System for responding to flexibility needs in a power grid

Abstract

System and method for responding to flexibility needs to be expressed by third parties regarding an electrical power grid. A forecast engine (15) performs operations of: - receiving a flexibility request from the booking engine (14), - selecting a subset (26) of algorithms adapted to an adjustment mechanism associated with the flexibility request (30a) and adapted to a type of distributed energy resources and reading the subset (26) of computer-program codes (27) implementing said selected subset of algorithms in said storage means (25), - loading said subset of computer-program codes (27) in the computer-readable medium, - running the loaded subset of computer-program codes (27), - delivering to the booking engine (14) a flexibility schedule (30b) adapted to the request.

Description

System for responding to flexibility needs in a power grid

[0001] The technical field of the invention is electrical power grid management, and more specifically, electrical power grids connecting aggregated controllable distributed energy resources which can be controlled to provide flexibility under request.

[0002] From Autogrid®, it is known to use different and distributed electrical devices connected to the electrical power grid as resources for giving back power to the grid. Some devices are used for their ability to deliver power, such as energy storage devices, or for their ability not to consume the available power at some times. This concept, known as a Virtual Power Plant, is self-defined as aggregating customer-owned flexible storage, distributed generation and demand-side resources to monetize in multiple energy markets and turn them into new revenues streams. The need for power is globally considered, and supply is permanently adjusted to demand. A global dispatch engine carries out this function of adjusting supply to demand.

[0003] From US2009326726A1, it is known to manage demand responses with a system including: - a database to store demand response data, said demand response data including demand response agreement parameters, demand response load and energy demand characteristics of one or more demand response customers, said demand response load characteristics including power consumption capacity of each of one or more demand response loads,- an aggregator to aggregate the demand response loads based on the demand response data and forecast data into a demand response portfolio,- a monitor to monitor the power demand of one or more demand response customers and one or more power grids, and - a dispatcher to notify one or more demand response customers of the demand response portfolio and to notify a utility of a response from one or more demand response customers whether to control the demand response load to return the power consumption capacity of the demand response load to the one or more power grids.

[0004] Other examples are known from EP3301635 and US2009 / 326726.Technical Problem

[0005] The existing dispatch engine does not consider specific data of the local resources when forecasting the opportunities of availability of power over the grid. So then, it is only possible to forecast an opportunity benefiting from optimized aggregated resources.Solution to Problem

[0006] An object of the invention is asystem for responding to flexibility needsto be expressed by third parties regarding an electrical power grid, having a dispatch engine able to control distributed energy resources according to a flexibility schedule, a booking engine, a forecasting engine, all connected for exchanging data,

[0007] characterized in that the booking engine comprises a computer-readable medium with instructions encoded thereon and processors connected to the medium and configured to, when executing the instructions, perform operations of:

[0008] - interfacing with a third party's agent,

[0009] - receiving and storing a flexibility request provided by the third party's agent,

[0010] - checking the feasibility of the stored flexibility request with a flexibility schedule received from the forecast engine,

[0011] - in case of feasibility, triggering the controlling of the distributed energy resources by the dispatch engine according to the flexibility schedule,

[0012] - otherwise, declining the flexibility request

[0013] and the forecast engine comprises storage means for storing a set of different subsets of computer-program codes implementing different subsets of algorithms, each subset of algorithms being adapted to at least one specific adjustment mechanism and at least one specific type of distributed energy resources, a computer-readable medium with instructions encoded thereon and processors connected to the medium and configured to, when executing the instructions, perform operations of:

[0014] - receiving a flexibility request from the booking engine, associated with at least one adjustment mechanism,

[0015] - selecting a subset of algorithms adapted to the at least one adjustment mechanism associated with the flexibility request and adapted to a type of distributed energy resources, and reading the subset of computer-program codes implementing said selected subset of algorithms in said storage means,

[0016] - loading said subset of computer-program codes in the computer-readable medium,

[0017] - running the loaded subset of computer-program codes,

[0018] - delivering to the booking engine a flexibility schedule adapted to the request.

[0019] In this text, "flexibility needs" means that over a given time, there is a need to modify the power temporarily. The flexibility can be expressed by time slots or days. Flexibility need is a generalization of load shedding, where the demand respond only consists in turning off services in the grid. With "flexibility need", the invention covers any kind of temporary modification of power, whether upwards or downwards.

[0020] In this text, an "electrical power grid" means an interconnected network for electricity delivery from producers to consumers.

[0021] In this text, "distributed energy resources" means electric units – typically in the range of 3 kW to 50 MW – located within the electric distribution system at or near the end user. Examples of distributed energy resources are generation units such as batteries or consumption units such as electric heaters. The word "asset" is also used for the electric units considered as distributed energy resources.

