Energy management algorithm evaluation method and device, equipment and medium
By comparing the measured and predicted resource return information, the performance and potential capabilities of the energy management algorithm are evaluated, and the problem of inaccurate evaluation results in the prior art is solved, and the evaluation accuracy and stability of the energy management algorithm are improved.
Patent Information
- Application Number
- CN202411174909.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-23
- Publication Date
- 2024-12-10
AI Technical Summary
Existing energy management algorithms are limited to simple cost-benefit analysis in terms of resource return assessment, resulting in inaccurate evaluation of the evaluation results and incomplete evaluation of the current performance and potential capabilities of the energy management algorithm.
By obtaining the measured operating parameters and historical operating parameters of the energy management algorithm to be evaluated in the system to be evaluated, combining the algorithm prediction to obtain the predicted operating parameters, comparing the measured resource return information and predicted resource return information, and determining the evaluation results of the energy management algorithm to be evaluated. Improve the accuracy of evaluation results and achieve predictive maintenance.
A more comprehensive evaluation of the energy management algorithm is achieved, the accuracy and reliability of the evaluation results are improved, the possibility of continuous optimization is provided, and the stability and reliability of the algorithm are enhanced.
Smart Images
Figure CN119106271A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present invention relate to the field of energy management technology, and in particular to an energy management algorithm evaluation method, device, equipment and medium. Background Art
[0002] With the rapid development of renewable energy technology, photovoltaic energy storage systems have become an important means to solve energy problems. As the core of photovoltaic energy storage systems, energy management algorithms can achieve effective scheduling and management of energy through intelligent analysis of photovoltaic power generation, energy storage status, and charging requirements.
[0003] By evaluating the resource return of the energy management algorithm, a reference can be provided for the optimization of the energy management algorithm or the optimization of the photovoltaic energy storage system. At present, the evaluation of resource return of energy management algorithms is usually limited to simple cost-benefit analysis, which leads to inaccurate evaluation results. Summary of the invention
[0004] The present invention provides an energy management algorithm evaluation method, device, equipment and medium, which can improve the accuracy of determining the evaluation result of the energy management algorithm to be evaluated in terms of resource return.
[0005] In a first aspect, an embodiment of the present invention provides an energy management algorithm evaluation method, comprising:
[0006] Obtaining measured operating parameters obtained by running the energy management algorithm to be evaluated in the system to be evaluated, and determining measured resource return information corresponding to the measured operating parameters;
[0007] Acquire historical operating parameters of the system to be evaluated, and obtain predicted operating parameters based on the historical operating parameters combined with the energy management algorithm to be evaluated;
[0008] Determining predicted resource return information corresponding to the predicted operating parameters;
[0009] The measured resource return information and the predicted resource return information are compared to determine an evaluation result of the energy management algorithm to be evaluated.
[0010] In a second aspect, an embodiment of the present invention provides an energy management algorithm evaluation device, including:
[0011] A first processing module is used to obtain measured operating parameters obtained by running the energy management algorithm to be evaluated in the system to be evaluated, and determine measured resource return information corresponding to the measured operating parameters;
[0012] A second processing module is used to obtain historical operating parameters of the system to be evaluated, and obtain predicted operating parameters based on the historical operating parameters combined with the energy management algorithm to be evaluated;
[0013] A third processing module, used to determine the predicted resource return information corresponding to the predicted operation parameter;
[0014] The fourth processing module is used to compare the measured resource return information with the predicted resource return information to determine the evaluation result of the energy management algorithm to be evaluated.
[0015] In a third aspect, an embodiment of the present invention provides an electronic device, including:
[0016] at least one processor; and
[0017] a memory communicatively connected to the at least one processor; wherein,
[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method according to the first aspect.
[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.
[0020] The technical solution of the embodiment of the present invention determines the measured resource return information through the measured operating parameters of the energy management algorithm to be evaluated in the system to be evaluated, obtains the predicted operating parameters through the historical operating parameters of the system to be evaluated combined with the prediction of the energy management algorithm to be evaluated, determines the predicted resource return information based on the predicted operating parameters, and determines the evaluation result of the energy management algorithm to be evaluated by comparing the measured resource return information and the predicted resource return information. The above technical solution quantifies the resource return actually achieved and the resource return expected to be achieved by the energy management algorithm to be evaluated in the system to be evaluated through the two aspects of measured resource return information and predicted resource return information; compares the actually achieved resource return and the expected resource return to determine the evaluation result, which can more comprehensively evaluate the current performance and potential capabilities of the energy management algorithm to be evaluated, and realizes the predictive maintenance function while improving the accuracy of the determined evaluation results, which provides the possibility for the continuous optimization of the energy management algorithm to be evaluated, and is further conducive to improving the reliability and stability of the energy management algorithm to be evaluated.
[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0023] Figure 1 is a flow chart of an energy management algorithm evaluation method provided according to Embodiment 1 of the present invention;
[0024] Figure 2 is a flow chart of an energy management algorithm evaluation method provided according to Embodiment 2 of the present invention;
[0025] Figure 3 is a flow chart of an energy management algorithm evaluation method provided according to Embodiment 3 of the present invention;
[0026] Figure 4 is a flowchart of another energy management algorithm evaluation method provided according to Embodiment 3 of the present invention;
[0027] Figure 5 is a flowchart of another energy management algorithm evaluation method provided according to Embodiment 3 of the present invention;
[0028] Figure 6 is a structural schematic diagram of an energy management algorithm evaluation device provided according to a fourth embodiment of the present invention;
[0029] Figure 7 It is a schematic diagram of the structure of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION
[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.
