Classifications

• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
  • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
  • H02J3/38—Arrangements for feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel
  • H02J3/381—Dispersed generators
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
  • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
  • H02J3/12—Arrangements for adjusting voltage in AC networks by changing a characteristic of the network load
  • H02J3/14—Arrangements for adjusting voltage in AC networks by changing a characteristic of the network load by switching loads on to, or off from, the networks, e.g. progressively balanced loading
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
  • H02J3/28—Arrangements for balancing of the load in networks by storage of energy
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J2101/00—Supply or distribution of decentralised, dispersed or local electric power generation
  • H02J2101/40—Hybrid power plants, i.e. a plurality of different generation technologies being operated at one power plant
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J2103/00—Details of circuit arrangements for mains or AC distribution networks
  • H02J2103/30—Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks
  • H02J2103/35—Grid-level management of power transmission or distribution systems, e.g. load flow analysis or active network management

Definitions

• the present disclosure relates to optimized delivery and storage of electrical energy. More specifically, the aspects and embodiments of the present disclosure relate to systems and methods for optimizing delivery and storage of electricity in an electrical grid network.
• a method for optimized delivery of electricity in an electricity distribution network comprises obtaining from a sensor system, sensor data associated with electricity consumption of at least one electricity-consumer and/or sensor data associated with electricity production of at least one electricity-producer coupled to the electricity distribution network. Further, the method comprises predicting a probability of a future electricity need of the at least one electricityconsumer and/or predicting a probability of future electricity production of the at least one electricity-producer based on the obtained sensor data.
• the method further comprises optimizing, based on the predicted probability of the future electricity need of the at least one electricity-consumer and/or the predicted probability of the future electricity production of the at least one electricity-producer, any one of an electricity storage scheme for an operationally decentralized distributed energy storage system coupled to the electricity distribution network, and an electricity delivery scheme for the at least one electricityconsumer. Further, the method comprises delivering, by the distributed energy storage system to the at least one electricity-consumer, an electricity need of the at least one electricity-consumer based on the optimized electricity storage scheme and/or the optimized electricity delivery scheme.
• the inventors have realized that one of the outstanding advantages of the presented solution is to decrease CO2 emissions and increase share of renewable energy in the electric grid, to supply Distribution System Operators (DSO) and their consumers' industry, households and transport sector with dependable and secure energy and power capacities. This will most likely minimize any "range anxiety" for the electric vehicle owners and increase savings of additional CO2 by the use of electric vehicles and thus reach the Paris Agreement's zero-emission target 3-5 years earlier than today's forecast.
• the electricity distribution network may comprise an electricity grid network or may be in communication with or coupled to an electricity grid network.
• the predicting of the probability of the future electricity production of the at least one electricity-producer may comprise predicting a probability of any one of a future power production capacity of the at least one electricity-producer at a certain time-point, a future energy production capacity of the at least one electricity-producer under a certain period of time and/or under a certain time interval, a future combination of the power and energy production capacity of the at least one electricity-producer, one or more future combination(s) of the power and energy production capacity of the at least one electricity-producer to be delivered under one or more certain time period(s), one or more future combination(s) of the power and energy production capacity of the at least one electricity-producer to be delivered under one or more certain set of conditions.
• the predicting of the probability of the future electricity production of the at least one electricity-producer may further comprise predicting a probability of a future over-production of electricity by the at least one electricity producer, wherein the overproduction of electricity is originated from one or more renewable electricity production source(s).
• predicting the probability of the future electricity need of the at least one electricity-consumer may further comprise predicting a probability of any one of a future power need of the at least one electricity-consumer at a certain time-point, a future energy need of the at least one electricity-consumer under a certain period of time and/or under a certain time interval, a future combination of the power and energy need of the at least one electricity-consumer, one or more future combination(s) of the power and energy need of the at least one electricity-consumer to be delivered under one or more certain time period(s), one or more future combination(s) of the power and energy need of the at least one electricity-consumer to be delivered under one or more certain set of conditions.
• the method may further comprise predicting the probability of the future electricity need of the at least one electricity-consumer and/or predicting the probability of the future electricity production of the at least one electricity producer based on any one of an obtained frequency and/or voltage and/or electrical current change in the electricity distribution network, an obtained weather forecast information, an obtained electrical current and/or voltage and/or frequency parameters of an electricity grid network, coupled to the electricity distribution network, and/or of the at least one electricity-consumer and/or electricity-producer, an obtained electricity consumption and/or production rate under certain time intervals and/or at certain time-points, an obtained previous trend of electricity consumption and/or production in the electrical grid network.
• the distributed energy storage system may be a mixed energy storage system comprising one or more operationally decentralized distributed mixed energy storage unit(s), each mixed energy storage unit comprising at least one energy storage module being of an energy type of a plurality of energy types.
• optimizing the electricity storage scheme for the distributed energy storage system may comprise optimizing any one of a geographical location of the one or more distributed mixed energy storage unit(s), a time-point and/or time period and/or time interval for storing electricity in the one or more distributed mixed energy storage unit(s), an amount of electrical power and/or electrical energy and/or energy type to be stored in the one or more distributed mixed energy storage unit(s), obtaining and storing the overproduction of electricity from the one or more renewable electricity production source(s) in the one or more distributed mixed energy storage unit(s), and life-time of each of the one or more distributed mixed energy storage unit(s).
• optimizing the electricity delivery scheme for the at least one electricity-consumer may comprise optimizing any one of a timepoint and/or time period and/or time interval for delivery of electricity, stored in the one or more distributed mixed energy storage unit(s), to the at least one electricity-consumer, an amount of electrical power and/or electrical energy and/or energy type stored in the one or more distributed mixed energy storage unit(s), to be delivered to the at least one electricityconsumer, delivery of the over-production of electricity from the one or more renewable electricity production source(s) stored in the one or more distributed mixed energy storage unit(s).
• the method may further comprise selecting at least one distributed mixed energy storage unit of the one or more distributed mixed energy storage unit(s) for delivering the electricity need of the at least one electricity-consumer based on the optimized electricity storage scheme and/or the optimized electricity delivery scheme.
• predicting the probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity-producer may comprise providing the sensor data as input to a trained machine-learning algorithm configured to use the obtained sensor data to predict the probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity producer. Further, the method may comprise obtaining an output signal of the machine-learning algorithm comprising the predicted probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity-producer.