[0022] In this text, ''flexibility request'' means that, over a given time, there is a request to meet a flexibility need with the help of the at least one adjustment mechanism.

[0023] In this text, ''flexibility schedule'' means a plan of available power at different times of the day for different energy resources. A table usually expresses it. For example, an elementary flexibility schedule table may comprise only a starting date (date means day and time), an ending date and a power value. The flexibility schedule may comprise several slots, each slot may cover a period when the load is shed or even increased, by dispatching orders to the resources, possibly in combination with activating at least one adjustment mechanism.

[0024] In this text, an ''adjustment mechanism'' is either one of the following actions that can be activated to serve the flexibility schedule:

[0025] - a purchase or sale on an energy market, such as Block Exchange Notification of Demand Response (''NEBEF'') in France, or

[0026] - a contract change, such as a contract pricing option, or

[0027] - a technical change, such as a frequency adjustment or smart peaking.

[0028] According to a particular embodiment of the system, the booking engine is configured to carry out any one or several of these actions:- send flexibility forecasts to the third party,- allow the requester to ask to stop the flexibility commands on existing assets urgently,- allow the requester to bid / nominate assets on relevant markets.

[0029] The third party may be the person requesting the flexibility need, also named the requester, but it can also be the market. In this last case, the system of the invention is used to bid in response to market demand.Advantageous Effects of Invention

[0030] According to the invention, flexibility requests will be answered depending on available adjustment mechanisms and available distributed energy resources.

[0031] Adjustment mechanisms can be selected among the list consisting of a type of energy market, a type of contract pricing, a type of technical change such as a frequency adjustment.

[0032] An example of a subset of algorithms comprises four algorithms:

[0033] 1. An algorithm for assessing the energy need of users of a specific type of distributed energy resources.

[0034] 2. An algorithm for predicting the consumption of said users on a timeline.

[0035] 3. An algorithm for predicting the available adjustment mechanisms when considering constraints set by said users.

[0036] 4. An algorithm for predicting the expected earnings that would result from activating the thus made available adjustment mechanisms.

[0037] According to a particular embodiment of the system, the set of algorithms is a library organized so as to gather the algorithms in subsets each dedicated to at least one adjustment mechanism and at least one type of distributed energy resources.

[0038] According to a particular embodiment of the system, the sets of algorithms are organized as the cells of a table. In this table, each column is a type of activable asset, such as heat pumps, electric heaters, recharge stations, and so on, and each row is an adjustment mechanism, such as spot market, price optimization, frequency adjustment, or any network service. Of course, rows and columns can be inverted. In such a table, each cell is the equivalent of a subset as described above.

[0039] According to a particular embodiment of the system, a decision tree filters out the subsets of algorithms based on tags used as selection criteria.

[0040] Tags can also be used to manage the algorithms as a criterion to filter out algorithms depending on their version.

[0041] Tags can also be put on asset types and contracts. For instance, if the requester has signed a contract with the flexibility manager, the contract ID can be used as a tag. The tag helps select the subset of algorithms adapted to manage the appropriate assets, such as electric vehicle charging stations.

[0042] According to a particular embodiment of the system, the forecast engine is configured to take into account anyone or several of these parameters:

[0043] - market constraints,

[0044] - unexpected events, such as maintenance, failures, accidents,

[0045] - risk predefined as acceptable by the requester,

[0046] - frequency, timestep and horizon of the forecasts can be customized.

[0047] According to a particular embodiment of the system, algorithms can improve and auto-define new subsets of algorithms.

[0048] According to a particular embodiment of the system, successive versions of the algorithms are stored for later use.

[0049] According to a particular embodiment of the system, an instant report is sent back to the booking engine to help discuss with the third party's agent.

[0050] The instant report helps the agent with its flexibility activation strategy. The need is analyzed and discussed, and the gain can be increased by helping the agent review his request. The third party's agent may not possess as much information as the manager of the resources, such as market prices, fines, and so. Taking benefit from such knowledge may generate more profit than the a priori request of the agent.

[0051] According to a particular embodiment of the system, the dispatch engine is configured to dispatch flexibility to a portfolio of individual assets or a subpart.

[0052] One advantage is the ability to dispatch the flexibility to a portfolio of individual assets, or a subpart of it, provides the advantage of taking into account that some of the controlled assets have setpoints, possibly in another physical unit than Watts or Volts. For instance, electrical heaters are usually set at Celsius degrees. Then, a model for conversion can be part of the configuration of the dispatch engine. Another advantage is optimizing the portfolio's assets pool by verifying that the expected power is generated. The dispatch engine gathers all the information enabling the power gain check. If all the resources have reacted as expected, the check is positive. If some resources show latency or failure, the delay or failure information is returned to the booking engine. Then, either the delay or failure is overcome, unsolicited resources are involved in replacing the defective ones, or the following availability schedule is adjusted to consider this new data.

