182] ,

[0183] The optimized formula for the energy storage risk carrying capacity value is:

[0184] ,

[0185] In the formula, Configure the optimized energy storage capacity; Configure the optimized thermal energy storage capacity; The optimized energy storage capacity allocation value; The optimized thermal energy storage capacity allocation value; The weight for optimizing energy storage capacity is set to 0.6; The weight for optimizing thermal energy storage capacity is set to 0.4.

[0186] The formula for improving the risk tolerance of energy storage is:

[0187] ,

[0188] The results show that the proposed method enables the optimized configuration of electrical and thermal energy storage capacity to better adapt to multiple disturbance operation scenarios.

[0189] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to computer program instructions. The aforementioned computer program can be stored in a non-volatile storage medium, and when executed, it performs the steps of the above method embodiments. Alternatively, if the integrated unit of the present invention is implemented as a software functional module and sold or used as an independent product, it can also be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention.

[0190] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive energy storage capacity optimization method based on multiple perturbation risk quantification, characterized in that, The optimization method includes: Obtain operational data from the integrated energy system and acquire the parameters required for optimization; Based on the parameters required for optimization, a multi-disturbance risk index is constructed and normalized to obtain a quantitative value of the multi-disturbance risk. Based on the quantified values ​​of the multiple disturbance risks, the capacity gap value of the integrated energy storage system is obtained. The capacity gap value is allocated to obtain the capacity allocation value; The feasibility of the capacity allocation value is verified to obtain a feasible capacity allocation value; Based on the capacity optimization factor, the parameters required for optimization are optimized to obtain the optimized parameters of the integrated energy storage system. Based on the optimized parameters, the risk tolerance improvement rate of comprehensive energy storage capacity is obtained.

2. The comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification as described in claim 1, characterized in that, The acquisition of operational data from the integrated energy system and the obtaining of parameters required for optimization include: Real-time acquisition of operational data from integrated energy systems based on power sensors; The operational data of the integrated energy system includes both theoretical and actual data for all operations, which are recorded to obtain the parameters required for optimization.

3. The comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification as described in claim 1, characterized in that, The process of constructing a multiple disturbance risk index based on the parameters required for optimization, and performing normalization processing to obtain a quantitative value for the multiple disturbance risk, includes: A perturbation risk index is constructed for all the parameters required for optimization, and then normalized. The normalized index equation is as follows: ， in, This represents the normalized photovoltaic power output fluctuation risk. The normalized N-1 fault disturbance risk quantity; This represents the risk of disturbance to the normalized electrothermal conversion efficiency. For the first Actual photovoltaic output during the specified time period; For the first Forecasted photovoltaic output for the specified time period; For the first Solar power curtailment during the specified time period; For the first Electrical load power during each time period; For the first Thermal load power during each time period; For the first Electrical load power during each time period; For the first Heat load power for each time period; This is the thermal power conversion factor; The reference electrothermal conversion efficiency for the heat pump; For the first Actual electrothermal conversion efficiency of time-limited heat pumps; To optimize the total number of time periods in the cycle; Number the time period; This represents the maximum permissible value for the risk of fluctuations in photovoltaic power output. The maximum permissible value for fault disturbance risk is N-1; This represents the maximum permissible risk level for electrothermal conversion. To prevent extremely small positive numbers with a denominator of zero; T represents the upper limit of the time period. Weights are assigned to all normalized disturbance risk indicators, and they are then merged to obtain a quantified value for multiple disturbance risks. The equation for the quantified value of multiple disturbance risks is as follows: ， in, , and These represent the normalized weights for photovoltaic power output fluctuation risk, normalized weights for N-1 fault disturbance risk, and normalized weights for electrothermal conversion efficiency disturbance risk, respectively. ; To optimize the quantification value of multiple disturbance risks within the cycle.

