TECHNICAL FIELD OF THE DISCLOSURE

The present disclosure is in the field of load management of clusters of charge points for vehicles. In particular, the present disclosure relates to a load management system and an associated method for load management of at least one cluster of charge points for vehicles.

BACKGROUND

In recent years, environmental concerns coupled with relatively high fuel prices have contributed to a surge in demand for vehicles that are, at least in part, electrically powered.

Various electric vehicles have been implemented, ranging from Hybrid Electric Vehicles (HEV) that may generate their own electrical power from fuel, to plug-in hybrid electric vehicles (PHEV) and plug-in fully electric vehicles (PEV) that may be charged by means of connecting the vehicle to an electrical power supply, e.g. the grid. PHEVs and PEVs are hereafter referred to collectively as ‘electric vehicles’.

As the number of electric vehicles on the road continues to increase, increasing demands may be placed upon electrical power supplies for charging such vehicles. A demand for charging points for charging such vehicles is also gradually increasing. Furthermore, as battery technology advances and also due to a consumer demand for long-range electric vehicles, a storage capacity of batteries used in electric vehicles is also generally increasing, thereby further contributing to demand on electrical power supplies.

The power demand of electric vehicles may pose substantial challenges for both overall electricity grid capacity, and for local distribution capacity. For example, high demands on electrical power supplies may exacerbate a risk of overloading local distribution transformers.

As such, it may be desirable to take steps to balance or moderate the provision of electrical power to charging points for vehicles, especially when multiple electric vehicles are being simultaneously charged, to mitigate the risk of overloading power supplies to the charging points. However, any such balancing or moderation of the provision of electrical power must account for specific demands of consumers, and in particular the need to sufficiently recharge a vehicle in a commercially acceptable timeframe.

It is therefore desirable to provide means for managing the charging of electric vehicles at charge points that may avoid overloading electrical power supplies to the charge points, yet also adequately meet the particular charging requirements of a user.

It is therefore an aim of at least one embodiment of at least one aspect of the present disclosure to obviate or at least mitigate at least one of the above identified shortcomings of the prior art.

SUMMARY

The present disclosure is in the field of load management of clusters of charge points for vehicles. In particular, the present disclosure relates to a load management system and an associated method for load management of at least one cluster of charge points for vehicles.

According to a first aspect of the disclosure, there is provided a method for load management of at least one cluster of charge points for vehicles, the method comprising: receiving, by a load manager, data relating to usage of one or more charge points in a cluster; and controlling, by the load manager, an amount of power that each charge point in the cluster can provide to a respective load based on the received data. The load manager is configured to limit a total power collectively provided by all of the charge points in the cluster to their respective loads to below a threshold level associated with the cluster.

Advantageously, by limiting a total power collectively provided by all of the charge points in the cluster, a risk of overloading power supplies, e.g. a local transformer, to the charging points may be mitigated. Furthermore, as described in more detail below, by limiting to a threshold that is associated with the cluster, e.g. a threshold representative of a maximum power that may be available to the whole cluster, a charging strategy for vehicles charged by the cluster may be optimized accordingly.

The method may comprise receiving, by the load manager, data corresponding to the threshold level from a system for providing electrical power to the cluster. The system may be the electrical grid. The system may be an Energy Management System (EMS). The system may be a Building Energy Management System (BEMS).

The threshold level may comprise a dynamic threshold level. It is noted that the term ‘load’ is used herein as a reference to a load on a power supply, e.g. a reference to how much the cluster may load the power supply. In other words, a power supply can supply a limited amount of power and therefore can support a limited load.

That is, in some examples, a dedicated, e.g. fixed, load may not be available to the cluster of charge points. Furthermore, in such examples there may be no device local to the cluster that may enable local dynamic load management.

In such an example, the cluster of charge points may need to adapt to a dynamic availability of load, e.g. power, at the location where the charger points are connected to the power supply, e.g. the grid.

In embodiments, dynamic load management described herein may be handled local to the cluster, e.g. not through a software-as-a-Service (SaaS) system wherein delays in communication may occur.

Optionally, the dynamic threshold level may be communicated in real-time and/or periodically to the load manager.

The dynamic threshold level may be communicated in real-time and/or periodically to the load manager by an energy management system coupled to the cluster.

Advantageously, the dynamic threshold level may be updated in real-time, or periodically, in response to changes or forecast changes in levels of electrical power available to the cluster.

In a non-limiting example, an energy management system such as a Building Energy Management System (BEMS) may indicate at a first time that a certain amount of power is available to the cluster. However, the BEMS may subsequently forecast that at a second time a reduced amount of electrical power is available to the cluster due to another load managed by the BEMS, such as scheduled air conditioning, requiring a proportion of the available electrical power budget.