• the method may further comprise optimizing, by the trained machinelearning algorithm, the electricity storage scheme for the distributed energy storage system, and the electricity delivery scheme for the at least one electricity-consumer based on the obtained output signal of the machine-learning algorithm.
• the trained machine-learning algorithm may comprise a decentralized federated machine learning algorithm arranged at each one of the one or more distributed mixed energy storage unit(s).
• a system for optimized delivery of electricity in an electricity distribution network comprising processing circuity configured to obtain from a sensor system, sensor data associated with electricity consumption of at least one electricity-consumer and/or sensor data associated with electricity production of at least one electricity-producer coupled to the electricity distribution network.
• the processing circuitry may be further configured to predict a probability of a future electricity need of the at least one electricity-consumer and/or predicting a probability of future electricity production of the at least one electricity-producer based on the obtained sensor data.
• the processing circuitry may be configured to optimize, based on the predicted probability of the future electricity need of the at least one electricity-consumer and/or the predicted probability of the future electricity production of the at least one electricity-producer, any one of an electricity storage scheme for an operationally decentralized distributed energy storage system coupled to the electricity distribution network, and an electricity delivery scheme for the at least one electricityconsumer.
• the processing circuitry may further be configured to deliver, by the distributed energy storage system to the at least one electricity-consumer, an electricity need of the at least one electricity-consumer based on the optimized electricity storage scheme and/or the optimized electricity delivery scheme.
• a computer program carrier carrying one or more computer programs configured to be executed by one or more processors of a processing circuitry, the one or more programs comprising instructions for performing the method according to any one of the embodiments of the method herein, and wherein the computer program carrier is one of an electronic signal, optical signal, radio signal or a computer-readable storage medium.
• a computer program product comprising instructions which, when the program is executed by one or more processors of a processing circuitry, causes the processing circuitry to carry out the method according to any one of the embodiments of the method herein.
• a distributed energy system comprising one or more distributed mixed energy storage unit(s) configured to store and/or deliver electricity; and a system according to any one of the embodiments of the system according to the second aspect of the present disclosure configured to control storage of electricity in the one or more distributed mixed energy storage unit(s) and/or delivery of electricity to at least one electricity-consumer coupled to an electricity distribution network in communication with the distributed energy storage system.
• Figs, la-b are schematic block diagrams illustrating an electricity distribution network according to the present disclosure
• Fig. 2 is a schematic block diagram of a control system in accordance with several embodiments of the present disclosure
• Fig. 3 is a schematic flowchart illustrating a method in accordance with several embodiments of the present disclosure.
• Fig. 4 is a schematic illustration of a distributed energy system in accordance with an embodiment of the present disclosure.
• Fig. la illustrates a schematic view of an example of an optimized electricity distribution network 1 for optimized storage and delivery of electricity according to several aspects and embodiments of the present disclosure.
• the electricity distribution network 1 may comprise an electricity grid network 50 or may be in communication with or coupled to one or more electricity grid networks 50.
• the optimized electricity storage and delivery network 1 which may simply be referred to as the network 1, comprises one or more electricity-producer(s) 30a-30c which in some embodiments may be coupled to the electricity grid network 50 via a plurality of power lines 50a.
• the network 1 further comprises one or more electricity-consumers 40a-40c which may similarly be coupled to the electricity grid network 50 via a plurality of power lines 50b. It should be appreciated by the person skilled in the art that electricity-consumer in the present context is not only limited to the end users 40a-40c such as buildings as shown in Fig.
• conditions and intended applications may include any entity which consumes any form of electricity such as the end users 40a-40c coupled to the grid network 50, the electricity grid 50 itself, other electricity grid networks (not shown) in connection with the electricity grid network 50, distributed storage units lOOa-lOOm as described in the following, etc.
• the at least one electricity-consumer may be at least one of a chargeable electrical vehicle, a home charging station, a public charging station, a transmission system operator, TSO, a distribution system operator, DSO and a charging station operator, CSO, the electricity grid network owner, commercial building complexes, smart homes, various energy-intensive industrial entities, etc.
• an electricity-producer in the present context it is meant an entity capable of producing electrical power and electrical energy which may be consumed by at least one electricity-consumer. Accordingly, electricity-producer in the present context is not only limited to the power plants 30a-30c as shown in Fig. la but depending on various circumstances, conditions and intended applications may include several entities such as the end users 40a-40c coupled to the grid network 50 e.g. in installations involving battery-to-grid or vehicle-to-grid configurations providing their stored surplus electricity to the grid.
• the electricity-producer may include the electricity grid 50 itself, other electricity grid networks (not shown) in connection with the electricity grid network 50, and the distributed energy storage units lOOa-lOOm supplying the grid 50 and the one or more electricity consumers with electricity, etc.
• the power plants 30a-30c illustrated in Fig. la may include the electricity grid 50 itself, other electricity grid networks (not shown) in connection with the electricity grid network 50, and the distributed energy storage units lOOa-lOOm supplying the grid 50 and the one or more electricity consumers with electricity, etc.
• the network 1 further comprises an operationally decentralized distributed energy storage system 100 as shown in the example of Fig. 4 comprising one or more operationally decentralized distributed mixed energy storage unit(s) lOOa-lOOn, lOOn-lOOm, wherein a ⁇ n ⁇ m being any suitable number of distributed units of similar or varying properties such as electricity storage and delivery capacity, etc.
• Each one of the distributed energy storage units lOOa-lOOm may also be in communication with the grid network 50 via one or more transmission lines 101.
• the distributed energy storage units lOOa-lOOm may herein also be referred to as modular energy-storage hubs lOOa-lOOm.
• the transmission lines 101 between the energy storage units lOOa-lOOm and the grid network 50 may be configured for transmitting electricity including electrical power and electrical energy as well as information such as sensor measurements, operation statuses, log files, electricity consumption and/or production data, power and/or energy needs of the at least one electricity-consumer, power and/or energy production of the at least one electricity-producer, etc.
• the one or more electricity-producers and/or the one or more electricityconsumers and/or the distributed energy storage system 100 may be connected to and/or be in communication with each other in the distribution network 1 without being connected to the electricity grid network 50.
