Control method for distributed energy supply system

Abstract

A control method for a distributed energy supply system of the present invention includes a plurality of energy supply devices and a control module or control system configured to control operation of the plurality of energy supply devices to supply power to a power grid. For example, the control method includes inputting first data into an optimization algorithm to determine power supply parameters corresponding to one or more of the plurality of devices, transmitting a power supply command to the one or more devices to supply power based on the power supply parameters, acquiring second data for each of the one or more devices from measurements of the device while power is being supplied, inputting the second data into a device behavior model corresponding to the device, estimating one or more device parameters of the device using the device behavior model, and updating the optimization algorithm corresponding to each of the one or more devices using the one or more estimated device parameters. [Representative diagram] Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to the operation of a distributed energy supply system, such as a virtual power plant (VPP), comprising a plurality of energy supply devices and configured to supply power to a power grid. [Background technology]

[0002] Optimization of distributed energy supply systems, including virtual power plants (VPPs), typically relies on models of individual energy supply devices and facilities, such as medium-scale power generation units, micro-cogeneration units, gas engines, small-scale wind power generation units, solar power units, hydroelectric power plants, biomass power generation units, backup power generation units, and energy storage systems. These models simulate how the energy supply devices connect to the distributed energy supply system, one or more power sources, other energy supply systems, and how they interact with the energy market. These models can be incorporated into optimization models used by the distributed energy supply system to control the operation of individual or groups of energy supply devices. Such models typically have a fixed configuration and cannot adapt to changing conditions that affect the performance of individual energy supply devices, such as the storage capacity and / or power supply capability of the device, weather conditions such as temperature and humidity that can affect the performance of the energy supply device, and changes in operating efficiency due to age.

[0003] Device parameters (e.g., state-of-charge (SoC) range, operating power range, operating temperature range, etc.) specified by manufacturers to describe various operating characteristics of energy supply devices often lack precision and specificity, and these device parameters may change over time. Furthermore, many of these device parameters may vary depending on factors such as operating conditions (e.g., temperature), device power and / or SoC, and past behavior. Therefore, device parameters often cannot be accurately specified even based on manufacturer laboratory testing. As a result, conventional models used to optimize the operation of energy supply devices participating in distributed energy supply systems, even if initially validated in factory testing, may not accurately represent actual behavior.

[0004] Therefore, improved methods for controlling the operation of energy supply devices in distributed energy supply systems are desirable. Summary of the Invention [Means for solving the problem]

[0005] In consideration of the above-mentioned problems, one aspect of the present technology provides a computer-implemented method for controlling a distributed energy supply system, the distributed energy supply system comprising a plurality of energy supply devices and a control module or control system configured to control operation of the plurality of energy supply devices and configured to supply power to a power grid, the method including the following steps: inputting first data into an optimization algorithm to determine power supply parameters corresponding to each of the one or more energy supply devices; sending a power supply command to each of the one or more energy supply devices to operate the one or more supply devices to supply power based on the respective power supply parameters; for each of the one or more energy supply devices, acquiring second data by measuring the energy supply device while power is being supplied; inputting the second data into an equipment behavior model corresponding to the energy supply device; estimating one or more equipment parameters of the energy supply devices using the equipment behavior model; and updating the optimization algorithm based on the one or more estimated equipment parameters corresponding to each of the one or more energy supply devices.

