Patent Description:
Energy storage represents a growing asset class in the energy system and opportunity to help manage and shift supply from low carbon generation resources such as wind and solar, and to help manage the shape of energy demand profiles, and electrical system management. The management challenge increases when large numbers of energy storage and flexibility resources are present on a grid, particularly with the rise in electric vehicle adoption and the increased pressure on local networks in accommodating large swings in power consumption - such as with increasingly higher rate electric vehicle charging.

The challenge is also increased when energy systems are 'islanded' or limited in connection, e.g. for large island nations, or locations/networks with few interconnection, or when planning new sites, whether for new building or campuses, or for new smart cities. UK and Japan for example are large island nations, with low (e.g. <NUM>%) interconnection so have to manage flexibility within their own energy system as the swings from large scale deployment of distributed wind and solar resources result in changes over the solar day or with the weather. Similarly large scale adoption of distributed batteries, such as home storage, electric vehicles and personal mobility devices, robotics or growing Internet of Things/battery operated devices, requires significant charge management over the day.

In the UK for example an electrification strategy for mobility, could result in over a Terawatt (TWH) of batteries across UK transport that need to be managed and optimised on a daily and location basis. This creates significant infrastructure challenges, in investing in new generation and network resource, and also opportunities of vehicles aggregating power to help grids (e.g. <CIT> V2 Green Inc.

There are a number of prior art examples (including from Moixa, <CIT>, <CIT>) which discuss aspects of this challenge from the viewpoint of individual solutions (e.g. solar batteries at Moixa, Tesla, STEM, Sunverge, Sonnen), energy data collection and secure exchange (e.g. <CIT>, <CIT>) or via ledgers (<CIT> ) or for solutions on EV management (<CIT>), or rate arbitrage between on or off peaks (e.g. <CIT> on co-ordinating storage resources as emergency power on a micro-grid and in response to market price) and aggregate applications for virtual power plants (e.g. <CIT>, <CIT>).

There are also various academic papers modelling challenges of Electric Vehicle management and charging, including "<NPL>), "<NPL>), "<NPL> et al, which outline mathematical optimisation problems in electric vehicle flows and charging.

However, such and other examples do not properly consider how multiple types of assets and interests need to be managed on a group and local level, and optimised to achieve a balance between, individual motives and benefits (e.g. the home owner) or EV user, or regulated entities (such as suppliers or networks), or device manufacturers. In particular they do not present how technologies need to be combined to offer solutions that are adaptive to different energy systems and regulations or changes in billing and approach over time, or in how machine learning and other optimisation technologies can combine to deliver real-time and self-regulating control of groups of assets in a location. Neither do the prior approaches properly address how such groups can be managed reliably over time, with technologies that are resilient over time and a changing energy, communication and software environment, nor do prior approaches address how to manage such assets financially, such as cash-flow payments from counter-parties or contracts, to maximise returns to stakeholders or asset funders. Nor do prior approaches properly address how to minimise life-time operations and maintenance costs, in maintaining connectivity and managing and updating fleets of distributed assets over time.

In view of these challenges and issues, there is therefore a need for systems, methods and devices that can collectively address these and other problems in the energy system, and enable groups of different types of batteries or devices with batteries to be managed as collective assets as energy infrastructure.

<CIT> describes a photovoltaic generation energy-storage system and energy dispatching method in which neural network predictions of energy usage are utilised. <CIT> describes charging an electrical power storage device based on predictions of temporal changes in power consumption based on a long-term prediction model.

The present invention is defined by appended independent claims <NUM>, <NUM> and <NUM>, with preferred embodiments being defined by the dependent claims.

In embodiments, a management and optimisation system is provided, comprising software systems and protocols, connectivity and exchange means to and between end devices and resources, to gather data and monitor usage, process external data and market signals, and perform algorithms that analyse and identify characteristics and update predictions, in order to co-ordinate how flexibility in said resources, can be scheduled, shared or orchestrated to enable various interventions of individual or aggregate groups of resources, to achieve certain goals or reliable performance objectives over time, for an individual site, local environment, wider community or nation.

Said end resources typically include distributed energy storage resources, such as "behind the meter" electrical storage batteries or heat storage sources, co-located or centralised larger battery resources, electric vehicles or their charger apparatus, other devices with embedded batteries such as drones, telecom masts, robotics, end customer devices, Internet of Things (IoT) and consumer electronic devices that require periodic charging and management, or distributed energy generation sources such as solar panels, wind resources, fuel cells, waste to energy, or energy loads or appliances that can act as a flexible resource by shifting consumption, e.g. mechanical, heating or cooling elements.

Said end devices typically include physical apparatus co-located with resources such as smart meters, clamps and sensors, routers and controllers, smart hubs and gateways, communication apparatus, consumer access devices and displays, charger apparatus or smart plugs or control actuators, processing chips or circuitry connected to end resources, or as sensors or other devices ostensibly performing an alternate function such as smart speakers, smart thermostats, smart phones, or methods of determining or extracting data from third-party sources such as GPS signals, traffic cameras, remote imagery (such as of weather patterns or solar availability for roof areas).

An example embodiment would be to use said devices, to provide real-time data on energy supply or usage or needs of said resources across a location or low voltage network, to algorithms or a 'brain' software system, e.g. in the cloud or at a central server, or on end devices and resources, to calculate a current position and next predicted position or forward profile of resources to aid with an intervention, such as managing the rate of charging of distributed energy resources at such as a plurality of batteries or electric vehicles.

Said connectivity means typically include standard communication technologies such as fixed and wireless telephony and mobile networks (GPRS, <NUM>-<NUM>, LTE), local communication technologies such as WiFi, Z-Wave, Zigbee, mesh networks, Powerline or signals carried over an electrical circuit, together with leverage of the Internet and remote servers, and cloud hosted components and technologies, and on end customer devices.

