A photovoltaic power plant photovoltaic-storage joint dispatch method and system considering multiple assessment criteria

Abstract

This invention relates to the field of power system technology, specifically to a photovoltaic power plant photovoltaic-storage joint dispatching method and system that considers multiple types of assessments. The method provided by this invention deeply integrates the "two detailed rules" assessment rules, constructing a comprehensive optimization model that considers multiple assessment types under these rules. It comprehensively covers medium-term, short-term, and ultra-short-term forecast assessments, as well as assessment scenarios such as power curtailment and ramp-up. It quantifies the mileage calculation and cost differences for different assessment types, and from the perspective of minimizing the mileage and cost of multi-type forecast assessments, it builds a photovoltaic-storage joint dispatching model to ensure optimal comprehensive performance of the photovoltaic-storage system under complex assessment systems. Based on a real-time data-driven rather than predictive dispatching strategy, it dynamically adjusts the dispatching scheme according to real-time renewable energy output capacity and equipment operating condition data, thus overcoming the reliance on prediction in traditional dispatching and addressing the risk of prediction deviation assessments based on prediction errors.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a photovoltaic power plant photovoltaic-storage joint dispatching method and system that takes into account multiple types of assessments. Background Technology

[0002] With the advancement of new power systems, the proportion of renewable energy generation continues to rise. The intermittent and fluctuating nature of wind and solar power poses a severe challenge to the stable operation of the power system. Driven by the "dual carbon" target, the installed capacity of renewable energy is rapidly expanding, and its proportion in the power system is constantly increasing. However, the uncertainty of its output significantly increases the difficulty of grid dispatch. To ensure the safe, stable, and efficient operation of the power grid, energy authorities are continuously improving the "two detailed rules," with increasingly refined and stringent assessment indicators. From the initial assessment of power generation plan deviations, the assessment has gradually expanded to include multi-dimensional assessments such as power prediction accuracy, ramp-up rate, and curtailment assessment. The combined operation of solar and energy storage is gradually becoming a key strategy for improving the stability and reliability of renewable energy power plants.

[0003] Currently, common technical solutions for the joint scheduling of photovoltaic and energy storage systems include:

[0004] Model predictive control-based methods: By establishing detailed models of photovoltaic power plants, energy storage systems, and loads, the photovoltaic output and load demand in the future are predicted. Then, based on the optimization objective function (such as minimizing scheduling costs and maximizing the absorption of new energy), model predictive control algorithms are used to formulate the charging and discharging strategies of energy storage, so as to achieve the coordinated operation of photovoltaic and energy storage systems.

[0005] Machine learning-based methods utilize historical data to train machine learning models (such as neural networks and support vector machines) to predict photovoltaic power output and load, or directly learn the optimal scheduling strategy for energy storage systems. When data is abundant and of good quality, machine learning models can effectively capture the nonlinear relationships of the system, improving the accuracy of prediction and scheduling.

[0006] Control strategy-based methods: Designing reasonable control strategies (such as fuzzy control, sliding mode control, etc.) to achieve coordinated operation of photovoltaic-energy storage systems. These control strategies can quickly adjust the charging and discharging power of the energy storage system according to the real-time system state and control objectives to maintain stable system operation. They have good real-time performance and dynamic performance and are often used in applications such as primary frequency regulation.

[0007] Existing photovoltaic-storage joint scheduling technologies have the following limitations:

[0008] First, the "two detailed rules" lack a systematic approach to the multi-faceted assessments, resulting in a narrow perspective on dispatch optimization. Most technologies focus on optimizing a single or a few assessment indicators. When facing medium-term, short-term, and ultra-short-term forecast assessments, as well as multi-faceted assessments such as power curtailment and ramp-up, there is a lack of a holistic optimization strategy, making it difficult to ensure that the power plant meets the overall assessment standards.

[0009] Second, there is an over-reliance on forecasts. Energy storage operation plans are mostly based on photovoltaic forecasts within the scheduling window. Photovoltaic forecasts have inherent biases, and forecast bias is a key performance indicator. Optimizing energy storage plans based on biased forecasts to address these biases creates a logically repetitive and inconsistent problem, which can easily lead to performance risks. Summary of the Invention

[0010] The purpose of this invention is to provide a photovoltaic power plant photovoltaic-storage joint scheduling method and system that takes into account multiple types of assessments, in order to solve the above-mentioned problems in the prior art.

