Micro-grid energy management system

Abstract

An energy management system for a microgrid system. The energy management system includes a controller. The controller is configured to: receive forecasted weather condition data; predicting a power generation profile and / or a power demand profile of the microgrid system in response to the forecasted weather condition data; in response to the power generation profile and / or the power demand profile, determining a performance model and / or a degradation model for one or more system components of the microgrid system; deriving an energy management optimization strategy in response to a performance model and / or a degradation model of one or more system components of the microgrid system; and controlling an operating mode of one or more system components of the microgrid system in response to the energy management optimization strategy.

Description

Technical Field

[0001] This disclosure relates to a microgrid energy management system. The microgrid energy management system can integrate weather forecast data with operating strategies for electrolyzers used in microgrids. Background Technology

[0002] Electrolyzer systems, including electrolyzer stacks, are capable of producing clean hydrogen. The input to the electrolyzer stack is water, an environmentally friendly and abundant material. The electrolyzer system uses electricity to break down water molecules. Hydrogen ions accept electrons from the electricity to form hydrogen gas (H2) at the cathode and produce oxygen gas (O2) as a byproduct at the anode. Electrolyzer stacks produce no direct emissions—hydrogen can be used as a clean fuel source, and oxygen is harmlessly released into the atmosphere and can be used for other purposes.

[0003] The electricity used in the electrolyzer system can be produced from renewable energy sources. Using renewable energy for electricity production ensures that the process of producing hydrogen from the electrolyzer stack is emission-free, thus guaranteeing clean hydrogen production. Summary of the Invention

[0004] According to one embodiment, an energy management system for a microgrid system is disclosed. The energy management system includes a controller. The controller is configured to: receive forecasted weather condition data; predict a generation profile and / or a power demand profile for the microgrid system in response to the forecasted weather condition data; determine a performance model and / or a degradation model for one or more system components of the microgrid system in response to the generation profile and / or the power demand profile; derive an energy management optimization strategy in response to the performance model and / or degradation model for one or more system components of the microgrid system; and control the operating mode of one or more system components of the microgrid system in response to the energy management optimization strategy.

[0005] According to a second embodiment, an energy management system for a microgrid system is disclosed. The energy management system includes a controller. The controller is configured to: receive first forecast weather condition data associated with a first microgrid system and second forecast weather condition data associated with a second microgrid system; predict a first generation profile and / or a first power demand profile for the first microgrid system in response to the first forecast weather condition data, and predict a second generation profile and / or a second power demand profile for the second microgrid system in response to the second forecast weather condition data; determine a first performance model and / or a first degradation model for the first microgrid system in response to the first generation profile and / or the first power demand profile, and determine a second performance model and / or a second degradation model for the second microgrid system in response to the second generation profile and / or the second power demand profile; derive an overall energy management optimization strategy for the first microgrid system and the second microgrid system in response to the first performance model and / or the first degradation model and the second performance model and / or the second degradation model; and control a first operating mode of the first microgrid system and a second operating mode of the second microgrid system in response to the overall energy management optimization strategy. For example, the first microgrid system and the second microgrid system can be different microgrids deployed in a modular manner and connected to a common energy management system and forecasting system for mutual optimization.

[0006] According to another embodiment, an energy management system for a microgrid system is disclosed. The energy management system includes a controller. The controller is configured to: receive forecasted weather condition data associated with the microgrid system, the microgrid system having a first electrolyzer stack of a first electrolyzer type and a second electrolyzer stack of a second electrolyzer type different from the first electrolyzer type; predict a first power generation profile of the first electrolyzer stack in response to the forecasted weather condition data and the first electrolyzer type, and predict a second power generation profile of the second electrolyzer stack in response to the forecasted weather condition data and the second electrolyzer type; determine a first performance model and / or a first degradation model of the first electrolyzer stack in response to the first power generation profile, and determine a second performance model and / or a second degradation model of the second electrolyzer stack in response to the second power generation profile; derive an overall energy management optimization strategy for the first and second electrolyzer stacks in response to the first performance model and / or the first degradation model and the second performance model and / or the second degradation model; and control a first operating mode of the first electrolyzer stack and a second operating mode of the second electrolyzer stack in response to the overall energy management optimization strategy. Attached Figure Description

[0007] Figure 1 A schematic diagram of an energy management system according to one or more embodiments is depicted.