Said software systems may be aided by suitable protocols which act as distributed control means, standards, frameworks and APIs, and mechanisms for self-regulating large volumes of distributed entities to achieve a collective objective or benefit. For example, a charging protocol on distributed resources may be configured to respond to a local constraint, congestion or local limit, to optimise flow (e.g. energy or data) at a local position, in such a manner as the aggregate stochastic and network performance is predictable and beneficial. As an example it has been found particularly advantageous to use approaches from telephony to inform energy control, such as TCP (Transmission Control Protocol) where bandwidth was managed by enabling distributed resources to self-regulate and manage bandwidth (TCPIP) flows as local congestion was observed (Jacobson <NUM>). In a similar manner, an object of the invention is to use a combination of central software systems and protocols to help govern at a distributed level how an overall energy system performs, and aid for example local voltage limits, local and overall system balancing. This has been remarkably effective in bandwidth management where in effect a decentralised system of 'routing' stochastically to local constraints, achieves an overall optima - in effect as a decentralised parallel algorithm that achieves and solves an optimisation problem (Kelly).

In the same way such charging protocols may help govern a goal of the software system, by ensuring that distributed resources such as batteries or electric vehicle charger rates, initially respond to local constraints in a predictable fashion and in a manner which favours a preferred aggregate behaviour, and where such charging protocols might act to maximize e.g. 'power flow' or capacity at certain sites or maximise proportional fairness to balance resources and access more equitably, such as access to charging at low price or access and suitable fair distribution in rates of charge when energy networks are congested, and suitable management or 'throttling' to actively manage the charge rates to optimise participant demands within constraints of a system.

The system can be combined with other aspects and embodiments of the invention where prediction of energy usage at an end site is used.

In an embodiment, the mode of use is a seasonal or calendar related pattern, arrival, night-time slow-down, holiday.

In an embodiment, the event represents an EV charging, operation of wet-goods appliance or heat-appliance or cooling appliance.

In an embodiment, dedicated neural networks are provided for a plurality of target appliances and/or modes.

In an embodiment, a primary network dynamically branches to a further neural network arranged to:.

Whilst aspects of the innovation and above description of embodiments is given by way of example only, and by reference to figures and diagrams forthwith, it will be appreciated that various aspects and embodiments can be modified in accordance with other aspects and embodiments. The scope of the invention is not to be limited by details of the embodiments but is capable of numerous modifications within the scope of the invention as defined in the accompanying claims.

Preferred embodiments of the invention will now be described in detail with reference to the accompanying drawings.

Referring to <FIG>, which shows a high-level schematic of a management and optimisation system <NUM>, comprising software systems <NUM> and protocols <NUM>, connectivity <NUM> and exchange <NUM> means for linking the software system to and between end devices <NUM> and resources <NUM> at various end sites <NUM> in the energy distribution system <NUM>. The software gathers data <NUM> and monitor usage <NUM> of end devices <NUM> and resources, as well as processing external data <NUM> such as market signals <NUM>, weather forecasts <NUM>, and location presence <NUM>. The software performs algorithms <NUM> e.g. Al & neural network <NUM> approaches that analyse and identify characteristics and/or events <NUM> from the data <NUM> and monitored usage <NUM> and, based on this, creates/updates predictions <NUM> of energy use in upcoming time periods and stores learnings <NUM> and calendar patterns <NUM> relating to insights into energy usage at the end sites. These predictions <NUM>, learnings <NUM> and calendar patterns <NUM> are used in order to co-ordinate how flexibility <NUM> in said resources, can be scheduled <NUM>, shared <NUM> or orchestrated to enable various interventions of individual or aggregate groups of resources <NUM>, to achieve certain goals or reliable performance objectives over time, for an individual site <NUM>, local environment <NUM>, wider community <NUM>, or nation <NUM>.

Flexibility is the ability to provide resources that can increase or decrease demand, store or provide power to aid the energy network in managing variability and volatility and balance supply and demand on the network. Traditionally this was done by the energy suppliers bringing new generation resources on line to meet increases in demand. Increasingly, efforts now focus on demand side response where flexibility in how and when resources consume energy is managed to help balance the network. As described in this document, the ability to manage and optimise the energy resources and their flexibility at end sites provides a range of advantages at all levels of the network, and becomes increasingly important as more variable energy supplies, such as wind and solar are added to the network, or with the electrification of mobility and heat, that add increasing loads onto the network that vary with location, time and season.

The flexibility <NUM> of resources may be traded via exchange means <NUM> such as data, contracts, marketplace platforms, with energy actors <NUM> such as aggregators, suppliers, local networks, grid, or peer-to-peer or communities <NUM>, via contracts <NUM> and enable financial payments <NUM>, or other benefits <NUM> such as carbon offsets.

Also illustrated is an electrical distribution system <NUM>, typically comprised of a central grid <NUM> and central energy generation sources, providing high-voltage power. This is transmitted over networks <NUM> to medium-voltage networks and substations <NUM>. This is then distributed to low-voltage networks <NUM> and step-down transformers or distribution sub-stations <NUM> which provide end customer power. The end customer power may be provided, potentially on different electrical phases, to end sites <NUM>, typically via meter devices <NUM>, <NUM>, or to unmetered loads such as street lamps or network attached charging points <NUM>, which typically use a virtual measured central management system approach. Within an example site <NUM> there is shown example resources <NUM> on the distribution network such as solar <NUM> and battery resources <NUM>, mobile phone network masts with batteries <NUM>, sites and buildings <NUM> with flexible demand side resources, and similarly an electric vehicle charger cluster <NUM> formed of individual Electric Vehicle charger apparatus <NUM> (that may also be co-located in a home or street), and an example electric vehicle <NUM>. Similarly around an example house site <NUM> is shown a residential solar system <NUM> and battery <NUM>, a high-load/duration appliance <NUM>, and site data / patterns <NUM>, consumer access devices <NUM>, such as smart phones <NUM> and internet browsers on computers <NUM>.

