CN120725399B - Distributed resource aggregation analysis method and system - Google Patents
Distributed resource aggregation analysis method and systemInfo
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Abstract
The invention relates to the technical field of intelligent power management and discloses a distributed resource aggregation analysis method and a distributed resource aggregation analysis system, wherein the distributed resource aggregation analysis system comprises a resource processing module, a data analysis module, an aggregation analysis module and a cooperative regulation and control module; the system comprises a resource processing module, a data analysis module, a cooperative regulation and control module, a class II energy supply end, a data processing module, a cooperative regulation and control module and a corresponding energy supply end, wherein the resource processing module is used for carrying out data acquisition and protocol analysis on an accessed distributed energy supply end, the data analysis module is used for receiving and processing multi-source heterogeneous data from the resource processing module to construct a resource portrait, the class I energy supply end is used for constructing a load model and outputting load prediction data, the class II energy supply end is used for constructing a random process model of output and response of the class II energy supply end and outputting new energy output data, the aggregation analysis module is used for setting an optimal charge and discharge plan of each energy storage unit and a reference scheduling scheme of adjustable load, and the cooperative regulation and control module is used for generating a scheduling instruction and issuing the scheduling instruction to the corresponding energy supply end. The invention can efficiently integrate the power supply resources and precisely quantize the power supply resources, and effectively improves the considerable, measurable and controllable level of the distributed power supply resources.
Description
Technical Field
The invention relates to the technical field of intelligent power management, in particular to a distributed resource aggregation analysis method and system.
Background
At present, new energy represented by wind power and photovoltaic becomes an important component of an electric power system, and the characteristics of dispersion and intermittence of the new energy form a great challenge for a traditional centralized scheduling mode of 'source follow-up'. Particularly in regional power grids like Xinjin areas, the air conditioner load surge during summer high temperature is overlapped with the midday output peak of the photovoltaic power station, the peak-valley difference is further increased, and the photovoltaic high emission problem can occur during the night load valley period. The traditional power grid dispatching mode is difficult to accurately perceive and efficiently coordinate massive distributed resources, and needs to aggregate and optimally regulate and control the distributed power sources, energy storage systems, controllable loads and other resources through digital technical means such as virtual power plants and the like, so that the flexibility, reliability and new energy consumption capacity of a power grid are enhanced, and the method has important significance in building a novel power system, guaranteeing regional energy safety and promoting energy low-carbon transformation.
In the technical field of virtual power plant resource aggregation, a series of researches and practices have been carried out at home and abroad. Prior art schemes typically implement data collection and monitoring based on supervisory control and data acquisition (SCADA) systems or ENERGY MANAGEMENT SYSTEM (EMS) and utilize optimization algorithms to schedule aggregated resources. However, most of the existing systems still focus on the scheduling of traditional controllable loads, and the adaptability to high-proportion new energy access scenes is insufficient. In modeling, the traditional method mostly adopts deterministic prediction or simple statistical model to describe new energy output, and the inherent randomness and fluctuation nature of the new energy output cannot be fully characterized. In architecture design, the existing platform often adopts a relatively centralized data processing mode, and is difficult to support real-time access and efficient calculation of massive heterogeneous resources. In addition, the prior system mostly ignores the space-time characteristics such as geographic distribution, weather association and the like when resources are aggregated, so that the reliability and the precision of an aggregation result are limited.
Although the virtual power plant technology has been widely focused, in the scene of facing high-proportion new energy access, constructing an efficient distributed resource aggregation system still faces a plurality of technical difficulties. Firstly, the new energy output is highly dependent on meteorological conditions, the strong randomness and intermittence of the new energy output make accurate prediction and reliable modeling extremely difficult, and the traditional deterministic model is difficult to apply. And secondly, the distributed resources are various in variety and different in characteristics, not only comprise power source side resources such as photovoltaic and energy storage, but also comprise various flexible loads, and the unified modeling and collaborative optimization complexity is extremely high due to the difference of isomerism, dispersity and response characteristics. Thirdly, the coordination and connection problem between day-ahead-day-real-time multi-time scale scheduling is outstanding, and how to realize rolling optimization and quick decision in a high-dimensional uncertainty environment and ensure the robustness and economy of a scheduling plan is a core difficult problem in practical application. In addition, the problems of real-time access, high-frequency data acquisition and processing, low-delay communication and control and the like of mass equipment also put high demands on the infrastructure and computing power of the system.