[0011] This invention is achieved through the following technical solution:

[0012] A photovoltaic power plant photovoltaic-storage joint dispatch method considering multiple types of assessments includes:

[0013] Under the condition of receiving the target power instruction given by the superior dispatcher, the multi-resource coordinated output of the power station meets the operational indicators of effectively following the operation target;

[0014] System constraints are constructed based on the aforementioned operational indicators. These constraints include constraints on the real-time scheduling flexibility of new energy sources, constraints on the real-time scheduling flexibility of electrochemical energy storage, constraints on system power balance, and constraints on system power flow.

[0015] A multi-product assessment mileage evaluation model is established based on constraints. The multi-product assessment mileage evaluation model includes a power curtailment assessment model, a predictive assessment model, and a ramp assessment model.

[0016] An objective function is constructed based on system constraints and a multi-variety assessment mileage evaluation model to minimize curtailment behavior during station operation, and the final result is output based on the objective function.

[0017] Preferably, the objective function includes:

[0018]

[0019] In the formula, For multi-variety forecasting and assessment of comprehensive mileage; To assess unit mileage cost; To schedule this time segment after scheduling Reference Deviation count, This is the primary assessment cost; This is the count of deviations from the superior scheduling instructions within this scheduling time segment. This is the second assessment cost; This is the penalty count for abandoned light behavior within this scheduling time segment. This is the third assessment cost. The comprehensive mileage for hill climbing assessment This is the fourth assessment cost.

[0020] Preferably, the constraints on the real-time scheduling flexibility of the new energy source include:

[0021]

[0022] In the formula, For the unit The final actual power generation output of the current scheduling time segment; For the unit The theoretical maximum output power; For the unit The minimum output power under startup condition is usually 0 for new energy units; These are the current grid-connected power generation indication variables 0 and 1 for the unit. 0 indicates that the unit is not connected to the grid for power generation, while 1 indicates that the unit is in the grid-connected power generation state during this scheduling time segment.

[0023] The penalty count for abandoned light behavior within this scheduling time segment includes:

[0024]

[0025] In the formula, This represents the total number of new energy generating units that can be dispatched at the power station. For the unit The rated installed capacity.

[0026] Preferably, the constraints on the real-time scheduling flexibility of the electrochemical energy storage include:

[0027]

[0028] In the formula, For the expected completion of this scheduling Energy Storage ; , Energy storage Maximum and minimum , Current time section Energy Storage ; , Energy storage The scheduled charging power and scheduled discharging power; , Energy storage The charging efficiency and discharging efficiency; For energy storage Battery capacity; For energy storage Current battery ; For equipment scheduling time step;

[0029] The energy storage scheduling is pre-set to coincide with the current scheduling time segment. The deviation count includes:

[0030]

[0031] In the formula, For energy storage After the current cycle is scheduled Reference The difference, The amount of electrochemical energy storage available for dispatch at the site. For energy storage Reference for current cycle scheduling .

[0032] Preferably, the system power balance constraints include:

[0033]

[0034] In the formula, This represents the total power of all grid-connected stations at the current time section. For the unit The final actual power generation output of the current scheduling time segment; This represents the total auxiliary power consumption of the station.

[0035] Preferably, the system power flow constraints include:

[0036]

[0037] In the formula, This represents the available power distribution capacity at the power plant's grid connection point for the current month.

[0038] Preferably, the power rationing assessment model includes:

[0039]

[0040] In the formula, The target power for the entire station as set by the current superior dispatcher. If the scheduling window exists ,but It equals 0.

[0041] Preferably, the predictive assessment model includes:

[0042] Obtain historical prediction accuracy:

[0043]

[0044] In the formula, To determine the forecast accuracy for the corresponding assessment varieties; for the three forecast assessment varieties, To predict power, for Available power or actual power of the power station at any given time.

[0045] Based on the given assessment type, obtain the assessment power benchmark for the current scheduling time segment, and determine the station's comprehensive power exemption interval for the given assessment type according to the assessment power benchmark.

[0046] Based on the definition of deviation assessment mileage within the exemption assessment interval, the multi-product predicted comprehensive assessment mileage is constructed using the deviation assessment mileage:

[0047]

[0048] In the formula, This refers to the set of varieties with prediction bias considered in the algorithm. This is the adjustment coefficient for the corresponding variety's assessment mileage. For the evaluation varieties The deviation assessment mileage.