In an embodiments of the management and optimisation system <NUM>, methods would seek to orchestrate and manage distributed energy resource assets on an individual and aggregate basis to deliver an optimal return for such assets and their owners (customers or asset vehicles) as an "Energy as a Service" (EaaS) model or as a battery operator 'BOP' by providing flexibility and services across a spectrum of potential beneficiaries, from BTM - "Behind the Meter", typically for end customers or buildings, ATM - "At the Meter", typically for energy suppliers or energy service companies, LTM - "Local to Meter", typically for local distribution networks, developers or communities, FTM - "Front of the Meter", for wider grid actors and system benefits. Said optimisation method typically involves optimising for a single or co-operating cluster of beneficiaries, and learning energy patterns and managing flexibility to maximise income on a daily basis, and deliver extra return by making flexibility available on demand via contracts with certain parties for when certain situations arise, such as local network constraints or high value opportunities on the electricity grid.

Within such an approach, an optimisation and orchestration method may seek to manage a pure BTM - in home/building customer benefit, or to align objectives between, say, a Utility supplying the customer (BTM+ATM) or across a local group of customers as peers (in a peer to peer model) or as a group (BTM+ATM+LTM) such as houses and EV customers, Utility suppliers and local network. In such a situation algorithms need to consider <NUM>) the data and identity characteristics and manage according to goals such as a) local limits on the network that may act as constraints on supply or timing and rates of charging, or b) limit export of energy from renewable or battery/EV resources, and <NUM>) constraint scoring (e.g. risk of the power network not having enough capacity to meet demand) and <NUM>) prediction of shiftable demand or flexibility in homes or vehicles and <NUM>) risk scoring of the flexibility and predictability of the resource to account for where it may be limited e.g. by forecast energy demand needs, the size and availability of battery resources, knowledge of occupancy or non-occupancy of building, location of an electric vehicle (e.g. if not connected), or contract or market constraints, e.g. where an energy supplier may not wish to provide flexibility if it impacts their trading position or where flexibility may be desired for a wider grid issue or contract opportunity.

According to an embodiment, a method of optimising behind-the-meter (BTM) benefits by the management and optimisation system is provided, where the system processes real-time or periodic data from end devices to manage distributed energy storage resources to inform and manage charging or trading. A method comprising:.

Such algorithms may make use of machine learning, pattern recognition and feature and event detection (e.g. of a high load, occupancy event, start of a charge cycle), training of neural networks to aid recognition of patterns or classifying patterns that are unusual, use of modelling, convolution and comparison, forecasting and probabilistic modelling (e.g. of energy load profiles on event detection, solar profiles, EV charge patterns), or Markov modelling to model probabilistic transitions and paths between likely subsequent states and duration of energy devices in use, or transition states in EV charging, feedback networks, predictive learning, linear programming.

Said event detection and short term forecast may make use of simple multi-layer perceptron or recurrent neural network, or disaggregation or profile information to determine and focus on events that have a prolonged impact on a forward profile, such as detecting the start of a high-load appliance such as a cooker, air-conditioner or washing machine, by detecting substantial step change in energy use, and disaggregation and pattern recognition approaches, such as referring to past profiles and learnt behaviour. This has been found to be particularly advantageous for informing forward predictions for such high-loads, or standard electric vehicle charging events, as well as rises in consumption triggered by occupancy (e.g. detection of return to work, away - e.g. holiday modes, night time slow down), and various tools such as risk-profiles can lend weight to the stability of such forecasts and past reliability to inform energy management and how predictions are used for trading, battery charge plan adjustments, wider flexibility availability. Such approaches can also be particularly advantageous in aiding accuracy of short term interval or half-hourly settlement approaches, in making adjustments to household load, e.g. via changing the battery charge/discharge pattern, or updating trading positions - typically reported ahead of a time-gate. Similarly, such approaches on EV detection and charge profile prediction can be valuable to local network managers, and to inform setting, throttling or limit setting on other charging requests on the same local network.

Event detection, can make use of various approaches, such as creating or matching to appliance signatures, by recording significant and sizeable change events over a time interval of measurement of aggregated active power, and knowledge based learning, and storing of signatures to a database, or by removing certain probabilistic signatures from a profile and comparing to performance, or labelling of unlearned patterns to inform risk profiles. Of particular relevance for reliable prediction is events that have a high probability of duration, thereby influencing larger power flows or availability for flexibility, than say short duration events. As such a probability map and risk weightings on predictive load change can be assigned by focus on selective disaggregation and identification of high and long probabilistic duration events, within a typical energy load profile also characterised by a general background of multiple shorter event activities, and improved by machine learning techniques as patterns or cluster correlations of activity repeat.

In an example embodiment of said management and optimisation system, a software system, enables real-time connectivity or interval processing of data from end measurement devices, to make data available for consumer presentation or analysis and processing to enact a remote control change or program a local control change on an end resource, such as adjusting a battery management system or charge plan, by for example: the end user, in response to an external request or as an optimisation using data from I) local sources: such as battery State of charge, energy use, solar supply, EV demand, or II) from a predictive forecast using such data and additional insight from prior patterns, detection of large loads, occupancy awareness, start or expectation of EV charging (e.g. GPS geo-fencing), or learnt behaviours associated with detected events or date patterns, or III) optimisation to external signals such as current, short-term and days ahead forecasts in weather, solar irradiance, market pricing, or time of use tariffs, or IV) to real-time price information and e.g. time interval such as Half Hourly price data from suppliers, price signals and requested adjustments or opportunities (e.g. low cost), V) or from a recommendation from modelling to show the benefit of an alternative tariff or energy resource opportunity.

Said consumer presentation, may include selectively displaying real-time or interval energy use data on a consumer access device (such as a mobile phone, tablet, home energy display, internet browser) of building energy use and energy from grid, solar production and use, battery charging status, percentage and capacity KWh and energy flow, electric vehicle battery charging status, and show energy flows, together with analysis or time based views, such as usage graphs, pricing information and savings totals, benefits, together with alerts on status or choices, forward predictions, comparisons of historic data with current or peer group, and allow settings or changes to be selected by end users, together with administrative functions such as data management, updates on user data such as WiFi, account information, addresses, tariff information, and customer support areas such as documentation, product and warranty information, service information and fault/investigation requests, visibility of price plans or flexibility access, settings and contract choices.