Disclosure of Invention
The invention aims to provide a distributed resource aggregation analysis method and a distributed resource aggregation analysis system, which can efficiently integrate power supply resources and accurately quantify the power supply resources, and effectively improve the considerable, measurable and controllable level of the distributed power supply resources.
In order to achieve the above purpose, the present invention provides the following basic scheme:
Scheme one
A distributed resource aggregation analysis system comprises a resource processing module, a data analysis module, an aggregation analysis module and a cooperative regulation and control module;
The resource processing module is used for carrying out data acquisition and protocol analysis on the accessed distributed energy supply end, wherein the distributed energy supply end comprises a I type energy supply end with energy supply sources being controllable loads and a II type energy supply end with energy supply sources being new energy sources;
The data analysis module is used for receiving and processing multi-source heterogeneous data from the resource processing module to construct a resource portrait, constructing a load model and outputting load prediction data aiming at a class I energy supply end, constructing a random process model of output and response of the class II energy supply end and outputting new energy output data;
The aggregation analysis module is used for receiving and processing the data from the data analysis module, and setting an optimal charge-discharge plan and a reference scheduling scheme of an adjustable load of each energy storage unit according to a preset strategy, wherein the preset strategy comprises the steps of solving the optimal charge-discharge plan and the reference scheduling scheme by adopting an MILP algorithm on the basis of load prediction data and new energy output data on a day-ahead time scale;
the collaborative regulation and control module is used for generating a scheduling instruction based on an optimal charge and discharge plan and a reference scheduling scheme and transmitting the scheduling instruction to a corresponding energy supply end.
Scheme II
A distributed resource aggregation analysis method applies the distributed resource aggregation analysis system according to the scheme I to carry out resource aggregation analysis, and comprises the following steps:
Carrying out data acquisition and protocol analysis on the accessed distributed energy supply terminals, constructing resource portraits for each energy supply terminal based on historical data, and generating resource portrait labels representing the operation characteristics of the resource portraits;
Aiming at the I-type energy supply end, a load model is built and load prediction data are output, and aiming at the II-type energy supply end, a random process model of the output and response of the II-type energy supply end is built and new energy output data are output;
Setting an optimal charge-discharge plan and a reference scheduling scheme of an adjustable load of each energy storage unit according to a preset strategy, wherein the preset strategy comprises the steps of solving the optimal charge-discharge plan and the reference scheduling scheme by adopting an MILP algorithm based on load prediction data and new energy output data on a day-ahead time scale, and periodically updating on the day-ahead time scale;
Based on the optimal charge-discharge plan and the reference scheduling scheme, a scheduling instruction is generated and issued to a corresponding energy supply end, execution deviation is monitored in real time, when the deviation between the actual output of the new energy and the predicted value is monitored to exceed a preset threshold value and lasts for a certain time, an emergency cooperative stabilization strategy is automatically triggered, and an energy storage unit in an optimal working interval is preferentially invoked to perform power compensation.