[0031] It should be noted that the terms "first", "second", etc. in the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0032] Embodiment 1
[0033] Figure 1 This is a flow chart of an energy management algorithm evaluation method provided according to Embodiment 1 of the present invention. This embodiment is applicable to the case of determining the evaluation result of the energy management algorithm in terms of resource return. The method can be executed by an energy management algorithm evaluation device, which can be implemented in the form of software and / or hardware and integrated in an electronic device. Further, the electronic device includes but is not limited to: a computer, a laptop, a server, etc.
[0034] like Figure 1 As shown, the method includes:
[0035] S110: Acquire measured operating parameters obtained by running the energy management algorithm to be evaluated in the system to be evaluated, and determine measured resource return information corresponding to the measured operating parameters.
[0036] The energy management algorithm to be evaluated may be an energy management algorithm to be evaluated in terms of resource returns. The system to be evaluated may be a system that carries the operation of the energy management algorithm to be evaluated, such as a photovoltaic energy storage system. Among them, the photovoltaic energy storage system can be understood as a comprehensive system that combines solar power generation and energy storage technology. The system can convert solar energy into electrical energy and store it through energy storage devices (such as batteries) for use during non-sunshine time or peak hours, thereby improving energy utilization efficiency and power supply stability. The energy management algorithm may be an algorithm for scheduling and managing energy, such as scheduling and managing energy in a photovoltaic energy storage system.
[0037] The measured operating parameters may be parameters obtained by actually operating the system to be evaluated that carries the energy management algorithm to be evaluated, and there is no limitation on the measured operating parameters. In this step, the measured operating parameters may be obtained in units of days, such as obtaining the measured operating parameters within a measured time that is an integer multiple of a day.
[0038] In one embodiment, the measured operating parameters include at least measured light power, measured load power, measured charging power and measured energy storage power.
[0039] Among them, the measured optical power may be the measured value of the optical power in the system to be evaluated; the measured load power may be the measured value of the load power in the system to be evaluated; the measured charging power may be the measured value of the charging power in the system to be evaluated; and the measured energy storage power may be the measured value of the energy storage power in the system to be evaluated.
[0040] When the system to be evaluated is a photovoltaic energy storage system, the light power can be the ability of the photovoltaic panel to convert solar energy per unit time; the load power can be the electrical energy used by the load connected to the photovoltaic energy storage system per unit time, which can also be understood as the power demand that the system must meet; the charging power can be the ability of the energy storage device in the photovoltaic energy storage system to charge per unit time; the energy storage power can be the ability of the energy storage device in the photovoltaic energy storage system to store and release electrical energy per unit time.
[0041] In this step, the measured resource return information can be determined based on the measured operating parameters such as the measured optical power, the measured load power, the measured charging power and the measured energy storage power by determining the algorithm for determining the resource return information. Among them, the measured resource return information can be the resource return information determined according to the measured operating parameters. The resource return information can be understood as information indicating the resource return achieved through energy management, such as information on resource returns in daily units. There is no limitation on the algorithm for determining the resource return information, and it can be any algorithm that can determine the resource return information based on the optical power, the load power, the charging power and the energy storage power.
[0042] S120, acquiring historical operating parameters of the system to be evaluated, and obtaining predicted operating parameters based on the historical operating parameters and the energy management algorithm to be evaluated.
[0043] The historical operating parameters of the system to be evaluated can be understood as the parameters obtained by the actual operation of the system to be evaluated before the system to be evaluated adopts the energy management algorithm to be evaluated. There is no limitation on the historical operating parameters. The predicted operating parameters can be the parameters of the system to be evaluated that are predicted based on the historical operating parameters combined with the energy management algorithm to be evaluated.
[0044] In this step, the historical operating parameters may be obtained in units of days, such as obtaining the historical operating parameters within an actual measurement time that is an integer multiple of a day.
[0045] In one embodiment, the historical operating parameters include at least historical light power, historical load power, and historical charging power.
[0046] The historical optical power may be the historical measured value of the optical power in the system to be evaluated; the historical load power may be the historical measured value of the load power in the system to be evaluated; and the historical charging power may be the historical measured value of the charging power in the system to be evaluated. The historical measured value is the measured value determined before the system to be evaluated adopts the energy management algorithm to be evaluated.
[0047] In this step, predicted operating parameters are obtained based on historical operating parameters combined with predictions of the energy management algorithm to be evaluated. Specifically, the predicted parameters corresponding to the historical operating parameters can be determined based on historical operating parameters such as historical optical power, historical load power and historical charging power. The predicted parameters are parameters of the system to be evaluated that are predicted based on the historical operating parameters. The operating time corresponding to the predicted parameters may be a time after the time corresponding to the historical operating parameters. The calculated parameters are calculated based on the predicted parameters combined with the energy management algorithm to be evaluated. The calculated parameters are parameters calculated based on the predicted parameters combined with the energy management algorithm to be evaluated, such as the calculated energy storage power. The predicted parameters corresponding to the calculated parameters and the historical operating parameters are determined as the predicted operating parameters.
[0048] It should be noted that the execution order of S110 and S120 is not limited, and they can be executed sequentially or in parallel, as long as the measured resource return information and the predicted operation parameters can be determined. S130 is executed after S120.