[0006] An embodiment of the present technology provides a method for controlling the operation of multiple energy supply devices or systems in a distributed energy supply system (e.g., a virtual power plant) that is changeable and autonomously adaptively correctable to updated operating conditions. In particular, an optimization algorithm (such as, but not limited to, a mixed integer linear programming (MILP) algorithm) can be used to generate control commands for each participating energy supply device or group of devices to supply power. Input to the control algorithm is obtained, for example, from device behavior models, each of which describes the operating behavior of one (or a group of) energy supply devices. According to an embodiment, the device behavior models or control algorithm include, for example, manufacturer-specified operating parameters for the multiple energy supply devices. Power supply parameters (e.g., supply rate, total energy supply, etc.) are determined by the control algorithm, and commands for the participating energy supply devices (e.g., individual commands for each energy supply device or common commands for each group of energy supply devices) are generated. The commands are then transmitted to the participating energy supply devices, which operate to supply power based on the respective power supply parameters. While the energy supply device (or group of energy supply devices) is operating to supply power, the device is independently instrumented (e.g., various parameters are measured) to obtain a second set of data, which may include, for example, operating power, operating temperature, total energy delivered, etc. The obtained second data is input into an equipment behavior model for the associated energy supply device (or group of energy supply devices), and an updated set of equipment parameters for the energy supply device can be estimated by querying or analyzing the updated equipment behavior model. The updated set of equipment parameters for each participating energy supply device (or group of participating energy supply devices) is passed to the control algorithm to update the control algorithm. In this way, the equipment operation model, and therefore the control / optimization algorithm that generates the energy supply device's operating instructions, is continuously updated in response to changing operating and environmental conditions, degradation of the device and other meters, and socio-economic factors such as market fluctuations.

[0007] In some embodiments, the method may further include storing the second data in an observation database, which allows for analysis of historical operation and trends of the energy delivery device, thereby further improving the device behavior model and, therefore, the optimization algorithm.

[0008] In some embodiments, the method may further include obtaining, for each of the one or more energy delivery devices stored in the observation database, respective second-by-second historical data and inputting the respective second-by-second historical data into a device behavior model for each of the one or more energy delivery devices. Here, "second-by-second historical data" refers to second-by-second historical data obtained from the energy delivery device being measured from a driving cycle preceding a current driving cycle (time period) in which the device is being measured. This may include previous cycles on the same or different days, and may be configurable over any time scale. Optionally, a threshold may be set to limit the amount of second-by-second historical data within a relevant time period.

[0009] In some embodiments, the first data may include manufacturer-specified device parameters for one or more energy delivery devices, such as operating power, maximum / minimum state of charge, operating temperature, efficiency curve, response time, environmental parameters such as ambient temperature, humidity, or operating cost, or any combination thereof.

[0010] In some embodiments, the second data may include device parameters obtained through measurement, including operating power, operating temperature, SoC, changes in device configuration, or any combination thereof.

[0011] In some embodiments, the one or more device parameters for each energy delivery device may include operating efficiency and / or maximum operating power at a given state of charge.

[0012] In some embodiments, a device behavior model corresponding to each energy supply device may be configured to simulate the operation of each energy supply device based on second data acquired from the energy supply device, meters associated with the energy supply device, and power supply instructions associated with the energy supply device. For example, the device behavior model may include a set of rules defining relationships between device operating parameters. By way of example, these relationships may be defined using one or more linear equations. However, nonlinear equations or other forms of constraints may also be used. Such a device behavior model may be based on, for example, one or more physical processes within the device (e.g., a chemical battery process, power input / output to / from electrical components such as inverters or wiring, etc.), or may be configured as an abstract representation of observed data based on bench testing. In the former case, a mapping between a set of observed device parameters and a set of model rules is defined. In the latter case, for example, because the model is an abstract representation of a data set, changes in the observed data may require the model to be rebuilt. In some embodiments, the set of rules defining the device model may be determined automatically (or with minimal human intervention) using a machine learning algorithm.

[0013] In some embodiments, the power supply instructions may include an operation start time, an operation end time, an operation duration, an operating power at a specific point in time, an average operating power, a total amount of energy to be supplied, or any combination thereof. In these embodiments, the power supply instructions express a desired behavior for the associated device, and may be specified specifically as an action the device should perform (e.g., set power to x level and maintain it for y minutes) or as a condition to be met (e.g., provide x amount of energy for the next y minutes). Either form, or a combination of both, may be employed depending on the type of device and control system.

[0014] In some embodiments, the optimization algorithm may be a solution to a mixed integer linear programming problem (MILP). Other examples of optimization algorithms include nonlinear optimization such as gradient descent or quadratic programming, deep neural networks, large-scale neural networks, or hybrid models based on classical statistical inference or Bayesian inference.

[0015] Another aspect of the present technology provides a computer readable medium comprising machine readable code that, when executed by a processor, causes the processor to perform the method described above.