Said external request may be from an energy system stakeholder, and form a demand side response for flexibility, e.g. from an energy supplier for a tariff or imbalance motive, or from a local network for a local network constraint, voltage, power or fault issue, or from the overall grid system for a frequency response, demand turn-up, demand-turn-down, capacity, or balancing market requirement.

Said software system and modelling may make use of decision logic, such as binary classification of events and decision trees on probabilistic evolution of the event (such as an energy load or set of consumption behaviours), or neural networks to detect if patterns of use are within normal limits or represent exceptions or patterns attached to particular sets of events, data or calendar days, or use models to schedule and allow recovery time from events or uses of flexibility.

Said optimisation and decision logic, may also make use of linear programming techniques to focus an optimisation between maximising various properties (e.g. demand, PV supply, grid tariff price, weather) within a specific interval and time unit (TU), and establish a typical flow chart of measured or expected characteristics, and how, e.g. by varying a battery charge rate/discharge parameter in a household battery or electric vehicle charging plan, a local optimisation could occur for predicted time interval (see <FIG>).

Similarly a data store or vector may store a measurement or expected profile of such predictions, or general predictive forecasts from the algorithms, for a series of periods 'programme time units' (PTU), preferably in units of an hour or less, e.g. <NUM> minutes, and optimise as a rolling window across a suitable period (e.g. settlement, or day ahead - <NUM> PTU intervals; T<NUM>-T<NUM>) for variables including:.

Data stores may also include customer or site rules or preferences, reference charge plans, calendar records and default modes, occupancy, learnt or detected behaviours and modes, energy device signatures, lists of known devices at a site and typical use times, and risk profiles. Data stores may also be used to capture market signals or flexibility needs, such as designation of times of day for peak-off/peak, periods of limit e.g. congestion or constraints on networks, contract periods or needs for flexibility, DSR turn-up/turn-down, availability, at a local level (e.g. excess/demand from solar/battery/chargers), utility, network or system operator level.

Within a management and optimisation system, a software system method of optimising a behind the meter customer benefit, may include a method of generating a plan (for flexibility e.g. charging/discharging of an asset), based on sharing current and monitored data with a prediction engine and an economic model, wherein said economic model calculates an impact of the example plan subject to other data (e.g. battery, PV sizing, choices, tariffs) and with reference to a tariff model or store; and said prediction engine calculates a forward model of consumption and generation for applying such a plan, along with other factors and data (e.g. weather or other consumption predictions) and stores the prediction, to enable performance monitoring and feedback to the system or requests for new predictions, if there is divergence of measured variables from the forecast, and manages the storage and deployment of the plan to ensure end assets perform in accordance with the plan objectives (see for instance <FIG>).

According to another embodiment, a method of optimising "Behind-the-Meter" (BTM) and Utility supply "At-the-Meter" benefits by the management and optimisation system is provided, where the system processes real-time or periodic data from end devices to manage distributed energy storage resources to help inform and manage the overall energy shape of the trading and supply of energy by managing and adjusting charging. A method comprising:.

Within said management and optimisation method, modelling and decision logic may look at considering alternate tariff presentations or offers to end customers that favour the overall trading position and offer mutual advantages or reflect more accurately supply costs and charges, such as by settling on shorter time periods (such as half hourly) than longer period averages, or by helping to provide or encouraging access to flexible assets - such as Demand Side Response (DSR), storage or flexible EV charging, or by offering tariffs that reward or encourage certain time of day characteristics (e.g. off-peak charging), or by agreeing increased data access to households, such as EV location (GPS or vehicle sensors), occupancy or other sensors, additional real-time meter data, to improve prediction capabilities.

Within said management and optimisation method, approaches may consider multiple factors in choosing how multiple assets (e.g. batteries, EV, Heat storage, DSR) and flexibility across a group of sites under management and energy supply responsibility, can be deployed across data and forecast accuracy, availability likelihood, temperature/season and calendar, customer impact, flexibility risk, depreciation cost, and opportunity cost.

Within said management and optimisation method, approaches may need to consider how participants may mitigate or take advantage of alternate local mechanisms and rewards from flexibility, such as peer-to-peer, or indeed offer and host peer-to-peer benefits to end users and seek to optimise how resources (outside of the supplier responsibility) could be procured or obtained to benefit the end customer and supplier trading position, e.g. by offering to alter charging plans, trade excess supply or demand at the household level, to local participants, to improve its overall trading position and imbalance exposure.

As an example embodiment a group of co-located or co-operating households may opt to form a community for peer or shared resource benefits, such as for batteries, solar, EV charging, and opt to offset and settle as a group, e.g. on a half-hourly or interval basis, against each other, or against and with a site that is already settled on a half hourly basis (such as a larger wind or solar generator) or business. This has been found to enable some local trading by virtual metering, and enable greater virtual pooling of solar and battery resource within a community, even within restrictions on how households are metered and settled. An optimisation method within such a community may seek to share, energy data vectors and status on demand, batteries and solar, across a community, and to trade by various exchange mechanisms (discussed earlier) or platforms spare flexibility and capacity. From an overall supplier perspective, a supplier may settle and offset against the business/larger site, such local trades and offset each individual meter record directly.

According to another embodiment, a method of optimising a group of "Behind-the-Meter" (BTM) and Utility supply "At-the-Meter" requirements alongside Local-to-Meter (LTM) benefits by the management and optimisation system is provided, where the system processes real-time or periodic data across a plurality of end devices within the location to inform software systems to manage an aggregate performance of energy storage resources within local constraints. A method comprising:.

In an example embodiment, a method of actively managing and throttling rates of electric vehicle charging across a site or low voltage network is provided by a management and optimisation system, to allow a greater freedom, equality and access to faster charging rates, than might be allowed at the site without further upgrades, charges or inequality. Where said method involves some of i) actively managing and throttling charge rates, ii) setting a rolling forecast and forward charge curve governing such charge rates, iii) using price signals or incentives to encourage adjusting of rates or times of charge, iv) curtailing charging or high rates of charging at certain times or events v) establishing suitable charging protocols or automatic response and self-regulating mechanisms at individual electric vehicle chargers that in aggregate act to improve the controlled performance of the network. Said method typically involving stages of:.