[0049] S130: Determine predicted resource return information corresponding to the predicted operation parameters.
[0050] As can be seen from the above S120, the predicted operating parameters may include the calculated parameters and the predicted parameters corresponding to the historical operating parameters. In this step, the predicted resource return information may be determined based on the predicted parameters corresponding to the above historical operating parameters, such as the predicted optical power, the predicted load power, and the predicted charging power, and the above calculated parameters, such as the energy storage power calculated based on the predicted parameters combined with the energy management algorithm to be evaluated, by using an algorithm for determining the resource return information.
[0051] The predicted resource return information may be resource return information determined according to the predicted operation parameters. The predicted resource return information may indicate the resource return that the system to be evaluated is expected to achieve after the energy management algorithm to be evaluated is adopted in the system to be evaluated.
[0052] S140: Compare the measured resource return information with the predicted resource return information to determine an evaluation result of the energy management algorithm to be evaluated.
[0053] In this step, the measured resource return information is compared with the predicted resource return information to evaluate whether the actual application of the energy management algorithm to be evaluated in the system to be evaluated has achieved the expected effect, and obtain the evaluation result. The evaluation result can be understood as the result of evaluating the resource return of the energy management algorithm to be evaluated.
[0054] For example, when the resource return indicated by the measured resource return information is not lower than the resource return indicated by the predicted resource return information, the evaluation result may indicate that the performance of the energy management algorithm to be evaluated in actual application in the system to be evaluated has achieved the expected effect; otherwise, the evaluation result may indicate that the energy management algorithm to be evaluated has not achieved the expected effect.
[0055] In one embodiment, the evaluation result can be displayed visually. Feedback of the evaluation result helps to understand the performance of the energy management algorithm to be evaluated, so as to determine whether the energy management algorithm to be evaluated needs to be optimized.
[0056] It should be noted that, when the measured operating parameters and historical operating parameters are obtained on a daily basis, the determined measured resource return information and predicted resource return information also reflect the resource return information on a daily basis.
[0057] The technical solution of the embodiment of the present invention determines the measured resource return information through the measured operating parameters of the energy management algorithm to be evaluated in the system to be evaluated, obtains the predicted operating parameters through the historical operating parameters of the system to be evaluated combined with the prediction of the energy management algorithm to be evaluated, determines the predicted resource return information based on the predicted operating parameters, and determines the evaluation result of the energy management algorithm to be evaluated by comparing the measured resource return information and the predicted resource return information. The above technical solution quantifies the resource return actually achieved and the resource return expected to be achieved by the energy management algorithm to be evaluated in the system to be evaluated through the two aspects of measured resource return information and predicted resource return information; compares the actually achieved resource return and the expected resource return to determine the evaluation result, which can more comprehensively evaluate the current performance and potential capabilities of the energy management algorithm to be evaluated, and realizes the predictive maintenance function while improving the accuracy of the determined evaluation results, which provides the possibility for the continuous optimization of the energy management algorithm to be evaluated, and is further conducive to improving the reliability and stability of the energy management algorithm to be evaluated.
[0058] In one embodiment, the method further comprises:
[0059] In the case of determining the evaluation result of the energy management algorithm to be evaluated, determining whether the running time of the energy management algorithm to be evaluated in the system to be evaluated exceeds a first set time, and if so, terminating the evaluation; if not, returning to execute the operation of obtaining the measured running parameters;
[0060] When the predicted resource return information corresponding to the predicted operating parameters is determined, it is determined whether the predicted time exceeds the second set time period, and if so, the prediction is terminated; otherwise, the operation of obtaining the historical operating parameters is returned to be executed.
[0061] In practical applications, the evaluation life of the energy management algorithm to be evaluated can be preset, and the evaluation is continuously performed at a certain period during the life. For example, the life is 5 days, and the algorithm is evaluated once a day, which is not limited here.
[0062] Each time the evaluation result of the energy management algorithm to be evaluated is determined, it is determined whether the running time of the energy management algorithm to be evaluated in the system to be evaluated exceeds a first set time period. The first set time period can be understood as the above-mentioned life span. If so, the evaluation ends. Otherwise, it returns to execute the operation of obtaining the measured operating parameters to continue the next cycle of evaluation.
[0063] Each time the predicted resource return information corresponding to the predicted operation parameters is determined, it is determined whether the predicted time exceeds the second set time. In order to facilitate the comparison of the subsequent measured resource return information and the predicted resource return information, the second set time can be set to the above-mentioned life cycle, and the predicted resource return information is continuously predicted and determined in the same cycle as the evaluation. If the predicted time exceeds the second set time, the prediction ends; if it does not exceed, it returns to the operation of obtaining the historical operation parameters to continue the prediction of the next cycle.
[0064] Embodiment 2
[0065] Figure 2 It is a flowchart of an energy management algorithm evaluation method provided according to Example 2 of the present invention. This embodiment is based on the above-mentioned Example 1 and further refines the predicted operating parameters obtained based on the historical operating parameters combined with the prediction of the energy management algorithm to be evaluated.
[0066] like Figure 2 As shown, the method includes:
[0067] S110: Acquire measured operating parameters obtained by running the energy management algorithm to be evaluated in the system to be evaluated, and determine measured resource return information corresponding to the measured operating parameters.