• the transmission lines 101 for transmitting electricity may be implemented separately from transmission lines 101 for relaying information.
• the transmission of electricity and information between the energy storage units lOOa-lOOm and the grid network 50 may be integrated in the same transmission lines 101.
• the transmission lines 101 may also be configured to enable both wired and wireless information transmission technologies.
• each of the at least one distributed mixed energy storage units lOOa-lOOm may be in communication with a communication network 20a-20m which may be an external communication network or a cloud network 20a-20m.
• the at least one electricity-consumer 40a-40c and the at least one electricity-producer 30a-30c may also be connected to the external communication networks 20a-20m.
• the external communication networks 20a-20m may be configured for transmission of information and data among the distributed energy storage units lOOa-lOOm and the at least one electricity-producer 30a-30c and electricity-consumers 40a-40c as well as the grid network 50.
• the electricity producers 30a-30c and/or electricity consumers 40a-40c may be directly e.g. via the at least one external communication network 20 and power transmission lines and/or indirectly e.g. via the grid network 50 and/or via a command center 200 be coupled to distributed energy storage system 100.
• the electricityconsumer 40a is connected to the distributed storage unit 100m via the external network 20m.
• the electricity-consumer 40b is similarly connected to the storage unit lOOn via the external network 20n and the electricity-consumer 40c connected to the storage unit 100a via the external network 20a.
• the electricity-producer 30a is connected to the storage unit 100a via the external network 20a
• the electricity-producer 30b is connected to the storage unit lOOn via the external network 20n
• the electricity-producer 30c is connected to the energy storage unit 100m via the external network 20m.
• the electricityconsumers 40a-40c are enabled to communicate their electricity consumption such as historic and/or real-time electricity consumption data to the distributed energy storage system 100.
• the one or more electricity-producers 30a-30b are also enabled to communicate the historic and/or real-time electricity production capacity data such as power and/or energy production capacity to the distributed energy storage system 100.
• the energy storage units lOOa-lOOm are enabled to obtain sensor data associated with electricity consumption of at least one electricity-consumer 40a-40c and/or sensor data associated with electricity production of at least one electricity-producer 30a-30c coupled to the electrical grid network 50. This way a variety of predictions, estimations, and optimizations can be carried out by a control systems 10 of the distributed energy storage system 100 for optimized storage and delivery of electricity to the electricity-consumers based on the realtime and/or future electricity needs and/or available or future production capacity in the electricity grid network 50.
• the control system 10 of the distributed storage units lOOa-lOOm may in several embodiments be in communication with the command center 200 as shown in the example of Fig. 4.
• the command center 200 may oversee the entire operation of the distributed energy storage system 100, be responsible to handle communications with the grid network 50, be responsible to handle communications with the at least one electricityconsumer 40a-40c and/or the at least one electricity-producer 30a-30c, decide over the operation standards, prediction and optimization models used by the energy storage units lOOa-lOOm, etc.
• the electricity-producers 30a-30c and/or electricityconsumers 40a-40c may optionally be connected to several energy storage units e.g. to any one of energy storage units lOOa-lOOm in Fig. la via the respective external networks 20a- 20m.
• each electricity-producer/consumer may communicate electricity production and/or consumption to several energy storage units lOOa-lOOm to maximize the opportunities of optimized delivery of power needs, energy needs or any combination of power and energy needs at certain points in time or over certain time periods.
• each electricityconsumer will be served by the most suitable electricity storage unit lOOa-lOOm, among the plurality of available energy storage units lOOa-lOOm, matching the specific needs of each electricity consumer at any given time.
• the electricityproducers 30a-30c and/or electricity-consumers 40a-40c may be connected to several energy storage units e.g. to any one of energy storage units lOOa-lOOm in Fig. la via their respective power transmission lines (not shown).
• each electricity-producer 30a-30c and/or electricity-consumer 40a-40c may transmit its information, sensor measurements, or parameters such as power and/or energy capacity or needs or any combination thereof to the grid network 50 via the power lines 50a, 50b and the grid network 50 will in turn communicate and transmit these information and data to the respective energy storage units via the transmission lines 101.
• the grid network 50 may transmit the information obtained from the at least one electricity-consumer 40a-40c and/or from the at least one electricity producer 30a-30c to the distributed energy storage units lOOa-lOOm via the external networks 20a- 20m.
• the grid network 50 may transmit the information received from each electricity-consumer 40a-40c to the electricity-producers 30a-30c and the respective electricity-producers 30a-30c will in turn communicate, e.g. via the external networks 20a- 20m, such information to the distributed energy storage system 100.
• the distribution network 1 may comprise one or more standalone units 210 wherein the at least one electricity-consumer and the distributed energy storage system 100 or one or more of the energy storage units lOOa-lOOm comprised in the energy storage system 100 may be coupled together without the need to be connected to the electricity grid network 50.
• This exemplary embodiment enables implementation of self- sustaining energy storage and delivery units 210 to be deployed in so-called “island operation" configurations without the need to be connected to an electricity grid network 50.
• Each of the distributed energy storage unit(s) lOOa-lOOm may be implemented as operationally decentralized units which in the present context is to be understood that each unit is independently operated by its own dedicated control system 10 configured to process the obtained electricity consumption and/or production data locally without any influence from the outside of each energy-storage hub, thus conforming with the prevailing user privacy standards.
• each distributed energy storage system lOOa-lOOm may be implemented as a mixed energy storage unit meaning that various types of energy sources may be implemented in energy storage modules, such as the energy storage modules Mi-Mk, Mk-M n shown in Fig. 4, l ⁇ k ⁇ n being any suitable number of modules of different energy types implemented in each energy storage unit lOOa-lOOm.
• chemical battery modules hydrogen energy storage modules, hydrogen carrier energy storage modules such as NH3, CH3OH-H2O and cycloalkanes for large-scale distribution and for on-site hydrogen generation and storage
• biofuel energy storage modules fossil fuel-based energy storage modules such as diesel electricity generator modules, solar energy storage modules, wind power energy storage modules, etc.
• fossil fuel-based energy storage modules such as diesel electricity generator modules, solar energy storage modules, wind power energy storage modules, etc.
• lOOa-lOOm providing a collection of different energy types and energy storage technologies ready to store and deliver electricity to the grid network 50 and/or electricity consumers.