In an example preferred embodiment, said optimisation and management system, may connect to measure or control an Electric Vehicle battery state through a variety of software or physical device mechanisms including i) standards for EV, chargers, APIs for data exchange, common or device control protocols, ii) electric vehicle operating systems iii) controls to or embedded in Battery management systems and battery cells iv) EV charger apparatus and standards such as SAE CCS, OCPP, CHAdeMO v) smart hubs and attachments to EV charger apparatus vi) Smart Meters and signals to connected chargers vii) retrofittable controls such as connection plugs between an Electric Vehicle and a charger viii) wireless or inductive means of charging an electric vehicle in close proximity to a suitably configured supply.

Within said optimisation and management system, to consider and measure and forecast different usage across phases, and factor active management into devices that can adjust demands on particular phase, or move demand onto alternate phases to help balance, e.g. through electric vehicle charge-points that may optionally select or draw power from different phases in response to a request.

Within said optimisation and management system, the method may actively manage or recommend addition, of extra battery resource on a low voltage network, either as a central resource or as an aggregate of distributed resources, so as to aid management and balancing on the network, for example storing of excess local solar generation at peak solar hours, or discharging at peak domestic demand, or managing local solar and night /off-peak charging to create extra capacity in batteries to discharge at times of peak electric vehicle demand. In future such approaches can also apply to vehicle-to-grid, vehicle to vehicle or vehicle-to-home applications, where said software system may help to co-ordinate the charging and discharging of such electric vehicle chargers to achieve different outcomes.

Within said optimisation and management system, to use charging protocols based on a TCP like approach, that distribute a decision to vary charging rate, based on measurement of a local property, such as voltage changes, limits, or frequency, so as to proportionally delay charging or reduce charging rates in stress or high load events, or to gradually increase charging rates on measurement of low load or low stress events, and so to self-regulate in a predictable fashion how a charging event behaves.

Within said optimisation and management system, types of modelling that may be used may be, for example, based on decision trees, pattern recognition of charge behaviour and expected duration, patterns learnt from networks of events and characteristics typically proceeding a high-load or fault (e.g. sudden time and clustering of charge events at a point in the day or season/calendar).

Within a management and optimisation system, the software system and protocols may use approaches that can achieve results by consensus or sharing and establishing a price between parties as a form of exchange or market, or achieve an overall optimum for competing objectives, such as by finding a Nash equilibrium or maximising an entropy function. Thus for the case of a network constraint there is an ultimate limit to the aggregate (max power flow) of EV charging (or heat activity) that can occur at a particular rate and time, but through such mechanisms the result can favour a shared outcome that balances different parties objectives or achieves a proportionally fair result. Similarly where Utilities may act in self-interest (managing charging flexibility to their trading position or exposure on imbalance), said management and optimisation system may seek to achieve a group optimal result that favours managing to the network constraint, of wider benefit on sharing or reducing upgrade costs, whilst minimising or compensating for imbalance or change to each utilities' impact on their trading or inconvenience to end customers on access and charging rate. Within the forecast and vectors certain times may favour open trading and charging driven by price signals and nudges, whereas others, and in particular peak seasonal charging, may be driven by proportional fairness approaches.

Within a management and optimisation system, where local peer to peer trading can be supported, the measurement and forecasting of local flexibility may be advantageous in identifying local flexibility that might otherwise not be visible or known to other parties. Price signals and visibility of local charging or constraints may aid such availability and help provide extra flexibility that can be co-ordinated by the system.

According to another embodiment, a method of optimising a group of resources across BTM, ATM, LTM and FTM front-of-meter benefits by the management and optimisation system is provided, where the system processes real-time or periodic data across a plurality of end devices to achieve their objectives whilst calculating or optimising spare capacity to participate in other flexible markets. A method comprising:.

Within a management and optimisation system, a software system method of delivering flexibility is provided, wherein individual assets can report their monitored status, generated charge plans, predictions, to a flexibility engine, which can turn a flex request (for availability of delivery of flexibility to a market), into a constraint and adjustment to a plan, and model and calculate the cost, risk and recovery by applying such a constraint to a plan, in order to validate whether it can be assigned and aggregated into a group for dispatch to deliver such flexibility to a flex request, and to enact and manage performance of the delivery of such flexibility across a group, including managing the order, delivery, reporting and allocating reward from such performance.

Within a management and optimisation system, a software system may also aid management and modelling of a bid-engine, which manages a pipeline of potential requests for flexibility from different parties, preferably through standard approaches, APIs, protocols and frameworks, such as the USEF (Universal Smart Energy Framework) framework for expressing flexibility in universal terms, and to aid matching and scheduling suitable sets of resources, that can bid or offer prices and contracts into such bid-engines, and where this is for example market functions for managing local market flexibility such as DSO (Distributed System Operator) or network markets and platforms, or to peer-to-peer markets and trading platforms, or as part of whole system auctions and contracts managed by the system operator.

Within a management and optimisation system, a software system may aid the management of resources to participate in a peer-to-peer or peer-to-community offering of flexibility, by means of aiding matching supply of available or aggregates of flexibility from some participants or central resources, to demands for local flexibility from other participants or central resources, and aid managing such transactions, through e.g. providing data on availability, providing forecasts on energy usage, providing exchange means to manage such data or transaction, providing control and management over interventions, such as changing charge plans, providing performance monitoring and auditing of exchange and compensation or accounting of changes to meters or other settlement charges. Such a system has been found to be particularly advantageous even when flexibility and battery resources at locations are small (e.g. <NUM>-3KWh) as sharing such resources in aggregate can have the impact of switching off entire aggregates of houses or resources from the grid, or the overall energy demand more closely matching a predicted average profile, aiding local and wider grid stability. Pilots of peer-to-peer and peer-to-community exchanges (https://localisedenergyeric. com) have been found valuable to share end user resources (such as solar and battery) across different types of customer groups (private, social housing, schools, community centres and EV charge points) to be orchestrated for an overall benefit such as reduction in energy cost, sharing resources, reducing network constraints, or switching groups off grid during high price periods.