[0068] S121. Obtain historical operating parameters of the system to be evaluated.
[0069] S122: Input the historical operating parameters into a corresponding prediction model to obtain prediction parameters of the historical operating parameters.
[0070] The prediction model may be a model that predicts corresponding prediction parameters based on historical operating parameters. Different historical operating parameters may correspond to different prediction models. The model structure of the prediction model is not limited here.
[0071] For example, inputting historical optical power into the optical power prediction model can obtain the predicted optical power; inputting historical load power into the load power prediction model can obtain the predicted load power; inputting historical charging power into the charging power prediction model can obtain the predicted charging power.
[0072] In one embodiment, when the prediction model predicts the prediction parameters, in addition to inputting the above-mentioned historical operating parameters, data such as weather forecasts at the time corresponding to the historical operating parameters can also be input as a reference for the prediction.
[0073] S123. Determine calculation parameters based on the prediction parameters and the energy management algorithm to be evaluated, wherein the calculation parameters at least include calculating energy storage power.
[0074] In this step, the energy storage power can be calculated based on the prediction parameters combined with the energy management algorithm to be evaluated, as the above-mentioned calculated energy storage power.
[0075] S124: Determine the calculated parameters and the predicted parameters of the historical operating parameters as predicted operating parameters.
[0076] In this step, the predicted optical power, predicted load power, predicted charging power and the calculated energy storage power can be determined as predicted operating parameters.
[0077] S130: Determine predicted resource return information corresponding to the predicted operation parameters.
[0078] S140: Compare the measured resource return information with the predicted resource return information to determine an evaluation result of the energy management algorithm to be evaluated.
[0079] The technical solution of the embodiment of the present invention obtains prediction parameters by inputting historical operating parameters into the corresponding prediction model, determines the calculated energy storage power based on the prediction parameters in combination with the energy management algorithm to be evaluated, and determines the prediction parameters and the calculated energy storage power as predicted operating parameters, thereby realizing the prediction of the operating parameters in the system to be evaluated, so as to determine the predicted resource return information based on the predicted operating parameters, and then evaluate whether the performance of the energy management algorithm to be evaluated in the actual application of the system to be evaluated achieves the expected effect.
[0080] In one embodiment, the method further comprises:
[0081] Determining an update algorithm for the energy management algorithm to be evaluated;
[0082] Based on the historical operating parameters combined with the update algorithm prediction, updated operating parameters are obtained;
[0083] Determine update resource report information corresponding to the update operation parameter;
[0084] The updated resource return information, the measured resource return information and the predicted resource return information are compared to determine an evaluation result of the update algorithm.
[0085] The update algorithm of the energy management algorithm to be evaluated may be an algorithm obtained by updating the energy management algorithm to be evaluated, and the updating method is not limited.
[0086] The updated operating parameters are obtained based on the historical operating parameters combined with the update algorithm prediction. Specifically, the updated operating parameters can be implemented as follows: the historical operating parameters are input into the corresponding prediction model to obtain the prediction parameters of the historical operating parameters, such as the predicted light power, the predicted load power, and the predicted charging power; based on the obtained prediction parameters combined with the update algorithm, the calculation parameters are determined, such as the calculated energy storage power; the obtained prediction parameters and the obtained calculation parameters are determined as the updated operating parameters. Among them, the updated operating parameters can be the parameters of the operation of the system to be evaluated obtained based on the historical operating parameters combined with the update algorithm prediction.
[0087] Based on the updated operation parameters, the updated resource return information is determined by determining the algorithm for determining the resource return information. The updated resource return information may be the resource return information determined according to the updated operation parameters. The updated resource return information may indicate the resource return that the system to be evaluated is expected to achieve after the updated algorithm is adopted in the system to be evaluated, and may reflect the resource return information in units of days.
[0088] Compare the updated resource return information, the measured resource return information, and the predicted resource return information to determine the evaluation result of the updated algorithm. For example, when the resource return indicated by the updated resource return information is not lower than the resource return indicated by the predicted resource return information and the resource return indicated by the measured resource return information, the evaluation result may indicate that the updated algorithm has achieved the expected effect relative to the update of the energy management algorithm to be evaluated, and further optimization can be continued; for another example, when the resource return indicated by the updated resource return information is lower than the resource return indicated by the predicted resource return information but higher than the resource return indicated by the measured resource return information, the evaluation result may indicate that the updated algorithm has not achieved the expected effect relative to the update of the energy management algorithm to be evaluated, but is better than the performance of the energy management algorithm to be evaluated in the actual application of the energy management algorithm to be evaluated, and further optimization can be continued or the update can be stopped; for another example, when the resource return indicated by the updated resource return information is lower than the resource return indicated by the predicted resource return information and the resource return indicated by the measured resource return information, the evaluation result may indicate that the updated algorithm has not achieved the expected effect relative to the update of the energy management algorithm to be evaluated, and the update can be stopped.
[0089] By comparing the updated resource return information, the measured resource return information, and the predicted resource return information, the evaluation results of the updated algorithm can be determined. It can be determined whether the updated algorithm is better than the actual application performance of the energy management algorithm to be evaluated in the system to be evaluated, or whether it has achieved the expected effect. The advantage of this setting is that it can evaluate the optimization of the energy management algorithm to be evaluated to the updated algorithm, and more accurately measure the potential of the algorithm in improving energy storage and utilization efficiency; it can also provide a reference for the optimization of resource allocation in the system to be evaluated, which is conducive to the sustainable development of the system to be evaluated.
