Grid adapter system and method

Abstract

Embodiments described herein include systems and methods for managing power in various energy generation, storage, and consumption systems, including microgrids or nanogrids. Computing systems and electrical hardware transmit / receive power to / from various energy storage, transmission, and consumption sites, particularly where certain nanogrids are not connected to energy generation and storage subsystems via wires. The power grid adapter receives energy from various sources, reduces noise (e.g., harmonics) in the electrical waveform, and determines the amount of power delivered to the nanogrid through an electrical connection or transport vehicle to achieve acceptable operation in accordance with power grid specifications or other operational configurations. The storage system may include a flow battery that exchanges power with the transport vehicle according to an electrolyte exchange of the flow battery based on the required power or the remaining power.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 431,472, filed on December 9, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present application generally relates to managing the delivery of electrical energy from power generation sources to various grid systems, including nanogrid systems. Background Art

[0004] The way electricity is generated has changed significantly, especially with the rise of renewable energy generators such as wind turbines and solar photovoltaic (PV) systems. In addition, electricity consumption is also changing with the electrification of transportation (such as electric vehicles (EVs)) and home heating (such as heat pumps). Policies aimed at reducing emissions and combating climate change have also accelerated these production and consumption trends. Governments at all levels in various countries are formulating policies around net zero emissions targets to accelerate the energy transition to clean, renewable energy. This net zero emissions transition target requires distribution system operators to accommodate the integration of solar PV generation behind the meter and EV charging / discharging systems.

[0005] However, the proliferation of electric vehicle charging stations (EV-stations) can introduce harmonic pollution into the distribution system. Similarly, increasing the number of generators based on intermittent inverters reduces the inertia of the distribution network, exposing the power system to low-frequency oscillations. Harmonics and oscillations can exacerbate the wear and tear of existing distribution assets, necessitating significant upgrades to distribution system hardware and management to address power quality issues. Summary of the Invention

[0006] This disclosure details a power system device (sometimes referred to herein as a "grid adapter") that allows for greater protection and flexibility when adopting distributed energy resources (DERs) and electric vehicle stations, while limiting the amount of network upgrades required to achieve stable power system operation. Furthermore, high-frequency harmonics can distort the sinusoidal AC power of the distribution grid. This can negatively impact power system operation, manifesting, for example, in the failure of protective relays. The grid adapter blocks unwanted noise (e.g., harmonics) through a series of power conversions and manipulations of the sinusoidal AC power.

[0007] Embodiments described herein include systems and methods for managing power within various energy generation, storage, and consumption systems, including microgrids or nanogrids. Computing systems and electrical hardware send and receive power to and from various energy storage, transmission, and consumption sites, particularly where certain nanogrids are not connected to energy generation and storage subsystems via electrical wires. A grid adapter receives energy from various sources, reduces noise (e.g., harmonics) in the electrical waveform, and determines the amount of energy to deliver to the nanogrid via an electrical connection or a transport vehicle. The storage system may include a flow battery that exchanges required energy or surplus energy with a transport vehicle based on an exchange of electrolytes in the flow battery.

[0008] In one embodiment, a system includes a grid adapter. The grid adapter includes an internal storage system configured to store a certain amount of internal energy storage. A first power processing unit is configured to convert power associated with the nanogrid. The first power processing unit includes a bidirectional converter configured to convert input power or output power according to operating instructions indicating the direction of power flow. The grid adapter includes a grid adapter controller configured to execute software programming. The grid adapter controller is configured to: receive power data indicating power requirements of the nanogrid from one or more data sources; and transmit instructions to the internal storage system via the first power processing unit to exchange power with the nanogrid based on the power requirements.

[0009] The operating instructions may be based on one or more configurations, including a target configuration. The target configuration of the operating instructions may indicate a direction for converting energy of the operating target, including at least one of exporting output power from the grid or importing input power into the grid. The operating instructions may indicate an amount of energy or power associated with the nanogrid.

[0010] The system may also include an external storage system for storing energy according to the grid adapter controller, including an amount of energy dictated by the power demand of the nanogrid.

[0011] The external storage system may include a first flow battery for exchanging electricity with at least one of a second flow battery of the energy storage vehicle or a vehicle electrolyte storage tank of the energy storage vehicle through electrolyte exchange, the vehicle electrolyte storage tank being configured to contain an electrolyte.

[0012] The external storage system may include a reactor stack of the first flow battery and a storage system electrolyte tank configured to hold an electrolyte.

[0013] The system may also include one or more power processing units, including a first power processing unit. Each power processing unit includes one or more power converters configured to reduce the amount of noise on the electrical waveform.

[0014] The system may further include a second processing unit configured to convert a second amount of electricity related to the distributed energy resources. The second processing unit includes a DC-DC converter for converting input power or output power according to the second amount of electricity.

[0015] The system may further include a second processing unit configured to convert a second amount of electricity related to the distributed energy resources. The second processing unit includes an AC-DC converter for converting input power or output power according to the second amount of electricity.

[0016] The system may further include a second processing unit including a multi-level converter configured to convert input power received from a power distribution network coupled to the grid adapter.

[0017] The grid adapter can be disconnected from the power distribution grid.

[0018] In another embodiment, a system includes a grid adapter and an external storage system. The grid adapter includes an internal storage system configured to store internal energy storage; and a first power processing unit configured to convert power for exchange with a nanogrid. The first power processing unit includes a bidirectional converter configured to convert power exchanged between the internal storage system and the nanogrid according to an operating instruction indicating the direction of power flow. The external storage system includes a first flow battery configured to exchange power with at least one of a second flow battery of an energy storage vehicle or an electrolyte storage tank of the energy storage vehicle through electrolyte exchange.

[0019] The operating instructions may be based on one or more configurations, including a target configuration. The target configuration of the operating instructions may indicate a direction for converting energy of the operating target, including at least one of exporting output power from the grid or importing input power into the grid. The operating instructions may indicate an amount of energy or power associated with the nanogrid.

[0020] The system may also include a grid adapter controller for executing software programming, configured to: receive power data indicating power requirements of the nanogrid from one or more data sources; and transmit instructions to the internal storage system through the first power processing unit to exchange power with the nanogrid based on the power requirements.

[0021] The external storage system may include a reactor stack of the first flow battery and a storage system electrolyte tank configured to hold an electrolyte.

[0022] The system may further include one or more power processing units, including a first power processing unit. Each power processing unit includes one or more power conversions configured to reduce the amount of noise on the electrical waveform.

[0023] The system may further include a second processing unit configured to convert a second amount of electricity related to the distributed energy resources. The second processing unit includes a DC-DC converter for converting input power or output power according to the second amount of electricity.

[0024] The system may further include a second processing unit including a multi-level converter configured to convert input power received from a power distribution network coupled to the grid adapter.

[0025] The grid adapter can be disconnected from the power distribution grid.

[0026] In yet another embodiment, a system includes a grid adapter. The grid adapter includes an internal storage system, a first power processing unit, and a second processing unit. The internal storage system is configured to store a certain amount of internal energy storage. The first power processing unit is configured to convert an amount of power associated with a nanogrid. The first power processing unit includes a bidirectional converter configured to reduce noise on a first electrical waveform from an external storage system. The second processing unit is configured to convert distributed energy resources from a distribution network. The second processing unit includes a multi-stage converter configured to reduce noise on a second electrical waveform from the distributed energy resources in the distribution network, the distribution network being coupled to the grid adapter.

[0027] The operating instructions may be based on one or more configurations, including a target configuration. The target configuration of the operating instructions may indicate a direction for converting energy of the operating target, including at least one of exporting output power from the grid or importing input power into the grid. The operating instructions may indicate an amount of energy or power associated with the nanogrid.

[0028] The external storage system may include a first flow battery configured to exchange charge with at least one of a second flow battery of the energy storage vehicle or an electrolyte storage tank of the energy storage vehicle through electrolyte exchange.

[0029] The system may further include a grid adapter controller for executing software programming. The grid adapter controller may be configured to: receive power data indicating power requirements of the nanogrid from one or more data sources; and transmit, based on the power requirements, operating instructions to the internal storage system via the first power processing unit to exchange power with the nanogrid.

[0030] The system may further include a third processing unit for converting a second amount of electricity associated with the distributed energy resource. The third processing unit includes at least one of a DC-DC converter or an AC-DC converter and is configured to reduce noise on a third electrical waveform of the second amount of electricity from the distributed energy resource.

[0031] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present disclosure may be better understood with reference to the following drawings. Components in the drawings are not necessarily drawn to scale, with emphasis instead placed on illustrating the principles of the present disclosure. In the drawings, reference numerals designate corresponding parts throughout the different views.

[0033] Figures 1A to 1F A block diagram of a system implementing a Mobile Electric Grid (MEG) including a nanogrid subsystem according to one or more embodiments is shown.

[0034] Figure 2 Components of the communication and control layers of a MEG system are shown according to one embodiment.

[0035] Figure 3 A system including various implementation use cases and implementation areas for grid adapter variations according to one embodiment is shown.

[0036] Figure 4 The overall architecture of a grid adapter according to one embodiment is shown, including different power electronics stages and a hybrid energy storage system (HESS).

[0037] Figure 5A A grid adapter co-located and configured for distributed energy resource (DER) devices according to one embodiment is shown.

[0038] Figure 5B A grid adapter is shown that is co-located or remotely located and configured for a managed or controllable microgrid or nanogrid according to one embodiment.