[0049] Preferably, the hill-climbing assessment model includes:

[0050] ;

[0051]

[0052] In the formula, This represents the total power of all grid-connected points at the start of this dispatch window; The limit for ramp power per unit time; Penalty mileage for climbing hills.

[0053] A photovoltaic power plant photovoltaic-energy storage joint dispatch system considering multiple types of assessments, used to execute the aforementioned photovoltaic power plant photovoltaic-energy storage joint dispatch method considering multiple types of assessments, includes:

[0054] The model building module is configured to, under the condition of obtaining the target power command given by the superior dispatcher, ensure that the multi-resource coordinated output of the power station meets the operational indicators of effectively following the operational target; based on the operational indicators, system constraints are constructed, including constraints on the flexibility of real-time dispatch of new energy sources, constraints on the flexibility of real-time dispatch of electrochemical energy storage, constraints on system power balance, and constraints on system power flow; based on the constraints, a multi-variety assessment mileage evaluation model is established, including a power curtailment assessment model, a predictive assessment model, and a ramp-up assessment model;

[0055] The output module is configured to construct an objective function based on system constraints and a multi-variety assessment mileage evaluation model to minimize curtailment behavior during station operation, and outputs the final result based on the objective function.

[0056] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0057] The method provided by this invention deeply integrates the assessment rules of the "two detailed rules" and constructs a comprehensive optimization model for multi-type assessments that considers the two detailed rules. It comprehensively covers medium-term, short-term, and ultra-short-term forecast assessments, as well as assessment scenarios such as power curtailment and ramp-up. It quantifies the mileage calculation and cost differences of different assessment types. From the perspective of minimizing the mileage and assessment cost of multi-type forecast assessments, it builds a photovoltaic-storage joint scheduling model to ensure the optimal comprehensive performance of the photovoltaic-storage system under complex assessment systems.

[0058] A real-time data-driven, rather than prediction-based, scheduling strategy. Based on real-time renewable energy output capacity and equipment operating data, the scheduling plan is dynamically adjusted. This eliminates the reliance on prediction in traditional scheduling, addresses the risks associated with prediction deviation assessments, reduces the impact of prediction deviations, enhances scheduling flexibility and adaptability, and ensures stable output and efficient operation of the photovoltaic-storage system in actual operation. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a schematic diagram of the process of the present invention;

[0061] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0063] The module division in this application is a logical division. In actual application, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not executed.

[0064] The independently described modules or sub-modules may or may not be physically separated; they may be implemented in software or hardware, and some modules or sub-modules may be implemented in software, with the processor calling the software to implement the function of these modules or sub-modules, while other modules or sub-modules may be implemented in hardware, such as through hardware circuits. Furthermore, some or all of the modules can be selected to achieve the purpose of this application's solution according to actual needs.

[0065] Please refer to Figures 1-2 This invention addresses the increasingly stringent assessment criteria for new power plants under the new power system framework and the growing importance of integrated photovoltaic (PV) and energy storage (ESS) operation. It constructs a PV-ESS joint scheduling algorithm that considers the differences in assessment mileage calculation and costs for different types of power plants under concurrent assessment scenarios, including medium-term, short-term, and ultra-short-term forecasts, curtailment assessments, and ramp-up assessments. It integrates historical feedback prediction curves and assessment rules, real-time new energy output capacity, and real-time resource equipment operating conditions. Starting from minimizing the predicted assessment mileage for multiple types of power plants, i.e., minimizing assessment costs, it constructs a wind-PV-ESS joint scheduling model based on mixed integer programming for a single time segment. This model serves the daily scheduling of centralized or distributed new energy power plants and addresses common prediction assessment types in the two sets of regulations.

[0066] This invention specifically provides a photovoltaic power plant photovoltaic-storage joint dispatch method that considers multiple types of assessments, including:

[0067] S101: Under the condition of obtaining the target power instruction given by the superior dispatcher, the multi-resource coordinated output of the power station meets the operational indicators of effectively following the operation target;

[0068] S102: Construct system constraints based on the aforementioned operating indicators. The constraints include constraints on the flexibility of real-time dispatching of new energy sources, constraints on the flexibility of real-time dispatching of electrochemical energy storage, constraints on system power balance, and constraints on system power flow.

[0069] S103: Establish a multi-product assessment mileage evaluation model based on constraints. The multi-product assessment mileage evaluation model includes a power curtailment assessment model, a predictive assessment model, and a ramp assessment model.