• the concept of mixed energy storage unit in the present context should also be understood as various combinations of electrical power and electrical energy which will be delivered to the electricity consumers. Accordingly, delivery of combinations of power and energy may be provided from different combinations of energy sources. For instance, if an electricity consumer requires a certain amount of power at a certain time-point, this power need may be provided from energy source Mi being of energy type 1 e.g. chemical battery modules. However, the same electricity-consumer may require delivery of electrical energy under a certain period of time. Accordingly, the selected mixed energy storage unit may provide the required energy need of the electricity-consumer from the energy source Mk being of energy type k e.g. hydrogen energy module.
• mixed energy storage unit in the present context not only includes a combination of various energy sources and types implemented in each of the distributed energy storage units but also comprises various combinations and ratios of power needs and energy needs of the at least one electricity-consumer which will be delivered by the mixed energy storage units.
• some energy modules may be suitable for delivery of high power needs and some energy modules might be suitable for long-term storage and/or delivery of energy needs.
• power need of an electricity consumer, it is meant the rate at which the electricity consumer will consume electricity at a certain time-point or per unit of time having the SI unit "Watt”.
• energy need in the present context it is meant the amount of electrical power consumed by the electricity-consumer over a certain period of time having the SI unit "Joule”.
• the present inventors have realized that the proposed solution of the modular mixed energy storage system 100 comprising the modular energy-storage hubs lOOa-lOOm enables to efficiently provide any one of power needs of an electricity consumer at a certain instance and/or energy needs of an electricity consumer under a certain period of time and/or a combination of power and/or energy needs of the electricity consumer. Further, it is made possible to accurately predict what combination of the above will be required by a certain electricity consumer at a certain time point, under a certain period of time or under specific circumstances such as specific weather or traffic conditions.
• Each distributed energy storage unit lOOa-lOOm comprises a control system lOa-lOm collectively referred to as the control system 10 hereinafter.
• the control system 10 comprises processing circuitry 11 which is configured for optimized delivery of electricity which in the present context encompasses any one of prediction, optimization, storage and delivery of electricity in an electrical grid network 50 in accordance with several aspects and embodiments of the present disclosure.
• the control system 10 is configured to obtain from a sensor system 6, sensor data associated with electricity consumption of at least one electricity-consumer 40a-40c and/or sensor data associated with electricity production of at least one electricity-producer 30a-30c coupled to the electrical grid network 50.
• the sensor system 6 may comprise any number of sensor devices 6a-6n, and any type of sensor devices configured to measure various parameters of the electricity network 1 such as voltage, current, frequency, temperature, power, etc.
• the sensor system 6 may be installed and implemented in various entities and nodes of the grid network 50, be connected to utility meters or smart meters for measuring and reporting the electricity usage by the at least one electricity consumer 40a-40c, be connected to several entities of the at least one electricityproducer 30a-30c to monitor and measure electricity production capacity, be connected to each distributed energy storage unit lOOa-lOOm for measuring associated parameters such as voltage, current, frequency, temperature, power, state of charge or energy/power capacity of each unit lOOa-lOOm, etc.
• the control system 10 may obtain the sensor data directly from the one or more electricity-consumers 40a-40c and/or from one or more electricity-producers 30a-30c. Alternatively or additionally, the control system 10 may obtain the sensor data via the electricity grid network 50 by the transmission lines 101 and/or via the respective external networks 20a-20m for each distributed unit lOOa-lOOm. In various embodiments, the obtained sensor data may comprise information about a state of charge of the at least one electricity-consumer 40a-40c, a charging duration of the at least one electricity-consumer, a power and/or energy consumption level of the at least one electricityconsumer, etc.
• the control system 10 of each distributed energy storage unit may be implemented as a stand-alone decentralized processing and control system which only has access to a predefined amount, or category of user data.
• the user data including the data obtained from the one or more electricity-consumers 40a-40c may be anonymized and utilized by the control system 10 without any user identification information.
• the data obtained from the grid network 50 and/or the data obtained from the at least one electricity-producer 30a-30c may also be anonymized by the control system 10.
• a scheme of access rights may be defined to regulate the access rights of each entity to the data.
• federated learning algorithms may be implemented in each energy storage hub to ensure that the obtained data remains anonymized and is only shared with the parties having the necessary security access rights.
• the decentralized distributed energy storage units lOOa-lOOm are prevented from sharing user information with other entities or energy units as well as with a central system such as the command center 200 without the proper access rights. Therefore, the distribution network 1 of the present disclosure operates in line with fulfilling the prevailing user data and privacy protection standards.
• Another advantage of the decentralized control system 10 is to delegate the processing and decision making based on the obtained user data to each of the distributed energy storage units lOOa-lOOm which in turn makes the whole process considerably faster and computationally efficient compared to conventional system which centrally gather and process user data.
• the control system 10 of each distributed energy storage unit lOOa-lOOm is configured to predict a probability of a future electricity need of the at least one electricity-consumer 40a-40c and/or predict a probability of future electricity production of the at least one electricity-producer 30a-30c based on the obtained sensor data from the sensor system 6.
• control system 10 may be configured to predict the probability of the future electricity production of the at least one electricity- producer by predicting a probability of a future over-production of electricity by the at least one electricity producer 40a-40c, wherein the over-production of electricity is originated from one or more renewable electricity production source(s) 60.
• the renewable energy produced by the renewable electricity production sources could be injected into the grid network 50 and/or directly employed to address the needs of the one or more electricity consumers 40a-40c.
• the exemplary unit 210 comprises the electricity consumers 40a-40c in communication with the distributed energy storage system 100, as well as the renewable energy production resources 60.
• the node 61a created from such an interaction is subject to various sensor measurement as mentioned above.
• node 61b may be created to monitor the over-production of the electricity of the renewable source 60 by the grid network 50.
• the sensor system 6 may be couple to nodes 61a, 61b and measure any one of frequency, voltage or current variations in the nodes 61a, 61b. Based on the obtained sensor data from nodes 61a, 61b the electricity needs of the one or more electricity-consumer 40a-40c may be provided by the distributed energy storage system 100.
• the electricity stored in the distributed energy storage system 100 may have been provided from the renewable sources 60 or from the electricity grid 50.