In another example, the electrical grid, e.g. a utility provider, may communicate the dynamic threshold to the load manager.

The dynamic threshold level may be communicated to the load manager using an Open Smart Charging Protocol (OSCP).

The method may comprise transmitting, by the load manager, a signal to the energy management system for enabling the energy management system to manage a power available to the cluster and/or a load area. The signal may enable management of energy production and/or provision by the energy management system. The signal may be a feedback signal. The signal may comprise data such as messages and/or data packets. The signal, and more specifically data forming the signal, may be termed “flexibility messages”.

In an example, the load manager may provide the signal to indicate to the energy management system an amount of flexibility any clusters and/or load areas managed by the load manager may have in terms of the load it may place on the energy management system.

The dynamic threshold level communicated to the load manager may comprise a forecast.

That is, an energy management system (EMS), such as the above-described BEMS or the electrical grid managed by a utility provided, may use an open communication protocol, such OSCP, to provide a forecast of the electrical power that will be available to the cluster, e.g. a forecast of the capacity of the grid to provide electrical power. In a non-limiting example, the forecast may comprise a 24 hourforecast.

The threshold level may comprise a static threshold level. Optionally, the static threshold level may be communicated to the load manager by a system for providing power to the cluster.

As an example, a dedicated load may be available for a cluster of charging points. In an example, of the total electrical power available from the EMS to support loads of the EMS, a defined amount or proportion of that total electrical power may be made available to the cluster. Based on a configuration of the cluster, the load manager may receive data from the charge points and may apply the load management logic to prevent the dedicated load being exceeded by the cluster. The load manager may track electrical power consumption by each charge point, and may adapt a charging load on charge points included in the cluster.

The load manager may be configured to receive the data, e.g. usage data, from each charge point in the cluster.

That is, each charge point may be configured to communicate, such as wirelessly, with the load manager.

The load manager may be configured to receive the data, e.g. usage data, from a vehicle communications device.

The load manager may be configured to receive the data, e.g. usage data, from a portable communications device. In some examples, a user may use an installed application, or access a web application or website, on a portable communications device such as a smartphone or the like. The user may provide at least a portion of the usage data, such as an expected departure time of the vehicle from the charge point.

The usage data may be communicated to the load manager using an Open Charge Point Protocol (OCPP).

An amount of power and/or a scheduling of power that each charge point in the cluster may provide to a respective load may be based on the data. The data may comprise a user's preferences. The user's preferences may, for example, be preferences associated with a respective charge point and/or vehicle and/or usage habits, needs or the like.

For example, the data, e.g. the user’s preferences, may comprise a vehicle arrival time at a respective charge point.

The data may comprise a predicted vehicle departure time from a/the respective charge point;

The data may comprises a determined amount of energy required by the vehicle.

The data may comprises a charging phase of the vehicle.

The load manager may be configurable to apply a charging policy to control a provision of power to one or more charge points in the cluster to adapt a time taken to charge one or more vehicles and/or to charge vehicles based on a first-come-first-served service policy.

The charging policy of adapting a time taken to charge one or more vehicles may be based on a fairness level determined by the load manager.

In an example, a charge point in the cluster may be configured by the load manager to charge a vehicle ‘as fast as possible’, based at least in part on an available electrical power to provide to the vehicle. Charging ‘as fast as possible’ may comprise charging with full amount of power available and/or based upon user preferences.

In some embodiments, charging as fast as possible may be based on a fairness level, which is described in more detail below with reference to example embodiments of Figures 1 and 2. That is, in some examples the fairness level may be a parameter of the ‘as fast as possible' charging strategy/policy.

The fairness level may be provided as an input to the load manager relating to a configuration of the cluster, e.g. a manual input to an algorithm executed by the load manager, relating to a configuration of the cluster. The fairness level may be provided in addition to data corresponding to the user preferences and/or charging strategy/policy, e.g. the above described 'fast-as-possible' strategy or the like, to the load manager.

In an example, a charge point in the cluster may be configured by the load manager to charge a vehicle ‘as slow as possible’. In such an example, the load manager may configure the charge point to charge a vehicle as slowly as possible yet still fulfill user preferences such as a required departure time and/or required amount of energy.

In yet a further example, the load manager may configure the cluster to charge vehicles based on a first come first served service policy. In this example, a vehicle that arrived at the cluster first may receive a maximum remaining current/power, and user preferences may be ignored.

The method may be for load management of at least one load area comprising a plurality of clusters. The plurality of clusters may be configured to receive power from a common power source.

The plurality of clusters may comprise at least one cluster wherein the threshold level is a static threshold level. The plurality of clusters may comprise at least one cluster wherein the threshold level is a dynamic threshold level.