[0039] Figure 5C A grid adapter is shown that is co-located or remotely located and configured for consumers and prosumers behind the meter (as a nanogrid), according to one embodiment.

[0040] Figure 6 A control system architecture of a grid adapter according to one embodiment is shown.

[0041] Figure 7 A control block diagram of a multi-level converter of a grid adapter according to one embodiment is shown.

[0042] Figure 8 An example of virtual power plant control according to one embodiment is shown.

[0043] Figure 9 The data flow of the autonomous control mode of the multi-level converter according to one embodiment is shown.

[0044] Figure 10 Included is a block diagram illustrating data flow for a grid support control system according to one embodiment.

[0045] Figure 11 The data flow and control architecture with external components according to one embodiment is shown.

[0046] Figure 12A A system is shown implementing a grid adapter co-located or remotely located and configured as a managed or controllable microgrid or nanogrid for continuous islanding using an electrical charging and discharging method, according to one embodiment.

[0047] Figure 12B A system is shown that implements a co-located or remotely located and configured grid adapter for uncurtailed power generation using electrical charging and discharging methods, according to one embodiment.

[0048] Figure 13 A grid adapter co-located or remotely located and configured for a managed or controllable microgrid or nanogrid for continuous islanding using non-electrical charging and discharging methods is shown according to one embodiment. DETAILED DESCRIPTION

[0049] Reference will now be made to the illustrative embodiments shown in the accompanying drawings, and specific language will be used herein to describe these embodiments. However, it should be understood that this is not intended to limit the scope of the invention. For those skilled in the art and those familiar with the present disclosure, changes and further modifications to the inventive features shown herein, as well as additional applications of the inventive principles shown herein, will be deemed to be within the scope of the invention.

[0050] In general, an enhanced power grid architecture is described herein that includes various techniques for sending power from various energy storage stations to various microgrid or nanogrid systems. In some cases, the energy production station or energy storage station and the nanogrid system are not electrically connected to each other (e.g., air-gapped), but embodiments include hardware and software components to monitor, manage, and implement energy exchanges; particularly for determining and managing situations where the nanogrid system currently requires a specific amount of power or predicts that a specific amount of power will be required at a specific time / date. For example, a system may include: a grid control server (e.g., an application, virtual), a plurality of energy storage station client computers (energy supply clients), a plurality of vehicle client computers (manned or unmanned), and a plurality of nanogrid client computers (energy demand clients and / or prosumers), wherein the grid control server communicates in real time with the energy storage station client computers, the vehicle client computers, and the nanogrid client computers.

[0051] Each vehicle associated with a vehicle client computer carries a payload of vehicle energy devices, including a rechargeable battery or rechargeable battery pack. The vehicle client instructs or receives instructions regarding whether the rechargeable battery or rechargeable battery pack participates in the vehicle client's energy supply actions (e.g., charging or discharging energy). The server receives various energy data or energy information from the energy storage station client, such as charge status or energy supply (e.g., current or predicted capacity, technical characteristics, location, current or predicted local weather, current or predicted local traffic conditions). The server receives various vehicle information (e.g., type, technical characteristics, current driving range, location, route, current or predicted local weather or en route, current or predicted local traffic conditions) and vehicle energy data, such as energy storage information (e.g., capacity, current charge level, time to recharge), from the vehicle client. The server receives various energy data or information regarding energy demand (e.g., current available amount, charge status, energy demand, specific energy amount required, time / date when the specific amount is required, current or predicted local weather, current or predicted local traffic conditions).

[0052] Based on this information, the server is programmed to receive a request from a nanogrid client requesting a specific amount of energy at a specific time / date (time factor). Thus, based on this information, the server is programmed to develop an optimized dispatch route for the vehicle client to travel to the energy storage station client (e.g., a few miles, tens of miles, or hundreds of miles) and use the energy from the energy storage station client to charge the vehicle client's onboard energy storage device (e.g., a battery). Furthermore, based on this information, the server is programmed to develop an optimized dispatch route for the vehicle client to travel from the energy storage station client to the nanogrid client after charging the vehicle client's battery with the energy storage station client, so as to timely deliver the energy released from the vehicle client's battery in response to the request sent from the nanogrid client to the server. Additionally or alternatively, the vehicle client may also transfer energy between local energy storage stations of various nanogrid clients. This transfer (between nanogrids) may be based on calculated factors, such as the vehicle client's charge level, the charge level of the local energy storage station (of each nanogrid), and / or time factors.

[0053] Embodiments described herein may include a grid architecture, a Mobile Electric Grid (MEG), that achieves availability, reliability, and flexibility in electricity supply without requiring an extensive wired distribution network and associated substations. MEG involves a method for reliably supplying electrical energy to consumers while minimizing or eliminating the need for static power lines between various energy storage stations and various nanogrid systems. This approach can be a data-driven grid that matches real-time and predicted energy demand with real-time and predicted energy supply.

[0054] Embodiments also include one or more grid adapter hardware and software components that couple electrical components and manage the distribution and quality of power to and from a power distribution network or grid. The grid adapter hardware and software implement a set of operational functions for allocating and distributing power among components of a distributed power system.

[0055] Non-limiting examples of functions, benefits, or advantages of a grid adapter include:

[0056] Grid adapters can mitigate or eliminate the penetration of high-frequency harmonics generated from EV charging stations and inverter-based DERs into the grid, thereby maintaining standard AC grid power quality levels regardless of the increased penetration of power electronics-based systems in the nanogrid;

[0057] Grid adapters can increase the DER carrying capacity of a given feeder or feeder network;

[0058] Grid adapters can enable downstream microgrids or nanogrids to act as controllable balancing loads from the grid’s perspective;

[0059] Grid adapters can use HESS to provide ancillary services to the upstream grid, such as voltage and frequency support;

[0060] Grid adapters can enable downstream microgrids or nanogrids to operate autonomously (isolated from the grid); and

[0061] The grid adapter may provide the ability to exchange data regarding the status of the microgrid or nanogrid with a distribution control center to allow smart grid operations that rely on communication between different elements of the distribution grid. Any number of additional or alternative features, functions, and advantages may be realized.

[0062] In some embodiments, the grid adapter is capable of air-gap (or "islanding") downstream microgrid and nanogrid systems over a wide range of timeframes. This is achieved by connecting the DC / DC converter of the grid adapter to an Energy Storage System (ESS). In some cases, the DC / DC converter is connected to an ESS based on a stationary flow battery, wherein the flow battery is charged and discharged by electrolyte exchange. The flow battery ESS can be transported to or from the nanogrid system by a SEV. As a result of this electrolyte exchange, energy supply of varying durations can be achieved for microgrids and nanogrids via the grid adapter. The grid adapter and electrolyte exchange approach facilitates flexible islanding durations, providing rapid and / or long-term energy deployment and updates for remote nanogrids, which can be beneficial in supporting uses such as community disaster relief, backup power for communities and businesses, or reliability backup solutions (particularly for mining areas and oil and gas wells and pump jacks).

[0063] Grid adapters and electrolyte exchange methods also facilitate reduced energy exchange times. The grid adapter's power electronics and control software reduce or eliminate the need for certain components in the external ESS. Flow battery-based external ESSs can accommodate electrolyte exchange, which can reduce energy exchange (e.g., charging or discharging) times compared to conventional energy exchange or battery replacement. It should be understood that, while each term generally refers to an electrical commodity resource, terms such as power, energy, and electricity may be used in this context to describe certain functions or structures.

[0064] System components and architecture

[0065] Figures 1A to 1F Various exemplary embodiments of the MEG system 100 are shown, including variations of subsystems. Figure 1A is a block diagram illustrating components of a MEG system 100, including various subsystems that may include various charging stations, central energy storage (CES) stations 104a-104n (collectively, CES stations 104), and nanogrid systems 108a-108n (collectively, nanogrid 108), which may transmit power or data.

[0066] System 100 may include one or more distribution networks 111a, 111b (collectively, distribution network 111), including hardware and software components for distributing energy from a source to a destination. In some cases, distribution network 111 includes hardware and software components of CES station 104, LES station 110, or non-grid 108, or hardware and software components coupled to CES station 104, LES station 110, or non-grid 108. Distribution network 111 may include a low voltage (LV) distribution network, a medium voltage (MV) distribution network, or a low / medium voltage (LV / MV) distribution network.

[0067] like Figure 1A As shown, MEG 100 includes one or more energy generators 102, one or more central energy storage (CES) stations 104, one or more stored energy vehicles (SEVs) 106, and one or more nanogrids 108. The example MEG system 100 includes several variations of nanogrids 108.

[0068] The MEG 100 may include a system of fixed and mobile hardware that manages, controls, and delivers a reliable supply of electricity to end consumers 112 within a given geographic area. One or more computing devices, such as servers or other forms of computing devices (e.g., personal computers, laptops, tablets), may execute software (referred to herein as a "MEG controller" or "controller") that receives data from various input sources to control and manage the operation of the components of the MEG 100. The computer-implemented MEG controller may include or be in operational communication with a demand controller software component that monitors and obtains data on the energy consumed by end consumers 112 and controls the specific electrical loads available to a particular end consumer 112.