[0016] In accordance with a further aspect of the present technology, there is provided a control module for operating a plurality of energy supply devices in a distributed energy supply system to supply power to a power grid, the control module including one or more processors configured to execute machine-readable code stored in memory to perform the methods described above.

[0017] According to yet another aspect of the present technology, there is provided a distributed energy supply system configured to supply power to a power grid, the distributed energy supply system including: a plurality of energy supply devices configured to output power; and a control module for controlling operation thereof, the control module including a memory that stores machine-readable code and one or more processors configured to execute the code, wherein execution of the machine-readable code causes the one or more processors to perform the following: inputting first data into an optimization algorithm to determine power supply parameters corresponding to each of the plurality of energy supply devices; sending power supply commands to the plurality of energy supply devices to operate the plurality of energy supply devices to supply power based on the corresponding power supply parameters; obtaining, for each of one or more energy supply devices, second data from measurements of the energy supply device while it is supplying power; inputting the second data into an equipment behavior model of the energy supply device; estimating one or more equipment parameters of the energy supply device using the equipment behavior model; and updating the optimization algorithm corresponding to each of the one or more energy supply devices using the one or more estimated equipment parameters.

[0018] In some embodiments, the distributed energy supply system may be a Virtual Power Plant (VPP).

[0019] In some embodiments, the plurality of energy supply devices may include one or more medium-scale power generating units, one or more micro-cogeneration units, one or more natural gas-fired reciprocating engines, one or more small-scale wind power plants, one or more solar power generating units, one or more hydroelectric power plants, one or more biomass power plants, one or more standby generators, one or more energy storage systems and devices, or any combination thereof.

[0020] Each embodiment of the present technology will have at least one, but not necessarily all, of the above-described objects and / or aspects, and it should be understood that some aspects of the present technology that arise from an attempt to achieve the above-described object may not meet that object and / or may meet other objects not specifically set forth herein.

[0021] Additional and / or alternative features, aspects, and advantages of embodiments of the present technology will become apparent with reference to the following description, accompanying drawings, and claims. [Brief explanation of the drawings]

[0022] Hereinafter, embodiments will be described with reference to the accompanying drawings. [Figure 1] FIG. 1 is a schematic diagram illustrating an example distributed energy supply system configured to supply energy to a power grid. [Figure 2] FIG. 1 illustrates an example of a method for controlling a distributed energy supply system. DETAILED DESCRIPTION OF THE INVENTION

[0023] FIG. 1 shows a schematic diagram of an example of a distributed energy supply system 100 , such as a virtual power plant, that is configured to supply power to a power grid 190 .

[0024] In this embodiment, a distributed energy supply system 100 includes a plurality of energy supply devices and systems and a control module or control system 110 configured to control the operation of the plurality of energy supply devices and systems, which in this embodiment include one or more solar panels or photovoltaic farms 120, one or more residential or commercial electric vehicle batteries 130, one or more solar panels 140 that provide energy to one or more energy storage devices or systems 150, and one or more wind turbines 160, although other energy supply devices or systems and / or power generation devices or systems are possible.

[0025] The plurality of energy delivery devices and systems 120-160 communicate with a control module or control system 110 via a network 170 to receive commands from the control module / system 110 and to enable the control module / system 110 to obtain measurements and observations (e.g., through receiving meter readings) regarding the operation of the energy delivery devices and systems 120-160. The control module / system 110 communicates with an electrical power grid 190 to regulate the delivery of electrical power, and the plurality of energy delivery devices and systems 120-160 are individually connected to the electrical power grid 190 and deliver electrical power to the grid 190 based on commands from the control module / system 110.