In further examples, the method may be for load management of a plurality of load areas, each load area comprising at least one cluster.

A load area may be an area that has electrical power provided from a common source, e.g. a transformer.

The load manager may be configured to limit a total power collectively provided by all of the charge points in the plurality of clusters to their respective loads to below a further threshold level associated with the load area.

The plurality of clusters may comprise at least one further cluster having a local load manager configured to control, for each charge point in the further cluster, an amount of power that each charge point in the further cluster can provide to a respective load based on the received data.

According to a second aspect of the disclosure, there is provided a load manager computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to the first aspect.

According to a third aspect of the disclosure, there is provided a load management system comprising a processing system implementing a load manager configured to receive data relating to usage of one or more charge points in a cluster, and to control an amount of power that each charge point in the cluster can provide to a respective load based on the received data; wherein the load manager is configured to limit a total power collectively provided by all of the charge points in the cluster to their respective loads to below a threshold level associated with the cluster.

The load management system may comprise at least one cluster of charge points for vehicles. The at least one cluster of charge points may be communicatively coupled to the processing system implementing the load manager.

The processing system may comprise a cloud-based processing system.

In other examples, the processing system may comprise a server or other processing device that may be remote from one or more clusters that are managed by the load manager executed on the processing system.

In yet further examples, the processing system may be local to a cluster that is managed by the load manager, such as integrated into a charge point of the cluster.

The above summary is intended to be merely exemplary and non-limiting. The disclosure includes one or more corresponding aspects, embodiments or features in isolation or in various combinations whether or not specifically stated (including claimed) in that combination or in isolation. It should be understood that features defined above in accordance with any aspect of the present disclosure or below relating to any specific embodiment of the disclosure may be utilized, either alone or in combination with any other defined feature, in any other aspect or embodiment or to form a further aspect or embodiment of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of the present disclosure will now be described, by way of example only, with reference to the accompanying drawings, wherein:

Figure 1 depicts a high-level block diagram of an implementation of a system for load management of at least one cluster of charge points, according to an embodiment of the disclosure;

Figure 2 depicts a block diagram of an implementation of a system for load management of load areas comprising clusters of charge points, according to an embodiment of the disclosure; and

Figure 3 depicts a flow diagram of a method for load management of at least one cluster of charge points for vehicles, according to an embodiment of the disclosure.

DETAILED DESCRIPTION OF THE DRAWINGS Figure 1 depicts a block diagram of an example of a system 100 for load management of at least one cluster of charge points, according to an embodiment of the disclosure.

The example system 100 comprises a load manager 105. The load manager is depicted as a cloud symbol to illustrate that the load manager 105 may be implemented as a processing system, wherein the processing system comprises a cloud-based processing system. In other examples, the load manager 105 may be implemented on a server or other processing device that may be remote from one or more clusters that are managed by the load manager 105.

In yet further examples, the load manager 105 may be local to a cluster that is managed by the load manager 105.

For purposes of illustration, a first vehicle 110 is depicted as coupled to a first charge point 115 for charging the first vehicle 110. A second vehicle 120 is depicted as coupled to a second charge point 125 for charging the second vehicle 120. A third charge point 130 available for charging a third vehicle is also depicted.

A structure 135 is depicted. An electrical power supply 140 is provided from structure 135 to each of the first, second and third charge points 115, 125, 130.

As an example, the structure 135 may be a building such as a car park or an office, or may be a transformer substation or the like.

A smart meter 145 is depicted. The smart meter 145 is an electricity meter with communications functionality. The smart meter 145 meters electrical power provided to the first, second and third charge points 115, 125, 130. The smart meter 145 communicates measurements of a total electrical power provided to the first, second and third charge points 115, 125, 130 to the load manager 105.

Power is provided to each of the first, second and third charge points 115, 125, 130 by an the electrical grid 150, which for purposes of illustration only is simply depicted as a pylon for transporting electrical power and wind turbines for power generation. It will be appreciated that one or more transformers, substations or the like may be implemented between the grid 150 and the smart meter 145.

The first, second and third charge points 115, 125, 130 form a cluster 155. Although the example embodiment of Figure 1 depicts only a single cluster 155, although it will be appreciated that the load manager 105 may be configured to manage a plurality of clusters, as described in more detail below with reference to the example embodiment of Figure 2. Returning to Figure 1 , each of the first 115, second 125 and third charge points 130 are communicatively coupled to the load manager 105.

The grid 150 is also communicatively coupled to the load manager 105. That is, in examples a system which may be generally termed the electrical grid and operated by a utility provider, may be communicatively coupled to the load manager 105.