[0069] In some implementations, the demand controller of the MEG controller can control the load based on the demand predicted by the artificial intelligence, based on instructions from each end consumer 112 and / or based on instructions from the Mobile Grid Control Centre (MGCC). The artificial intelligence operation can be trained based on various types of historical energy data received by the MEG controller from various data sources. During the training phase of the artificial intelligence, the values ​​of the historical data are translated and converted into training feature vectors and input into the artificial intelligence engine. The MEG controller then applies one or more layers of the artificial intelligence to the training vectors to train the various parameters of each layer. During the testing phase (sometimes referred to as "deployment" or "inference time"), the MEG controller receives the corresponding type of data, which is then translated and converted into test vectors and input into the artificial intelligence. The artificial intelligence engine then outputs, for example, the required predicted or projected energy demand, shortage or surplus.

[0070] The computing device used herein may include any device having hardware (e.g., a processor, a programmable logic controller) and software capable of performing the various functions and features described herein, such as a controller (not shown) including executable controller software for controlling the operation of the various subsystems of MEG 100.

[0071] The controller receives data from various data sources, including components of the subsystem and external data sources (e.g., a weather server hosting meteorological data), to perform various processes and tasks for the subsystem. These processes include, for example, monitoring energy data, determining energy shortages or surpluses, and managing various subsystems (e.g., nanogrid 108, LES station 110).

[0072] Controllers managing, for example, generators 102 and CES stations 104 can monitor and issue instructions for energy generation, storage, and output. For example, a controller can manage the power converters of renewable energy generators 102a and 102c (e.g., wind turbines or solar generators), which typically require power converters to output a maximum amount of acceptable power in a stable manner. Operating the power converters may require continuous data monitoring. The controllers contain and execute software algorithms to ensure stable energy output. For example, a wind farm may experience voltage spikes due to wind surges. A controller monitoring the wind turbines can detect the voltage spikes and immediately determine and issue instructions for corrective action. Corrective action may include instructing the renewable generators 102a and 102c to reduce the amount of power generated or to modulate the amount of reactive power generated. Additionally or alternatively, rather than reducing the amount of power generated, the corrective action may include sending excess energy to one or more energy devices within the subsystems of MEG 100. The controllers can determine to send the excess energy to one or more energy storage devices and store the energy for later release at another time. The controller described herein affirmatively controls any variable energy generation within the MEG 100 and maintains the stability of the MEG 100, whereas most traditional grid monitoring software solutions rely on grid components to maintain stability. Utilizing vehicle delivery and controller-based metering, electrical energy can be delivered to end consumers across varying distances while maintaining predictable energy without, for example, transmission lines and power transformers.

[0073] In some implementations, the onboard computing device monitors the charge status of the onboard storage device. The onboard computing device can instruct the onboard storage device to charge, discharge, or exchange a predetermined amount of energy at one or more nanogrids 108. In some cases, the onboard computing device manages and controls the voltage or amount of energy so that the predetermined amount of energy delivered to each geographic location of the one or more nanogrids 108 does not increase (or rise).

[0074] The energy generator 102 is a system of hardware and software components that generates electricity and is operated and managed by a software controller executed by one or more computers. The energy generator 102 can be of any size and operate at any scale and can implement any form of power generation (e.g., natural gas, coal, nuclear energy). In some cases, the energy generator 102 is a renewable energy generator 102a, such as a wind or solar power generation system. The energy generator 102 can be stand-alone (e.g., "air-gapped"), connected to one or more additional energy generators 102, or connected to a wider power grid. The end consumers 112 of the MEG 100 system can include any one or any combination of residential, commercial, and industrial electricity consumers.

[0075] The CES station 104 stores electricity generated by one or more energy generators 102. The CES 104 can be co-located with the generator 102 system or remote from the generator 102 system. Figure 1B As shown, CES 104a includes a CES controller 128, a charging and discharging station 122, and a primary energy storage system 124. Primary energy storage system 124 includes hardware components for storing electrical loads generated by respective energy generators 102, which may be renewable energy generators 102a, 102c electrically coupled to CES 104a. Charging and discharging station 122 manages and processes energy moved from energy storage system 124 to SEVs 106, or from SEVs 106 to energy storage system 124. CES controller 128 includes hardware and software components for controlling the movement and distribution of electrical loads between generators 102, energy storage system 124, and / or SEVs 106.

[0076] Local Energy Storage (LES) stations 110 store electricity for one or more nanogrids 108. Figures 1C to 1E As shown, each LES 110 includes an energy storage station system 124 located within or co-located with the corresponding nanogrid 108, and includes an energy storage device 124 (e.g., a battery) and a grid forming / tracking inverter 126 (e.g., DC to AC). In some cases, the LES 110 includes a charging and discharging station 122 for interacting with the SEV 106 to meet the energy required for service delivery goals. It should be understood that power delivery to the LES 110 can be achieved through one or more methods and media. For example, power delivery to the LES 110 can be achieved through the above-mentioned charging and discharging station 122. However, in some cases, power delivery can also be achieved by replacing or exchanging depleted battery storage devices within the LES 108. In these cases, the battery payload can be carried by the SEV 106.

[0077] Mobile Grid Control Centre (MGCC) Figures 1A to 1FThe MGCC (not shown) may include a control room within a building, underground facility, mobile facility (e.g., truck, bus, ship, airplane), or data center that houses command and control software and personnel who monitor and operate the MEG 100 for a specified geographic area. Computing devices of the MGCC, such as data center servers and / or other computing devices (e.g., personal computers, laptops, tablets), may be communicatively coupled to computing devices of the MEG 100 system, such as a server executing a controller for controlling the energy generator 120, via one or more public or private networks. The MGCC computing devices may receive various data inputs, such as energy data (e.g., current or historical state of charge; current or historical energy consumption), and store the data in servers at the MGCC data center. The MGCC computing devices may transmit instructions and data updates to various computing devices in the MEG 100 system. These instructions may be automatically generated by algorithms executed by the controller, or may be input by an administrative user through a graphical user interface (GUI) of the user's computing client device.

[0078] Nanogrid 108 may include a collection of one or more (e.g., two, three, four, or more) end consumers 112a-112f (collectively, end consumers 112), which may be consumers 112d, 112f or prosumers 112c, 112e, where a prosumer is an end consumer 112 that has the ability to self-generate (e.g., solar, wind) some or all of its electrical energy needs. End consumers 112 may be located within a common geographic boundary (e.g., a geographic region / area) and connected to LES station 110 via high-voltage or low-voltage distribution lines through associated power electronics. In some cases, nanogrid 108 may be an electrical island that is not connected to conventional grid power lines.

[0079] The nanogrid 108 may have a computing device (e.g., a server) that executes Nano-Grid Controller (NGC) 120 software, which may include energy management algorithms that maintain a reliable power supply for a given nanogrid 108 and monitor energy usage by end consumers 112 of the nanogrid 108.

[0080] The MEG 100 system is served by one or more SEVs 106 according to the instructions of the MGCC equipment. The SEVs 106 may include transport vehicles that carry electrical energy stored in batteries for charge / discharge transportation between various CES104 and LES stations 110. The SEVs 106 may be land vehicles (e.g., cars, buses, trucks), aircraft, or ships, whether manned or unmanned, whether autonomous or non-autonomous. Land vehicles may be powered by internal combustion engines or batteries, and may include bidirectional electric or hybrid vehicles. In some implementations, the SEVs 106 may be automatically or manually dispatched by one or more devices of the MEG system 100 to charge or discharge a specific amount of electrical energy from their batteries at a CES station 104 or a LES station 110.

[0081] In operation, the servers execute one or more controllers of the MEG system 100, creating a data-driven power grid that matches real-time and predicted energy demand with real-time and predicted energy supply. To achieve this matching, the MEG system 100 includes computer-implemented controllers at each level of the MEG system 100 that incorporate data acquisition and analysis, machine learning, and deep learning algorithms that determine the optimal use of various battery storage technologies, power generation electronics, and SEVs 106 carrying energy storage. As a result, the MEG 100 enables a reliable and flexible power delivery system that can meet at least some of the needs of energy end consumers 112 as their respective demands evolve. The MEG 100 represents a novel approach to delivering electricity in an informed and quantified manner.

[0082] The MEG 100 includes a network of nanogrids 108, the stability of which is maintained by an on-site nanogrid controller 120. According to the nanogrid controller 120, the energy needs of end consumers 112 (e.g., prosumers and consumers) connected to these nanogrids are met through on-site generation and / or energy harvested from LES stations 110 within the respective nanogrids 108. The LES stations 110 manage and provide energy supply to consumers 112a, 112d, 112f, as well as energy supply and reliable backup solutions for prosumers 112b, 112c, 112e. The schedule and amount of energy delivered by the SEVs 106 to the LES stations 110 of the nanogrid 108 is automatically and / or manually determined and scheduled by a computing device executing controller software in the MGCC, taking into account demand forecasts from the prosumers 112b, 112c, 112e and consumers 112a, 112d, 112f in the nanogrid 108 and the available energy at the CES stations 104. The MEG system 100 may include redundancy in the form of additional storage capacity at the LES stations 110 to account for any delayed or missed deliveries by the SEVs 106.

[0083] Each LES station 110 includes a grid-forming inverter for controlling the quality of (at least part of) the power delivered to the end consumer 112. A stationary energy storage system 124 may be located at the charging station 122 and at the interface between the SEV 106 and the nanogrid 108. The SEV 106 may include a mobile energy storage system on the SEV 106 that may have different characteristics and requirements than the energy storage system 124 at the CES station 104 or the LES station 110.