The working principle and the advantages of the invention are as follows:
The distributed resource aggregation analysis method and system can efficiently integrate the power supply resources and accurately quantify the power supply resources, and effectively improve the considerable, measurable and controllable level of the distributed power supply resources. The key point is that the scheme is particularly used for carrying out differentiated aggregation analysis aiming at a high-proportion new energy access scene. The data analysis module is used for carrying out corresponding modeling particularly aiming at different energy supply types, wherein a load prediction model is adopted for capturing a human power consumption behavior rule for an I-type energy supply end, a random process model is established for quantifying uncertainty of wind, light and other energy sources for an II-type energy supply end, and the accuracy of modeling of the resource characteristics can be remarkably improved through the differentiation processing method, so that reliable input is provided for follow-up optimal scheduling. Compared with the existing research which is concentrated on scheduling of a single time scale, the scheme solves the optimal plan by adopting a Mixed Integer Linear Programming (MILP) algorithm on a day-ahead scale, and periodically rolls and updates on the day-ahead scale, so that the prospective of the plan is ensured, and the flexibility of real-time adjustment is also considered. In addition, the controllable load and the new energy are placed under a unified frame for coordination and optimization, and the complementary regulation and control of the cross-resource type can be realized through the joint solution of the energy storage charging and discharging plan and the load scheduling scheme, so that the scheduling mechanism is more flexible and comprehensive.
Drawings
Fig. 1 is a schematic diagram of a system structure of an embodiment of a distributed resource aggregation analysis method and system according to the present invention.
Detailed Description
The following is a further detailed description of the embodiments:
The embodiment is basically shown in the figure 1, and the distributed resource aggregation analysis system comprises a resource processing module, a data analysis module, an aggregation analysis module, a cooperative regulation and control module, a resource aggregation visualization module and a simulation deduction module.
The resource processing module is used for carrying out data acquisition and protocol analysis on the accessed distributed energy supply end. The distributed energy supply end comprises an I-type energy supply end with energy supply sources being controllable loads and an II-type energy supply end with energy supply sources being new energy sources.
The resource processing module is used for collecting data, the collected data types comprise operation data and environment data of the distributed energy supply ends, the operation data comprise real-time active power, reactive power, charge states, energy supply equipment temperatures, grid-connected point voltages and historical charging and discharging data of the energy storage units, the environment data comprise real-time meteorological data in a radius range of 5 km with the geographic positions of the distributed energy supply ends as centers, and the real-time meteorological data comprise irradiance, environment temperatures, wind speeds and wind directions.
Specifically, in this embodiment, data is collected from Supervisory Control And Data Acquisition (SCADA), ENERGY MANAGEMENT SYSTEM (EMS), each station Remote Terminal Unit (RTU) or Internet of Things (IoT) gateway, where macro electricity data such as total active power, total reactive power, switching status, critical loop current, etc. are collected for class I energy supply terminals (controllable loads, including industrial tunable devices, flexible residential loads, etc.) with a period of minutes (e.g., 5-15 minutes). For a class II energy supply end (new energy source, including a photovoltaic power station, distributed photovoltaic power/wind power and the like), detailed running state data of the class II energy supply end (including real-time active/reactive power, direct-current side voltage/current, alternating-current side voltage/current, built-in temperature, alarm information, grid-connected point frequency, and specific state of charge (SOC), health Status (SOH), charge and discharge times and the like of energy storage equipment are collected at a high frequency of a second level (such as 1-5 seconds).
Furthermore, the resource processing module is internally provided with a protocol adaptation engine for executing a protocol analysis function, and supports the Profinet protocol and the MPI/PPI protocol which are commonly used for simultaneously communicating with a class I energy supply end (such as an industrial Programmable Logic Controller (PLC)), and establishes connection with the IEC 61850, modbus TCP, MQTT, DNP3 and other protocols which are commonly used for communicating with a class II energy supply end (a photovoltaic inverter, an energy storage converter (PCS) and a fan controller).
The resource processing module is also provided with a preprocessing unit, and the preprocessing unit is used for cleaning data and checking quality. Specifically, in this embodiment, a preset data validity rule is applied to perform real-time cleaning and verification, where the data validity rule includes range verification, checking whether the data is within a reasonable physical range (for example, SOC value is between 0 and 100%), jump verification, identifying abrupt changes between adjacent data points (for example, power change rate exceeds 20% of rated value), smoothing or marking the abrupt changes, and relevance verification, and cross-verifying meteorological data (for example, photovoltaic output should be zero when irradiance at night) for a class II energy supply end.