• the control system 10 may be configured to set a cap on the peak amount of electricity which may be received from the grid network 50, so as to alleviate or prevent electricity price surcharges for the one or more electricity consumers.
• the one or more electricity consumers may advantageously obtain their electricity needs extending above the peak cap, from the distributed energy storage system 100 for a lower price.
• control system is configured to predict the probability of the future electricity need of the at least one electricity-consumer and/or predicting the probability of the future electricity production of the at least one electricity producer based on various parameters of the electricity grid network 50.
• the parameters may be obtained via real-time or historical sensor measurements of the sensor system 6 and/or from historical data regarding electricity consumption and production in the electricity grid network 50.
• the historical data may be stored on external servers or on cloud networks such as cloud network 20 and be obtained by the control system 10 on demand.
• the real-time and/or historical data and parameters may comprise any one of an obtained frequency and/or voltage and/or electrical current change in the electricity distribution network 1, an obtained weather forecast information, an obtained electrical current and/or voltage and/or frequency parameters of the electricity grid network 50, coupled to the electricity distribution network, and/or of the at least one electricityconsumer and/or producer, an obtained electricity consumption and/or production rate under certain time intervals and/or at certain time-points, an obtained previous trend of electricity consumption and/or production in the electrical grid network 50.
• the control system 10 may be configured to predict the probability of the future electricity production of the at least one electricity-producer 30a-30c by predicting a probability of a future electrical power production capacity of the at least one electricity-producer at a certain time-point; further, by predicting a future electrical energy production capacity of the at least one electricity-producer 30a-30c under a certain period of time and/or under a certain time interval. Additionally or alternatively, the control system 10 may be configured to predict the probability of the future electricity production of the at least one electricity-producer 30a-30c by predicting a future combination of the electrical power and energy production capacity of the at least one electricity-producer 30a-30c.
• control system 10 may be configured to predict one or more future combination(s) of the electrical power and energy production capacity of the at least one electricity-producer to be delivered under one or more certain time period(s). Further, the control system 10 may be configured to predict one or more future combination(s) of the electrical power and energy production capacity of the at least one electricity-producer to be delivered under one or more certain set of conditions.
• the control system 10 may also be configured to predict the probability of the future electricity need of the at least one electricity-consumer by predicting a probability of any one of a future electrical power need of the at least one electricity-consumer at a certain timepoint, a future energy need of the at least one electricity-consumer under a certain period of time and/or under a certain time interval, a future combination of the power and energy need of the at least one electricity-consumer, one or more future combination(s) of the power and energy need of the at least one electricity-consumer to be delivered under one or more certain time period(s), one or more future combination(s) of the power and energy need of the at least one electricity-consumer to be delivered under one or more certain set of conditions.
• the certain time-points and or certain time periods in the present context may comprises examples such as a certain time of a day, a week, a month, a year, a certain time period such as a peak electricity demand period during a day, during a weeks, during a month, during a year, etc.
• Certain set of conditions in the present context may comprise examples such as certain weather conditions e.g. rainfall, snow conditions, wind or storm conditions, etc.
• peak traffic conditions, traffic congestion states and similar in a geographical region peaked usage of electrical appliances in commercial centers, office spaces, etc., certain times of day for certain periods of time correlated with worker commute information, etc. which may affect the electricity consumption of the at least one electricity consumer.
• control system 10 obtains information based on historic and/or real-time data of various parameters and in turn provides predictions based on such obtained data. This way real-time and/or future needs of each electricity-consumer is matched with the real-time and/or future production capacity of each electricity-producer and any off-balance in the production/consumption cycle could be compensated for by the distributed energy storage system 100.
• the electricity needs of the at least one consumer may be delivered by the distributed energy storage system 100, e.g. by providing better electricity pricing, suitable matching of power/energy need combinations, etc.
• the control system 10 predicts based on the obtained data and the above-mentioned parameters that the future peak usage time on a certain day may shift based on several factors such as specific traffic conditions on that specific day, weather conditions in an area on that specific day, planned construction work in an area under a certain time period during that specific day, affected electricity production rates by the electricity producers on that specific day, variations in the price of electricity, variation of production and demand in different times of the year such as in the tourist season, consumer behavior changes, driver behavior changes in charging/driving electric vehicles, or the like.
• control system 10 dynamically and efficiently optimizes and delivers the specific electricity demands of the electricity consumers at the right time based on the variations in multiple impacting factors as mentioned above. As such any shifts or variations in peak usage time and/or power and energy demands can be addressed with high accuracy.
• the dynamic prediction of the future electricity needs as well as electricity production based on the obtained data enables the system 10 to store and deliver electricity based on optimized storage and delivery schemes updated dynamically based on the conditions of the electricity network 1. This will be further elaborated on in the following.
• control system 10 may further be configured to optimize, based on the predicted probability of the future electricity need of the at least one electricity-consumer 40a-40c and/or the predicted probability of the future electricity production of the at least one electricity-producer 30a-30c, an electricity storage scheme for the operationally decentralized distributed energy storage system 100 coupled to the electrical grid network 50.
• control system 10 may further be configured to optimize, based on the predicted probability of the future electricity need of the at least one electricity-consumer 40a-40c and/or the predicted probability of the future electricity production of the at least one electricity-producer 30a-30c, an electricity delivery scheme for the at least one electricity-consumer 40a-40c.
• the processing circuitry 11 of the control system 10 may comprise a data processing unit 11a for receiving and processing the obtained real-time sensor data and/or historic data and information to be used for predicting the probability of the future electricity need of the at least one electricity-consumer and/or a the probability of the future electricity production of the at least one electricity-producer.
• the data processing unit 11a may employ any of available data processing methods and mathematical models known in the art such as simple logic or neural networks or trained learning algorithms to process the obtained data. Further, the data processing unit 11a may be configured for optimizing the electricity storage scheme for the distributed energy storage system, and the electricity delivery scheme for the at least one electricity-consumer.
• the data processing unit 11a may comprise a trained machine learning algorithm lib or a trained artificial intelligence (Al) algorithm lib such as a supervised and/or an unsupervised machine learning algorithm lib for performing the prediction and optimization tasks.
• the control system 10 may be further configured for providing the sensor data as input to the trained machinelearning algorithm lib configured to use the obtained sensor data to predict the probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity producer.
• control system 10 may be configured for obtaining an output signal of the machine-learning algorithm lib comprising the predicted probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity-producer.