Referring now to <FIG>, this shows a particular configuration of the system <NUM> of <FIG> used for developing predictions for energy usage at end sites. Thus, <FIG> shows a schematic of a management and optimisation system <NUM>, where data from third-party or meter resources <NUM>, or from batteries <NUM> or smart-hubs <NUM>, is received and processed by a software system <NUM> or by algorithms <NUM>, alongside inputs <NUM> such as weather <NUM>, supplier time of use or market prices <NUM>, location/occupancy <NUM>, stored learnings <NUM>, calendar reference <NUM>, to aid updating predictions <NUM>. These predictions are used to inform flexibility for trading <NUM>, network or grid balancing opportunities <NUM>, <NUM>, or to drive a charge plan <NUM> for a connected asset such as a battery <NUM>, <NUM>, electric vehicle charger <NUM>, or smart hub <NUM> controlling a resource.

Referring now to <FIG>, this shows a schematic as to how a configuration of the management and optimisation system <NUM> can manage a central battery resource <NUM>, or a virtual battery <NUM> formed as an aggregate of resources, such as a group of distributed energy storage resources (e.g. batteries <NUM> and electric vehicle chargers <NUM>) associated with houses <NUM>, a cluster of electric vehicle chargers <NUM>, a cluster of telecom masts with batteries <NUM>, demand turn up and turn down resource assets in buildings <NUM>.

Referring now to <FIG>, this shows a schematic of a configuration of the management and optimisation system <NUM> aiding control of a local voltage network <NUM>, wherein an active management of resources could deliver a saving <NUM> or deferral of upgrade cost, and local resources such as EV chargers <NUM>, flexible building or site resources <NUM>, could be balanced by managed charging of central resources <NUM>, <NUM> and community assets (e.g. <NUM>) by the software system <NUM> and algorithms <NUM> with local data feeds (e.g. <NUM>, <NUM>, <NUM>).

Thus, for example, a new EV charging park for multiple EVs might be planned on a local branch of the network, where the existing branch does not have the capacity, e.g. too far from the substation or the physical wires carrying the power are underrated. etc., to provide peak power to the EV charging points used simultaneously and at maximum charging rates and/or at peak times. This might result in the local energy distributor refusing permission for the new park unless a new substation or new branch to the park was installed, which typically would be very expensive. The present system <NUM> can be used to reduce the cost or avoid the need for upgrading the branch line by actively managing the EV charging points, i.e. rates and times of charging, to limit the maximum demand on the local network to an acceptable figure, or to co-ordinate how other local resources such as household batteries charge or discharge, or demand side response resources to help enable larger EV charging loads. Based on a cost analysis, battery storage capacity can be installed at the park to allow further flexibility and active management and allow the possibility of trading flexibility or together with a virtual battery formed by aggregating batteries in nearby locations. Similar considerations apply to building a new town, installing a wind farm and other situations where it is desirable to actively manage around constraints in the network, where providing additional battery or control resources could help reduce this cost whilst also providing these resources for other benefits behind the meter or to the Utility and wider grid.

Referring now to <FIG>, this shows a schematic of a management and optimisation system <NUM> configured to measure, schedule and control or "throttle" the rate of EV charging. Various methods <NUM> may be utilised to control EV charging, such as via, cloud and APIs or common protocols <NUM>, on-board EV operating systems <NUM> and programs on the vehicle <NUM>, a battery management system <NUM> and battery system, a smart hub controller <NUM> configured as a universal communication board for device level integration and hosting device control protocols for electric vehicle chargers or other storage resources typically using a D-Bus <NUM> and connection to an Internet of Things (loT) client in the software system <NUM>, software on electric vehicle charger apparatus <NUM>, smart meter <NUM> communications, retrofittable connectors <NUM> or devices that attach between a charge apparatus <NUM> and electric vehicle plug connector <NUM>.

Referring now to <FIG>, this shows a schematic of an overall battery operator model <NUM>, where a management and optimisation system <NUM> manages sets of assets <NUM> and delivers benefits and services <NUM> across a range of 'BTM' <NUM> behind the meter, "ATM' <NUM> at the meter, 'LTM' <NUM> local to meter, 'FTM' <NUM> front of meter beneficiaries. This is done through a series of modules that provide client-side device management and analysis <NUM>, Partner or Utility side tools and services <NUM>, tools for managing aggregates of resources for local network or grid services <NUM>, which are configured <NUM> to integrate and communicate with end devices <NUM> and resources <NUM>, <NUM>. These services are typically delivered on a SaaS (Solution As A Service) approach involving design work, business models and methodologies <NUM>, integration work and use of API's and protocols in <NUM>, software modules and platforms <NUM>, <NUM>, <NUM>, and contracted service delivery <NUM> such as sales, set-up, installation, operations and maintenance.

Referring now to <FIG>, this shows a schematic of a special purpose vehicle <NUM>, lenders <NUM> and shareholders <NUM>, solution provider <NUM>, distributed energy assets <NUM>, and energy actors <NUM>, and example cash flows or contract relations between participants, such as loan capital <NUM>, loan and shareholder agreements <NUM>, payments <NUM> to a solution provider <NUM> under an engineering, performance and construction (EPC) contract <NUM> and operations and maintenance (O&M) contract <NUM>, for support on sales and contracting <NUM>, procurement and installation <NUM> e.g. of a battery <NUM> and solar system <NUM>, operations and maintenance of assets over time <NUM> and use a management and optimisation system <NUM> to optimise savings from assets and access income streams <NUM> from other energy actors such as communities <NUM>, suppliers <NUM>, network operators, aggregators or grid, via contracts <NUM> or marketplaces <NUM>. Where end customers <NUM> may for example assign <NUM> roof space leases and solar feed in tariff incomes <NUM> to the SPV <NUM> or pay a rental or PAYS (pay as you save) rate <NUM>, and have a contract <NUM> with the solutions provider <NUM> for services (e.g. battery services agreement), payments, incomes or rebates from flexibility trading <NUM>. Said management and optimisation system also using software systems <NUM>, protocols and exchange means <NUM>, to deliver benefits over the term of the asset funding or contract, and to help mitigate differences in income over time as markets, regulation and technologies evolve.