The data analysis module is used for receiving and processing the multi-source heterogeneous data from the resource processing module and constructing a resource portrait. And building a random process model of the output and response of the class II energy supply end and outputting new energy output data.
Specifically, when constructing the resource portrait, the method comprises the step of adding a resource identifier for each energy supply end based on data of various energy supply ends. The resource identification comprises a class I resource label, namely an interruptible load, a translatable load, a maximum reduction power, a minimum response time length and a comfort degree constraint, and a class II resource label, namely a photovoltaic/energy storage type, a rated capacity, a current adjustable capacity, a weather dependency degree, a volatility grade and an SOH health state.
The data analysis module comprises the following steps when constructing a random process model:
modeling the output fluctuation of the II-type energy supply end by adopting a random differential equation, wherein the new energy is the photovoltaic II-type energy supply end of solar energy, and the photovoltaic output of the II-type energy supply end Is described by the Jacobi process:
;
wherein, the Is the variation of the output with time, the average recovery rateMean value of conditionsCoefficient of fluctuation ratioThe dynamic mapping generation of the gradient lifting decision tree model is realized through pre-training by the real-time irradiance and the ambient temperature. Specifically, meteorological data such as total irradiance, scattered irradiance, cloud cover, ambient temperature, wind speed, humidity and the like acquired in real time are formed into a characteristic vector M (t). Then a pre-trained gradient lifting decision tree (GBDT) regression model is used, M (t) and historical window data thereof are used as input, and Jacobi process parameters at the current moment are output on line in real time. The GBDT model is good at capturing complex nonlinear relations among characteristics, and can effectively learn how meteorological conditions influence fluctuation characteristics of output.
The Jacobi process is selected to replace the traditional single deterministic prediction point, and the natural existence of the Jacobi process is utilized to restrict the random variable to a fixed intervalCan perfectly fit the characteristics of normalized photovoltaic outputThe fluctuation range of the photovoltaic output (limited by 0 and the maximum available output) and the mean recovery and random diffusion characteristics in the fluctuation range can be simultaneously described, and the accurate evaluation of the photovoltaic output is realized.
The data analysis module is provided with an energy storage health state evaluation unit which is used for calculating accumulated throughput electric quantity by an ampere-hour integration method based on collected historical charge and discharge data (including cycle start/end SOC, total charge quantity, total discharge quantity, average charge and discharge power, duration time, average temperature and the like) of the energy storage unit, and combining the rated cycle life of the accumulated throughput electric quantity according to the formula:
the state of health is dynamically estimated and the upper limit of the adjustable capacity of the energy storage unit with SOH value lower than 80% is automatically reduced to 90% of its nominal value.
The latest SOH value is calculated by the energy storage health assessment unit periodically (e.g. daily). If the SOH value is lower than 80%, automatically generating a capacity correction coefficient (0.9), sending the coefficient to an aggregation analysis module and a cooperative regulation and control module, and multiplying the maximum chargeable and dischargeable power and the maximum available capacity of stored energy by the capacity correction coefficient when the downstream modules make a scheduling plan, so that capacity attenuation caused by equipment aging is effectively considered in regulation and control, an instruction exceeding the actual capacity of the equipment is avoided, and the service life of the equipment is prolonged.