• control system 10 may be further configured for optimizing, by the trained machine-learning algorithm lib, the electricity storage scheme for the distributed energy storage system, and the electricity delivery scheme for the at least one electricity-consumer based on the obtained output signal of the machinelearning algorithm.
• the trained machine-learning algorithm lib may comprise a decentralized federated machine learning algorithm arranged at each one of the one or more distributed mixed energy storage unit(s) lOOa-lOOm.
• the command center 200 may be configured to decide on which machine learning models and algorithms to be used by the distributed energy storage system 100 and the control system 10 in each energy-storage hub lOOa-lOOm. For instance, the command center 200 may transmit a machine learning model to one or some or a subset of the energystorage hubs lOOa-lOOm, wherein the model may be trained, tested and verified with the realtime and historically obtained data in each of the elected hubs lOOa-lOOm.
• the federated learning structure of the machine learning algorithms implemented in each energy storage hub lOOa-lOOm ensures that the obtained data remains anonymized and is only processed locally without any influence from the outside of each energy-storage hub, thus conforming with the prevailing user privacy standards.
• the results obtained from testing the proposed model by the command center 200 can be remitted to the command center 200 which in turn may decide to make improvements and modifications to the model and communicate a fixed operational version to some or all of the energy storage units 100a- 100m.
• This process of training, testing and verification of the machine learning algorithm is an iterative and dynamic process leading to continuous improvements of the data processing and optimization by the control system 10. Further, considerably faster processing of the obtained data is provided.
• the training process of the machine learning algorithm may comprise pre-processing, training, testing, and validating phases.
• the machine learning algorithm developed by the command center 200 may thus be trained and tested with training and testing datasets offline prior to transmission to the control systems lOa-lOm of the distributed energy storage units lOOa-lOOm to be trained or validated further with the obtained sensor data.
• the first trained version of the machine learning algorithm may be transmitted to the energy storage units lOOa-lOOm where obtained real-time or historic data i.e. validation dataset obtained from energy consumptions and/or productions is input to the machine learning algorithm.
• the validation dataset should never be presented to the machine learning algorithm before the first trained version of the machine learning algorithm passes the performance evaluation by the command center 200.
• the outcome of training and validating the trained machine learning algorithm with the validation dataset may be transmitted to the command center 200 as mentioned above.
• the command center 200 may evaluate the performance of the machine learning algorithm and make adjustments and improvements to the model before arriving at a fixed machine leaning algorithm to be implemented in the distributed energy storage system 100.
• Various parameters of the electricity distribution network 1 and the electricity consumers and producers may be measured by the sensor system 6 and be used in training and validation of the machine learning algorithm.
• the parameters may include any one of sensor measurements of voltage, current, frequency, frequency fluctuations, voltage and/or current fluctuations under a variety of time units such as under duration of a year, a month, a day, an hour, a minute, a second, a millisecond, etc.
• a pre-processing phase may be applied for data cleanout or data reduction to accurately extract the data which may then be fed to the machine learning algorithm.
• Further parameters which may be measured and processed may include power and energy limitations of the grid network 50, voltage and/or current and/or frequency of the grid network 50 at the coupling nodes to each of the energy storage units lOOa-lOOm, input and output current and/or voltage and/or frequency at each of the energy storage units 100a- 100m, frequency and phase angles of the grid network 50 (which may be obtained from the voltage and current measurements), electricity prices in energy bidding marketplaces, probabilities of new electricity prices in the bidding markets which may be based on the previous predictions from the machine learning algorithms, etc.
• Other variables to consider may also include the wind conditions in different areas which affects the wind power productions, the amount of sunlight in different areas and under certain time periods leading up to variations in the solar power production, other weather conditions such as rain may also be considered.
• state of charge of each energy-storage hub may be measured and processed, other services such as price support services by frequency and/or power control may be considered, temporary power limitations of the energy storage units may be measured and processed.
• the collective effect of processing the above obtained data and information may also lead to better decision making processes to identify what types of energy to store in certain energy storage units for which type of electricity consumers as well as providing valuable insights on different electricity delivery technologies.
• the control system 10 may be configured to optimize the electricity storage scheme for the distributed energy storage system by optimizing a geographical location of the one or more distributed mixed energy storage unit(s) lOOa-lOOm which may be decided based on the predictions of future electricity needs and/or production capacity. Further, the control system 10 may be configured to optimize a time-point and/or time period and/or time interval for storing electricity in the one or more distributed mixed energy storage unit(s) lOOa-lOOm. Thus, based on the future predictions, the control system 10 will receive electricity from the grid network 50 and store in each or a selected number of distributed energy storage units lOOa-lOOm based on the optimized parameters. Thus, electricity may be obtained from the grid network 50 and be stored in the distributed energy units e.g.
• control system 10 may be configured to optimize an amount of electrical power and/or electrical energy and/or energy type to be stored in the one or more distributed mixed energy storage unit(s). Even further, the control system 10 may optimize obtaining and storing the over-production of electricity from the one or more renewable electricity production source(s) 60 in the one or more distributed mixed energy storage unit(s), as well as optimize a life-time of each of the one or more distributed mixed energy storage unit(s).
• control system 10 may be configured to optimize the electricity delivery scheme for the at least one electricity-consumer by optimizing a time-point and/or time period and/or time interval for delivery of electricity, stored in the one or more distributed mixed energy storage unit(s), to the at least one electricity-consumer.
• control system 10 may be configured to deliver the required amount or type of energy by means of the distributed energy storage units lOOa-lOOm.
• control system 10 may be configured to optimize an amount of electrical power and/or electrical energy and/or energy type stored in the one or more distributed mixed energy storage unit(s), and to be delivered to the at least one electricity-consumer. Even further, the control system 10 may be configured to optimize delivery of the over-production of electricity from the one or more renewable electricity production source(s) 60 stored in the one or more distributed mixed energy storage unit(s) to the at least one electricity-consumer 40a-40c.
• control system 10 may also be configured for delivering, by the distributed energy storage system 100 to the at least one electricityconsumer 40a-40c, a real-time and/or future electricity need of the at least one electricityconsumer based on the optimized electricity storage scheme and/or the optimized electricity delivery scheme.