[0084] The mobile energy storage system of the SEV 106 may include a payload system (e.g., a payload battery) and an operating system (e.g., a car battery). The payload system includes those mobile energy storage systems and components of the SEV 106 (e.g., payload system, payload battery) used for power delivery, and charging or discharging according to power data and power demand. The operating system includes those mobile energy storage systems and components of the SEV 106 involved in operation. In some cases, the payload system of the SEV 106 is electrically, structurally, and operationally different from the operating system of the SEV 106. In some cases, the payload system of the SEV 106 is not operationally separate and different from the operating system of the SEV 106 involved in operation. In some embodiments, the payload system and energy storage system of the SEV 106 include one or more flow batteries or electrolyte storage tanks for electrolyte exchange, wherein the flow battery of the payload system of the SEV 106 can be charged or discharged by electrolyte exchange according to the power data and power demand.

[0085] System 100 includes various energy storage systems, such as energy storage system 124 of nanogrid 108, energy storage system 124 of LES station 110, energy storage system 124 of CES station 104, and mobile energy storage system of SEV 106. The structure and operating principles of the various energy storage systems may vary depending on the type of energy storage system implemented. Non-limiting examples of energy storage systems may include flow batteries, lithium-ion batteries, and lead-acid batteries.

[0086] Controller software executed by various servers continuously monitors the type of power storage system to ensure health, thereby achieving predictable power costs over a certain duration.

[0087] Figure 1F An example system 100 is shown including a non-grid variant 108f that implements a grid adapter 117 for energy management, exchange, and storage as described herein. Figure 1FAs shown, the grid adapter 117 is coupled to the LES 110 including or coupled to one or more distribution grids 111 (e.g., a low voltage distribution grid, a low voltage / medium voltage distribution grid), and coupled to an energy storage station system 124 located within or co-located with the corresponding nanogrid 108f, wherein the energy storage system includes one or more energy storage devices 124 (e.g., batteries). In some cases, the energy storage system 124 includes an interface with the SEV 106 to meet the energy required to serve the delivery target.

[0088] It should be understood that power delivery to the LES 110 can be accomplished through one or more methods and media. For example, power delivery to the LES 110 can be accomplished through the aforementioned charging and discharging station 122. In some cases, power delivery can also be accomplished by replacing or swapping depleted battery storage devices within the LES 108. In these cases, the battery payload can be carried by the SEV 106. In some cases, power delivery can also be accomplished by exchanging electrolytes between flow batteries in the SEV 106 and depleted battery storage devices within the LES 108.

[0089] Grid adapter 117 includes hardware and software components that air-gap (or "island") downstream microgrids and nanogrids over a wide range of timeframes. Grid adapter 117 also facilitates interchange between distribution grid 111 and energy storage system 124. This can be accomplished through a DC / DC converter (not shown) or other hardware components of grid adapter 117 that connects distribution grid 111 to storage system 124.

[0090] In some embodiments, the storage system 124 includes an interface and battery structure based on a stationary flow battery that is charged and discharged through the exchange of electrolytes transported by the SEVs 106 .

[0091] Figure 2 Figure 2 shows the components of the communication and control hierarchy of the MEG system 200 according to one embodiment. The MEG system 200 includes various subsystems, including servers that execute controller software to control the operation of specific subsystems, such as the MEG controller (of the MGCC 202) or the nanogrid controller (NGC 209). The MEG system 200 includes a control and communication network architecture that carries data communications between the various subsystems of the MEG system 200.

[0092] The network architecture includes various hardware and software components of one or more public or private networks that interconnect the various subsystem components of the MEG system 200. Non-limiting examples of such communication networks may include a local area network (LAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), and the Internet. Network communications may be performed in accordance with various communication protocols, such as the Transmission Control Protocol and Internet Protocol (TCP / IP), the User Datagram Protocol (UDP), and IEEE communication protocols.

[0093] The control of the MEG system 200 relies on a centralized concept to operate the MEG system 200 in an efficient and secure manner, wherein the MEG system 200 is operated and controlled by one or more servers that execute software controllers located in each subsystem. These controllers receive and evaluate information used to manage the operation of the subsystems, such as operating data of generators, geographic information of the location of each subsystem, energy storage indicating inventory at a given location, the amount of energy in transit, historical and predicted energy consumption, weather forecasts (e.g., meteorological data 214), etc.

[0094] The Local Demand Controller (LDC) 216 and the NGC 210 at the nanogrid or generator can be configured to ensure safe and reliable operation of the network even in the event of a loss of the communication network.

[0095] The servers (or other computing devices) at the MGCC 202 can oversee some, most, or all operations of the MEG system 200 to enable optimal utilization of various available energy sources while maximizing the power quality levels required by consumers and prosumers. For example, the servers at the MGCC 202 send set points to the CES stations 206 and, in conjunction with the CES stations 204, issue commands to the SEVs 208 to charge or discharge specific amounts of electricity, thereby achieving various set goals for the MEG system 200. In some implementations, the servers at the MGCC 202 receive the status and health of specific CES stations 204 from the servers at the CES stations 206. The servers at the MGCC 202 evaluate the status, health, and operational details to ensure reliable and safe operation of these subsystems.

[0096] CES station 204 communicates (e.g., wirelessly, wired) with LES station 210 to schedule and control the operation of LES station 210 while ensuring safe and reliable conditions. LES station 212 communicates (e.g., wirelessly, wired) with NGC 209 to exchange setpoints and monitoring information.

[0097] The nanogrid's NGC 210 supports various operations of the low voltage (LV) distribution network, which includes home controllers, distributed generation controllers, and network elements to achieve some, many, most, or all of the goals of safe and reliable operation of the nanogrid. The NGC 210 includes various distribution management system functions, such as network connectivity analysis, state estimation, voltage and reactive power control, load reduction application, load and generation forecasting, feeder reconfiguration, fault management and system restoration, and demand-side management (DSM). These functions can help the MEG system 200 achieve various goals, such as improving system reliability, reducing outage time, improving system efficiency, improving power quality indicators, increasing the integration of renewable energy, strengthening asset management, and reducing operation and maintenance costs.

[0098] In some embodiments, NGC 210 includes the functionality and features of a grid adapter controller of a grid adapter described herein. In some embodiments, system 200 includes a grid adapter having a grid adapter controller (not shown) that is distinct from the nanogrid and NGC 210 .

[0099] The MGCC 202 evaluates various types of data inputs from various data sources. For example, the servers of the MGCC 202 evaluate and model energy data from various utility systems 224 and other subsystems of the MEG 200. Non-limiting examples of data inputs include: real-time and projected geographic locations of SEVs 208; real-time and forecasted SoC of SEVs 208 within geographic boundaries (e.g., service areas); real-time and forecasted energy surplus or shortage at LES 212 for a given NGC 210; real-time and forecasted SoC at CES 206; consumption metrics for consumers connected to the NGC 210, which may indicate a projected reduction in electricity consumption for a given monetary or non-monetary credit (consumer incentive) over a defined forward-looking period; consumption and production metrics from the utility system 224, which indicate the amount of energy projected to be supplied or absorbed at a given price (utility system 224 incentive) at a particular LES station 212 connected to the MEG 200; and production metrics from generators, which indicate the amount of energy supplied to the CES station 206 at a given price (generator incentive).

[0100] The MGCC 202 executes controllers to receive and / or determine real-time and predicted parameters from the grid adapter controller, LDC 216, NGC 210, SEV 208, and CES 206. The MGCC 202 can predict and / or identify power shortages or surpluses at various subsystems of the MEG 200 based on data inputs from various data sources (e.g., subsystem inputs, database 408) and / or based on Virtual Mobile Power Plant (VMPP) model outputs. The MGCC 202 can determine which subsystems to charge / discharge to achieve balance and appropriate power at the subsystems of the MEG 200 and transmit machine-readable charge / discharge instructions or human-readable instructions (e.g., incentives) to, for example, consumers, generators, and the utility system 224. After determining how to achieve energy balance for the MEG 200 using, for example, price commitments and credits, the MGCC 202 determines a schedule and delivery routes for the SEV 208 fleet to achieve energy balance for the entire MEG 200 at the lowest operating cost within a given time step.

[0101] Figure 3 A system 300 is shown that includes grid adapter variations 317a-317n (collectively, grid adapters 217) for various implementation use cases and implementation areas. The system 300 includes an upstream power distribution network 315 or grid that is coupled to various energy subsystems 320a-320n (collectively, energy subsystems 320) via corresponding grid adapters 317.

[0102] Grid adapter 317 is an integrated hardware and software system that prevents voltage violations, harmonic contamination, and low-frequency oscillations from penetrating distribution network 315 due to the increased uptake of various types of energy subsystems 320, such as electric vehicle (EV) stations and distributed energy resources (DER), which are typically used to supplement or augment traditional centralized power generation and distribution infrastructure (e.g., solar panels, wind turbines). Grid adapter 317 provides these functions by decoupling the structure and operation of energy subsystems 320 (e.g., DER, EV stations) from the structure, operation, management, and control of distribution network 315 or the power grid. Grid adapter 317 can be deployed at various locations on distribution network 315 or the power grid to facilitate the unrestricted adoption of new forms of energy subsystems 320 (e.g., DER, EV stations), thereby ensuring smooth and reliable power system operation for energy subsystems 320 and other entities in system 300 (including generators, consumers, and prosumers). For example, a first grid adapter variant 317a can be deployed and used with a DER device 320a (e.g., an energy storage system, a renewable energy generator, or an EV charging station). For another example, a second grid adapter variant 317b can be deployed upstream of a given microgrid 320b on a given feeder or feeder network. For another example, a third grid adapter variant 317c can also be deployed behind the meter at a nanogrid 320c for use by individual electricity consumers or prosumers connected to the distribution grid 315 or the grid as a subsystem of the nanogrid 320c.