Referring now to <FIG>, this shows a flow diagram for an example linear programming simple charge-discharge optimisation of a battery based on data models for home energy demand <NUM>, solar supply <NUM>, grid tariff price <NUM>, at the start of a time period <NUM> and after <NUM> a programme time period (PTU) and after <NUM>, and applied e.g. to minimise a property like cost.

Referring now to <FIG>, this shows a schematic of an example of a plan generator <NUM> method performed by software <NUM> within a management and optimisation system <NUM> of generating a plan <NUM> (e.g. for flexibility, charging/discharging of an asset in a location <NUM>) under various constraints and predictions <NUM>, <NUM>, and external data e.g. weather <NUM>, tariff information <NUM> and flexibility requests <NUM>. The plan generator method involves sharing monitored data, learnt behaviours and models <NUM> with a prediction engine <NUM> and an economic model <NUM>. The economic model calculates an impact of the example plan <NUM> e.g. by considering data <NUM> such as battery, PV sizing, choices, with reference to a tariff model or store <NUM> and predictions <NUM>. The prediction engine <NUM> may for example calculate a forward model of consumption and generation for applying such a plan, along with other factors and data <NUM>, <NUM>. The prediction <NUM>, <NUM> is stored to enable performance monitoring <NUM> and feedback to the system, or requests for new predictions if there is divergence of measured variables from the forecast. The method manages the storage <NUM> and deployment <NUM> of the plan to ensure end devices and resources <NUM> perform in accordance with the plan objectives.

Referring to <FIG>, this shows a schematic of an example of a recurrent neural network (RNN) <NUM> (such as neural network <NUM> shown in <FIG>) to aid in a pattern recognition of an input sequence <NUM> or classification of a typical event <NUM> or set of behaviour previously observed from a time series energy measurement or forecast <NUM>, <NUM> and that have consequence on a forecast load or flexibility <NUM>, <NUM> for a period of time. In particular, the neural network may be configured for identifying a time dependent or occupancy mode (seasonal or calendar related pattern, arrival, night-time slow-down, holiday) or to help disaggregate and detect a high load, long duration event (e.g. an EV charging, wet-goods appliance, heat-appliance or cooling appliance). These identified modes and events have been found to be particularly helpful to aid the prediction and risk profile of the forecast <NUM>, and in informing a battery charging and discharging plan.

In such a scheme, a dedicated neural network (<NUM>, <NUM>,. ) may be established for each of various target appliances (e.g. an Electric Vehicle) or to represent various modes (holiday, summer solar day, arrival, night-time) An initial feature detection process <NUM> may be applied to the input sequence <NUM> before being passed to the neural network to aid such classification. Dedicated neural networks (<NUM>, <NUM>,. ) may also to help validate scalar real-time outputs <NUM>, <NUM> from other neural networks on key properties aiding the forecast <NUM>, such as device or mode type, start-time, time and power load duration expectation. For example a dedicated neural network may recognise that a change in load corresponds to the start of an electric vehicle charge event, and then use additional learnt behaviour or data (e.g. size and type of vehicle) to make a prediction on the duration of charge and therefore aid informing the forecast load for the next few hours. This then aids a utility on a supply and trade position, or a local network in forward knowledge of load demand on the network. Networks and other mechanisms could also be used to aid classification of new events (such as a new appliance) or an unusual load behaviour in a property (such as a device or resource not responding or indicating a fault). A network output such as solar fault or lower solar output than expected could therefore aid a local prediction and aid a plan.

Similarly outputs <NUM>, <NUM> from such neural networks can inform a risk-profile and confidence on whether a mode, or dominant device use, and expected probability of duration. The risk profile enables scoring the reliability of a forecast or showing there is not enough flexibility for energy sources to meet demand, and so by developing a measure of confidence in predictions of flexibility, i.e. the probability of the prediction being right/wrong, flexibility in the network can be better managed to avoid possible failures, and e.g. over charge rates. The higher confidence in the forward load prediction allows for example greater freedom in charging rates, whereas lower confidence may be used by a network of electric vehicle charging plan to hold back spare capacity in the network.

Such a neural network may branch dynamically where new patterns are identified or not matching prior learnings and create a new, secondary network <NUM>, <NUM>. The secondary network may be arranged for testing the data <NUM> from the primary network or to recognise a separate set of characteristics once the primary network has made an identification of a mode or event. Alternatively, the secondary network may be arranged to create and re-enforce and train a network <NUM>, <NUM> when measurement of a pattern is within range of an output threshold <NUM> of a primary network, and then decide to undertake a 'forward-pass' classification in a series of adjacent networks <NUM>, <NUM>, or selectively learn and undertake a 'backwards-pass' update of weights <NUM>, <NUM> within the network, when a network match is found. Weights may also be assigned as a dimensional array relating to specific modes (e.g. seasons), to ensure learning reinforces a seasonal or mode related pattern. Thus, the neural network is re-enforced and trained when an event or device has been recognised.

Such a branch or distribution of networks can both aid a mode or primary event detection, aid a risk-profile scoring of where the current pattern fits on a distribution, to aid forecast and decision logic. Thus, the adjacent neural networks may represent a distribution of patterns away from a base representing typical patterns for said event such that the outputs of the adjacent networks represents a probability (e.g. binomial) distribution of the primary network being accurate in identifying the event or mode. This can aid how the forecast is used, e.g. for example knowing how well a current situation fits with prior experience and decisions, or in a wider example such as automated financial trading, informing that such rules should not be used when the market is in an unknown or less familiar pattern.