[0053] According to a particular embodiment of the system, the distributed energy resources are aggregated.

[0054] In this text, aggregated means distributed energy resources are brought together to reach a critical power, for instance, more than 100 kW. Aggregated resources behave like one only.

[0055] According to a particular embodiment of the system, it comprises a database containing data used by the forecast engine.

[0056] According to a particular embodiment of the system, the database is structured so as to store resources' metadata, such as the hierarchy between resources or geographic addresses.

[0057] According to a particular embodiment of the system, the database is structured so as to store flexibility constraints such as activation time, delay, maximum tolerated activation, and activation costs.

[0058] According to a particular embodiment of the system, the database is structured so as to store dynamic states such as communication status, telemetries, time series, and user needs.

[0059] According to a particular embodiment of the system, the database is structured so as to store user-expressed constraints such as electric vehicle mobility, heating schedule, and request constraints retrieved through tailored means.

[0060] User-expressed constraints can be collected through apps, such as APIs, which stands for application programming interfaces, or HCI which stands for human-computer interaction. Any other input means can be used.

[0061] According to a particular embodiment of the system, the database is structured so as to store unexpected events, such as electricity cuts or information system-related incidents.

[0062] According to a particular embodiment of the system, the database is structured so as to store technical, functional, contractual and / or regulatory constraints.

[0063] According to a particular embodiment of the system, the database is structured so as to store weather forecasts.

[0064] According to a particular embodiment of the system, the database is structured so as to store market prices.

[0065] According to a particular embodiment of the system, the database is structured so as to store the CO2 content of the power generated or saved by the resources.

[0066] According to a particular embodiment of the system, it comprises a reporting module configured to carry out any one or several of these actions:- gather all the required data from various other in-house software and databases and produce various aggregated outputs as needed;- allow retroaction on flexibility commands.

[0067] Another object of the invention is a method for responding to flexibility needs to be expressed by third parties regarding an electrical power grid, characterized in that it comprises steps.

[0068] When answering to a flexibility need expressed by a requester (usually named as client for the network service provider implementing the invention), the appropriate set of algorithms is selected. The selection depends on the adjustment mechanism and activable resources.

[0069] The whole process for being able to address a flexibility request can be described in five steps, from the prediction of available flexibilities upstream of the day of flexibility to post-clearance feedback:

[0070] Possible pre-studies may have previously taken place on the statistical behavior of assets and users.

[0071] Then, in a first step, taking place several days before the intended flexibility day:

[0072] Using historical consumption data and external flows, such as weather, calendar, data from statistics bureaus, the probability distribution of the consumption of the portfolio of assets is estimated. The requester’s constraints at the required time are taken into consideration.

[0073] Then, the appropriate subset of algorithms, adapted to the available adjustment mechanisms and available resources, is selected. Performance of these algorithms allow to estimate the probability distribution of each available network service, considering the constraints associated to the network service in the asset portfolio and predicted consumption, the technical constraints of the assets, the regulatory constraints of the mechanism(s), and the constraints of the consumers of the assets.

[0074] In a second step, depending on the risk and business criteria (maximum number of slots, slot duration, tolerated risk of overestimation of load shedding, etc.) specified by the requester, various load shedding slots, or, conversely, slots for on-site generation of electricity, are offered by the subset of algorithms for all or part of the asset portfolio, comparing the potential gains on the various targeted markets / adjustment mechanisms.

[0075] Algorithms of the selected subset are also able to estimate the quality of the predictions. Then, the requester is also transparently shown a risk assessment that the proposed slots may be difficult to achieve on the intended flexibility day, based on recent variations in the behavior of assets in relation to history, their abnormal unavailability, unusual weather forecasts, etc.

[0076] In a third step, once the requester has decided to activate the offered flexibility schedule, partially or entirely, or possibly slightly altered, the booking takes place.

[0077] If the flexibility need requires that an adjustment mechanism be activated in relation with an energy market, the order is sent to the market. In this case, market results are fed back.

[0078] In a fourth step, the flexibility schedule is executed. The plan is converted into orders to the resources, sent at the proper time by the dispatch engine to the distributed energy resources.

[0079] A monitoring takes places while ordering the resources, to enable feedback and making corrections on the fly if some resources became unable to respond.

[0080] In a fifth step, taking place after the flexibility day, the actual load shedding or load increase is accurately computed from the perspective of the market operator, i.e., after monitoring actual values, different from the values measured on the assets themselves. KPIs relating to load flexibility, and comparison between the planned schedule, the orders sent, the values actually measured on the assets and, where applicable, control of the regulatory achievement, are presented to the requester. Then, business KPIs showing the impact on resource end-user, financial gains, observed unavailability of assets are presented to the requester.