And the data analysis module is also provided with an output characteristic index calculation unit for calculating the fluctuation rate index and the climbing risk index of the photovoltaic output. Wherein, in calculating the fluctuation rate, the photovoltaic output is calculated by rolling within a time window (such as 15 minutes)The standard deviation of the first order difference (difference between successive data points) is used as a quantization index of the short-term fluctuation rate, i.e., a fluctuation rate index. When the climbing risk is calculated, firstly, based on historical photovoltaic output data and corresponding environmental data, the maximum rising and falling climbing rates of the photovoltaic output in unit time (such as 1 minute and 5 minutes) under different weather types are counted to form climbing event probability distribution, and in the embodiment, when the rising climbing rate exceeds a certain larger value (such as 50 kW/min), or the absolute value of the falling climbing rate exceeds a certain larger value (such as-60 kW/min, the absolute value is taken to be 60 kW/min), the climbing event is considered to be an extreme climbing event. And calculating the probability of the occurrence of the extreme climbing event based on the climbing event probability distribution obtained in the previous step, and taking the probability as a climbing risk index. For example, in a certain weather type and a certain unit time, the probability of an extremely ascending climbing event=the number of samples/the total number of samples of the type that the ascending climbing rate exceeds the threshold value per unit time.
The aggregation analysis module is used for receiving and processing the data from the data analysis module, and setting an optimal charge and discharge plan of each energy storage unit and a reference scheduling scheme of an adjustable load according to a preset strategy.
The preset strategy comprises the steps of solving an optimal charge-discharge plan and a reference scheduling scheme by adopting an MILP algorithm based on load prediction data and new energy output data in a day-ahead time scale, and periodically updating in the day-ahead time scale.
When an MILP algorithm is adopted to solve an optimal charge-discharge plan and a reference scheduling scheme, an MILP model aiming at minimizing the total running cost of a virtual power plant is established, and the objective function of the MILP model is as follows:
;
wherein, the Cost of purchasing electricity from the main network for period t,The electricity price of the online shopping is set,The power is purchased; the benefits of selling electricity to the main network for period t, The electricity price of the network is the electricity price of the internet,Is the electricity selling power, and the negative sign represents the income; In order to save the energy consumption cost, Is the energy storage depreciation coefficient,Is the sum of the absolute values of the charge and discharge powers of the i energy storage units.
The constraint conditions of the MILP model comprise a system power balance constraint, an energy storage SOC dynamic update constraint, an operation boundary constraint, a new energy output uncertainty constraint, a reducible constraint and a duration constraint.
In this embodiment, the system power balance constraint is specifically expressed as:
。
wherein, the Indicating that the power is purchased from the main network in the period t,Represents the sum of the output of all photovoltaic units (i) in the t period,Representing the sum of the charge and discharge powers (charge is negative and discharge is positive) of all the energy storage units (i) in the t period; representing the load demand during the period t, Indicating the amount of load reduction in the t period.
The energy storage SOC dynamic update constraint is specifically expressed as:
;
wherein, the The charge state of the ith energy storage unit at the time t is represented (the value is 0-100%); Representing the charge and discharge power of the ith energy storage unit in the t period; The time step is represented by a time step, Indicating the rated capacity of the i-th energy storage unit.
The operation boundary constraint of the energy storage SOC is specifically expressed as:
。
In the present embodiment of the present invention, The value of (2) can be 10% -20%,The value of (2) can be 90% -95%.
The uncertainty constraint of the new energy output is specifically expressed as:
。
wherein, the The maximum charging power of the ith energy storage unit is represented by a negative sign, and the power is negative during charging; is the maximum discharge power of the ith energy storage unit.
The curtailable quantity constraint is specifically expressed as: . Wherein, the Representing the curtailed power of the jth controllable load during the t period,Representing the maximum allowable curtailed power of the jth controllable load during the t period.
The duration constraint is expressed in particular as that a certain load is invoked at most N times a day, or that the continuous invocation time must not exceed M time periods. M, N is set according to the actual power supply requirement.
Through the constraint condition limitation, the feasible domain of the MILP model operation can be jointly framed, so that the MILP model can optimize the scheduling strategy on the premise of meeting the physical rule.
In the preset strategy, updating is carried out once in a daily time scale with 15 minutes as a period, based on the latest actual SOC data and new energy output data, with the adjustment amount minimized as a target, an MPC framework (model predictive control framework) is adopted to correct the optimal charge-discharge plan and the reference scheduling scheme before the day, and an equivalent adjustable power upper limit, an equivalent adjustable power lower limit and a climbing rate curve of the polymer are output.