[0090] In one embodiment, determining an update algorithm for the energy management algorithm to be evaluated includes:
[0091] Determining a plurality of candidate update algorithms for the energy management algorithm to be evaluated;
[0092] Based on the algorithm selection model, with the goal of updating the resource return information of the energy management algorithm to be evaluated, an update algorithm of the energy management algorithm to be evaluated is determined from the multiple candidate update algorithms.
[0093] The candidate update algorithm of the energy management algorithm to be evaluated may be an algorithm to be determined as the update algorithm obtained by updating the energy management algorithm to be evaluated, and the updating method is not limited. The algorithm selection model may be a model for selecting a candidate update algorithm from multiple candidate update algorithms as the update algorithm, and the model structure of the algorithm selection model is not limited here.
[0094] Based on the algorithm selection model, an optimization is performed with the goal of improving the resource return that can be achieved by the energy management algorithm to be evaluated, and a candidate update algorithm is selected from multiple candidate update algorithms as the update algorithm of the energy management algorithm to be evaluated.
[0095] In one embodiment, the measured resource report information is determined in units of days, and accordingly, the method further includes:
[0096] Inputting the measured operating parameters and first reference data into a resource return reference model to determine daily reference resource return information in units of days, wherein the first reference data includes data required for reference in determining the daily reference resource return information;
[0097] The measured resource return information and the daily reference resource return information are compared to determine an evaluation result of the energy management algorithm to be evaluated.
[0098] The resource return reference model can be a model for determining the resource return that may be generated by the photovoltaic energy storage system, and the determined resource return can be used as a reference for evaluating the energy management algorithm to be evaluated. The resource return reference model can be a pre-established model, such as a model established based on the average level of resource returns that can be achieved by energy management algorithms on the market, which can achieve an estimate of resource returns in years, and is not limited here.
[0099] Since the resource return reference model can estimate resource returns in years, it is necessary to input the first reference data and the measured operating parameters obtained in days into the resource return reference model as a reference for the model to determine the daily reference resource return information in days. The daily reference resource return information may be the information on resource returns in days determined by the resource return reference model, which may indicate the average level of daily resource returns that can be achieved by energy management algorithms on the market. The first reference data may be the data required for reference in determining the daily reference resource return information, such as but not limited to photovoltaic attenuation rate, photovoltaic utilization rate, charging utilization rate corresponding to energy storage equipment, etc. The first reference data may be a fixed value determined based on the average level on the market.
[0100] By comparing the measured resource return information with the daily reference resource return information, when the resource return indicated by the measured resource return information is higher than the resource return indicated by the daily reference resource return information, the evaluation result can indicate that the performance of the energy management algorithm to be evaluated in actual application in the system to be evaluated is better than the average level of energy management algorithms on the market.
[0101] In one embodiment, the method further comprises:
[0102] Inputting the second reference data and the annual measured resource return information corresponding to the measured resource return information into the resource return reference model to determine the annual comprehensive measured resource return information, wherein the second reference data includes a plurality of annual reference resource return information in units of years;
[0103] Acquire annual comprehensive reference resource return information, wherein the annual comprehensive reference resource return information is determined in advance based on a reference energy management algorithm combined with the system to be evaluated when the energy management algorithm to be evaluated is not running in the system to be evaluated;
[0104] The annual comprehensive measured resource return information and the annual comprehensive reference resource return information are compared to determine an evaluation result of the energy management algorithm to be evaluated.
[0105] The measured resource return information may reflect the resource return information in daily units, and the measured resource return information in daily units may be accumulated to obtain the annual measured resource return information in annual units. The annual measured resource return information may be understood as the accumulation of the resource returns actually generated by the evaluated system implementing the evaluated energy management algorithm in one year.
[0106] The second reference data may be data required for reference in determining the annual comprehensive measured resource return information, and may include a plurality of annual reference resource return information in units of years. The annual reference resource return information may be information indicating the average level of annual resource returns that can be achieved by energy management algorithms on the market. The annual comprehensive measured resource return information may be information on resource returns determined by combining the annual measured resource return information and a plurality of annual reference resource return information.
[0107] The annual measured resource return information and the multiple annual reference resource return information included in the second reference data are input into the resource return reference model to determine the annual comprehensive measured resource return information. If the evaluation period is five years, the annual measured resource return information indicates the actual annual resource return in the first year, and the multiple annual reference resource return information indicates the annual resource return for reference from the first year to the fifth year. The resource return reference model can replace the annual resource return for reference in the first year of the multiple annual reference resource return information with the annual measured resource return information while the rest remain unchanged, and calculate the annual comprehensive measured resource return information based on the replaced data. Exemplarily, the annual comprehensive measured resource return information may include an internal rate of return or a resource payback period.
[0108] The annual comprehensive reference resource return information may be an internal rate of return or resource payback period determined in advance in years for reference, and may indicate the average level of internal rate of return or resource payback period that can be achieved by energy management algorithms on the market. When the energy management algorithm to be evaluated is not running in the system to be evaluated, the internal rate of return or resource payback period in years that can be achieved by running the reference energy management algorithm in the system to be evaluated may be estimated by an empirical formula. Among them, the reference energy management algorithm may be an algorithm that can indicate the average level of energy management algorithms on the market; the empirical formula may be a formula for calculating the internal rate of return or resource payback period determined based on the average level of energy management algorithms on the market; the specific implementation of the reference energy management algorithm and the empirical formula is not limited here.