[0103] Figure 4 Components of system 400 are shown, and in particular, various components of the overall architecture of grid adapter 417 are described. System 400 includes an electron source system (shown as DC load 402), an upstream distribution network 415 (e.g., Figure 1F 406 ), a target electronic system, such as a DER 420 (e.g., AC DER, DC DER), a nanogrid 408, an EV station 422 coupled to the SEV 406, and an external power storage system (ESS 424) located, for example, at an end-consumer location or a remote storage location.

[0104] The hardware of the grid adapter 417 includes a combination of energy storage technology and power electronics technology. The grid adapter 417 includes power processing units for different power electronics levels, and a hybrid electrical storage system (HESS), including an internal HESS 442. As used herein, "power electronics level", "conversion level" and similar terms known in the art refer to electronic device-based power processing units that perform the required power processing, such as converting a voltage waveform from alternating current (AC) to direct current (DC). The power processing units of the grid adapter include, for example, a DC / DC converter 445, an AC / DC converter 446, a multi-level converter 440, and a bidirectional converter 444. The specific combination of converters within the grid adapter will depend on the specific use case (e.g., the target application area) for the distribution network 415. Figures 5A to 5C ). The internal HESS 424 is connected to a set of unidirectional and bidirectional AC / DC converters 446 and DC / DC converters 445 via an internal DC link.

[0105] Each internally facing DC side of converters 440, 444, 445, 446 is connected to an internal HESS 442, while the corresponding externally facing AC side or DC side of converters 440, 444, 445, 446 is connected to devices external to grid adapter 417, such as electronic DC load 402, DER 420 (e.g., AC DER, DC DER), nanogrid 308, EV station 422 coupled to SEV 406, and external ESS 424. The interface with upstream distribution grid 415 is through multi-level converters 440 or bidirectional AC / DC converters to control power flow with upstream grid 415. In some cases, transformer 416 is located between grid adapter 417 and distribution grid 415. Transformer 416 can manage, for example, medium voltage (MV) or low voltage (LV) voltage as needed. Meanwhile, other power processing units for the power electronics level provide interfaces with, for example, other elements of the downstream nanogrid 408, including DC loads 402, AC loads (not shown), external ESS 424, EV charging stations 422, and DERs 420 (e.g., AC DERs and DC DERs), which may be on-site at the nanogrid 408 or remote from the nanogrid 408.

[0106] Each processing unit and internal HESS 442 at the converter level may include a different local controller (not shown). The local controller includes software programming for managing the performance of the specific processing unit or internal HESS 442 and establishing and tracking reconfigurable set points. In some implementations, the set points or thresholds for each local controller may be received from a higher-level central controller (e.g., a grid adapter controller 419 or a grid controller (not shown) of the distribution network 415), which configures and manages the operation of the components of the grid adapter 417 to implement a desired energy management strategy (collectively referred to as an "energy management system" or "EMS"). The EMS optimizes the operation of the grid adapter based on the needs of the components connected to the grid adapter while complying with the requirements of the utility distribution management system (DMS).

[0107] Figures 5A to 5C A system 500 is shown with various architectures of a grid adapter 517 for various implementations and uses according to an exemplary embodiment. Figures 5A to 5C The grid adapter 517 shown includes a power processing unit for the power electronics stage and an internal HESS 542. In some cases, a transformer 516 is located between the grid adapter 517 and the upstream distribution network 515. The components of the system 500 are similar to Figure 4 Similar features and functions are described in Figures 5A to 5C No need to repeat certain details.

[0108] like Figure 5A As shown, the grid adapter 517 is co-located with the DER 520 device (e.g., DC DER, AC DER) and is configured to receive / send power from / to the DER 520 device. The system 500 includes a power distribution network 515 (e.g., Figure 1F515 ), DER 520 (e.g., AC DER, DC DER), nanogrid 508, EV station 522 coupled to SEV 506, and external power storage system (ESS 524) located, for example, at an end-consumer location or a remote storage location. DER 520 may include multiple subsystems, including hardware and software components used to supplement distribution grid 515 or enhance power generation and distribution infrastructure (e.g., solar panels, wind turbines). For DC-based DER 520, grid adapter 517 includes a DC / DC converter 545 having an externally facing DC interface and an internally facing DC interface, converting or applying an electronic stage to reduce harmonics or other noise on the electrical waveform of the input power or output power. For AC-based DER 520, grid adapter 517 includes an AC / DC converter 546 having an externally facing AC interface and an internally facing DC interface, converting or applying electronic stages to reduce harmonics or other noise on the electrical waveform of the input power or output power.

[0109] For AC loads and generators 504, grid adapter 517 includes a multi-level converter 540 with an externally facing AC interface and an internally facing DC interface. The converter converts or applies electrical levels to reduce harmonics or other noise in the electrical waveform of the input or output power. Grid adapter 517 includes multi-level converter 540 for interfacing with AC loads and generators 504 or an upstream AC distribution grid 515. Multi-level converter 540 can exchange power between the upstream grid 515 or the AC loads and generators 504 and the DC side of grid adapter 517. Grid adapter controller 519 can reference a pre-configured or desired power factor that ensures the desired power quality level for the upstream grid 515 or the AC loads and generators 504. Based on this power factor, grid adapter controller 519 monitors and controls multi-level converter 540 to exchange power with the upstream grid 515 or the AC loads and generators 504, which has a very high level of pure sinusoidal current.

[0110] Grid adapter 517 can mitigate or eliminate the penetration of high-frequency harmonics generated by EV charging station 522 and inverter-based DER 520 into components of system 500 (e.g., nanogrid 508 or upstream distribution grid 515). In some cases, grid adapter controller 519 monitors and controls grid adapter 517 components to maintain a preconfigured power quality level within grid system 500 (e.g., standard AC grid; DC grid), regardless of the increased penetration of power electronics-based systems within nanogrid 508 or other power sources flowing into grid adapter 517. In some cases, grid adapter controller 519 of grid adapter 517 can increase the DER 520 carrying capacity of a given feeder or feeder network between grid adapter 517 and DER 520. When grid adapter 517 is used in conjunction with DER 520, grid adapter controller 519 can be the same device and software programming as, or different from, the local controller (not shown) of DER 520.

[0111] Figure 5B A grid adapter 517 is shown that is co-located or remotely located and configured for a managed or controllable microgrid or nanogrid 508. The system 500 includes a distribution network 515 (e.g., Figure 1F 508), an EV station 522 coupled to a SEV 506, and an external power storage system (ESS 524). The grid adapter also includes a grid adapter controller 519. In the case where the grid adapter 517 is used in conjunction with the nanogrid 508, the grid adapter controller 519 can be the same device and software programming as the nanogrid controller (not shown) of the nanogrid 508, or different.

[0112] Figure 5C Grid adapters are shown that are co-located or remotely located and configured for the meter back-end systems of end consumers (e.g., consumers and prosumers) that serve as various types of subsystems, such as DC generators 503, DC loads 502, AC loads and generators 504, nanogrids ( Figure 5C (not shown) and so on. Figure 5C As shown, grid adapter 517 may smooth harmonics or noise in electrical signals introduced into system 500 from various energy systems (eg, DC generator 503 , DC load 502 , or AC load and generator 504 ).

[0113] For DC generator 503 or DC load 502, grid adapter 517 includes a DC / DC converter 545 with an externally facing DC interface and an internally facing DC interface, converting or applying electronic stages to reduce harmonics or other noise in the electrical waveform of the input or output power. For AC load and generator 504, grid adapter 517 includes a multi-level converter 540 with an externally facing AC interface and an internally facing DC interface, converting or applying electronic stages to reduce harmonics or other noise in the electrical waveform of the input or output power. Grid adapter 517 includes multi-level converter 540 for interfacing with AC load and generator 504 or upstream AC distribution grid 515. Multi-level converter 540 can exchange power between upstream grid 515 or AC load and generator 504 and the DC side of grid adapter 517. Grid adapter controller 519 can reference a preconfigured or desired power factor, which ensures the required power quality level for upstream grid 515 or AC load and generator 504. Based on the power factor, the grid adapter controller 519 monitors and controls the multi-level converter 540 to exchange power with the upstream grid 515 or AC load and generator 504 with a very high level of pure sinusoidal current.

[0114] Figure 6 The architecture of a control system 600 for a grid adapter is shown. System 600 includes a grid adapter controller (referred to as GAC 619 or a central controller for the grid adapter) and one or more local controllers 620a-620n (collectively referred to as local controllers 620 or local controllers 620). System 600 includes any number of internal ESSs 624a-624n (collectively referred to as internal ESSs 624 or internal ESSs 624), a battery management system (BMS) 631, and a plurality of converters 628a-628n (collectively referred to as converters 628 or converters 628). In some embodiments, GAC 619 and local controllers 620 can be located in the same geographic location or cabinet, which is located at the geographic location of the grid adapter.

[0115] Internal ESS of the grid adapter (e.g. Figure 4The internal HESS 624 includes hardware and software components for storing electricity received from various upstream sources as internal energy storage. The internal ESS 624 includes a controller, processor, or other logic device that performs certain functions of the internal ESS 624 in accordance with control commands containing executable instructions received from an upstream or higher-level controller in the system 600. The internal ESS 624 can, for example, receive, store, and transmit electricity in accordance with the control commands. The internal ESS 624 can receive control commands from the BMS 631 or the GAC 619. In some implementations, the controller, processor, or other logic device of the internal ESS 624 can monitor the function and status of the internal ESS 624 to generate energy data containing status information of the internal ESS 624. The internal ESS 624 transmits the energy data to the BMS 631 or the GAC 619, which can reference the energy data obtained from the various data sources when generating control commands.