[0026] In traditional distributed energy supply systems, the operation of multiple energy supply devices is controlled by static equipment models that describe the operating characteristics of each device. Energy supply devices are bench-tested against a set of criteria in a laboratory or standardized environment, such as during manufacturing quality assurance, to measure the device's performance parameters (e.g., capacity, efficiency, etc.). These manufacturer-specified device parameters are input into the corresponding equipment model to determine operating parameters (e.g., power supply rate, operating time, total energy supply, etc.). These operating parameters are input into an optimization algorithm to generate control outputs or commands for controlling the operation of the energy supply devices. Traditionally, these equipment models are not updated (and thus are static) or are manually updated by a human operator only when significant changes in the device parameters are identified, for example, through post-mortem analysis of operating data. In some cases, secondary algorithms are used in addition to the primary optimization algorithm to compensate for inaccuracies in the primary optimization algorithm due to inaccurate or outdated equipment models.

[0027] Device parameters that determine the operation of an energy delivery device (e.g., maximum power output, response speed, state-of-charge range, efficiency, etc.) can vary depending on many factors, and the applicant recognizes that it is impractical to test every combination of parameters and parameter values ​​and consider how the parameters change over time before deploying the device. Furthermore, the location where the device is installed can also affect the device parameters. Therefore, it may be impossible to accurately determine these device parameters before installation, and it may be impossible to reuse device parameters determined in one location in another. For example, the amount of energy used by a device during service may change as market forces change. Some parameters (e.g., the amount of energy consumed while the device is running) may fluctuate due to incremental changes in market forces, which may also affect the operation of the device. Examples of changing market dynamics include an increase in the supply of batteries in the market, providing additional capacity and driving down usage, or the introduction of a secondary market that absorbs some of the activity and distribution of devices normally traded in existing markets.

[0028] Embodiments of the present technology therefore provide an improved method for controlling multiple energy supply devices or systems that participate in a distributed energy supply system, such as a virtual power plant.

[0029] Figure 2 is a schematic diagram of an example control mechanism, according to some embodiments of the present technology, for controlling the operation of multiple energy delivery devices or systems in a distributed energy delivery system, such as system 100 shown in Figure 1. One or more stages of the control mechanism may be performed by a control module / system 110.

[0030] In this embodiment, the method begins with a control algorithm, such as an optimization algorithm (e.g., an MILP algorithm or model) 200, being initialized by inputting (manually or autonomously) first data and operational constraints, such as market prices 220. In some embodiments, the first data includes default, manufacturer-specified, or lab-tested device parameters for each energy delivery device, such as maximum power or energy output, average operating power, maximum / minimum or range state of charge, environmental parameters such as operating temperature, ambient temperature and humidity, or operational costs, or any combination thereof. That's fine.

[0031] Depending on the available initial data and the structure of control algorithm 200, control algorithm 200 may use the received first data as input to determine appropriate operating parameters (e.g., operating power, energy output) for power delivery by the multiple energy delivery devices. Alternatively, the first data may first be input into a device behavior model describing the operating behavior corresponding to each of the multiple energy delivery devices, thereby determining appropriate operating parameters for the multiple energy delivery devices. Control algorithm 200 then generates operating commands 230 for the multiple energy delivery devices to power them based on the determined operating parameters, i.e., power delivery parameters. In some embodiments, the commands may be unique to each energy delivery device based on its respective power delivery parameters. In other embodiments, the same command or set of the same commands may be generated for an entire group of multiple energy delivery devices or devices, for example, if the devices have the same, similar, or interdependent device or power delivery parameters.

[0032] The commands 230 generated by the control algorithm 200 are sent to the corresponding energy supply devices to operate the multiple energy supply devices to perform the power supply 240 . In some embodiments, the power supply command 230 may specify an operation start time, an operation end time, an operation duration, an operating power, and optionally, for a series of time steps, an average operating power output, an average or total energy output, or any combination thereof.

[0033] As part of a distributed energy supply system, the energy supply devices operate according to received commands 230 to provide power 240. During power supply, each energy supply device or group of energy supply devices is independently metered, for example by the control module / system 110, to obtain second data. In some embodiments, the second data may include any measurements obtainable related to the operation of each energy supply device (e.g., power output during operation, operating temperature, state of charge, etc.). Operating and environmental conditions may also be taken into account. The metering function 250 of each energy supply device generates a set of observations 260 or secondary data corresponding to each device, which may be stored in a database 270 for independent analysis or for use in updating device behavior models.