[0109] By comparing the internal rate of return indicated by the annual comprehensive measured resource return information with the internal rate of return indicated by the annual comprehensive reference resource return information, if the former is higher than the latter, the evaluation result can indicate that the performance of the energy management algorithm to be evaluated in actual application in the system to be evaluated is better than the average level of energy management algorithms on the market.
[0110] By comparing the resource payback period indicated by the annual comprehensive measured resource return information with the resource payback period indicated by the annual comprehensive reference resource return information, if the former is lower than the latter, the evaluation result can indicate that the performance of the energy management algorithm to be evaluated in actual application in the system to be evaluated is better than the average level of energy management algorithms on the market.
[0111] The advantage of this setting is that it can intuitively demonstrate the competitiveness of the energy management algorithm to be evaluated relative to other energy management algorithms on the market, help evaluate the market position of the energy management algorithm to be evaluated, and provide direction for the improvement and optimization of the energy management algorithm to be evaluated.
[0112] Embodiment 3
[0113] This embodiment is an exemplary description of the above embodiments. The energy management algorithm is evaluated in terms of resource return through the following three different dimensions.
[0114] Dimension 1:
[0115] Figure 3 is a flow chart of an energy management algorithm evaluation method provided according to Embodiment 3 of the present invention. Figure 3 As shown, the following steps are included:
[0116] The measured operation parameters are collected in real time in the system to be evaluated, and the system to be evaluated carries the operation of the energy management algorithm to be evaluated; it is determined whether the measured time is an integer multiple of the day, and if so, the measured resource return information corresponding to the measured operation parameters is determined.
[0117] The historical optical power, historical load power, historical charging power, weather forecast and other data of the system to be evaluated are obtained. After the acquired data are processed accordingly by the optical power prediction model, the load power prediction model and the charging power prediction model, the predicted optical power, the predicted load power and the predicted charging power are obtained. The calculated energy storage power is determined based on the predicted optical power, the predicted load power and the predicted charging power in combination with the energy management algorithm to be evaluated. The predicted optical power, the predicted load power, the predicted charging power and the calculated energy storage power are determined as predicted operation parameters, and the corresponding predicted resource return information is determined.
[0118] The measured resource return information and the predicted resource return information are compared to determine the evaluation result of the energy management algorithm to be evaluated.
[0119] Dimension 1 can be used to evaluate whether the performance of the energy management algorithm to be evaluated in actual application in the system to be evaluated has achieved the expected effect.
[0120] Dimension 2:
[0121] Figure 4 is a flow chart of another energy management algorithm evaluation method provided according to Embodiment 3 of the present invention. Figure 4 As shown, the following steps are included:
[0122] The measured operation parameters are collected in real time in the system to be evaluated, and the system to be evaluated carries the operation of the energy management algorithm to be evaluated; it is determined whether the measured time is an integer multiple of the day, and if so, the measured resource return information corresponding to the measured operation parameters is determined.
[0123] The historical optical power, historical load power, historical charging power, weather forecast and other data of the system to be evaluated are obtained. After the acquired data are processed accordingly by the optical power prediction model, the load power prediction model and the charging power prediction model, the predicted optical power, the predicted load power and the predicted charging power are obtained.
[0124] The calculated energy storage power is determined based on the predicted optical power, the predicted load power, the predicted charging power and the energy management algorithm to be evaluated; the predicted optical power, the predicted load power, the predicted charging power and the calculated energy storage power are determined as predicted operation parameters, and the corresponding predicted resource return information is determined.
[0125] Based on the algorithm selection model, with the goal of updating the resource return information of the energy management algorithm to be evaluated, an update algorithm is determined from multiple candidate update algorithms of the energy management algorithm to be evaluated; the energy storage power is determined based on the predicted optical power, the predicted load power, the predicted charging power and the update algorithm; the predicted optical power, the predicted load power, the predicted charging power and the determined energy storage power are used as update operation parameters, and the corresponding updated resource return information is determined.
[0126] The updated resource return information, the measured resource return information and the predicted resource return information are compared to determine the evaluation result of the updated algorithm.
[0127] Dimension 2 can be used to evaluate whether the update algorithm has achieved the expected effect relative to the update of the energy management algorithm to be evaluated.
[0128] Dimension three:
[0129] Figure 5 is a flow chart of another energy management algorithm evaluation method provided according to the third embodiment of the present invention. Figure 5 As shown, the following steps are included:
[0130] The measured operating parameters are collected in real time in the system to be evaluated, and the system to be evaluated carries the operation of the energy management algorithm to be evaluated; it is determined whether the measured time is an integer multiple of the day, and if so, the measured resource return information corresponding to the measured operating parameters is determined, and the measured operating parameters and the first reference data are input into the resource return reference model to determine the daily reference resource return information in units of days; the measured resource return information and the daily reference resource return information are compared to determine the evaluation result of the energy management algorithm to be evaluated.
[0131] Determine whether the measured time is an integer multiple of years. If so, input the annual measured resource return information corresponding to the second reference data and the measured resource return information into the resource return reference model to determine the annual comprehensive measured resource return information; obtain the annual comprehensive reference resource return information, and compare the annual comprehensive measured resource return information with the annual comprehensive reference resource return information to determine the evaluation result of the energy management algorithm to be evaluated.