• control system 10 in communication with the grid network 50 or the command center 200 may be adapted to select at least one distributed mixed energy storage unit of the one or more distributed mixed energy storage unit(s) lOOa-lOOm for delivering the real-time and/or a future electricity need of the at least one electricity-consumer based on the optimized electricity storage scheme and/or the optimized electricity delivery scheme.
• the selection criteria may be based on the geographical location of the selected storage unit, the size i.e. delivery capacity, current state of charge, type of stored energy, etc. of the selected energy storage unit, specific needs of the respective electricity consumer e.g. the combination of power and energy needs, etc.
• the specific electricity needs of each electricity consumer is fulfilled by matching the most suitable energy storage unit capable of optimized delivery of the electricity to that electricity consumer.
• Fig. 3 shows a flow chart of a method 300 according to various embodiments and aspects of the present disclosure for optimized storage and delivery of electricity in an electricity distribution network.
• the electricity distribution network 1 may comprise an electricity grid network 50 or may be in communication with or coupled to one or more electricity grid networks 50.
• the method comprises obtaining 301 from a sensor system, sensor data associated with electricity consumption of at least one electricity-consumer 30a-30c and/or sensor data associated with electricity production of at least one electricity-producer 40a-40c coupled to the electricity distribution network 1.
• the method further comprises predicting 303 a probability of a future electricity need of the at least one electricity-consumer and/or predicting a probability of future electricity production of the at least one electricity-producer based on the obtained sensor data.
• the method comprises optimizing 305, based on the predicted probability of the future electricity need of the at least one electricityconsumer and/or the predicted probability of the future electricity production of the at least one electricity-producer, any one of an electricity storage scheme 302 for an operationally decentralized distributed energy storage system 100 coupled to the electricity distribution network 1, and an electricity delivery scheme 304 for the at least one electricity-consumer.
• the method further comprises delivering 307, by the distributed energy storage system 100 to the at least one electricity-consumer, a real-time and/or future electricity need of the at least one electricity-consumer based on the optimized electricity storage scheme and/or the optimized electricity delivery scheme.
• Real-time and/or future electricity needs of the at least one electricity-consumer may be determined and be scheduled for delivery based on the predictions and optimizations performed by the systems and methods according to the present disclosure.
• the method 300 may further comprise predicting 303 the probability of the future electricity need of the at least one electricity-consumer and/or predicting the probability of the future electricity production of the at least one electricity producer based on any one of an obtained frequency and/or voltage and/or electrical current change in the electricity distribution network, an obtained weather forecast information, an obtained electrical current and/or voltage and/or frequency parameters of the electricity grid network, coupled to the electricity distribution network, and/or of the at least one electricity-consumer and/or electricity-producer, an obtained electricity consumption and/or production rate under certain time intervals and/or at certain time-points, an obtained previous trend of electricity consumption and/or production in the electrical grid network.
• predicting 303 the probability of the future electricity production of the at least one electricity-producer 40a-40c may comprise predicting 303 a probability of any one of a future power production capacity of the at least one electricity-producer at a certain time-point, a future energy production capacity of the at least one electricity-producer under a certain period of time and/or under a certain time interval, a future combination of the power and energy production capacity of the at least one electricity-producer, one or more future combination(s) of the power and energy production capacity of the at least one electricity-producer to be delivered under one or more certain time period(s), one or more future combination(s) of the power and energy production capacity of the at least one electricity-producer to be delivered under one or more certain set of conditions.
• predicting 303 the probability of the future electricity production of the at least one electricity-producer may further comprise predicting a probability of a future over-production of electricity by the at least one electricity producer, wherein the over-production of electricity is originated from one or more renewable electricity production source(s) 60.
• predicting 303 the probability of the future electricity need of the at least one electricity-consumer may further comprise predicting a probability of any one of a future power need of the at least one electricity-consumer at a certain time-point, a future energy need of the at least one electricity-consumer under a certain period of time and/or under a certain time interval, a future combination of the power and energy need of the at least one electricity-consumer, one or more future combination(s) of the power and energy need of the at least one electricity-consumer to be delivered under one or more certain time period(s), one or more future combination(s) of the power and energy need of the at least one electricity-consumer to be delivered under one or more certain set of conditions.
• the distributed energy storage system 100 may be a mixed energy storage system comprising one or more operationally decentralized distributed mixed energy storage unit(s) lOOa-lOOm, each mixed energy storage unit comprising at least one energy storage module Mi-M n of an energy type of a plurality of energy types.
• optimizing 305 the electricity storage scheme 302 for the distributed energy storage system and/or optimizing 305 the electricity delivery scheme 304 for the at least one electricity-consumer may comprise, for the electricity storage scheme, optimizing any one of a geographical location of the one or more distributed mixed energy storage unit(s), a time-point and/or time period and/or time interval for storing electricity in the one or more distributed mixed energy storage unit(s), an amount of electrical power and/or electrical energy and/or energy type to be stored in the one or more distributed mixed energy storage unit(s), obtaining and storing the over-production of electricity from the one or more renewable electricity production source(s) 60 in the one or more distributed mixed energy storage unit(s), and life-time of each of the one or more distributed mixed energy storage unit(s).
• optimizing 305 may comprise optimizing any one of a time-point and/or time period and/or time interval for delivery of electricity, stored in the one or more distributed mixed energy storage unit(s), to the at least one electricity-consumer, an amount of electrical power and/or electrical energy and/or energy type stored in the one or more distributed mixed energy storage unit(s), to be delivered to the at least one electricity-consumer, delivery of the over-production of electricity from the one or more renewable electricity production source(s) 60 stored in the one or more distributed mixed energy storage unit(s).
• the method 300 may further comprise selecting 309 at least one distributed mixed energy storage unit of the one or more distributed mixed energy storage unit(s) for delivering the real-time and/or future electricity need of the at least one electricityconsumer based on the optimized electricity storage scheme and/or the optimized electricity delivery scheme.
• predicting 303 the probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity-producer may comprise providing 311 the sensor data as input to a trained machine-learning algorithm lib configured to use the obtained sensor data to predict the probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity producer; and obtaining 313 an output signal of the machine-learning algorithm comprising the predicted probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity-producer.