[0008] Figure 2AA graph depicting wind speed forecasts was created, showing the relationship between wind speed (km / h) and time of day.

[0009] Figure 2B A graph depicting the power generation profile plots the predicted power (kW) as a function of the time of day.

[0010] Figure 2C A graph depicts the implementation of optimal electrolyzer stack and energy management optimization strategies, and plots the electrolyzer (ELY) stack voltage (V) as a function of the time of day.

[0011] Figure 3 An exemplary computing device, which can be used in conjunction with an energy management system, is described according to one or more embodiments. Detailed Implementation

[0012] Embodiments of this disclosure are described herein. However, it should be understood that the disclosed embodiments are merely examples, and other embodiments may take various forms and alternative forms. The figures are not necessarily to scale; some features may be enlarged or minimized to show details of particular components. Therefore, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a representative basis for teaching those skilled in the art to adopt the embodiments in various ways. As will be understood by those skilled in the art, various features illustrated and described with reference to any of the figures may be combined with features illustrated in one or more other figures to produce embodiments not explicitly illustrated or described. The combinations of illustrated features provide representative embodiments for typical applications. However, various combinations and modifications of features consistent with the teachings of this disclosure may be desirable for a particular application or implementation.

[0013] Unless explicitly indicated in the examples or otherwise, all numerical quantities indicating the amount of material and / or the amount used in this description should be understood to be modified by the word "about" when describing the widest scope of the invention.

[0014] The first definition of an acronym or other abbreviation applies to all subsequent uses of the same abbreviation herein, and with necessary modifications, applies to normal grammatical variations of the abbreviation as originally defined; and, unless explicitly stated otherwise, the measurement of an attribute is determined by the same technique as the same attribute mentioned above or below.

[0015] This invention is not limited to the specific embodiments and methods described below, as specific components and / or conditions may vary. Furthermore, the terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting in any way.

[0016] As used in the specification and appended claims, the singular forms “a,” “an,” and “the” include plural objects unless the context clearly indicates otherwise. For example, a component mentioned in the singular is intended to include multiple components.

[0017] The term "substantially" may be used herein to describe the disclosed or claimed embodiments. The term "substantially" may modify values ​​or relative characteristics disclosed or claimed in this disclosure. In such instances, "substantially" may mean that the value or relative characteristic it modifies is within ±0%, 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, or 10% of that value or relative characteristic.

[0018] Hydrogen is a promising resource for transitioning from a fossil fuel economy. However, fully realizing this hope requires economically viable and scalable hydrogen production methods. Electrolyzer systems, including electrolyzer stacks, can be a key component of the hydrogen economy by providing clean sources of hydrogen production. Electrolyzer stacks can be constructed and implemented modularly. However, the adoption of electrolyzer stacks has been hampered by economic barriers, power-intensive demands, efficiency considerations, and durability issues.

[0019] With the emergence of hydrogen as a promising clean resource, the availability of a wide range of renewable energy sources has led to an increased demand for energy storage systems that can effectively utilize these renewable energy sources. Microgrids are considered as energy storage systems for these purposes due to their modularity, security, flexibility, and / or ease of transmission.

[0020] Combinations of electrolyzer systems and microgrid systems have been proposed to provide a clean source of fuel production, energy storage, and electricity within a single system. However, implementing such proposed solutions has been challenging because microgrid systems rely on intermittent power sources, which are irregular given the relatively small size of microgrids. The intermittent nature of renewable energy, combined with the intensive energy demand of the electrolyzer stack, makes dynamic operation of the electrolyzer stack an attractive economic option. For example, a strategy could be implemented where the electrolyzer stack operates at a high current density during periods of abundant renewable energy and operates near idle during periods of lower energy production. The high current density can be any one of the following values ​​or within a range of any two of the following values: 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, and 2.5 A / cm². 2 The current density of an electrolytic cell stack operating near idle can be 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, and 0.1 A / cm². 2 .