[0070] S104: Construct an objective function based on system constraints and a multi-variety assessment mileage evaluation model to minimize curtailment behavior during station operation, and output the final result based on the objective function.

[0071] The method provided by this invention deeply integrates the assessment rules of the "two detailed rules" and constructs a comprehensive optimization model for multi-type assessments that considers the two detailed rules. It comprehensively covers medium-term, short-term, and ultra-short-term forecast assessments, as well as assessment scenarios such as power curtailment and ramp-up. It quantifies the mileage calculation and cost differences of different assessment types. From the perspective of minimizing the mileage and assessment cost of multi-type forecast assessments, it builds a photovoltaic-storage joint scheduling model to ensure the optimal comprehensive performance of the photovoltaic-storage system under complex assessment systems.

[0072] A real-time data-driven, rather than prediction-based, scheduling strategy. Based on real-time renewable energy output capacity and equipment operating data, the scheduling plan is dynamically adjusted. This eliminates the reliance on prediction in traditional scheduling, addresses the risks associated with prediction deviation assessments, reduces the impact of prediction deviations, enhances scheduling flexibility and adaptability, and ensures stable output and efficient operation of the photovoltaic-storage system in actual operation.

[0073] In one exemplary embodiment of the present invention, the method aims to optimize the predicted assessment mileage (i.e., its cost) of multiple types of solar power using joint solar-storage scheduling, while minimizing solar curtailment during station operation, and ensuring that the multi-resource collaborative output of the station can effectively follow the target power command given by the superior scheduling authority. The specific objective function includes:

[0074]

[0075] In the formula, For multi-variety forecasting and assessment of comprehensive mileage; To assess the cost per unit mileage, it can be used as a reference benchmark for weight allocation in this method (often denoted as 1). To schedule this time segment after scheduling Reference Deviation count, This is the primary assessment cost, and a smaller value can be taken than the reference benchmark for weight allocation. This is the deviation count from the superior dispatch instructions within this dispatch time segment, i.e., the comprehensive mileage for power rationing assessment. This is the second assessment cost, and a larger value can be taken than the weighting reference benchmark. This is the penalty count for abandoned light behavior within this scheduling time segment. This is the third assessment cost, which can be a larger value than the weighting reference benchmark, but preferably smaller. , The comprehensive mileage for hill climbing assessment This is the fourth assessment cost, which can be compared with... The value is set according to the specific circumstances while maintaining a certain order of magnitude.

[0076] In one exemplary embodiment of the present invention, the real-time scheduling flexibility constraints of the new energy source include:

[0077] For photovoltaic (PV) power plants, the theoretical maximum output capacity can typically be accurately assessed based on real-time meteorological data, such as wind speed and irradiance. This method, however, quantifies the dispatch flexibility of PV power generation resources based on the theoretical maximum output capacity obtained from the real-time assessment, establishing the following constraints:

[0078]

[0079] In the formula, For the unit The final actual power generation output of the current scheduling time segment; For the unit The theoretical maximum output power; For the unit The minimum output power under startup condition is usually 0 for new energy units; These are the current grid-connected power generation indication variables 0 and 1 for the unit. 0 indicates that the unit is not connected to the grid for power generation, while 1 indicates that the unit is in the grid-connected power generation state during this scheduling time segment.

[0080] To reduce wind and solar curtailment during power plant scheduling, the cost of solar curtailment is added to the objective function as a penalty for real-time scheduling of new energy sources. The penalty count for solar curtailment within the current scheduling time segment includes:

[0081]

[0082] In the formula, This represents the total number of new energy generating units that can be dispatched at the power station. For the unit The rated installed capacity, included in , All of these aim to assist in the dimensionless processing of the penalty count for light-abandoning behavior, making it easier to select the value of the corresponding unit cost in the objective function.

[0083] In one exemplary embodiment of the present invention, electrochemical energy storage scheduling will be based on the rated battery capacity of the energy storage, the energy storage... Upper and lower limits, rated installed capacity of energy storage PCS, current energy storage capacity By considering the potential forecasting bias in the future time window and its impact on the demand preferences for energy storage dispatch, a constraint on the flexibility of electrochemical energy storage dispatch is formed.