[0034] In this embodiment, second data obtained from measurements of multiple energy delivery devices is input into device behavior models 280 corresponding to each energy delivery device. Each energy delivery device's operation model 280 then utilizes the second data of the device to simulate the operation of the device based on the second data obtained from the device, the meters associated with the device, and the power delivery commands associated with the device.

[0035] In some embodiments, the energy supply device's device behavior model 280 can obtain and use as input second historical data stored in the observation database 270 obtained from observations made to the energy supply device during past operating cycles. Thus, in this embodiment, the device behavior model 280 can be updated using not only observational data obtained during a current operating cycle, but also observational data obtained during past cycles, such as values, averages, trend values, etc. for one or more parameters, allowing the updated device behavior model to more accurately reflect the real-time behavior of the energy delivery device.

[0036] The device behavior model 280 corresponding to each energy delivery device can be queried and analyzed to obtain (updated) estimates of various parameters 290 associated with the energy delivery device. For example, a regression technique (e.g., least squares (linear or nonlinear), support vector regression, etc.) can be used to model the relationship between measurements and one or more variables. The updated device parameters 290 are used to update the control algorithm 200, which uses the updated parameters 290 to determine updated power delivery parameters for each energy delivery device and generate more accurate power delivery commands for each energy delivery device.

[0037] Thus, according to the present technology, control parameters are first determined using known optimization techniques. Measurements are then taken during operation to update the model describing each participating device. The updated model is then interpreted (e.g., using regression analysis techniques) to obtain updated parameters that more accurately reflect the device's current state, such as due to device aging. The updated parameters are used as inputs to the initial optimization algorithm or model, allowing it to output updated instructions for operating the energy delivery device with improved performance and efficiency. The potential for more accurate modeling of the operation of individual (or groups of) energy delivery devices can facilitate improved power delivery, leading to increased revenue from service provision, and reducing the likelihood of device damage by operating the devices within their practical limits.

[0038] According to current embodiments, in a distributed energy supply system including multiple energy supply devices, the multiple energy supply devices are controlled by respective models that describe the device's behavior. The techniques described herein enable operational observations obtained from the multiple energy supply devices during power delivery, e.g., through independent measurements for each device, to be integrated into the model that controls them through the above-described mechanisms, thereby enabling system optimization to be performed in response to current conditions. The techniques described herein thus improve the accuracy of the model without requiring formal testing or manual development by a human operator, thereby improving the effectiveness of the control or optimization algorithm used to control the multiple energy supply devices. Improving the accuracy of the model can reduce the frequency or probability of scheduled operations that may place unacceptable loads on the device, thereby potentially extending the device's operating life.

[0039] One or more aspects of the present technology may be implemented as one or more machine learning algorithms (MLAs). For example, the regression and / or optimization techniques described above, the estimation of device parameters using associated device models, and / or the smoothing of results generated by an optimization algorithm (e.g., an MILP algorithm) may be implemented through one or more appropriate MLAs. It should be understood that various types of MLAs with different structures and topologies may be used for various tasks. However, it should be noted that the use of MLAs in embodiments of the present technology is a non-limiting example of implementing the technology, and the use of MLAs is not required.

[0040] Broadly speaking, there are three types of MLAs: supervised learning-based MLAs, unsupervised learning-based MLAs, and reinforcement learning-based MLAs. The supervised learning MLA process is based on a target variable (outcome or dependent variable) to be predicted from a given set of predictor variables (independent variables). Using this set of variables, the MLA (machine learning algorithm) uses training data to generate a function that maps inputs to the desired output during training. The training process continues until the MLA achieves a predetermined level of accuracy on validation data. Unsupervised learning MLAs learn patterns from untagged data without predicting a target or outcome variable. These MLAs are capable of self-organization, viewing patterns as probability densities and are used, for example, to cluster a set of values ​​into distinct groups. Clustering is used in many fields, including pattern recognition, image analysis, bioinformatics, data compression, and computer graphics. Reinforcement learning MLAs are trained to take actions or make decisions that maximize a cumulative reward (e.g., a score provided by the user). During training, the MLA is exposed to a training environment where it learns through trial and error to develop an optimal or suboptimal policy that maximizes reward. During this process, the MLA learns from past experience and attempts to acquire the best knowledge possible to make desired decisions.