In use, the load manager 105 may be configured to receive usage data from each charge point 115, 125, 130 in the cluster 155. In other examples, the load manager 105 may additionally or alternatively be configured to receive usage data from the vehicle itself, e.g. a communications device in the vehicle. In yet further examples, the load manager 105 may additionally or alternatively be configured to receive usage data from a portable communications device, such as a smart phone, or an internet-connected device.

In examples, such usage data may comprise an arrival time of the vehicle 110, 120 at a respective charge point 115, 125. Such usage data may comprise a predicted departure time of a vehicle 110, 120 from the respective charge point 115, 125. Such usage data may comprise a determined amount of energy required by the vehicle 110, 125, e.g. an amount of energy needed to recharge the vehicle to a desired level. Such usage data may comprise a charging phase of the vehicle, e.g. single or three phase.

In some examples, if sufficient usage data is not provided, e.g. a user does not provide a “required energy” and/or a “departure time”, the load manager 105 may apply default values, or may generate values based upon preconfigured assumptions.

In the example embodiment of Figure 1 , the usage data is communicated to the load manager 105 by the smart meter and each charge point using an Open Charge Point Protocol (OCPP).

In use, the load manager 105 receives data, e.g. usage data relating to usage of one or more charge points in a cluster, from any/all of the charge points, the vehicles and/or the user via an application, a web-application or a website. For example, the user may access the application, web-application or a website by means of a portable communications device such as a smartphone.

The load manager 105 may be configured to control an amount of power that each charge point 115, 125, 130 in the cluster 155 may provide to a respective vehicle 110, 120 based on the received data.

An amount and/or a scheduling of power that each charge point 115, 125, 130 in the cluster can provide to a respective load, e.g. vehicle 110, 120, may be based on the data, wherein the data may comprise a user's preferences. In an example, a charge point 115, 125, 130 in the cluster may be configured by the load manager 105 to charge a vehicle 110, 120 based on a charging strategy/policy that may be based, at least in part, on an available electrical power to provide to the vehicle. For example, the charging strategy/policy may be a strategy/policy of charging a vehicle as quickly as possible.

That is, a charge point 115, 125, 130 in the cluster may be configured by the load manager 105 to charge a vehicle 110, 120 ‘as fast as possible’, based at least in part on an available electrical power to provide to the vehicle. Charging ‘as fast as possible’ may comprise charging with full amount of power available and/or based upon user preferences.

The above-described fairness level may be associated with the ‘fast as possible’ charging strategy, as described in the following example.

The fairness level may be determined by the load manager 105. For example, the load manager 105 may determine the fairness level based on any of: user preferences, available load; forecast of available load, vehicle type and requirements; vehicle charging times, departures times, or the like. In yet further examples, the fairness level may be a predefined value, such a stored value, a stored value in a look-up-table, or the like.

In some embodiments, charging as fast as possible may be based on the abovedescribed fairness level, which for purposes of example only may range from 0 to 100. Continuing with this example, if the fairness level equals 100 then user preferences, e.g. as defined by the usage data, may take priority over maximizing the electrical power provided by one or more charge points for charging vehicles. As an example, if the first vehicle 110 is configured for single-phase charging and the second vehicle 120 is configured for three-phase charging and both have the same user preferences, then the first vehicle 110 and the second vehicle 120 would both charge with the same current. Example user preferences may be correspond to the above-described usage data, e.g. an arrival time, a departure time an amount of energy required, etc.

Continuing with this example, if the fairness level equals 0 then user preferences may be ignored. In this example, the load manager 105 may maximize a power output of one or more charge points. That is, in this example, the second vehicle 120 configured for three-phase charging would receive its maximum current and the first vehicle 110 configured for single-phase charging would only receive any remaining current.

Continuing with this example, if the fairness level equals is between 0 and 100, then user preferences may be balanced against maximizing the electrical power provided by one or more charge points for charging vehicles, such that user preferences are taken into account to at least some extent, the extent being defined by the value of the fairness level.

That is, in examples the fairness level may be associated with the 'fast as possible' charging strategy, and the charging strategy may be associated with cluster.

In examples, charge points within the same cluster may have the same fairness level but may have different preferences (or user's on charge points have different preferences). Based on the fairness level, the some or all of the preferences may be either ignored or applied, or at least taken into account to some extent.

In another example, the charging strategy/policy may be a strategy/policy of charging a vehicle ‘as slow as possible’. That is, in an example, a charge point 115, 125, 130 in the cluster 155 may be configured by the load manager 105 to charge a vehicle 110, 120 ‘as slow as possible’. In such an example, the load manager 105 may configure the charge point 115, 125, 130 to charge the vehicle 110, 120 as slowly as possible yet still fulfil user preferences such as a required departure time and/or required amount of energy.