In the preset strategy, in the time scale in the day, an objective function constructed by taking the adjustment amount minimization as the objective is as follows:
;
wherein, the Is the power stored in the plan before the day,Is the adjusted power.
The collaborative regulation and control module is used for generating a scheduling instruction based on an optimal charge and discharge plan and a reference scheduling scheme and transmitting the scheduling instruction to a corresponding energy supply end. Specifically, in this embodiment, the cooperative regulation module receives the optimal charge-discharge plan and the reference scheduling scheme (JSON format) from the aggregation analysis module through a message queue (e.g., kafka), and extracts a scheduling instruction of each energy supply end at each time point (for example, for the energy storage unit, the scheduling instruction includes a power set value, an expected charge state and an operation mode of each time point, and for the controllable load, the scheduling instruction includes a planned cut-down power, a baseline load predicted value and an instruction state of each time point), and then issues the scheduling instruction to a device gateway or a controller corresponding to each energy supply end. All instructions are attached with unique instruction ID, time stamp generation and execution validity period (if no response occurs within 15 minutes, the execution validity period is automatically disabled), and the instructions are issued through standardized protocols (such as MQTT-SN) so as to adapt the communication capability of different energy supply ends.
The resource aggregation visualization module is used for performing the following operations that on a Web-based GIS map, different resource types including photovoltaics and energy storage are represented by circular marks with different colors, the area size of the mark represents the rated capacity of the mark, the shade of the mark represents the proportion of the current adjustable capacity of the mark to the rated capacity, for example, dark red represents the adjustable capacity to be close to 100%, and light pink represents the adjustable capacity to be less than 20%.
The simulation deduction module is used for enabling a user to customize meteorological scenes (such as customizing meteorological parameters in a sliding block, a drop-down frame, form input and other modes, including setting illumination intensity, temperature, wind speed, cloud cover coefficient, irradiation attenuation rate and the like), driving the data analysis module and the aggregation analysis module to carry out simulation calculation, and outputting an evaluation report of the total output range, expected benefit and expected scheduling risk (such as load loss risk, namely probability of load shedding caused by insufficient new energy output and energy storage electric quantity exhaustion) of the aggregate in the future 24 hours under the customized scenes.
When the simulation calculation is performed, the environmental data sources of the data analysis module and the aggregation analysis module are switched into user-defined meteorological scenes. Specifically, when the simulation deduction is triggered, the simulation deduction module intercepts a conventional environmental data acquisition request (such as acquiring data from a real-time weather monitoring system and a historical database) of the data analysis module and the aggregation analysis module through middleware (such as a message queue) and redirects the conventional environmental data acquisition request to a simulation scene data cache area. The simulated scene data buffer area generates simulated environment data including a photovoltaic irradiation prediction sequence, a temperature sequence and the like according to a meteorological scene customized by a user and in time sequence (15 minutes or 1 hour is taken as a time step), and the simulated environment data are injected into the data analysis module according to the original data format and frequency, so that the module logic can operate based on the simulated data without modification.
The embodiment also provides a distributed resource aggregation analysis method, which is applied to the distributed resource aggregation analysis system for resource aggregation analysis and comprises the following steps:
and carrying out data acquisition and protocol analysis on the accessed distributed energy supply terminals, constructing resource portraits for each energy supply terminal based on historical data, and generating resource portrait labels representing the operation characteristics of the resource portraits.
And constructing a random process model of the output and response of the class II energy supply end and outputting new energy output data.
Setting an optimal charge-discharge plan and a reference scheduling scheme of an adjustable load of each energy storage unit according to a preset strategy, wherein the preset strategy comprises the steps of solving the optimal charge-discharge plan and the reference scheduling scheme by adopting an MILP algorithm based on load prediction data and new energy output data in a day time scale, and periodically updating in the day time scale.