[0116] In some embodiments, system 600 includes a BMS 631, which includes hardware and software components for monitoring the function and status of one or more internal ESSs 624 of the grid adapters of system 600. BMS 631 generates energy data containing status information of one or more internal ESSs 624 and transmits the energy data to GAC 619. GAC 619 may reference energy data obtained from various data sources, including BMS 631, when generating control commands. BMS 631 may receive control commands from GAC 619 and then transmit the control commands to the appropriate internal ESS 624 based on the identification information of the internal ESS 624 contained in the control commands.

[0117] The GAC 619 executes various software processes to manage the operation of one or more grid adapters using energy received from various controllers or devices in the system 600 or based on control commands received from local controllers 620 (e.g., LDC 216, NGC 210) or other subsystems of the system 600 (e.g., LES stations, CES stations, distribution networks, MGCC servers). For example, the internal GAC 619 can determine, for example, the amount of energy input, the quality of energy conversion or output, and the control commands to be executed by the components of the grid controller. The GAC 619 then generates and transmits control commands to the components of the grid adapter. These components receive, store, and transmit power in accordance with the control commands. The GAC 619 can monitor the function and status of the components of the grid adapter to generate energy data containing status information about the grid adapter. The GAC 619 transmits the energy data to the local controller 620 or other processing components (e.g., MGCC servers), which can reference the energy data when generating further control commands.

[0118] In some cases, GAC 619 can capture energy data or transmit control commands directly to / from converter 628, which is a component of a grid adapter, including internal ESS 624. In some cases, where converter 628 is a component of a separate subsystem, GAC 619 receives control commands from a local controller 620 for the particular subsystem.

[0119] Grid adapters mitigate harmonics and oscillations

[0120] Figure 7 A control block diagram of a multi-stage converter for a grid adapter 700 is shown. High-frequency harmonics distort the sinusoidal AC power of the power distribution grid. This can negatively impact the operation of the power system, manifesting, for example, as malfunctioning protective relays. Grid adapter 700 includes a power processing unit (sometimes called a power electronic converter) that blocks unwanted harmonics from electrical signals. The power processing unit comprises a series of power conversion stages and controls for the sinusoidal AC power.

[0121] Grid adapter 700 uses power electronic converters to decouple the upstream distribution grid from downstream energy subsystems, which may include external ESS, EV stations, and DER stations. This mitigates or eliminates harmonics generated by the downstream grid and prevents them from penetrating the upstream grid, and vice versa.

[0122] In some implementations, the converter converts the AC voltage on the nanogrid side to a DC voltage, and then converts it back to an AC voltage on the distribution grid side. In such implementations, the grid adapter 700 includes a multi-level converter for interfacing with the AC upstream distribution grid. This multi-level converter is capable of exchanging power between the upstream grid and the DC side of the grid adapter 700 with very high levels of pure sinusoidal current and the required power factor to ensure the required power quality level for the upstream grid.

[0123] In some cases, the internal ESS acts as an energy storage buffer between the two sides of system 700. This allows grid adapter 700 to smooth out long-term disturbances in voltage or frequency. The multi-level converter is controlled to allow for grid-following operation, where the converter exchanges required power and reactive power with the upstream grid based on the needs of the downstream system to maintain a balance between generating and load elements. This balance is achieved by maintaining the DC link voltage at a desired level based on the operating mode determined by the overall EMS.

[0124] System 700 may include a compensator controller 701 (shown as "Compensator in dq frame"). A compensator controller is a special controller used to achieve the desired current in the converter. In some implementations, compensator controller 701 implements a control technique called "dq control."

[0125] The system 700 may include a reference signal generator 703. The reference signal generator 703 includes hardware and software components for executing various equations or functions that convert a power stage set point to a current set point.

[0126] System 700 may include a droop controller 705. Droop controller 705 includes computing hardware and software that implements a control technique in a power system where the power level set point is automatically changed based on frequency or voltage to support the grid. The operation of droop controller 705 may be equivalent to proportional control.

[0127] Grid adapters increase feeder carrying capacity

[0128] The grid adapter increases feeder capacity to carry additional DERs by interacting with an external ESS and coordinating the physical delivery of power using SEVs. The additional DER capacity is determined by modeling and analyzing the quasi-static time series behavior of existing feeder elements, SEV delivery, and the proposed DERs in a central EMS with the grid adapter. This approach unlocks variable adoption rates of DERs and EVs downstream of the grid adapter, eliminating the need for utility operators to repeatedly assess and upgrade distribution lines as consumers purchase EVs, install EV stations, or deploy rooftop solar.

[0129] A DC / DC converter connected to a bidirectional charging station enables the Mobile Electric Grid (MEG)'s SEVs to charge and discharge from and to the ESS in the grid adapter. SEV power delivery ensures that the energy available to the feeder or household is sufficient to meet its forecasted needs. This, in turn, reduces the amount of power drawn from the distribution grid upstream of the grid adapter.

[0130] Added to this is the internal ESS, which is able to provide energy to the EV or loads if required, and can also absorb excess energy from renewable energy sources or the upstream grid during low electricity price hours during the day.

[0131] Grid adapters create controllable microgrids and nanogrids

[0132] By controlling the various power electronics stages and a central EMS controller, a grid adapter can control the downstream grid to act as a controllable load or generator with a pre-specified power setpoint, which can be tied to time of use and electricity price. Thus, the grid adapter transforms the downstream grid into a microgrid or nanogrid, which can then support the upstream grid and alleviate any congestion or overload on feeders, such as those in the distribution network. Embodiments can implement the basic control concept, virtual power plant control, to achieve this functionality.

[0133] Virtual power plant control enables the grid adapter to operate as a controllable generator or load from the grid's perspective. The grid adapter receives setpoints from the grid operator to supply or absorb a certain amount of power. In some cases, network feeders may become overloaded due to increased load. Therefore, if the grid adapter is able to generate power and supply nearby loads, this will reduce future power on the feeder, which is considered to alleviate congestion.

[0134] Figure 8 A control block diagram of a system 800 implementing virtual power plant control is shown. System 800 includes a proportional / integral (PI) controller 801 that operates on the error between a set point (Pgrid,ref) and an actual measured signal (Pgrid). This controller 801 ensures zero error in steady state, thereby ensuring correct set point tracking. Figure 8 Also shown is a frequency droop controller 803 which is a proportional controller for minimizing the error between the frequency set point (Wref) and the actual measurement (Wgrid) to produce a power set point (Pref).

[0135] Grid adapters ensure the required power quality

[0136] The combination of short-duration storage (such as supercapacitors and lithium-ion) and long-duration storage (such as flow batteries) addresses power quality behavior at different time scales. For example, supercapacitors can smooth out peaks in power drawn from multiple EV charging stations that are turned on simultaneously, or compensate for a rapid drop in solar PV power generation due to the passage of clouds. Similarly, long-duration ESS (or HESS) can address diurnal and seasonal variations in feeder energy demand. This is achieved by controlling different combinations of AC / DC converters and DC / DC converters to achieve a balance between generation and load even if the upstream grid is unavailable. Different power electronics levels have local converter controllers to implement current control, voltage control or power control. The local converter controllers receive set points from a higher-level controller (EMS) to ensure optimal operation of the system. Even in the event of a complete loss of the upstream grid, the grid adapter can form the downstream grid and provide the required power to the critical loads using the autonomous control concept of the Voltage Source Converter (VSC), such as Figure 9 The example shown in .

[0137] Figure 9 The data flow between components of a system 900 for autonomous control of a multi-level converter (e.g., multi-level converters 440, 540) for a grid adapter, which performs inverter current control 907 functionality, is shown. System 900 includes a multi-level converter, but embodiments may include any number of additional or alternative types of VSCs or other types of power processing units (e.g., DC / DC converters 445, 545; AC / DC converters 446, 546; bidirectional DC / DC converters 444, 544). The multi-level converter includes a controller that implements certain control functions and algorithms, including functions for grid forming control 901 and grid tracking control 905, including processor-executed control algorithms and functions 901, 905, and 907 that perform control functions on input electrical signals. The multi-level converter functionality may include algorithms for state detection and operating mode selection 903. State detection and operating mode selection 903 can detect and identify various conditions of an external grid or external storage system. The control algorithms and functions 901 , 905 and 907 of the controller of the multi-level converter may perform control functions based on the detected conditions resulting from the state detection and operation mode selection 903 .

[0138] Grid adapters enhance the stability of power system operations

[0139] The grid adapter is also capable of supporting the voltage and frequency of the upstream distribution grid using droop control concepts to provide the required active and reactive power based on the deviation of the voltage and frequency from their nominal values. This will allow the grid to accommodate additional renewable energy sources, such as DERs and EV charging stations, even outside the nanogrid's grid adapter, without having to strengthen the grid to accommodate these additional elements. This active power support is achieved thanks to the inclusion of an internal HESS in the grid adapter.

[0140] Figure 10 Included is a block diagram illustrating the data flow of a grid support control system 1000. The control block diagram illustrates proportional control operations for implementing droop control 1001 to support the grid with active power proportional to the frequency deviation and reactive power proportional to the voltage deviation. Droop control employs proportional control to support the grid with active power (Pref) proportional to the frequency deviation (Wdref, W) and reactive power (Qref) proportional to the voltage deviation (Vdref, Vd).