[0084] Electrochemical energy storage begins based on the current time segment. And this energy storage charging and discharging dispatch command, estimate the energy storage after dispatch. And ensure it is in the device preset state. Within the upper and lower limits, to protect battery life, the constraints on the real-time scheduling flexibility of electrochemical energy storage include:

[0085]

[0086] In the formula, For the expected completion of this scheduling Energy Storage ; , Energy storage Maximum and minimum , Current time section Energy Storage ; , Energy storage The scheduled charging power and scheduled discharging power; , Energy storage The charging efficiency and discharging efficiency; For energy storage Battery capacity; For energy storage Current battery ; For equipment scheduling time step;

[0087] In electrochemical energy storage dispatch, the charging and discharging states are mutually exclusive, and the charging and discharging power is limited by the charging and discharging states and the installed capacity of the PCS, resulting in the following constraints:

[0088]

[0089]

[0090]

[0091] In the formula, , Energy storage The charging status indicator and discharging status indicator are 0 and 1 variables; This is electrochemical energy storage. The rated power of the PCS. If there are multiple energy storage systems working together within the site, constraints on ensuring consistent charging and discharging states of these systems can be considered.

[0092] The timing coupling between energy storage scheduling and the preceding and following events is relatively large. Therefore, it is necessary to reserve adjustment capacity for possible future prediction deviations while assessing the current prediction deviation. Although it is generally assumed that there are deviations in the medium-term, short-term, and ultra-short-term predictions of renewable energy output, the accuracy of ultra-short-term predictions is generally much better than that of medium-term predictions. Therefore, the difference between the ultra-short-term and medium-term predictions of renewable energy output within a certain time window in the future (i.e., the ultra-short-term and medium-term predictions reported in the two detailed assessment rules) is used as a reference to design the reference SoC for energy storage scheduling at the current moment.

[0093]

[0094] In the formula, That is, energy storage Reference for this scheduling ; , These are the new energy generating units at the power station. Ultra-short-term and medium-term power forecasts; For a preset time window; , It is a constant, and can be configured appropriately. , Values ​​can form scheduling references. The characteristic curve aims to achieve the following: within a preset future time window, when the ultra-short-term predicted renewable energy output exceeds the medium-term forecast (the risk of exceeding the original forecast for power generation output is high), the reference for energy storage scheduling in the current cycle... The capacity will be smaller to ensure that energy storage reserves charging capacity for periods when future renewable energy output is higher than expected; conversely, when the ultra-short-term forecast of renewable energy output is lower than the medium-term forecast (the risk of assessment is high if power generation output is lower than the original forecast), the reference for energy storage dispatch in the current cycle will be smaller. It will be larger, ensuring that energy storage reserves power for future discharges.

[0095] Finally, after the current scheduling Reference Deviation counts are performed and incorporated into the objective function as a penalty. The energy storage scheduling is pre-set to match the current scheduling time segment. The deviation count includes:

[0096]

[0097] In the formula, For energy storage After the current cycle is scheduled Reference The difference, The amount of electrochemical energy storage available for dispatch at the site. For energy storage Reference for current cycle scheduling .

[0098] In one exemplary embodiment of the present invention, for the current scheduling time segment, the comprehensive power generation output of the new energy power station system grid connection point is obtained after balancing the power of the new energy power generation equipment, energy storage output, and possible auxiliary power equipment within the system. The system power balance constraints include:

[0099]

[0100] In the formula, This represents the total power of all grid-connected stations at the current time section. For the unit The final actual power generation output of the current scheduling time segment; This represents the total auxiliary power consumption of the station.

[0101] In one exemplary embodiment of the present invention, under reasonable system scheduling, the comprehensive power at the system grid connection point should not exceed the power distribution transmission capacity limit, wherein the system power flow constraints include:

[0102]

[0103] In the formula, This represents the available power distribution capacity at the power plant's grid connection point for the current month.

[0104] In one exemplary implementation of the present invention, if the superior dispatcher issues a target power to the power station at the current time segment, the actual system dispatch should fully consider the target power to coordinate equipment operation. Deviations from the dispatched target power will be assessed by two detailed rules, namely, power curtailment assessment. This method references the power curtailment assessment methods of the two detailed rules, adjusts and quantifies the power curtailment assessment mileage, incorporates it into the objective function as a penalty rather than a hard constraint, and integrates it into the algorithm. This achieves power station operation tracking of the target power while reducing the risk of algorithm misunderstanding leading to dispatch process anomalies. The power curtailment assessment evaluation model includes:

[0105]

[0106] In the formula, The target power for the entire station as set by the current superior dispatcher. If the scheduling window exists ,but It equals 0.