[0041] As will be readily understood by those skilled in the art, the present technology can be implemented as a system, a method, or a computer program product, and therefore can be implemented as an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines software and hardware.

[0042] Furthermore, the present technology may take the form of a computer program product embodied in a computer-readable medium having computer-readable program code thereon. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof.

[0043] Computer program code for carrying out the techniques of the present technology can be written in any combination of one or more programming languages, including object-oriented and conventional procedural programming languages.

[0044] For example, program code for carrying out operations according to the present techniques may include source code, object code, executable code, or assembly code in a conventional programming language (interpreted or compiled) such as C, code for configuring or controlling an ASIC (application-specific integrated circuit) or FPGA (field-programmable gate array), or code for a hardware description language such as Verilog™ or VHDL (very high speed integrated circuit hardware description language).

[0045] The program code may be executed entirely on the user's computer, partly on the user's computer and partly on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network. The code components may be implemented in the form of procedures, methods, etc., and, as subcomponents, may take the form of instructions or sequences of instructions at any level of abstraction, from direct machine instructions in a series of native instructions to higher-level compiled or interpreted language constructs.

[0046] It will also be appreciated by those skilled in the art that the logical methods according to preferred embodiments of the present technology may be implemented in whole or in part as a logic device including logic elements that perform the steps of the method, and these logic elements may include components such as logic gates in a programmable logic array, an application specific integrated circuit, etc. Such logical configurations may further be embodied as implementation means for temporarily or permanently constructing logic structures in said arrays or circuits, for example using a virtual hardware description language, that may be stored or transmitted on a fixed or transmissible carrier medium.

[0047] The examples and conditional expressions described herein are provided for the purpose of understanding the principles of the present technology, and are not intended to limit the scope of the present technology to these descriptions. It will be understood that those skilled in the art can devise various configurations that embody the principles of the present technology and are encompassed within the scope defined by the appended claims of this application, even if they are not explicitly described or shown in the present specification.

[0048] Also, to aid in understanding, the above description may describe relatively simplified embodiments of the technology, and those skilled in the art will appreciate that various embodiments of the technology may be more complex.

[0049] In some cases, modifications that are considered useful examples of modifications to the present technology are also described. These modifications are described merely to facilitate understanding and, again, are not intended to limit the scope of the present technology or to define its boundaries. These modifications are not an exhaustive list, and those skilled in the art may make other modifications while remaining within the scope of the present technology. Furthermore, the absence of a modification should not be interpreted as meaning that modifications are impossible or that the description is the only way to implement elements of the present technology.

[0050] Furthermore, all descriptions herein of principles, aspects, and embodiments of the present technology, as well as specific examples thereof, are intended to encompass structural and functional equivalents thereof, whether now known or developed in the future. Thus, for example, those skilled in the art will understand that any block diagrams herein are conceptual diagrams of illustrative circuitry embodying the principles of the present technology. Similarly, flowcharts, flow diagrams, state transition diagrams, pseudocode, and the like, will be understood to substantially represent various processes that can be stored on a computer-readable medium and executed by a computer or processor, whether or not the computer or processor is explicitly shown.

[0051] The functionality of each element illustrated in the figures (including functional blocks labeled "processor") may be provided by dedicated hardware and hardware capable of executing software in conjunction with appropriate software. When provided by a processor, the functionality may be provided by a single dedicated processor or by multiple individual processors, some of which may be shared. Furthermore, the explicit use of the terms "processor" and "controller" does not necessarily limit the functionality to hardware that executes software, but implicitly includes, for example, digital signal processor (DSP) hardware, network processors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), read-only memory (ROM), random access memory (RAM), and non-volatile storage for storing software. Other general-purpose or custom hardware may also be included.

[0052] A software module, or simply a "module" meaning software, may be represented herein as any combination of flowchart elements, other elements, and / or textual descriptions that represent performing process configurations. Such modules may be implemented by explicitly or implicitly shown hardware.