[0141] Grid Adapter as aggregator node for DMS

[0142] The grid adapter features a central controller (EMS) capable of communicating with the Distribution Management System (DMS) using standard communication protocols and media, which is central to the concept of smart grid operation. This communication link will allow the transfer of measurements and setpoints to improve the operation of the entire distribution system through the different functions of the DMS, such as voltage / var control, optimal operation, state estimation, etc. In the case of a distribution network with multiple such grid adapters, these grid adapters will act as control agents for the entire DMS, thereby acting as data concentrators and enhancement agents in the smart grid environment.

[0143] Figure 11The data flow and control architecture within a MEG system 1100 with external components is shown. System 1100 includes a utility distribution management system (DMS) 1115, an MGCC 1102, a LES 1112, a grid adapter 1121 with a GAC ​​1123 and a data aggregator 1125, an LDC 1116, a DG 1118, local meteorological data (Met 1114), and a LVNE 1120. MEG system 1100 includes various subsystems, including servers that execute controller software for controlling the operation of specific subsystems, such as the MEG controller (of MGCC 1102 or GAC 1123). MEG system 1100 includes a control and communication network architecture that carries data communications between the various subsystems of MEG system 1100. Various aspects of the system 1100 (e.g., LDC 1116, DG 1118, Met 1114, public DMS 1115, and LVNE 1120) have been Figure 2 and other paragraphs, some details need not be repeated here.

[0144] The network architecture includes various hardware and software components of one or more public or private networks that interconnect the various subsystem components of the MEG system 1100. Non-limiting examples of such communication networks may include a local area network (LAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), and the Internet. Network communications may be performed in accordance with various communication protocols, such as the Transmission Control Protocol and Internet Protocol (TCP / IP), the User Datagram Protocol (UDP), and IEEE communication protocols.

[0145] The control of the MEG system 1100 relies on a centralized concept to operate the MEG system 1100 in an efficient and secure manner, wherein the MEG system 1100 is operated and controlled by one or more servers that execute software controllers located in each subsystem. These controllers receive and evaluate information used to manage the operation of the subsystems, such as operating data of generators, geographic information of the location of each subsystem, energy storage indicating inventory at a given location, the amount of energy in transit, historical and predicted energy consumption, weather forecasts (e.g., meteorological data 1114), etc.

[0146] The servers (or other computing devices) at the MGCC 1102 can oversee some, most, or all operations of the MEG system 1100 to enable optimal utilization of various available energy sources while maximizing the power quality levels required by consumers and prosumers. For example, the servers at the MGCC 1102 send set points to the grid adapter 1121 and schedule or issue commands to the SEVs or other components of the system 1100 to charge or discharge specific amounts of electricity, thereby achieving various set goals for the MEG system 1100. In some implementations, the servers at the MGCC 1102 receive the status and health of downstream components from the controller of the system 1100 (e.g., the GAC 1123 of the grid adapter 1121). The servers at the MGCC 1102 evaluate the status, health, and operational details to ensure reliable and safe operation of these subsystems.

[0147] The LES 1112 can communicate (e.g., wirelessly, wired) with the MGCC 1102 or other components of the system 1100 to schedule and control the operation of the LES station 1112 while ensuring safe and reliable conditions. The LES station 1112 communicates (e.g., wirelessly, wired) with the GAC 1121 to exchange setpoints and monitoring information.

[0148] Grid adapter 1121 includes GAC 1123 and data aggregator 1125. GAC 1123 includes software and hardware components for managing grid adapter operation, as described herein. Data aggregator 1125 may include non-transitory machine-readable storage for collecting and storing energy data and weather data 1114 from various components of system 1100 that generate energy data for reference by GAC 1123.

[0149] Grid adapter for continuous operation in off-grid or island systems

[0150] As described above, the grid adapter enables continuous islanding of downstream microgrids or nanogrids over varying periods of time, where connectivity to the distribution grid is nonexistent or inconsistent. The grid adapter can also facilitate uncurtailed power generation by downstream renewable energy generators (REGs). In some implementations, the grid adapter can simultaneously charge and discharge an external ESS connected to the grid adapter system. This simultaneous charging and discharging is achieved by controlling a bidirectional DC / DC converter and isolating the internal DC link within the grid adapter. Simultaneous charging and discharging, along with energy delivery via SEVs in coordination with a mobile grid control system (MGCC), enables the grid adapter to achieve continuous islanding of downstream microgrids or nanogrids and uncurtailed power generation by REGs. In some cases, the grid adapter's bidirectional DC / DC converter also enables ESS charging and discharging to be achieved through both electrical and non-electrical methods, as described below.

[0151] FIG. 12A to FIG. 12B A system 1200 is shown having various architectures of a grid adapter 1217 according to an exemplary embodiment for implementing various implementations of the islanding effect for any duration of time when the grid adapter is disconnected from the power distribution network 1215. Figure 12A The grid adapter 1217 shown in FIG12C includes a power processing unit for the power electronics stage and an internal HESS 1242. The components of the system 1200 are similar to Figure 4 and Figures 5A to 5C Therefore, in describing Figures 12A to 5B No need to repeat certain details.

[0152] Figure 12A A grid adapter 1217 is shown co-located or remotely located and configured for a managed or controllable microgrid or nanogrid 1208, with no wired connection to the distribution grid 1215. System 1200 includes nanogrid 1208, EV station 1222 coupled to SEV 1206, and external ESS 1224a-1224b (collectively, external ESS or external ESS 1224). The grid adapter also includes a grid adapter controller 1219. When grid adapter 1217 is used with nanogrid 1208, grid adapter controller 1219 can be the same or different device and software programming than a nanogrid controller (not shown) of nanogrid 1208.

[0153] like Figure 12AAs shown, SEV 1206 can discharge at EV station 1222 and then flow to bidirectional converter 1244. Bidirectional converter 1244 is also coupled to a first external ESS 1224a. When input energy from SEV 1206 arrives at EV station 1222, grid adapter controller 1220 determines that the input power received from SEV 1206 can be stored in first external ESS 1224a. In some cases, grid adapter controller 1217 determines that power input to grid adapter 1217 from EV station 1222 or second external ESS 1224b can be stored in internal HESS 1242. When input power arrives at EV station 1222 and grid adapter controller 1220 determines that the input power is to be stored in first external ESS 1224a, bidirectional converter 1244 can apply various power conversion stages. The first external ESS 1224a can then be charged by the input power from the SEV 1206, while the second ESS 1224b provides the necessary energy to maintain a reliable power supply to the downstream microgrid or nanogrid 1208 that is electrically disconnected from the distribution grid 1215. Thus, continuous islanding capability is achieved without the need to disconnect, power down, and re-power the downstream microgrid or nanogrid 1208.

[0154] Figure 12B A grid adapter 1217 is shown co-located or remotely located and configured for a managed or controllable microgrid or nanogrid 1208 that has no wired connection to the distribution grid 1215 and facilitates uncurtailed power generation from DER 1220 devices (e.g., DC DER, AC DER). System 1200 includes DER 1220 (e.g., AC DER, DC DER), an optional nanogrid (not shown), an EV station 1222 coupled to SEV 1206, and an external ESS 1224 located, for example, at an end-consumer location or a remote storage location. DER 1220 may include multiple subsystems, including hardware and software components, for supplementing the distribution grid 1215 or enhancing power generation and distribution infrastructure (e.g., solar panels, wind turbines). For DC-based DER 1220, grid adapter 1217 includes a DC / DC converter 1245 having an externally facing DC interface and an internally facing DC interface, and converting or applying an electronic stage to reduce harmonics or other noise on the electrical waveform of the input power or output power. For AC-based DER 1220, grid adapter 1217 includes an AC / DC converter 1246 having an externally facing AC interface and an internally facing DC interface, and converting or applying an electronic stage to reduce harmonics or other noise on the electrical waveform of the input power or output power.

[0155] When the REGs of DER 1220 generate electricity, DER 1220 transmits the generated electricity to grid adapter 1217. The input power is received and converted by the DC / DC converter 1245 or AC / DC converter 1256 of grid adapter 1217, which applies various power conversion and noise reduction operations to the input electrical signal of the input power. Grid adapter 1220 then instructs the internal HESS 1242 to store the input power generated by and received from DER 1220. To achieve uncurtailed power generation, grid adapter controller 1220 can instruct a fully charged first external ESS 1224a to discharge the stored power (or overflow power) from the internal HESS 1242 to the SEV 1206 via the bidirectional converter 1244. A second external ESS 1224b can continue to charge from the REGs of one or more DERs 1220.

[0156] Figure 13 A system 1300 is shown having an exemplary architecture of a grid adapter 1317 for implementing islanding using non-electrical charging techniques, wherein the grid adapter 1317 is disconnected from the power distribution grid 1315, according to an exemplary embodiment. Components of the system 1200 are similar to Figure 4 、 Figures 5A to 5C as well as FIG. 12A to FIG. 12B Therefore, in describing Figure 13 No need to repeat certain details.

[0157] System 1300 includes nanogrid 1308, EV station 1322 coupled to SEV 1306, and external ESS 1324 for exchanging power with SEV 1306. Grid adapter 1317 also includes grid adapter controller 1319. When grid adapter 1317 is used with nanogrid 1308, grid adapter controller 1319 can be the same device and software programming as, or different from, a nanogrid controller (not shown) of nanogrid 1308.