[0107] In one exemplary embodiment of the present invention, for photovoltaic power generation stations, the forecasting assessment is mainly divided into three types: medium-term forecasting assessment, short-term forecasting assessment, and ultra-short-term forecasting assessment. Referring to the definitions of these three assessment types in the two detailed rules, the assessment method evaluates the forecasting accuracy of the power station and performs daily statistics and monthly assessments.

[0108] The prediction assessment model includes: S201: Obtaining historical prediction accuracy:

[0109]

[0110] In the formula, To adjust the forecast accuracy for the corresponding assessment commodities; for the three forecast assessment commodities, the power curtailment time... for Available power (theoretical power generation output) of the power station at any time, during which there is no power curtailment. for Actual power at time, The definitions differ; the mid-term assessment is based on the assessment conducted 4 days prior. The predicted power at any given time, for short-term assessment, is the power predicted for the day-ahead. The predicted power at any given time, while the ultra-short-term assessment is... 16 pairs of pairs between 15 minutes and 4 hours prior to the time. The average value of ultra-short-term power forecast at any given time. The accuracy requirements for medium-term, short-term, and ultra-short-term assessments are 45%, 65%, and 70%, respectively. If the accuracy of any of the three assessments is not met, the cost assessment will be calculated as the installed capacity of the month * assessment ratio * assessment coefficient. If the predicted power, available power, and actual power at a certain time are all less than 10% of the installed capacity of the month, that time will not be included in the accuracy statistics.

[0111] S202: Based on the given assessment type, obtain the assessment power benchmark of the current scheduling time segment, and determine the station comprehensive power exemption interval for the given assessment type according to the assessment power benchmark.

[0112] As can be seen from the above definition of assessment categories, it is quite difficult to conduct assessment and calculation according to the detailed assessment methods specified in the rules and incorporate them into the scheduling algorithm during the equipment scheduling phase. Therefore, this method proposes a method for calculating the predicted assessment mileage based on the assessment-exempt interval.

[0113] For a given assessment variety The benchmark power for the current scheduling time segment can be easily calculated based on the two detailed rules. And the accuracy requirements for the corresponding varieties are known. To simplify the problem and facilitate the development of mixed-integer programming for practical deployment of control algorithms, and referencing the accuracy calculation methods in the two detailed rules, a range of power exemptions for a given type of facility is proposed. The definition method is as follows:

[0114]

[0115]

[0116]

[0117] In the formula, The minimum value of the exemption range, The maximum value within the exemption range. For the unit The theoretical maximum output power.

[0118] Furthermore, this method defines the assessment varieties as follows: Deviation assessment mileage The following constraints must be satisfied:

[0119]

[0120]

[0121]

[0122] In the formula, This is the total power of all grid-connected stations at the current time section; That is, the varieties to be assessed Deviation assessment mileage; This is an auxiliary constant for the algorithm, representing a maximum value; This refers to the 0 and 1 variables representing whether the current assessed variety is activated, and should satisfy the following constraints:

[0123] when or When, the following equation is satisfied:

[0124]

[0125] when and When, the following equation is satisfied:

[0126]

[0127]

[0128] In the formula, For auxiliary judgment and The size is determined by auxiliary 0 and 1 variables. Through the above constraints, the corresponding rule in the two detailed rules, "when the predicted power, available power, and actual power are all less than 10% of the monthly installed capacity at a certain moment, that moment can be excluded from accuracy statistics," can be implemented in the algorithm model.

[0129] S203: Define the deviation assessment mileage based on the exemption assessment interval, and construct the multi-variety predicted comprehensive assessment mileage through the deviation assessment mileage:

[0130]

[0131] In the formula, This refers to the set of varieties with prediction bias considered in the algorithm. This is the adjustment coefficient for the corresponding variety's assessment mileage. For the evaluation varieties The deviation assessment mileage.

[0132] In one exemplary embodiment of the present invention, in two detailed rules, the photovoltaic power station is subject to ramp-up assessment restrictions, satisfying the following constraints. The ramp-up assessment evaluation model includes:

[0133] ;

[0134]

[0135] In the formula, This represents the total power of all grid-connected points at the start of this dispatch window; The limit for ramp power per unit time; Penalty mileage for climbing hills.