In yet a further example, the load manager 105 may configure the cluster to charge the vehicles 110, 120 based on a first come first served service policy. In this example, a vehicle 110, 120 that arrived at the cluster 155 first will get maximum remaining current/power, and user preferences may be ignored.

As non-limiting example of various example parameters of the usage data that may be used on the above described charging scenarios, and the means by which said data is provided to the load manager 105, is as follows:

Status: Received through OCPP from the charge point 115, 125, 130;

Vehicle full (if full, flexibility is 0): Received through OCPP from the charge point 115, 125, 130. In some examples, this parameter may be calculated internally by the load manager based on a charging pattern. That is, OCPP may not define such a message/data;

Charging type (AC, DC, different default values): This may be received through OCPP from the charge point 115, 125, 130. In examples, the charging type may be a parameter manually input into the load manager. That is, in some examples OCPP may not send this information;

Required energy by vehicle 110, 120: User, such as vehicle driver, may enter the data through a user application (web/mobile app). If not entered, the load manager 105 may make its own assumptions based on statistics. Departure time: The vehicle driver may enter the data through user application (web/mobile app). If not inserted, load manager 105 may make its own assumptions based on statistics.

Phases used by vehicle 110, 120: Received through OCPP from the charge point 115, 125, 130. In examples, this parameter may be calculated internally by the load manager based on one or more OCPP Meter Values message. If the charge point has current only on one phase, then the vehicle uses only the one phase;

Meter values missing (if true, flexibility indicates failsafe current): Received through OCPP from the charge point 115, 125, 130. In examples, the load manager may set this parameter to true if the one or more OCPP Meter Value messages was not received after a defined period, for example after 2 minutes;

Is charging over the limit (if true, flexibility is installation current): Received through OCPP from the charge point 115, 125, 130. In examples, the load manager may determine this parameter based on the received one or more OCPP Meter Value messages; and/or

Is charging below the limit (if true, flexibility is installation current): Received through OCPP from the charge point 115, 125, 130. In examples, the load manager may determine this parameter based on the received one or more OCPP Meter Value messages.

As described above, several of the example parameters may be calculated based on one or more OCPP Meter Value messages. As such, said example parameters may be effectively, indirectly provided by OCPP.

The load manager 105 may be configured to limit a total power collectively provided by all of the charge points 115, 125, 130 in the cluster 155 to their respective loads, e.g. vehicles 110, 120, to below a threshold level associated with the cluster. This may advantageously mitigate a risk of overloading a power supplies, e.g. electrical grid 150 or a local transformer or the like.

In a first example, threshold level may comprise a dynamic threshold level. Use of such a dynamic threshold level by the load manager 105 may be known as Dynamic Load Management (DLM). In such an example, the cluster 155 of charge points 115, 125, 130 may need to adapt to a dynamic availability of an available power at the location where the charge points are connected to the power supply, e.g. the grid.

The electrical grid 150, or the source of power, may communicate data corresponding to the dynamic threshold level to the load manager 105. In example embodiments, the electrical grid 150 may communicate such data to the load manager 105 using an Open Smart Charging Protocol (OSCP). Although the electrical grid 150 is provided as an example, in other embodiments, an energy management systems such as a Building Energy Management System (BEMS) may provide such data to the load manager 105.

Furthermore, the data corresponding to the dynamic threshold provided to the load manager may, in some examples, be communicated via OSCP in real-time and/or periodically to the load manager 105. In examples, the data corresponding to the dynamic threshold may comprise a forecast. Such a forecast may be a forecast of the electrical power that will be made available to the cluster 150, e.g. a forecast of the capacity of the grid 150 to provide electrical power.

For purposes of example only, an OSCP compliant “UpdateGroupCapacityForecast” message for communicating data corresponding to a dynamic threshold is provided below. It can be seen that a threshold level “capacity” has a value of 210 between time 6:19:00 and 11 :56:00, and then the threshold level “capacity” is forecast to increase to a value of 350 between subsequent times 11 :56:00 to 16:37:00.

{

"groupjd": "LOC-SI-UC2_2 ",

"type": "CONSUMPTION",

"forecasted_blocks":

{

"capacity": 210,

"phase": "ALL",

"unit": "A",

"start_time": "2021-05-25T06:19:00.0000000Z",

"end_time": "2021-05-25T11 :56:00.0000000Z"

}, {

"capacity": 350,

"phase": "ALL",

"unit": "A",

"start_time": "2021-05-25T11 :56:00.0000000Z", "end_time": "2021-05-25T16:37:00.0000000Z" }

}

In another example, the threshold level may comprise a static threshold level. Optionally, the static threshold level may be communicated to the load manager 105 by a system, e.g. the grid 150 or an energy management such as a building energy Management System, for providing electrical power to the cluster 155. Use of such a static threshold level by the load manager 105 may be known as Static Load Management (SLM).