[0021] However, the dynamic operation of the electrolyzer stack can significantly accelerate degradation, especially when cycling between high and low voltages. In extreme cases, adverse weather conditions can lead to the cessation of renewable production, resulting in the shutdown of the electrolyzer stack, which can cause severe degradation.

[0022] Furthermore, using a combined microgrid and electrolyzer in a purely reactive manner may result in excessive or insufficient stored energy relative to the expected power generation, thus further exacerbating dynamic fluctuations in the operation of the combined system. These dynamic fluctuations may accelerate the degradation of energy storage systems (e.g., batteries) used in the combined system. For example, contrary to energy storage strategies that keep the electrolyzer in constant operation, a period of good weather supporting high power generation with one day intermingled with very low generation may lead to unnecessary dynamic operation of the electrolyzer.

[0023] The uncertainty inherent in power generation from intermittent sources can result in lower returns on capital compared to other possible scenarios. However, advancements in artificial intelligence, increased availability and centralized data, and a deepening of scientific knowledge regarding electrolyzer stack performance and degradation mechanisms allow for opportunities to improve system design.

[0024] Electrolyzers can be powered by renewable energy sources such as solar or wind power. However, when relying on these renewable energy sources, effectively managing the operation of electrolyzers can be difficult due to their intermittent nature. An energy management system is needed to effectively manage electrolyzers powered by intermittent renewable energy sources.

[0025] In one or more embodiments, an energy management system for managing intermittent renewable energy sources is disclosed. The energy management system may include a controller configured to receive forecasted weather condition data and, in response to the weather forecast data, apply energy management optimization strategies to power an electrolyzer reactor via an energy grid (e.g., a microgrid system). The microgrid system may be a local, independent grid. The microgrid system may operate independently of the main grid or may be connected to the main grid. Independent operation may be referred to as islanding mode. The microgrid system includes a power source. In one or more embodiments, the power source includes one or more renewable energy sources (e.g., solar panels and / or wind turbines). In one or more other embodiments, the power source includes both renewable and non-renewable energy sources (e.g., diesel generators and / or natural gas turbines).

[0026] The controller of the energy management system can use available weather data to predict the expected available power from renewable and / or intermittent sources. The available weather data can be used to adjust the operating strategies of the electrolyzer stack and / or energy storage system (e.g., a battery), which can be coupled to the electrolyzer stack. The energy management system can achieve one or more of the following benefits: improved hydrogen production, efficiency, renewable energy absorption, reliability of the microgrid in meeting forecasted demand, minimized degradation, and / or meeting one or more key performance indicators (KPIs) of the system. Electrolyzer stack KPIs may include, but are not limited to, hydrogen production rate, production efficiency, mean time between failures (MTBF), mean time to repair (MTTR), stack lifetime, start-up time, regulation ratio, and power consumption at idle. Energy storage system KPIs may include, but are not limited to, nominal capacity, available capacity, capacity degradation rate, maximum power output, response time, ramp rate, availability, MTBF, MTTR, and cycle efficiency. The energy management system can receive input from users to set weights among different KPIs for optimization (e.g., durability may be weighted more than efficiency).

[0027] In one or more embodiments, an energy management system is disclosed. The energy management system can manage the operation of one or more components (e.g., electrolyzer stacks, energy storage systems, etc.) associated with a microgrid system. The energy management system can use forecasted weather condition data to optimize the operation of one or more components associated with the microgrid system. Machine learning algorithms can be used, in response to forecasted weather condition data and historical generation profiles and / or electricity demand profiles, to predict generation profiles and / or electricity demand profiles over time increments at a geographic scale. The generation profiles can be associated with renewable energy power profiles.