[0136] Thus, based on the model described above, a joint photovoltaic-storage scheduling algorithm considering multi-variety assessment mileage calculation and cost differences can be implemented. As seen earlier, this algorithm utilizes weights to achieve multi-objective optimization, and the comprehensive objective function... middle, The principles for selecting values ​​are relatively clear. However, In China, different forecast assessment varieties The principle for selecting the value is not explicitly stated. In practical applications, one could consider using the aforementioned scheduling algorithms and heuristic algorithms (such as common particle swarm optimization and genetic algorithms) to... As the main decision variables, through time-series simulation within the complete settlement cycle and the calculation method of actual assessment costs for assessed products, the actual assessment costs of multiple products are calculated using simulation data, and an objective function is designed and optimized. Value selection. The detailed design of the relevant algorithms will not be elaborated upon in this invention.

[0137] Secondly, this invention also provides a photovoltaic power plant photovoltaic-storage joint scheduling system that considers multiple types of assessments, used to execute the aforementioned photovoltaic power plant photovoltaic-storage joint scheduling method that considers multiple types of assessments, including:

[0138] The model building module is configured to, under the condition of obtaining the target power command given by the superior dispatcher, ensure that the multi-resource coordinated output of the power station meets the operational indicators of effectively following the operational target; based on the operational indicators, system constraints are constructed, including constraints on the flexibility of real-time dispatch of new energy sources, constraints on the flexibility of real-time dispatch of electrochemical energy storage, constraints on system power balance, and constraints on system power flow; based on the constraints, a multi-variety assessment mileage evaluation model is established, including a power curtailment assessment model, a predictive assessment model, and a ramp-up assessment model;

[0139] The output module is configured to construct an objective function based on system constraints and a multi-variety assessment mileage evaluation model to minimize curtailment behavior during station operation, and outputs the final result based on the objective function.

[0140] This patented solution provides an in-depth analysis of the common assessment rules in the two detailed regulations, deeply integrating assessment details into the optimization model. It constructs a photovoltaic-storage joint scheduling model covering multiple concurrent scenarios such as medium-term forecasting, short-term forecasting, ultra-short-term forecasting, power curtailment assessment, and ramp-up assessment. It comprehensively considers the mileage calculation and cost differences of different assessment types, effectively addressing the multi-dimensional assessment requirements under the increasingly stringent "two detailed regulations." It avoids the problem of existing technologies focusing only on a single or a few assessment indicators, leading to overall assessment failure, and significantly improves the comprehensive performance of new energy power plants under multiple assessment types.

[0141] The scheduling scheme is dynamically adjusted based on real-time renewable energy output capacity and equipment operating data. This real-time data-driven approach breaks through the traditional forecast-based scheduling method, avoids the problem of dealing with deviation assessments based on forecast errors, improves the flexibility and adaptability of scheduling, and thus ensures stable output and efficient operation of the photovoltaic-storage system in actual operation.

[0142] The model proposed in this patent can form a mixed integer programming model by applying common linearization methods. It can be solved using mature commercial or open-source solvers, which facilitates production deployment and engineering applications.

[0143] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0144] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This computer software product, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A photovoltaic power plant photovoltaic-storage joint dispatch method considering multiple types of assessments, characterized in that, include: Under the condition of receiving the target power instruction given by the superior dispatcher, the multi-resource coordinated output of the power station meets the operational indicators of effectively following the operation target; System constraints are constructed based on the aforementioned operational indicators. These constraints include constraints on the real-time scheduling flexibility of new energy sources, constraints on the real-time scheduling flexibility of electrochemical energy storage, constraints on system power balance, and constraints on system power flow. A multi-product assessment mileage evaluation model is established based on constraints. The multi-product assessment mileage evaluation model includes a power curtailment assessment model, a predictive assessment model, and a ramp assessment model. Based on system constraints and a multi-variety assessment mileage evaluation model, an objective function is constructed to minimize the curtailment behavior of solar power stations during operation, and the final result is output based on the objective function. The objective function includes: In the formula, For multi-variety forecasting and assessment of comprehensive mileage; To assess unit mileage cost; To schedule this time segment after scheduling Reference Deviation count, This is the primary assessment cost; This is the count of deviations from the superior scheduling instructions within this scheduling time segment. This is the second assessment cost; This is the penalty count for abandoned light behavior within this scheduling time segment. This is the third assessment cost. The comprehensive mileage for hill climbing assessment This is the fourth assessment cost; The power rationing assessment model includes the deviation count between the current scheduling time segment and the superior scheduling instructions: In the formula, The target power for the entire station as set by the current superior dispatcher. This represents the total power of all grid-connected stations at the current time section. This represents the available power distribution capacity at the power plant's grid connection point for the current month. If the scheduling window exists ,but If the scheduling window does not exist ,but Equal to 0; The predictive assessment model includes: Obtain historical prediction accuracy: In the formula, To determine the forecast accuracy for the corresponding assessment varieties; for the three forecast assessment varieties, To predict power, for Available or actual power of the power station at any given time; Based on the given assessment type, obtain the assessment power benchmark for the current scheduling time segment, and determine the station's comprehensive power exemption interval for the given assessment type according to the assessment power benchmark. Based on the definition of deviation assessment mileage within the exemption assessment interval, the multi-product predicted comprehensive assessment mileage is constructed using the deviation assessment mileage: In the formula, This refers to the set of varieties with prediction bias considered in the algorithm. This is the adjustment coefficient for the corresponding variety's assessment mileage. For the evaluation varieties Deviation assessment mileage; The hill-climbing assessment model includes: ； In the formula, This represents the total power of all grid-connected points at the start of this dispatch window; The limit for ramp power per unit time; Penalty mileage for climbing hills.