[0053] As will be apparent to those skilled in the art, many improvements and modifications can be made to the above-described embodiments without departing from the scope of the present technology.

Claims

1. 1. A computer-implemented method for controlling a distributed energy supply system configured to supply power to a power grid, the method comprising: a plurality of energy supply devices; and a control module configured to control operation of the plurality of energy supply devices, the method comprising: inputting first data into an optimization algorithm to determine power delivery parameters for one or more energy delivery devices of the plurality of energy delivery devices; sending power supply instructions to the one or more energy supply devices to operate the energy supply devices to supply power based on the respective power supply parameters; For each of the one or more energy supply devices, acquiring second data from measurements of the energy supply device during power supply; inputting the second data into a device behavior model of the energy supply device; estimating one or more device parameters of the energy delivery device using the device behavior model; and updating an optimization algorithm using the one or more estimated device parameters for each of the one or more energy delivery devices; A method comprising:

2. The method of claim 1 , further comprising storing the second data in an observational database.

3. The method of claim 2, further comprising obtaining respective past second data for each of one or more energy supply devices stored in the observation database, and inputting each of the past second data into a respective device behavior model for the one or more energy supply devices.

4. 4. The method of claim 1, wherein the first data includes manufacturer-specified device parameters of the one or more energy supply devices, such as operating power, maximum / minimum state of charge, operating temperature, efficiency curve, response time, ambient temperature, environmental parameters including humidity, operational parameters including operating cost, or any combination thereof.

5. 5. The method of claim 1, wherein the second data comprises device parameters obtained through measurements including operating power, operating temperature, state of charge, changes in device configuration, or any combination thereof.

6. 6. The method of any one of claims 1 to 5, wherein the one or more device parameters of each energy supply device include operating efficiency and / or maximum operating power at a given state of charge.

7. A method described in any one of claims 1 to 6, wherein the device behavior model of each energy supply device is configured to simulate the operation of each energy supply device based on second data acquired from the energy supply device, meters associated with the energy supply device, and power supply instructions associated with the energy supply device.

8. 8. The method of claim 1, wherein the power supply command includes an operation start time, an operation end time, an operation duration, an operating power at a given time, an average operating power, a total amount of power to be supplied, or any combination thereof.

9. 9. The method of claim 1, wherein the optimization algorithm is the solution of a mixed integer linear programming problem.

10. A computer readable medium comprising machine readable code that, when executed by a processor, causes the processor to perform the method of any one of claims 1 to 9.

11. configured to operate a plurality of energy supply devices in a distributed energy supply system to supply power to a power grid; A control module including one or more processors configured to execute machine-readable code stored in a memory to perform the method of any one of claims 1 to 9.

12. 1. A distributed energy supply system configured to supply power to an electric power grid, comprising: a plurality of energy supply devices each configured to be able to output electric power; a control module configured to control operation of the plurality of energy delivery devices, the control module including a memory storing machine-readable code and one or more processors configured to execute the machine-readable code, wherein execution of the machine-readable code causes the one or more processors to: inputting first data into an optimization algorithm to determine power delivery parameters for one or more of the plurality of energy delivery devices; transmitting a power supply command to the one or more energy supply devices to operate the one or more energy supply devices to supply power based on the respective power supply parameters; For each of the one or more energy supply devices, obtaining second data from measurements of the device while powered; inputting the second data into a device behavior model of the energy supply device; estimating one or more device parameters of the energy delivery device using the device behavior model; updating an optimization algorithm using the estimated device parameters corresponding to each of the one or more energy delivery devices.

13. The distributed energy supply system of claim 12, wherein the distributed energy supply system is a virtual power plant.

14. 14. The distributed energy supply system of claim 12 or 13, wherein the plurality of energy supply devices comprises one or more medium-scale power generation units, one or more micro-cogeneration devices, one or more natural gas-fired reciprocating engines, one or more small-scale wind power plants, one or more solar power generation units, one or more hydroelectric power generation devices, one or more biomass power generation devices, one or more standby power generation devices, one or more energy storage systems and devices, or any combination thereof.

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