[0028] In one or more embodiments, artificial intelligence is used to implement machine learning algorithms. Machine learning algorithms can be algorithms that improve their accuracy through experience. Machine learning algorithms can enable one or more embodiments to learn and improve from forecast weather condition data without explicit programming. Machine learning algorithms can be neural network algorithms. Neural network algorithms can be trained to recognize complex patterns within forecast weather condition data and learn to represent a non-linear relationship between forecast weather condition data and predicted power generation or demand profiles over time. Machine learning algorithms can be implemented as machine instructions stored in non-transitory memory on a computer, where the machine instructions will be executed by the computer.

[0029] The time increment can be any one of the following values ​​or within a range of any two of the following values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 48, 72, and 96 hours.

[0030] The geographic scale of forecasted weather conditions can be, but is not limited to, microscale (e.g., less than 1 km), topographic scale (e.g., 1 to 10 km), and mesoscale (e.g., 10 to 1000 km). Computational fluid dynamics (CFD) can be used to simulate forecasted weather condition data at microscale and / or topographic scale. Finite area models (LAM) can be used to simulate forecasted weather condition data at mesoscale.

[0031] The forecast weather condition may be wind. When the forecast weather condition is wind, the forecast wind data may include, but is not limited to, wind speed, wind pressure, wind direction, gusts, and wind shear. When the forecast weather condition is wind, the historical power generation profile is the historical wind power generation profile, and the predicted power generation profile is the predicted wind power generation profile.

[0032] The forecast weather condition may be sunny. When the forecast weather condition is sunny, the forecast solar data may include, but is not limited to, solar irradiance, cloud cover, and temperature. When the forecast weather condition is sunny, the historical power generation profile is the historical solar power generation profile, and the predicted power generation profile is the predicted solar power generation profile.

[0033] In one or more embodiments, a machine learning algorithm may use forecasted wind data (e.g., wind speed and wind pressure) and be trained based on historical turbine wind power generation profiles to obtain a predicted wind power generation profile. A machine learning algorithm may use forecasted solar data (e.g., solar irradiance and cloud cover) and be trained based on historical solar panel power generation profiles to obtain a predicted solar power generation profile.

[0034] Forecasted wind power generation profiles and / or forecasted solar power generation profiles can be coupled with consumer electricity microgrid demand profiles (e.g., forecast profiles based on historical data) to obtain a composite generation / demand profile. The composite generation / demand profile can be input to performance / degradation models of one or more system components (e.g., electrolyzer stacks, energy storage systems, etc.) coupled to the microgrid system. Optimization algorithms can then be applied to the performance / degradation models(s) to derive one or more optimal power loads or other energy management optimization strategies for one or more KPIs coupled to the one or more system components of the microgrid system. The one or more optimal power loads output from the optimization algorithm can be transmitted to the microgrid controller. The microgrid controller can be electrically connected to one or more system components of the microgrid system.

[0035] Figure 1A schematic diagram of an energy management system 100 according to one or more embodiments is depicted. The energy management system 100 includes a microgrid system 102 and a microgrid energy management system (EMS) 104. Figure 1 As depicted, the microgrid EMS 104 includes an energy management controller 106 and a microgrid controller 108. The energy management controller 106 and / or the microgrid controller 108 can be electrically connected to the microgrid system 102. Although... Figure 1 The embodiments shown depict the energy management controller 106 and the microgrid controller 108 as separate and distinct entities, but in one or more embodiments the two controllers may be combined into a single controller.

[0036] As shown in operation 110 performed by energy management controller 106, KPI optimization algorithm 112 receives consumer load forecasts. As shown in operation 114 performed by energy management controller 106, KPI optimization algorithm 112 receives weather-based power forecasts. KPI optimization algorithm 112 is configured to determine an energy management optimization strategy. Energy management controller 106 is configured to transmit the energy management optimization strategy to microgrid controller 108.