2. The photovoltaic power plant photovoltaic-storage joint dispatch method considering multiple types of assessments according to claim 1, characterized in that, The constraints on the real-time scheduling flexibility of new energy sources include: In the formula, For the unit The final actual power generation output of the current scheduling time segment; For the unit The theoretical maximum output power; For the unit The minimum output power under startup condition is usually 0 for new energy units; These are the current grid-connected power generation indication variables 0 and 1 for the unit. 0 indicates that the unit is not connected to the grid for power generation, while 1 indicates that the unit is in the grid-connected power generation state during this scheduling time segment. The penalty count for abandoned light behavior within this scheduling time segment includes: In the formula, This represents the total number of new energy generating units that can be dispatched at the power station. For the unit The rated installed capacity.

3. The photovoltaic power plant photovoltaic-storage joint dispatch method considering multiple types of assessments according to claim 2, characterized in that, The constraints on the real-time scheduling flexibility of the electrochemical energy storage include: In the formula, For the expected completion of this scheduling Energy Storage ; , Energy storage Maximum and minimum , Current time section Energy Storage ; , Energy storage The scheduled charging power and scheduled discharging power; , Energy storage The charging efficiency and discharging efficiency; For energy storage Battery capacity; For energy storage Current battery ; For equipment scheduling time step; The energy storage scheduling is pre-set to coincide with the current scheduling time segment. The deviation count includes: In the formula, For energy storage After the current cycle is scheduled Reference The difference, The amount of electrochemical energy storage that can be dispatched at the site, For energy storage Reference for current cycle scheduling .

4. The photovoltaic power plant photovoltaic-storage joint dispatch method considering multiple types of assessments according to claim 3, characterized in that, The system power balance constraints include: In the formula, This represents the total power of all grid-connected stations at the current time section. For the unit The final actual power generation output of the current scheduling time segment; This represents the total auxiliary power consumption of the station.

5. A photovoltaic power plant photovoltaic-storage joint dispatch method considering multiple types of assessments according to claim 4, characterized in that, The system power flow constraints include: In the formula, This represents the available power distribution capacity at the power plant's grid connection point for the current month.

6. A photovoltaic power plant photovoltaic-storage joint dispatch system considering multiple types of assessments, characterized in that, A photovoltaic power plant photovoltaic-storage joint dispatch method considering multiple types of assessments, as described in any one of claims 1-5, includes: The model building module is configured to, under the condition of obtaining the target power command given by the superior dispatcher, ensure that the multi-resource coordinated output of the power station meets the operational indicators of effectively following the operational target; based on the operational indicators, system constraints are constructed, including constraints on the flexibility of real-time dispatch of new energy sources, constraints on the flexibility of real-time dispatch of electrochemical energy storage, constraints on system power balance, and constraints on system power flow; based on the constraints, a multi-variety assessment mileage evaluation model is established, including a power curtailment assessment model, a predictive assessment model, and a ramp-up assessment model; The output module is configured to construct an objective function based on system constraints and a multi-variety assessment mileage evaluation model to minimize curtailment behavior during station operation, and outputs the final result based on the objective function.

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