• the method 300 may further comprise optimizing 305, by the trained machine-learning algorithm lib, the electricity storage scheme 302 for the distributed energy storage system, and the electricity delivery scheme 304 for the at least one electricityconsumer based on the obtained output signal of the machine-learning algorithm.
• the trained machine-learning algorithm lib may comprise a decentralized federated machine learning algorithm arranged at each one of the one or more distributed mixed energy storage unit(s) lOOa-lOOm.
• Executable instructions for performing the above functions and features of the embodiments of the methods may, optionally, be included in a non-transitory computer-readable storage medium or other computer program product configured for execution by one or more processors of the processing circuitry.
• a computer program carrier carrying one or more computer programs configured to be executed by one or more processors of a processing circuitry, the one or more programs comprising instructions for performing any one of the embodiments of the method 300 according to this disclosure.
• the computer program carrier may be one of an electronic signal, optical signal, radio signal or a computer-readable storage medium.
• a computer program product comprising instructions which, when the program is executed by one or more processors of a processing circuitry, causes the processing circuitry to carry out any one of the embodiments of the method 300 according to this disclosure.
• Fig. 4 is a schematic illustration of a distributed energy storage system 100 for optimized storage and delivery of electricity in an electricity distribution network 1 according to several aspects and embodiments of the present disclosure.
• the distributed energy storage system 100 may in some aspects and embodiments be coupled to or be in communication with an electricity grid system 50.
• the operationally decentralized distributed energy storage system 100 is a mixed energy storage system comprising one or more operationally decentralized distributed mixed energy storage unit(s) lOOa-lOOm, each mixed energy storage unit comprising at least one energy storage module Mi-M n of an energy type of a plurality of energy types.
• the distributed energy storage system 100 may comprise any combination of different operationally decentralized distributed mixed energy storage unit(s) lOOa-lOOm having varying locations and placements, energy storage capacities, geographical footprints, number of modules Mi-M n of different energy types, etc.
• the skilled person thus realizes that implementation of the distributed energy storage units lOOa-lOOm is a design choice depending on the system requirements and intended applications and may be readily adapted to specific circumstances.
• Each operationally decentralized distributed energy storage unit lOOa-lOOm comprises a control system lOa-lOm or collectively referred to as the control system 10.
• the control system 10 is configured to operate the distributed energy storage units in conjunction with the command center 200 and perform any of the embodiments of the method 300 according to the present disclosure.
• the control system 10 may further comprise one or more processors 11, a memory 8, a sensor and module connection interface 13 for connecting the control system 10 to various sensor devices 6a-6n and/or energy storage modules Mi-M n .
• the control system 10 may further comprise a communication interface 14 for connecting each energy storage unit lOOa-lOOm to its respective external network 20a-20m and/or to the grid network 50 via dedicated transmission lines 101.
• the processor(s) 11 may also be referred to as a control circuit 11 or control circuitry 11 or processing circuitry 11.
• the control circuitry 11 is configured to execute instructions stored in the memory 8 to perform various embodiments of the method 300.
• the memory 8 of the control system 10 can include one or more (non-transitory) computer- readable storage mediums, for storing computer-executable instructions, which, when executed by one or more computer processors 11, for example, can cause the computer processors 11 to perform the techniques described herein.
• the memory 8 optionally includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid-state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices.
• the processing circuitry 11 of the control system 10 may comprise a data processing unit 11a for receiving and processing the obtained sensor data and/or historic data and information to be used for predicting the probability of the future electricity need of the at least one electricity-consumer and/or a the probability of the future electricity production of the at least one electricity-producer. Further, the data processing unit 11a may be configured for optimizing the electricity storage scheme for the distributed energy storage system, and the electricity delivery scheme for the at least one electricity-consumer. In several embodiments and aspects the data processing unit 11a may comprise a trained machine learning algorithm lib or a trained artificial intelligence (Al) algorithm lib for performing the prediction and optimization tasks.
• Al trained artificial intelligence
• control system 10 may be further configured for providing the sensor data as input to the trained machine-learning algorithm lib configured to use the obtained sensor data to predict the probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity producer. Further, the control system 10 may be configured for obtaining an output signal of the machine-learning algorithm lib comprising the predicted probability of the future electricity need of the at least one electricity-consumer and/or the probability of the future electricity production of the at least one electricity-producer.
• the control system 10 may be further configured for optimizing, by the trained machine-learning algorithm lib, the electricity storage scheme for the distributed energy storage system, and the electricity delivery scheme for the at least one electricity-consumer based on the obtained output signal of the machine-learning algorithm.
• the trained machine-learning algorithm lib comprises a decentralized federated machine learning algorithm arranged at each one of the one or more distributed mixed energy storage unit(s) lOOa-lOOm.
• the distributed energy storage system 100 may be connected to external network(s) 20 via for instance a wireless link or communication interface 14 via various technologies such as cellular long range or short range such as Wireless Local Area (LAN), WiFi, etc. communication technologies.
• Each distributed energy storage unit lOOa-lOOm may accordingly be configured to be connected to its own external network 20a-20m.
• a cloud computing system 20 can be configured to perform any one of or any combination of the embodiments of the method 300 presented herein.
• the cloud computing system may comprise distributed cloud computing resources that jointly perform the methods presented herein under control of one or more computer program products.
• the processor(s) 11 may be or include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory 8.
• the system 10 may have an associated memory 8, and the memory 8 may be one or more devices for storing data and/or computer code for completing or facilitating the various methods described in the present description.
• the memory may include volatile memory or non-volatile memory.
• the memory 8 may include database components, object code components, script components, or any other type of information structure for supporting the various activities of the present description. According to an exemplary embodiment, any distributed or local memory device may be utilized with the systems and methods of this description.
• the memory 8 is communicably connected to the processor 11 (e.g., via a circuit or any other wired, wireless, or network connection) and includes computer code for executing one or more of processes described herein.
• the term “if” may be construed to mean “when or “upon” or “in response to determining or “in response to detecting” depending on the context.
• the phrase “if it is determined' or “when it is determined” may be construed to mean “upon determining or “in response to determining” or “upon detecting and identifying occurrence of an event” or “in response to detecting occurrence of an event” depending on the context.
• the term “obtaining” is herein to be interpreted broadly and encompasses receiving, retrieving, collecting, acquiring, and so forth directly and/or indirectly between two entities configured to be in communication with each other or with other external entities.

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