As a non-limiting example, a dedicated load may be available for the cluster 155 of charging points 115, 125, 130. In the example, 100 kW or electrical power may be available to the three charge points 115, 125, 130. Based on a configuration of the cluster 155, the load manager 105 may receive data from the charge points 115, 125, 130 and/or vehicles 110, 120 and/or user(s) and may apply load management logic to prevent the dedicated 100 kW electrical power being exceeded by the cluster 155. That is, the load manager 105 may track electrical power consumption by each charge point 115, 125, 130, and may adapt the charging load on charge points 115, 125, 130 included in the cluster 155 to ensure the dedicated load is not exceeded.

Figure 2 depicts a block diagram of an example of a system 200 for load management of a load area comprising clusters of charge points, according to an embodiment of the disclosure.

The example system 200 comprises a load manager 205 which, similar to Figure 1 , is depicted as a cloud symbol. The load manager 205 may be implemented as cloudbased processing system, on a server or other processing device that may be remote from one or more clusters that are managed by the load manager 205, or local to a cluster that is managed by the load manager 205.

A first cluster 210 of charge points is depicted. A second cluster 215 of charge points is depicted. The first and second clusters 210, 215 may be managed by the load manager using Dynamic Load Management (DLM), as is described above with reference to Figure 1. Furthermore, the load manager 205 may also be configured to apply the charging strategies/policies described above with reference to Figure 1 , such as the ‘fast as possible’ charging based on a fairness level.

A third cluster 220 of charge points is depicted. A fourth cluster 225 of charge points is depicted. The third and fourth clusters 220, 225 may be managed by the load manager using Static Load Management (SLM), as is described above with reference to Figure 1.

It will be appreciated that four clusters 210, 215, 220, 225are depicted for purposes of example only, and fewer than four or greater than four clusters may be managed by the load manager 205.

An electrical power supply is provided to each of the first, second, third and fourth clusters 210, 215, 220, 225 by an energy management system, which in this example is the electrical grid 250. For purposes of illustration only, the grid 250 is simply depicted as a pylon for transporting electrical power and wind turbines and solar panels for power generation. It will be appreciated that one or more transformers, substations or the like (not shown) may be implemented between the grid 250 and the first, second, third and fourth clusters 210, 215, 220, 225.

All of the charge points in each cluster 210, 215, 220, 225 are communicatively coupled to the load manager 205.

The grid 250 is also communicatively coupled to the load manager 205. That is, in examples a system which may be generally termed the electrical grid and operated by a utility provider, may be communicatively coupled to the load manager 205.

In use, the load manager 205 may be configured to receive usage data from each charge point in each cluster 210, 215, 220, 225. In other examples, the load manager 205 may additionally or alternatively be configured to receive usage data from a vehicle coupled to a respective charge point in a cluster, e.g. a communications device in the vehicle. In yet further examples, the load manager 205 may additionally or alternatively be configured to receive usage data from a user of a charge point in a cluster 210, 215, 220, 225, e.g. from a portable communications device, such as a smart phone, or an internet-connected device.

Such usage data is as described above with reference to Figure 1 , and therefore is not described further for purposes of brevity.

In the example embodiment of Figure 2, the usage data is communicated to the load manager 205 by the each charge point in each cluster 210, 215, 220, 225 using OCPP. The load manager 205 may be configured to control an amount of power that each charge point in each cluster 210, 215, 220, 225 may provide to a respective vehicle based on the received data.

The first and second clusters 210, 215 may form a first load area 255. The clusters 210, 215 forming the first load area are configured to receive electric power from, e.g. the grid 250.

A threshold level associated with the first load area 255 may be a dynamic threshold level. As such, the first load area 250 may need to adapt to a dynamic availability of an available electrical power from the grid 250.

The grid 250 may communicate data corresponding to the dynamic threshold level to the load manager 205. As described with reference to Figure 1 , the grid 250 may communicate such data to the load manager 205 using OSCP. Again, although the electrical grid 250 is provided as an example, in other embodiments, an energy management systems such as a Building Energy Management System (BEMS) may provide such data to the load manager 205.

Furthermore, the data corresponding to the dynamic threshold provided to the load manager 205 may, in some examples, be communicated using OSCP in real-time and/or periodically to the load manager 205. In examples, the data corresponding to the dynamic threshold may comprise a forecast. Such a forecast may be a forecast of the electrical power that will be made available to the load area 250, e.g. a forecast of the capacity of the grid 250 to provide electrical power to the load area 255.