The recurrent neural network (RNN) may make use of an Elman or Hopfield feedback topology or deep learning techniques, as well as preparing signals by filtering and signal normalising techniques such as convolutional neural network approaches for pattern recognition, de-noising and auto-decoder approaches. The networks and hidden layers may also make use of additional memory nodes to aid e.g. LSTM (Long Short Term Memory) approaches, and used of synthetic and random training on real data, central preprocessing to data-sets, to aid learning of the network or for expediting application of prepared networks for generic application at end sites where networks deploy for forward classification use, with secondary learning to adjust to local patterns. Such networks can have an advantage of re-enforcing a current temporal pattern of activity, like in speech recognition, or in aiding recognising correlated activities, for example an EV charging event starting corresponding to an arrival and occupancy of a property, corresponding to the start of additional load detections in a home for increased lighting and appliance use, or for seasonal activity where an arrival and a heating load event maybe correlated. In either a branching network approach, or where networks have lots of hidden layers, different nodes may represent and learn such behaviours for modes, appliances, or correlations of modes and appliances, to further aid the prediction and forecast, and therefore the risk-profile and flexibility available.

In embodiments, the software systems and protocols, may make use of mechanisms of exchange, based around distributed ledgers, such as block-chain technologies, electronic coins or cryptography, such as energy block-chains based on the EnergyWeb approaches (themselves based on Ethereum approaches). Such approaches, whilst negating the need for an intermediary, typically require significant processing power and chains that become unwieldy. Therefore they often require a party to act as a trusted validator to confirm a trade or verify 'proof of work', or para-chain approaches such as in the Polkadot variation of splitting transactions into groups or sub-chains. Such approaches may have value in how they enable consistent and mathematically pure and long-lasting approaches for data to be exchanged, and for new forms of protocols that are independent of energy system actor, device and language, so have value within software systems described in this application as mechanisms to ensure reliable access and management over time. However, while such approaches are interesting in creating new models of grid-edge or peer-to-peer marketplace, other approaches may be beneficial for creating such local marketplaces.

Such ledger approaches may be used to help govern interactions for assets within a close community, building, site, community or low voltage network. Within these approaches, a para-chain model can be used where part of the local energy system, such as a substation or special meter, can be used for confirmation and validation of local transactions, negating the energy and data intensive issues with full distributed block-chains. An approach is also to use what are termed herein as 'mesh-chains', where ledgers or block-chains are created at stable nodes, representing an assumed level of trust, such as by smart meters, charger points, at particular locations, as well as within assets such as electric vehicles, each time they cross-over or interact with another ledger, thus each creating an audit trail of each transaction that has metered energy flow for, charge event by a charger, charge/discharge by a vehicle, with each transaction creating a shared hash and timestamp noting its interaction within the grid.

<FIG> - shows an example of forming an audit trail or ledger <NUM> as a "mesh-chain" by recording transactions <NUM> each time a cross-over <NUM>, <NUM> or chain-link event happens, here illustrated by an EV <NUM> which "transacts" to receive a charging event with a charger apparatus <NUM>, at a particular time recorded as a time stamp <NUM>, and illustrated as a set of electric vehicles (EV(i)-EV(n)) and a set of charger apparatus (Ch(i)-Ch(n)) at different locations, wherein each ledger for an electric vehicle (e.g. <NUM>) and each ledger for a charger (or meter) <NUM> records a "transaction" <NUM> each time <NUM> there is an event, and forms as shown in <FIG> a hash <NUM> as a combination e.g. of shared public and private keys between the EV <NUM> and charger <NUM>, and at a future event illustrated as an EV with another charger <NUM>. Said ledgers <NUM>, <NUM> can therefore form a historic record of transactions over time with different locational assets, to for example aid accounting of use of asset (e.g. battery) or monetization of power to (via a charger) or from stored energy in the asset.

Referring now to <FIG>, this shows a schematic of a management and optimisation system <NUM> configured to measure, schedule and control or "throttle" the rate of EV charging. Various methods <NUM> may be utilised to control EV charging, such as via, cloud and APIs or common protocols <NUM>, on-board EV operating systems <NUM> and programs on the vehicle <NUM>, a battery management system <NUM> and battery system, a smart hub controller <NUM> configured as a universal communication board for device level integration and hosting device control protocols for electric vehicle chargers or other storage resources typically using a D-Bus <NUM> and connection to an Internet of Things (IoT) client in the software system <NUM>, software on electric vehicle charger apparatus <NUM>, smart meter <NUM> communications, retrofittable connectors <NUM> or devices that attach between a charge apparatus <NUM> and electric vehicle plug connector <NUM>.

Claim 1:
A system (<NUM>) comprising:
a software system (<NUM>);
end devices (<NUM>) and energy resources (<NUM>), wherein at least one resource is a rechargeable battery and wherein the software system is arranged to receive from the end devices real-time data on energy supply or usage of the energy resources across an energy system at a location or low voltage network; and
wherein the software system is arranged to classify events or behaviours observed in energy usage in the energy system, and comprises:
a recurrent neural network (<NUM>;<NUM>,<NUM>) arranged to receive at an input a time series of measurements (<NUM>) indicative of energy usage or activity in the energy system derived from the real-time data from the end devices and to identify based on the input
<NUM>) a time or occupancy dependent mode of use of the energy system or
<NUM>) a high load, long duration event, indicative of use of a particular appliance, disaggregated from the measurements,
and to output a scalar real-time value (<NUM>,<NUM>) representing one or more properties associated with the mode of use or event, being one or more of the device or mode type, start-time of the event or mode, time and power load duration expectation;
a prediction engine (<NUM>) arranged to calculate a prediction (<NUM>) of load or flexibility (!<NUM>) in the energy system over a time period and/or a risk profile of the prediction based at least in part on the scalar value, and
the software system being further arranged (<NUM>) to determine a battery charging plan (<NUM>) for charging and/or discharging the rechargeable battery at least in part based on the prediction, the battery being arranged to charge or discharge responsive to the plan.