[0132] Dimension three can be used to evaluate whether the performance of the energy management algorithm to be evaluated in actual application in the system to be evaluated is better than the average level of energy management algorithms on the market.
[0133] The technical solution of the embodiment of the present invention evaluates the resource return of the energy management algorithm to be evaluated through three different dimensions, and can quantify the performance of the resource return of the energy management algorithm to be evaluated in multiple dimensions, providing a reference basis for optimizing the resource return of the energy management algorithm to be evaluated.
[0134] Embodiment 4
[0135] Figure 6 1 is a schematic diagram of a structure of an energy management algorithm evaluation device according to a fourth embodiment of the present invention. This embodiment is applicable to the case of determining the evaluation result of the energy management algorithm in terms of resource return. Figure 6 As shown, the specific structure of the device includes:
[0136] The first processing module 61 is used to obtain the measured operation parameters obtained by running the energy management algorithm to be evaluated in the system to be evaluated, and determine the measured resource return information corresponding to the measured operation parameters;
[0137] A second processing module 62 is used to obtain historical operating parameters of the system to be evaluated, and obtain predicted operating parameters based on the historical operating parameters combined with the energy management algorithm to be evaluated;
[0138] The third processing module 63 is used to determine the predicted resource return information corresponding to the predicted operation parameter;
[0139] The fourth processing module 64 is used to compare the measured resource return information with the predicted resource return information to determine the evaluation result of the energy management algorithm to be evaluated.
[0140] The technical solution provided in this embodiment is to obtain the measured operating parameters obtained by the energy management algorithm to be evaluated in the system to be evaluated through the first processing module, and determine the measured resource return information corresponding to the measured operating parameters; obtain the historical operating parameters of the system to be evaluated through the second processing module, and obtain the predicted operating parameters based on the historical operating parameters combined with the prediction of the energy management algorithm to be evaluated; determine the predicted resource return information corresponding to the predicted operating parameters through the third processing module; and compare the measured resource return information with the predicted resource return information through the fourth processing module to determine the evaluation result of the energy management algorithm to be evaluated. The above technical solution quantifies the resource return actually achieved and the expected resource return to be achieved by the energy management algorithm to be evaluated in the system to be evaluated through the two aspects of measured resource return information and predicted resource return information; compares the actually achieved resource return and the expected resource return to be achieved to determine the evaluation result, which can more comprehensively evaluate the current performance and potential capabilities of the energy management algorithm to be evaluated, and realizes the predictive maintenance function while improving the accuracy of the determined evaluation results, which provides the possibility for the continuous optimization of the energy management algorithm to be evaluated, and further helps to improve the reliability and stability of the energy management algorithm to be evaluated.
[0141] Further, the measured operating parameters include at least measured light power, measured load power, measured charging power and measured energy storage power;
[0142] The historical operating parameters include at least historical optical power, historical load power and historical charging power.
[0143] Furthermore, the second processing module 62 is specifically configured to:
[0144] Inputting the historical operating parameters into a corresponding prediction model to obtain prediction parameters of the historical operating parameters;
[0145] Determine calculation parameters based on the prediction parameters combined with the energy management algorithm to be evaluated, wherein the calculation parameters at least include calculating energy storage power;
[0146] The calculated parameters and the predicted parameters of the historical operating parameters are determined as predicted operating parameters.
[0147] Furthermore, the device also includes an update algorithm evaluation module, which is used to:
[0148] Determining an update algorithm for the energy management algorithm to be evaluated;
[0149] Based on the historical operating parameters combined with the update algorithm prediction, updated operating parameters are obtained;
[0150] Determine update resource report information corresponding to the update operation parameter;
[0151] The updated resource return information, the measured resource return information and the predicted resource return information are compared to determine an evaluation result of the update algorithm.
[0152] Furthermore, the algorithm evaluation module is updated, specifically for:
[0153] Determining a plurality of candidate update algorithms for the energy management algorithm to be evaluated;
[0154] Based on the algorithm selection model, with the goal of updating the resource return information of the energy management algorithm to be evaluated, an update algorithm of the energy management algorithm to be evaluated is determined from the multiple candidate update algorithms.
[0155] Furthermore, the measured resource report information is determined in units of days. Accordingly, the device further includes a fifth processing module, which is used to:
[0156] Inputting the measured operating parameters and first reference data into a resource return reference model to determine daily reference resource return information in units of days, wherein the first reference data includes data required for reference in determining the daily reference resource return information;
[0157] The measured resource return information and the daily reference resource return information are compared to determine an evaluation result of the energy management algorithm to be evaluated.
[0158] Furthermore, the device also includes a sixth processing module, which is used to:
[0159] Inputting the second reference data and the annual measured resource return information corresponding to the measured resource return information into the resource return reference model to determine the annual comprehensive measured resource return information, wherein the second reference data includes a plurality of annual reference resource return information in units of years;
[0160] Acquire annual comprehensive reference resource return information, wherein the annual comprehensive reference resource return information is determined in advance based on a reference energy management algorithm combined with the system to be evaluated when the energy management algorithm to be evaluated is not running in the system to be evaluated;
[0161] The annual comprehensive measured resource return information and the annual comprehensive reference resource return information are compared to determine an evaluation result of the energy management algorithm to be evaluated.