In the example of Figure 2, electrical power may be provided to the grid 250 by energy sources such as solar panels and/or wind turbines. As such, a total capacity of the grid 250 to provide power may vary depending upon environmental considerations. As such, a forecast of available power to supply to the load area may be provided to the load manager 205 from the grid 250 over OSCP. This forecast may comprise data corresponding to the dynamic threshold, e.g. limits to an available power.

The load manager 205 may impose such limits on the load area 255. As such, the load manager 205 may manage operation of the charge point clusters in the load area to ensure a total power consumed by the load area 255 does not exceed a total power indicted and/or forecast to be available by the grid 250, also taking into account that a fixed amount of power may be allocated to the third and fourth clusters 220, 225 undergoing static load management.

That is, the load manager 205 may be configured to limit a total power collectively provided by all of the charge points in the plurality of clusters 210, 215, 220, 225 to their respective loads to below a threshold level. Furthermore, the load manager 205 may be configured to limit a total power collectively provided by all of the charge points is a given load area to below a threshold level associated with that load area.

In some examples, the plurality of clusters 210, 215, 220, 225 comprises at least one further cluster having a local load manager (not shown) configured to control, for each charge point in the further cluster, an amount of power that each charge point in the further cluster can provide to a respective load based on received usage data. In such instances, the load manager 205 may be configured to virtualize a load area comprising the further cluster having a local load manager, such that power consumption of the further cluster may be accounted for in any power budget shared between clusters and/or load areas managed by the load manager. That is, the locally managed further cluster may be treated as a virtualized load area by the load manager 205. In other examples, the further cluster may be allocated a fixed power budget by the load manager, and thereby treated as a load area under SLM.

As described above with reference to Figure 1 , an amount and/or a scheduling of power that each charge point within any of the clusters 210, 215, 220, 225 can provide to a respective vehicle 110, 120, may be based on a fairness level associated with each charge point, which the load manager may determine based on usage data and/or threshold levels.

The load manager 205 may be operated by, or accessed by, a charge point operator (CPO). A CPO may provide inputs, such as limitations, parameters of the like to control or customize operation of a load management engine of the load manager 205. An EMP eMobility Provider (EMP), also known in the art as a Mobility Service Provider (MSP), may provide information to the CPO to control operation of the load management engine. For example, such information may comprise user details, contracts, tariffs, billing payment information, or the like. In an example, the load management engine may limit an amount of power a charge point may provide to a vehicle based upon a prepayment scheme of the user, or a maximum amount of power allowed by a user’s contract with the EMP.

Also indicted in Figure 2 is signal 260, denoted ‘Flexibility’, provided by the load manager 205 to the energy management system, e.g. grid 250.

The signal 260 may enable management of energy production and/or provision by the energy management system. The signal 260 may be a feedback signal. The signal 260 may comprise data such as messages and/or data packets. The signal 260, and more specifically data forming the signal, may be termed “flexibility messages”. The load manager 205 may provide the signal 260 to indicate to the energy management system an amount of flexibility any clusters and/or load areas managed by the load manager 205 has in terms of the load it may place on the energy management system.

That is, the load manager 205 may provide the signal to the energy management system to enable the energy management system to manage a power available to the cluster and/or a load area. Examples of uses of the signal 260 are provided below.

As a non-limiting example, with the signal 260 the energy management system may decide to not interrupt a user's priorities, yet still lower a load on a cluster. A nonlimiting example of a charging scenario is as follows.

In the example charging scenario 2 vehicles are to be charged by charge points within a cluster of charge points. Each vehicle must be charged with a current of 15 Amps to fulfill their user preferences. In the example, each vehicle has the same preferences. Both vehicles are 3 phase. The charging strategy/policy is to charge the vehicles as fast as possible. A limit of the cluster is sufficiently high to support the required charging, e.g. a limit of 60 amps. In this scenario, each vehicle ay charge with a current of 30 Amps, thereby using the full 60 amp budget in total.

Advantageously, such charging with higher current than what is required to fulfill the user's preferences means that the vehicle may be fully charge before a required departure time. In this example, the provided to the vehicles may change overtime based on how much energy is already provided to vehicle.

The signal 260 may be used to inform the energy management system that a maximum current that the cluster can handle is 60 amps, but also that a low current where the user's preferences are still fulfilled is 30 amps. Based on this signal 260, the energy management system may lower the load from 60 amps to 30 amps, thereby providing charging capability to the two vehicles while also fulfilling the user's preferences.

The energy management system may lower the load even further, for example to 10 amps or even 0 amps, although this may affect users preferences and satisfaction as charging of the vehicles may be paused or stopped.

As described above, some or all communication between the load manager 205 and the energy management system, e.g. grid 250, may be using OSCP protocol.