[0158] The external ESS 1324 includes a reactor stack 1350 and an electrolyte storage tank 1352. Figure 13As shown, the flow-based or hydrogen-based ESS 1324 can be non-electrically charged through an electrolyte exchange process or by refueling from a tank-based SEV 1306 carrying charged electrolyte or hydrogen fuel. The electrolyte exchange and / or refueling methods allow the ESS tank 1352 to be charged without interrupting the release of energy through the same reactor stack 1350 of the liquid flow or fuel cell ESS 1324. In some embodiments, simultaneous charging and discharging can also be achieved using two or more flow-based ESSs 1324 or hydrogen-based ESSs 1324, the ESS configuration having a reactor stack 1350, such as FIG. 12A to FIG. 12B As shown, one ESS 1324 is performing non-electrical charging or discharging, while the other ESS 1324 is performing electric discharging or charging to achieve continuous islanding effect or non-curtailed power generation function.

[0159] Example embodiments or use cases

[0160] The functional capabilities of the grid adapter are suitable for a variety of use cases and applications. The following lists the different categories of applications (such as Figures 5A to 5C shown):

[0161] Device level: The power level here depends on the device itself and can range from kilowatts (kW) to megawatts (MW). Therefore, the grid adapter can act as an interface between the distribution grid and any form of renewable energy, energy storage systems, EV charging stations, DC loads, etc. All of these devices require some level of control to allow integration into the grid while meeting the integration requirements according to applicable grid codes.

[0162] Microgrid level: Applicable to megawatt-scale systems, where feeder interfaces are connected to a distribution system or a microgrid within an industrial facility that has the required interfaces for different elements such as DC loads, AC loads, renewable energy generators, EV charging stations, etc.

[0163] Nanogrid scale: Applicable to kilowatt-scale systems, such as homes or small commercial facilities.

[0164] Non-limiting examples of the advantages of a grid adapter include:

[0165] Simplifies the planning and operation of power distribution system upgrades;

[0166] Improved reliability against cascading power outages through islanding;

[0167] Enable scalable sizes and ratings to allow multiple application areas while ensuring identical behavior and performance (and therefore, operational confidence) of hardware and software;

[0168] enabling faster grid transition to meet net-zero emissions targets;

[0169] Enabling the future capabilities of the distribution grid to facilitate and interconnect various energy storage devices and vehicle-to-grid (V2G) solutions;

[0170] Enable communities with weak grids to adopt EV and DER solutions without incurring expensive grid upgrades; and

[0171] It enables the upgrading of the generation capacity of existing wind power projects that are limited in capacity due to feeder constraints.

[0172] The various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been generally described above in terms of functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person can implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0173] Embodiments implemented in computer software can be implemented using software, firmware, middleware, microcode, hardware description language, or any combination thereof. A code segment or machine executable instruction can represent a process, function, subroutine, program, routine, subroutine, module, software package, set, or any combination of instructions, data structures, or program statements. A code segment can be connected to another code segment or hardware circuit by passing and / or receiving information, data, arguments, attributes, or storage contents. Information, arguments, attributes, data, etc. can be passed, forwarded, or sent by any suitable means, including memory sharing, message passing, token passing, network transmission, etc.

[0174] The actual software code or specialized control hardware used to implement these systems and methods does not limit the present invention. Therefore, the operation and behavior of the systems and methods are described without reference to specific software code, with the understanding that software and control hardware can be designed to implement the systems and methods based on the description herein.

[0175] When implemented in software, these functions can be stored as one or more instructions or codes on a non-transitory computer-readable or processor-readable storage medium. The steps of the method or algorithm disclosed herein can be embodied in a processor-executable software module, which can exist on a computer-readable or processor-readable storage medium. Non-transitory computer-readable or processor-readable media include computer storage media and tangible storage media, which are conducive to transferring computer programs from one place to another. Non-transitory processor-readable storage media can be any available medium that can be accessed by a computer. By way of example and not limitation, such non-transitory processor-readable media can include random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage or other magnetic storage device, or any other tangible storage medium that can be used to store the required program code in the form of instructions or data structures and can be accessed by a computer or processor. As used herein, disk and optical disc include compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs use lasers to reproduce data optically. Combinations of the above are also included within the scope of computer-readable media. Furthermore, the operations of a method or algorithm may reside as one or any combination or set of code and / or instructions on a non-transitory processor-readable medium and / or computer-readable medium, which may be incorporated into a computer program product.

[0176] The description of the foregoing disclosed embodiments is provided to enable one skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but rather to be construed in the widest possible manner consistent with the following claims and the principles and novel features disclosed herein.

[0177] While various aspects and embodiments have been disclosed, other aspects and embodiments are contemplated. The disclosed aspects and embodiments are for purposes of illustration only and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Claims

1. A system comprising: Grid adapter, including: an internal storage system configured to store an amount of internal stored energy; A first power processing unit configured to convert power associated with the nanogrid, the first power processing unit comprising a bidirectional converter for converting input power or output power according to an operation instruction indicating a direction of power flow; and A grid adapter controller configured to execute software programming configured to: receiving power data indicative of a power demand of the nanogrid from one or more data sources; and According to the power demand, an instruction is transmitted to the internal storage system through the first power processing unit to exchange the power with the nanogrid.

2. The system according to claim 1, further comprising: An external storage system is configured to store energy according to the grid adapter controller, including the amount of energy indicated by the power demand of the nanogrid.

3. The system according to claim 2, wherein: The external storage system includes a first flow battery for exchanging electricity with at least one of a second flow battery of the energy storage vehicle or a vehicle electrolyte storage tank of the energy storage vehicle through electrolyte exchange, wherein the vehicle electrolyte storage tank is configured to contain an electrolyte.

4. The system according to claim 3, wherein: The external storage system includes a reactor stack of the first flow battery and a storage system electrolyte tank configured to hold the electrolyte.

5. The system according to claim 1, further comprising: One or more power processing units, including the first power processing unit, each power processing unit includes one or more power conversions configured to reduce the amount of noise on the electrical waveform.

6. The system of claim 1 , further comprising: The second processing unit is configured to convert a second amount of electricity related to distributed energy, and the second processing unit includes a DC-DC converter for converting input power or output power according to the second amount of electricity.

7. The system of claim 1 , further comprising: The second processing unit is configured to convert a second amount of electricity related to distributed energy, and the second processing unit includes an AC-DC converter for converting input power or output power according to the second amount of electricity.

8. The system of claim 1 , further comprising: A second processing unit, including a multi-level converter, is configured to convert input power received from a power distribution network coupled to the grid adapter.

9. The system according to claim 1, wherein: The grid adapter is disconnected from the power distribution network.

10. A system comprising: Grid adapter, including: an internal storage system configured to store internal stored energy; and a first power processing unit configured to convert power for exchange with the nanogrid, the first power processing unit comprising a bidirectional converter configured to convert the power for exchange between the internal storage system and the nanogrid according to an operation instruction indicating a power flow direction; and The external storage system includes a first flow battery configured to exchange electricity with at least one of a second flow battery of the energy storage vehicle or an electrolyte storage tank of the energy storage vehicle through electrolyte exchange.

11. The system according to claim 10, further comprising: A grid adapter controller for executing software programming configured to: receiving power data indicative of a power demand of the nanogrid from one or more data sources; as well as According to the power demand, an instruction is transmitted to the internal storage system through the first power processing unit to exchange the power with the nanogrid.

12. The system according to claim 11, wherein The external storage system includes a reactor stack of the first flow battery and a storage system electrolyte tank configured to hold an electrolyte.

13. The system of claim 10, further comprising: One or more power processing units, including the first power processing unit, each power processing unit includes one or more power conversions configured to reduce the amount of noise on the electrical waveform.

14. The system of claim 10, further comprising: The second processing unit is configured to convert a second amount of electricity related to distributed energy, and the second processing unit includes a DC-DC converter for converting input power or output power according to the second amount of electricity.

15. The system of claim 10, further comprising: A second processing unit, including a multi-level converter, is configured to convert input power received from a power distribution network coupled to the grid adapter.

16. The system according to claim 10, wherein: The grid adapter is disconnected from the power distribution network.

17. A system comprising: Grid adapter, including: an internal storage system configured to store an amount of internal stored energy; a first power processing unit configured to convert power associated with the nanogrid, the first power processing unit comprising a bidirectional converter configured to reduce noise on a first electrical waveform from an external storage system; and A second processing unit is configured to convert distributed energy resources from a power distribution network, the second processing unit comprising a multi-level converter configured to reduce noise on a second electrical waveform of the distributed energy resources from the power distribution network, the power distribution network being coupled to the grid adapter.

18. The system according to claim 17, wherein: The external storage system includes a first flow battery configured to exchange electricity with at least one of a second flow battery of the energy storage vehicle or an electrolyte storage tank of the energy storage vehicle through electrolyte exchange.

19. The system of claim 17, further comprising: A grid adapter controller for executing software programming configured to: receiving power data indicative of a power demand of the nanogrid from one or more data sources; as well as According to the power demand, an operation instruction is transmitted to the internal storage system through the first power processing unit to exchange the power with the nanogrid.

20. The system of claim 17, further comprising: A third processing unit is used to convert a second amount of electricity related to distributed energy. The third processing unit includes at least one of a DC-DC converter or an AC-DC converter, and is configured to reduce noise on a third electrical waveform of the second amount of electricity from the distributed energy.

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