[0162] Furthermore, the device also includes:
[0163] A first judgment module is used to determine whether the running time of the energy management algorithm to be evaluated in the system to be evaluated exceeds a first set time when the evaluation result of the energy management algorithm to be evaluated is determined, and if so, the evaluation is terminated; if not, the operation of obtaining the measured operating parameters is returned;
[0164] The second judgment module is used to determine whether the prediction time exceeds the second set time period when the predicted resource return information corresponding to the predicted operation parameters is determined. If so, the prediction is terminated; otherwise, it returns to execute the operation of obtaining the historical operation parameters.
[0165] The energy management algorithm evaluation device provided in the embodiment of the present invention can execute the energy management algorithm evaluation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.
[0166] Embodiment 5
[0167] Figure 7 Schematic diagram of the structure of the electronic device implementing the embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.
[0168] like Figure 7As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0169] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0170] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the energy management algorithm evaluation method.
[0171] In some embodiments, the energy management algorithm evaluation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the energy management algorithm evaluation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the energy management algorithm evaluation method in any other appropriate manner (e.g., by means of firmware).
[0172] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0173] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0174] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0175] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).
[0176] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.
[0177] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.
[0178] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.
[0179] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims
1. A method for evaluating an energy management algorithm, characterized in that: include: Obtaining measured operating parameters obtained by running the energy management algorithm to be evaluated in the system to be evaluated, and determining measured resource return information corresponding to the measured operating parameters; Acquire historical operating parameters of the system to be evaluated, and obtain predicted operating parameters based on the historical operating parameters combined with the energy management algorithm to be evaluated; Determining predicted resource return information corresponding to the predicted operating parameters; The measured resource return information and the predicted resource return information are compared to determine an evaluation result of the energy management algorithm to be evaluated.
2. The method according to claim 1, characterized in that The measured operating parameters include at least measured light power, measured load power, measured charging power and measured energy storage power; The historical operating parameters include at least historical optical power, historical load power and historical charging power.
3. The method according to claim 1, characterized in that The predicted operating parameters are obtained based on the historical operating parameters combined with the energy management algorithm to be evaluated, including: Inputting the historical operating parameters into a corresponding prediction model to obtain prediction parameters of the historical operating parameters; Determine calculation parameters based on the prediction parameters combined with the energy management algorithm to be evaluated, wherein the calculation parameters at least include calculating energy storage power; The calculated parameters and the predicted parameters of the historical operating parameters are determined as predicted operating parameters.
4. The method according to claim 1, characterized in that: Also includes: Determining an update algorithm for the energy management algorithm to be evaluated; Based on the historical operating parameters combined with the update algorithm prediction, updated operating parameters are obtained; Determine update resource report information corresponding to the update operation parameter; The updated resource return information, the measured resource return information and the predicted resource return information are compared to determine an evaluation result of the update algorithm.
5. The method according to claim 4, characterized in that Determining an update algorithm for the energy management algorithm to be evaluated includes: Determining a plurality of candidate update algorithms for the energy management algorithm to be evaluated; Based on the algorithm selection model, with the goal of updating the resource return information of the energy management algorithm to be evaluated, an update algorithm of the energy management algorithm to be evaluated is determined from the multiple candidate update algorithms.
6. The method according to claim 1, characterized in that The measured resource report information is determined in daily units. Accordingly, the method further includes: Inputting the measured operating parameters and first reference data into a resource return reference model to determine daily reference resource return information in units of days, wherein the first reference data includes data required for reference in determining the daily reference resource return information; The measured resource return information and the daily reference resource return information are compared to determine an evaluation result of the energy management algorithm to be evaluated.
7. The method according to claim 6, characterized in that Also includes: Inputting the second reference data and the annual measured resource return information corresponding to the measured resource return information into the resource return reference model to determine the annual comprehensive measured resource return information, wherein the second reference data includes a plurality of annual reference resource return information in units of years; Acquire annual comprehensive reference resource return information, wherein the annual comprehensive reference resource return information is determined in advance based on a reference energy management algorithm combined with the system to be evaluated when the energy management algorithm to be evaluated is not running in the system to be evaluated; The annual comprehensive measured resource return information and the annual comprehensive reference resource return information are compared to determine an evaluation result of the energy management algorithm to be evaluated.
8. The method according to claim 1, characterized in that Also includes: When determining the evaluation result of the energy management algorithm to be evaluated, determining whether the running time of the energy management algorithm to be evaluated in the system to be evaluated exceeds a first set time period, and if so, terminating the evaluation; If not, return to execute the operation of obtaining the measured operating parameters; When the predicted resource return information corresponding to the predicted operating parameters is determined, it is determined whether the predicted time exceeds the second set time period, and if so, the prediction is terminated; if not, the operation of obtaining the historical operating parameters is returned to be executed.
9. An energy management algorithm evaluation device, characterized in that: include: A first processing module is used to obtain measured operating parameters obtained by running the energy management algorithm to be evaluated in the system to be evaluated, and determine measured resource return information corresponding to the measured operating parameters; A second processing module is used to obtain historical operating parameters of the system to be evaluated, and obtain predicted operating parameters based on the historical operating parameters combined with the energy management algorithm to be evaluated; A third processing module, used to determine the predicted resource return information corresponding to the predicted operation parameter; The fourth processing module is used to compare the measured resource return information with the predicted resource return information to determine the evaluation result of the energy management algorithm to be evaluated.
10. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 8.
11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.