[0037] like Figure 1 As shown, the microgrid controller 108 communicates electrically with system components 116 associated with the microgrid 102. System components 116 include a consumer load system 118, an electrolyzer (ELY) stack 120, an energy storage system 122, and a distributed generation system 124. The consumer load system 118 can refer to a system configured to detect, determine, and / or supply electricity to be consumed by consumer loads on the microgrid system. Types of consumer loads can include residential, commercial, industrial, and / or public institution loads. The distributed generation system 124 can refer to a system configured to manage the generation and distribution of electricity from several small, distributed sources located near the generation site. The consumer load system 118, the electrolyzer stack 120, the energy storage system 122, and the distributed generation system 124 can include one or more controllers for operating (one or more) the systems.

[0038] Figure 2A A graph 200 depicts wind speed forecasts, plotting the relationship between wind speed (km / h) and time of day. Wind speed forecasts can be input into an energy management system to optimize the operation of one or more system components associated with the microgrid system.

[0039] Figure 2B Figure 202, depicting a power generation profile, plots the predicted power (kW) as a function of time of day. Machine learning algorithms can be used in response to... Figure 2A The wind speed forecast shown in the figure predicts the power generation profile.

[0040] Figure 2C Figure 204 illustrates the implementation of optimal electrolyzer stack and energy management optimization strategies. Figure 204 plots the electrolyzer (ELY) stack voltage (V) as a function of time of day. Optimization algorithms can be applied to one or more performance / degradation models to derive operating strategies using one or more KPIs of the electrolyzer stack and energy storage system.

[0041] According to one or more embodiments, the output of the optimization algorithm can be dynamically updated upon receiving further forecast weather data. The optimization algorithm can be dynamically updated over consecutive time intervals. The consecutive time intervals can be any one of the following values ​​or within a range of any two of the following values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 48, 72, and 96 hours.

[0042] Energy management systems can be used to improve hydrogen production from electrolyzers by using renewable energy profiles, increasing efficiency, maintaining energy storage targets, limiting the commitment of microgrid systems to peak demand periods, and / or reducing dynamic loads between high and low voltage levels. For example, in the event of an impending extreme weather event that could severely destabilize renewable power generation, excess energy storage capacity can be preemptively built up to ensure that the electrolyzers can be idle during extreme weather conditions, rather than being hard-shut down.

[0043] In one or more embodiments, forecasted weather condition data is fed into a machine learning algorithm (e.g., a neural network) that predicts wind turbine power generation based on wind speed and pressure data to forecast one or more generation profiles and expected consumer load profiles for intermittent renewable energy sources in a microgrid system. The machine learning algorithm can be implemented on a controller. The controller can be configured to receive forecasted weather condition data to predict generation profiles. The controller can be electrically connected to one or more microgrid systems and / or one or more electrolyzer stacks. The controller can receive power forecasts from any microgrid system it is connected to. Generation profiles can be used as design criteria for electrolyzer stack design. Expected consumer load profiles on the microgrid system can be estimated based on historical data (e.g., empirical or algorithmic historical data).

[0044] Given a weather forecast, a machine learning algorithm can consider the dynamic properties of several renewable microgrids to balance the power output of multiple microgrid systems under at least two different weather patterns in at least two different geographic regions. For example, microgrid A in geographic region A may be under stable sunlight during the day, while microgrid B in geographic region B may experience intermittent wind. A machine learning algorithm based on weather forecasts for geographic regions A and B can be configured to adjust the power output of microgrids A and B to achieve a less dynamic but stable renewable power supply.

[0045] In one or more embodiments, machine learning algorithms can determine the optimal electrolyzer stack operation strategy for one or more prioritized KPIs in response to an expected power generation profile. Machine learning algorithms can also determine the optimal energy storage operation strategy for one or more prioritized KPIs in response to a consumer load profile. In response to weather forecast data, the electrolyzer stack can be optimized for stable hydrogen production with fewer interruptions over longer periods. For example, weather forecast data and algorithms can be used to predict the optimal time to charge energy storage devices. In other embodiments, when the cost of renewable electricity is low, the electrolyzer system can be optimized to maximize hydrogen production with a predictive dynamic output for a given weather forecast.