4. The comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification as described in claim 1, characterized in that, The process of obtaining the capacity gap value of the integrated energy storage system based on the quantified value of the multiple disturbance risks includes: Based on the capacity gap determination equation, the capacity gap value of the integrated energy storage system under the influence of multiple disturbance risk quantification values ​​is obtained. The capacity gap determination equation is as follows: ， in, This represents the energy storage capacity risk gap value. This is a correction coefficient for photovoltaic volatility risk. N-1 is the fault risk correction coefficient; This is a correction factor for the risk of electrothermal conversion; Configure capacity for electrical energy storage; Configure capacity for thermal energy storage.

5. The comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification according to claim 1, characterized in that, The allocation of the capacity gap value to obtain the capacity allocation value includes: The capacity gap value is allocated and determined, including the energy storage capacity allocation value and the thermal energy storage capacity allocation value; The equation for determining the energy storage capacity allocation value is as follows: in, This indicates the allocated energy storage capacity. This indicates the risk of fluctuations in photovoltaic power output; The fault disturbance risk is N-1. This represents the risk factor for disturbances in electrothermal conversion efficiency. The equation for determining the thermal energy storage capacity allocation value is as follows: in, This indicates the allocated value of thermal energy storage capacity.

6. The comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification according to claim 1, characterized in that, The feasibility verification of the capacity allocation value to obtain a feasible capacity allocation value includes: Based on the feasibility verification equation for capacity allocation, the feasibility index of energy storage capacity is obtained. The feasibility verification equation is as follows: ， in, Weighting for energy storage capacity margin; Weighting for thermal energy storage capacity margin; For the state-of-charge margin weight; Compare the feasibility indicators of energy storage capacity with the threshold values ​​of the feasibility indicators. ≥ At that time, the feasibility indicators of energy storage capacity were verified; when At that time, the feasibility indicators of energy storage capacity failed to pass the verification, and the risk quantification parameters needed to be optimized.

7. The comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification according to claim 1, characterized in that, The optimization of the parameters required for optimization based on the capacity optimization factor to obtain the optimized parameters of the integrated energy storage system includes: Based on the capacity optimization factor update equation, the capacity optimization factor is obtained. The capacity optimization factor update equation is as follows: ， in, For the first Capacity optimization factor in the next iteration; For the first Capacity optimization factor in the next iteration; Optimize the adjustment coefficient for capacity; Based on the capacity optimization factor, the parameters required for optimization are optimized to obtain the optimized parameters of the integrated energy storage system. The optimization equations for the required parameters are as follows: ， in, To optimize the actual photovoltaic output in time period t; To optimize the photovoltaic curtailment power in time period t; To optimize the electrical load power in the t-th time period; The optimized thermal load power for the t-th time period; To optimize the electrical load power in the t-th time period; The optimized heat load power for the t-th time period; To optimize the actual electrothermal conversion efficiency of the heat pump in time period t.

8. The comprehensive energy storage capacity optimization method based on multiple disturbance risk quantification according to claim 1, characterized in that, The step of obtaining the comprehensive energy storage capacity risk bearing improvement rate based on the optimized parameters includes: The energy storage risk carrying capacity value of the integrated energy system before optimization is obtained. The equation for the energy storage risk carrying capacity value before optimization is as follows: ， Obtain the optimized energy storage risk carrying capacity value of the integrated energy system. The equation for the optimized energy storage risk carrying capacity value is as follows: ， in, Configure the optimized energy storage capacity; Configure the optimized thermal energy storage capacity; The optimized energy storage capacity allocation value; The optimized thermal energy storage capacity allocation value; Optimize weights for energy storage capacity; Optimize weights for thermal energy storage capacity; G s To optimize the risk-bearing capacity value of front-end energy storage; To optimize the risk-bearing capacity value of front-end energy storage; Based on the energy storage risk carrying capacity values ​​before and after optimization, the energy storage risk carrying capacity improvement rate is obtained. The equation for determining the energy storage risk carrying capacity improvement rate is as follows: ， in, This indicates the improvement rate of energy storage risk tolerance.

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