Based on the optimal charge-discharge plan and the reference scheduling scheme, a scheduling instruction is generated and issued to a corresponding energy supply end, execution deviation is monitored in real time, when the deviation between the actual output of the new energy source and a predicted value exceeds a preset threshold (such as 15%, for example, when the photovoltaic prediction error of the cloudy day is large, the deviation can be temporarily widened to 20%), and when the deviation lasts for a certain time (such as 3 min-5 min), an emergency cooperative stabilization strategy is automatically triggered, and an energy storage unit in an optimal working interval is preferentially called to perform power compensation.
In the embodiment, the definition of the optimal working interval is that the SOC is 40-80% (both charge and discharge flexibility and life protection),(Good health status),(Low energy consumption), and no fault alarm within 30 minutes.
When power compensation is carried out, the set initial compensation quantity is equal to the actual output deviation value of the new energy source (if the actual output is lower than the predicted value, energy storage and discharge compensation is needed, otherwise, energy storage, charge and absorption are carried out), and the initial compensation quantity is distributed to the screened energy storage units according to the energy storage capacity proportion (if the total compensation requirement is 500kW, the energy storage capacity A is 2MWh, the energy storage capacity B is 3MWh, the energy storage A bears 200kW, and the energy storage B bears 300 kW), and meanwhile, the compensation power of a single energy storage is limited to be not more than 80% of the rated power of the single energy storage unit, so that overload is avoided.
In the execution process, the compensation effect is updated every 30 seconds, namely, if the deviation falls into a threshold value, the current compensation amount is kept, if the deviation still exceeds the threshold value, secondary energy storage (SOC 30% -40% or 80% -90%) is started to supplement compensation, and if the deviation is not converged after 3 continuous updating, the I-type energy supply end is linked to adjust (for example, the power is temporarily reduced when the interruptible load is called). When the deviation lasts for 2 minutes and is lower than the threshold value, the strategy automatically exits, the energy storage gradually recovers to the original charge-discharge plan (the recovery rate is not more than 50% of the maximum climbing rate of the energy storage, the power grid is prevented from being impacted), and meanwhile emergency process data (trigger time, compensation quantity, energy storage response speed and the like) are uploaded to the aggregation analysis module for optimizing the charge-discharge plan of the next day.
The distributed resource aggregation analysis method and system provided by the embodiment can efficiently integrate the power supply resources and accurately quantify the power supply resources, and effectively improve the considerable, measurable and controllable level of the distributed power supply resources.
In the resource access and perception level, the protocol adaptation engine realizes the wide compatibility of heterogeneous modeling equipment, ensures the comprehensiveness and instantaneity of data acquisition, and deeply fuses meteorological data and equipment operation data, thereby providing a reliable data base for subsequent accurate modeling and prediction. In the modeling layer, the scheme abandons the traditional certainty method, particularly adopts a random differential equation theory, particularly a Jacobi process to describe the non-Gaussian and non-steady fluctuation characteristics of the photovoltaic output, and model parameters are dynamically generated by real-time meteorological data through a machine learning model, so that the model not only has a firm mathematical physical foundation, but also can dynamically adapt to external environment changes, and the quantification precision of the uncertainty of the output of the new energy is obviously improved.
In the aggregation and scheduling layer, the scheme sets an optimization framework for tightly linking a plurality of time scales in the day ahead and the day ahead, the day ahead optimization makes a preliminary plan with optimal economical efficiency, the day ahead rolling optimization corrects the deviation in real time based on ultra-short-term prediction, the economical efficiency and the robustness of the scheduling plan are effectively balanced through a layered progressive optimization mechanism, and the improvement of the power grid on the digestion capacity and the operation safety of the distributed new energy is facilitated.
In addition, in order to verify the application effect of the scheme, a power utilization scheduling case of an industrial park in a Xinjin area in a summer peak period is selected for simulation analysis, an application scene is set to be 8 months and a week, weather forecast shows that the next day is a sunny and cloudy day, and short-time dispersive gusts exist in afternoon. The grid issues a next day 14:00-15:00 demand response command, asking the area to cut the load by 1.5MW.