[0046] In one or more embodiments, the energy management optimization strategy can be updated by the controller of the energy management system. For example, the electrolyzer stack operation strategy based on the updated forecasted power generation profile and / or the energy storage system operation strategy based on the updated forecasted electricity demand profile can be determined by the controller of the energy management system.

[0047] In one or more embodiments, the microgrid system may comprise two or more electrolyzer stacks (e.g., alkaline electrolyzer stacks and polymer electrolyte membrane (PEM) electrolyzer stacks) with different specifications, different KPIs, and / or dynamic operating tolerances. Machine learning algorithms can be configured to optimize operating strategies for the two or more types of electrolyzer stacks to maximize efficiency and / or output while minimizing degradation of the electrolyzer stack components. For example, one or more embodiments disclose a combination of an alkaline electrolyzer stack that maintains relatively static operation at lower current densities and a PEM electrolyzer stack that handles the dynamic aspects of power load. This combination utilizes a less expensive and less durable alkaline electrolyzer stack and a more expensive and more durable PEM electrolyzer stack. Machine learning algorithms can then use weather forecasts and power generation profiles to optimize the power load of the two or more different types of electrolyzer stacks and energy storage systems. This optimization can be performed on two or more competing objectives (e.g., simultaneously minimizing the total cost of hydrogen production and depreciation costs to maximize return on investment).

[0048] In one or more embodiments, the microgrid system may include an electrolyzer that generates hydrogen from electricity, a fuel cell and / or turbine that consumes hydrogen to generate electricity, and / or a hydrogen storage system. To extend the lifespan of an electrolyzer system comprising two or more electrolyzer stacks, a minimum current may be applied to each of the two or more electrolyzer stacks, resulting in a minimum voltage and hydrogen production rate to reduce the aging rate (even when renewable electricity is unavailable at the minimum current). Power may be supplied from the fuel cell or turbine during one or more periods of no or low renewable power generation, the fuel cell or turbine itself being powered by hydrogen generated by the electrolyzer system. The hydrogen storage system may be used to buffer the entire microgrid and compensate for round-trip inefficiencies of the electrolyzer and fuel cell and / or turbine.

[0049] Weather-related electricity supply forecasts and consumer-related demand forecasts can be used to predict the amount of hydrogen that should be stored in a hydrogen storage system to buffer the microgrid on the next time increment (e.g., minutes, hours, days, etc.) and / or optimal load profile, thereby producing that amount of hydrogen relative to one or more prioritized KPIs. For example, forecasted weather data can be used to predict wind turbine power generation, which makes it possible to predict the amount of hydrogen required to maintain the minimum current of an electrolyzer stack. Machine learning algorithms can then predict how to produce that amount of hydrogen while reducing or minimizing dynamic operations. Machine learning algorithms can be configured to collectively optimize system efficiency and durability while taking into account hydrogen storage that reduces the system's energy efficiency.

[0050] Figure 3 An exemplary computing device 300, which can be used in conjunction with an energy management system according to one or more embodiments, is depicted. As shown, the computing device 300 includes a processor 302, which can be operatively connected to a storage device 304, a network device 306, an output device 308, and an input device 310. Figure 3 This is just one example, and a computing device 300 with more, fewer, or different components can be used.

[0051] Processor 302 may include one or more integrated circuits that implement the functions of a central processing unit (CPU) and / or a graphics processing unit (GPU). In some examples, processor 302 may be a system-on-a-chip (SoC) that integrates the functions of both the CPU and GPU. The SoC may optionally include other components, such as storage device 304 and network device 306, into a single integrated device. In other examples, the CPU and GPU may be interconnected to each other via peripheral connectivity devices such as high-speed peripheral component interconnect (PCI) or other suitable peripheral data connections. In one example, the CPU may be a commercially available central processing unit that implements an instruction set such as x86, ARM, Power, or a family of non-interlocking pipelined microprocessors (MIPS).