Because OSCP does not support the above-described flexibility messages, custom messages may be sent by the load manager 205 to the energy management system. Such custom messages may be sent periodically. As a practical and non-limiting example, such custom messages may comprise: an identifier for identifying a cluster of charge points or a load area comprising one or more clusters of charge points; and parameters relating to use of the load area/cluster/charge points, such as user preferences and the like. An example format of such a custom message is as follows:

Definition:

Examples of the 'FlexibilityMeasurement' parameters of such custom messages is as follows:

FlexibilityMeasurement:

Such example custom messages may provide a snapshot of a current charging situation on each cluster at a given point in time, e.g. without forecasting. A non-limiting example of such a message is as follows (noting that “A’ is: amps and ‘W’ is watts:

"groupjd": "LOC-SI-00100151",

"flexibilities": [

{

"phase": "ONE",

"unit": "A",

"measure_time": "2022-09-20T10:44:32.4097866Z",

"excluded": 0.0,

"guaranteed": 0.0,

"minimum_respecting_ vehicle ": 6.0,

"user_preference": 1.6401065127374075,

"maximum": 32.0

},

{

"phase": "ONE",

"unit": "W",

"measure_time": "2022-09-20T10:44:32.4097866Z",

"excluded": 0.0,

"guaranteed": 0.0,

"minimum_respecting_vehicle ": 1380.0,

"user_preference": 377.22449792960373,

"maximum": 7360.0

}, {

"phase": "TWO",

"unit": "A",

"measure_time ": "2022-09-20T10:44:32.4097866Z", "excluded": 0.0,

"guaranteed": 0.0,

"minimum_respecting_ vehicle ": 6.0, "user_preference": 1.6401065127374075, "maximum": 32.0

},

{

"phase": "TWO",

"unit": "W",

"measure_time ": "2022-09-20T10:44:32.4097866Z", "excluded": 0.0,

"guaranteed": 0.0,

"minimum_respecting_vehicle ": 1380.0, "user_preference ": 377.22449792960373, "maximum": 7360.0

},

{

"phase": "THREE",

"unit": "A",

"measure_time ": "2022-09-20T10:44:32.4097866Z", "excluded": 0.0,

"guaranteed": 0.0,

"minimum_respecting_ vehicle ": 0.0, "user_preference": 0.0, "maximum": 0.0

},

{

"phase": "THREE",

"unit": "W",

"measure_time ": "2022-09-20T10:44:32.4097866Z", "excluded": 0.0, "guaranteed": 0.0,

"minimum_respecting_ vehicle 0.0,

"user_preference": 0.0,

"maximum": 0.0

}

]

}

The above example is of a single 2-phase vehicle (phase 3 has zero current). IN this example, the user's priority are very low, i.e. the departure time may be very long or a required energy may be very low, because it can be seen that if vehicle receives approximately 1.64 Amps from the moment of the message to the departure time, the vehicle would be fully charged by the departure time.

In this example, the parameter “minimum_respecting_vehicle=6” simply means that at least 6A needs to be provided to the vehicle, because (by standard) vehicles cannot charge with less than 6A. Flexibility may be provided in the provided current (Amps) or power (Watts).

Figure 3 depicts a flow diagram 300 of a method for load management of at least one cluster of charge points for vehicles, according to an embodiment of the disclosure.

In a first step 305, the load manager 105, 205, receives data corresponding to a threshold level from a system 100, 200 for providing electrical power to a cluster 155, 210, 215, 220, 225.

In a second step 310, the load manager 105, 205 controls an amount of power that each charge point in each cluster 155, 210, 215, 220, 225 can provide to a respective load, e.g. vehicle 110, 120, based on the received data.

The first and second steps 305, 310 may occur in either order or may occur concurrently.

In a third step, the load manager controls an amount of power that each charge point in each cluster 155, 210, 215, 220, 225 can provide to a respective load based on the received data.

In the example method of Figure 3, the load manager 105, 205 is configured to limit a total power collectively provided by all of the charge points in the cluster to their respective loads to below a threshold level associated with the cluster 155, 210, 215, 220, 225. Although the disclosure has been described in terms of particular embodiments as set forth above, it should be understood that these embodiments are illustrative only and that the claims are not limited to those embodiments. Those skilled in the art will be able to make modifications and alternatives in view of the disclosure, which are contemplated as falling within the scope of the appended claims. Each feature disclosed or illustrated in the present specification may be incorporated in any embodiments, whether alone or in any appropriate combination with any other feature disclosed or illustrated herein.

REFERENCE NUMERALS:

100 system

105 load manager

110 first vehicle

115 first charge point

120 second vehicle

125 second charge point

130 third charge point

135 structure

140 electrical power supply

145 smart meter

150 grid

155 cluster

200 system

205 load manager

210 first cluster

215 second cluster

220 third cluster

225 fourth cluster

250 grid

255 first load area

260 signal