The aggregate resources comprise a II-type energy supply end (new energy source), a garden roof photovoltaic power station (total capacity 4 MW), a distributed energy storage station (total capacity 2MW/4 MWh), a I-type energy supply end (controllable load), a A-type factory (controllable production line can be interrupted by 800kW at maximum), a B-technology company (adjustable central air conditioner, 400kW at maximum) and a C-type data center (adjustable refrigeration system, 300kW at maximum).
And in the day-ahead stage, an economic optimal plan is formulated, wherein the resource processing module acquires all resource states and displays that the energy storage average SOC is 65%. The data analysis module generates a probabilistic prediction of the photovoltaic output by a Jacobi process model based on weather forecast (clear-to-cloudy), showing the next day 14:00 output expectation of 3.2MW, but with higher uncertainty (70% probability between 2.8MW-3.5 MW). The aggregate analysis module performs MILP day-ahead optimization. It was calculated that the most economical solution to meet the 1.5MW cut target was to call a class I resource, let A plant cut 800kW, B company cut 400kW, and C data center cut 300kW (total 1.5 MW). The scheme has the advantages of lowest cost, no need of using energy storage and capability of avoiding depreciation loss. And then generating the reference scheduling scheme and submitting the reference scheduling scheme to a power grid scheduling center.
In the daytime, the real-time adjustment is carried out, the next day is 13:45, the weather suddenly changes, and the rainfall is advanced. The real-time monitoring of the resource processing module shows that the actual output of the photovoltaic power is drastically reduced to 1.8MW, which is 1.4MW lower than the predicted value before the day. If only 1.5MW of load is cut off as originally planned, the overall load of the park will be 1.4MW (photovoltaic gap) +1.5MW (planned cut-off) =2.9 MW of power shortage, which will lead to a decrease in the frequency of the grid inside the park and even to a power outage.
In this case, the data analysis module immediately triggers an ultra-short term predictive update, predicting that the future 1 hour photovoltaic output will continue to be low. MPC rolling optimization of the aggregate analysis module is triggered. Under new constraints (a substantial decrease in photovoltaic output), the goal is to recalculate with "minimize regulation and avoid power outage". And outputting a new decision, immediately terminating the reduction instruction (power supply is restored to ensure the safety of the server due to the high priority) of the C data center, and starting an energy storage collaborative stabilizing strategy. The cooperative regulation and control module issues instructions to the energy storage station to discharge at the maximum power of 1 MW. Instructions were issued to the a-plant and the B-plant to maintain the original curtailment plan (800 kw+400 kw). At this time, the total compensation power is 1MW (energy storage) +1.2MW (load reduction) =2.2 MW, and most of gaps are filled.
Therefore, the system can quickly make up for the power shortage, and intelligently avoid the fluctuation of the power grid frequency and potential power failure accidents. Under the condition of no disturbance, the system selects the load response scheme with the lowest cost through daily optimization, and saves the energy storage depreciation cost. Under extreme conditions, through quick adjustment, huge economic loss possibly caused by power failure is avoided, and production safety is protected. And photovoltaic power generation is utilized to the maximum extent through accurate random prediction and multi-time scale optimization, and the photovoltaic power generation is converted into stable output through energy storage even under the fluctuation condition, so that the light rejection rate is remarkably reduced.
The foregoing is merely an embodiment of the present application, and a specific structure and characteristics of common knowledge in the art, which are well known in the scheme, are not described herein, so that a person of ordinary skill in the art knows all the prior art in the application date or before the priority date, can know all the prior art in the field, and has the capability of applying the conventional experimental means before the date, and a person of ordinary skill in the art can complete and implement the present embodiment in combination with his own capability in the light of the present application, and some typical known structures or known methods should not be an obstacle for a person of ordinary skill in the art to implement the present application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present application, and these should also be considered as the scope of the present application, which does not affect the effect of the implementation of the present application and the utility of the patent.
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