[0052] In one or more embodiments, during operation, processor 302 executes stored program instructions that can be retrieved from storage device 304. Therefore, the stored program instructions include software that controls the operation of processor 302 to perform the operations described herein. Processor 302 may execute machine learning algorithms (e.g., neural networks). Storage device 304 may include both non-volatile memory and volatile memory devices. Non-volatile memory includes solid-state memory, such as NAND flash memory, magnetic and optical storage media, or any other suitable data storage device that retains data when the system may be disabled or lose power. Volatile memory includes static and dynamic random access memory (RAM) that stores program instructions and data during operation of the machine learning algorithms of one or more embodiments. Network device 306 or other components of the computing device may communicate with one or more components (e.g., power storage and distribution systems).

[0053] Output device 308 can be configured to present data from one or more embodiments. Output device 308 may include a graphics or visual display device, such as an electronic display screen, a projector, a printer, or any other suitable device for reproducing a graphic display.

[0054] Input device 310 may include any of a variety of devices that enable computing device 300 to receive control input from a user. Input device 310 enables a user to interact with the computing device to configure an active machine learning process, thereby improving the operating parameters of the active machine learning algorithm based on the input. Examples of suitable input devices for receiving human-machine interface input may include a keyboard, mouse, trackball, touchscreen, voice input device, graphics tablet, and the like.

[0055] Network devices 306 may each include any of a variety of devices that enable the device to send and / or receive data from external devices over a network. Examples of suitable network devices 206 include Ethernet interfaces, Wi-Fi transceivers, cellular transceivers, or Bluetooth or BLE transceivers, UWB transceivers, or other network adapters or peripheral interconnect devices that receive data from another computer or external data storage device, which may be useful for receiving large datasets in an efficient manner.

[0056] While exemplary embodiments have been described above, they are not intended to describe all possible forms covered by the claims. The terms used in this specification are descriptive and not limiting, and it should be understood that various changes may be made without departing from the spirit and scope of this disclosure. As previously described, features of various embodiments may be combined to form further embodiments of the invention, which may not be explicitly described or illustrated. While various embodiments may have been described as offering advantages or superiority over other embodiments or prior art implementations with respect to one or more desired characteristics, those skilled in the art will recognize that one or more features or characteristics may be compromised to achieve desired overall system properties depending on the particular application and implementation. These properties may include, but are not limited to, cost, strength, durability, lifecycle cost, merchantability, appearance, packaging, size, suitability, weight, manufacturability, ease of assembly, etc. Accordingly, to the extent that any embodiment is described with respect to one or more characteristics as less desirable than other embodiments or prior art implementations, such embodiments are not outside the scope of this disclosure and may be desirable for a particular application.

Claims

1. An energy management system, comprising: The controller is configured as follows: Receive forecast weather data; In response to the forecasted weather data, a generation profile and / or a power demand profile for the microgrid system are predicted. In response to the power generation profile and / or power demand profile, determine the performance model and / or degradation model of one or more system components of the microgrid system; In response to the performance models and / or degradation models of one or more system components of the microgrid system, an energy management optimization strategy is derived; and In response to the energy management optimization strategy, control the operating mode of one or more system components of the microgrid system.

2. The energy management system according to claim 1, wherein the prediction step is performed by a machine learning algorithm.

3. The energy management system according to claim 2, wherein the machine learning algorithm is a neural network.

4. The energy management system according to claim 2, wherein the machine learning algorithm is trained based on historical power generation profiles and / or historical power demand profiles to obtain power generation profiles and / or power demand profiles.

5. The energy management system according to claim 1, wherein the derivation step is performed by an optimization algorithm.

6. The energy management system of claim 1, wherein the derivation step comprises deriving an energy management optimization strategy in response to performance models and / or degradation models of one or more system components of the microgrid system and one or more key performance indicators (KPIs) of one or more system components of the microgrid system.

7. The energy management system of claim 6, wherein the one or more system components include an electrolyzer stack, and one or more KPIs include hydrogen production rate, production efficiency, mean time between failures (MTBF), mean time to repair (MTTR), stack lifetime, start-up time, control ratio, and / or power consumption during idle.

8. The energy management system of claim 6, wherein the one or more system components include an energy storage system, and the one or more KPIs include nominal capacity, available capacity, capacity degradation rate, maximum power output, response time, ramp rate, availability, mean time between failures (MTBF), mean time to repair (MTTR), and cycle efficiency.

9. The energy management system according to claim 1, wherein the forecasted weather condition data is forecasted wind data, and the forecasted wind data includes wind speed, wind pressure, wind direction, gusts, and / or wind shear.

10. The energy management system according to claim 1, wherein the forecasted weather data is forecasted solar data, and the forecasted solar data includes solar irradiance, cloud cover and / or temperature.

11. The energy management system of claim 1, wherein the one or more system components include an electrolyzer stack and / or an energy storage system.

12. The energy management system of claim 1, wherein one or more system components are electrolyzers, and the power generation profile and / or power demand profile are power generation profiles.

13. The energy management system of claim 1, wherein one or more system components are energy storage systems, and the power generation profile and / or power demand profile are power demand profiles.

14. An energy management system, comprising: The controller is configured as follows: Receive first forecast weather condition data associated with the first microgrid system and second forecast weather condition data associated with the second microgrid system; In response to first forecast weather data, a first generation profile and / or a first power demand profile of a first microgrid system are predicted, and in response to second forecast weather data, a second generation profile and / or a second power demand profile of a second microgrid system are predicted. A first performance model and / or a first degradation model of a first microgrid system are determined in response to a first generation profile and / or a first power demand profile, and a second performance model and / or a second degradation model of a second microgrid system are determined in response to a second generation profile and / or a second power demand profile. In response to the first performance model and / or the first degradation model and the second performance model and / or the second degradation model, an overall energy management optimization strategy for the first microgrid system and the second microgrid system is derived; and In response to the overall energy management optimization strategy, control the first operating mode of the first microgrid system and the second operating mode of the second microgrid system.

15. The energy management system of claim 14, wherein the first microgrid system is located in a first geographical region and the second microgrid system is located in a second geographical region.

16. The energy management system of claim 14, wherein the overall energy management optimization strategy is configured to adjust the first power output of the first microgrid system and the second power output of the second microgrid system.

17. An energy management system, comprising: The controller is configured as follows: Receive forecasted weather data associated with a microgrid system, the microgrid system having a first electrolyzer stack of a first electrolyzer type and a second electrolyzer stack of a second electrolyzer type different from the first electrolyzer type; In response to forecasted weather conditions and the type of the first electrolyzer, a first power generation profile of the first electrolyzer stack is predicted, and in response to forecasted weather conditions and the type of the second electrolyzer, a second power generation profile of the second electrolyzer stack is predicted. In response to a first power generation profile, a first performance model and / or a first degradation model of the first electrolyzer stack are determined, and in response to a second power generation profile, a second performance model and / or a second degradation model of the second electrolyzer stack are determined; In response to the first performance model and / or the first degradation model and the second performance model and / or the second degradation model, an overall energy management optimization strategy for the first electrolyzer stack and the second electrolyzer stack is derived; and In response to the overall energy management optimization strategy, control the first operating mode of the first electrolyzer stack and the second operating mode of the second electrolyzer stack.

18. The energy management system of claim 17, wherein the first electrolyzer type is an alkaline electrolyzer stack, and the second electrolyzer type is a polymer electrolyte membrane (PEM) electrolyzer stack.

19. The energy management system according to claim 18, wherein the first operating mode is a static operating mode.

20. The energy management system according to claim 19, wherein the second operating mode is a dynamic operating mode.

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