[0081] The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this description, illustrate embodiments of the invention and, together with the description, explain the invention's characteristics.

[0082] shows a system according to one embodiment of the invention, managing a group of electric vehicle-charging stations connected to a local electrical power grid.

[0083] is the same diagram as in, showing the components operating during regular consumption.

[0084] is the same diagram as in Figs.1 and 2, showing the components operating during a flexibility period.

[0085] is the same diagram as in Figs.1 to 3, showing the components resuming operation to regular consumption after the flexibility period.

[0086] shows a system according to another embodiment of the invention, managing a group of individual electric heaters 18 connected to an electrical power grid.

[0087] is a diagram of a system according to the invention with a third party expressing a flexibility need and a set of DERs.

[0088] is a block diagram of the steps carried out by the booking engine 14.

[0089] is a block diagram of the steps carried out by the forecast engine 15.

[0090] shows an example of a subset 26 of computer-program codes 27 with their associated types of flexibility requests.

[0091] is a block diagram illustrating a selection of a subset 26 of algorithms adapted to a type of flexibility request 30a.

[0092] In the figures, each distributed energy resource is considered an electric device. Each one consumes less than 100 kW. An aggregation of distributed energy resources is viewed on the grid as one resource with more than 100 kW of power.

[0093] A local electrical power grid, referred to as grid 1, is connected to an overall grid (not represented), also referred to as an electrical network. Grid 1 is considered an asset of the electrical network. The invention is intended for any power grid and enables cutting out any asset to make more power available in response to a flexibility request.

[0094] On, distributed energy resources are made of electric vehicle charging points 2 gathered in charging stations 3. Each charging station is connected to the grid 1. Electrical power is delivered to charging stations 3 through the grid 1. In the diagram, loop 4 illustrates the flow of power. The lower part 5 of loop 4 shows electricity delivered to the charging stations 3. The upper part 6 of loop 4 shows electricity returned by charging stations 3 to the grid 1.

[0095] Graph 7 on top shows the planned load profile 8 of the grid, i.e. the consumption capacity, i.e. the minimal power which should be delivered to the grid 1 by a power plant 9 under the regular operation of the power plant 9. In this example, power plant 9 provides a constant load profile to the grid 1.

[0096] Third parties express flexibility needs. In the present example, the third party requesting flexibility is the manager of the overall grid.

[0097] Peak consumption occurs in a time slot delimited by start time 10 and end time 11. During this peak load 12, the electricity demand is higher on the electrical network. The manager of the overall grid wants to reduce the demand on the grid 1. This decision impacts the operation of electric vehicle charging stations 3. To that purpose, a system is provided for responding to flexibility needs.

[0098] The system comprises a dispatch engine 13, a booking engine 14, and a forecast engine 15.

[0099] A data collection network 16 connects charging stations 3 to the forecast engine 15 and dispatch engine 13. Therefore, only the data flow is represented schematically on the diagram, not the network itself. Thanks to this data collection network 16, each charging point of charging stations 3 can send data to the forecast engine 15. Data sent by a charging point include, as non-limiting examples:

[0100] - An ID.- A charge status.

[0101] - A maximum charge value.

[0102] - A start-of-operation date.

[0103] - An efficiency.

[0104] The data collection network 16 also enables the dispatch engine 13 to send control instructions to each charging point in the charging stations 3.

[0105] All the charging points 2 are aggregated to be considered an electrical load resource on the grid 1. However, each one is too small to be considered an energy resource.

[0106] The booking engine 14 and the forecast engine 15 are provided with means to communicate with a third party through either a user graphic interface displayed on a personal computer 20, an API run by a server 21 (see) or any other adapted interface. In both cases, the communication between the third party and the booking engine / forecast engine makes it possible for the third party to express a flexibility need. In particular, the third party wants to manage better the peak load 12 between start time 10 and end time 11.

[0107] As shown in, during regular operation, power plant 9 delivers electricity to charging stations 3 according to the planned consumption schedule.

[0108] Graph 7, with a simple line (the actual electricity load), follows the planned load profile 8.

[0109] As shown in, after start time 10, the flexibility need previously expressed by the third party is met by controlling the charging points 2 of the charging stations 3.

[0110] The electricity consumption drops in the grid 1. Not only does the consumption of each charging point reduce, but, when possible, some of the charging points 2 send electricity back to power plant 9 through the grid 1 (upper part of loop 4 in the figure).

[0111] This flexibility increases the ability of the electrical network to face a more significant demand between start time 10 and end time 11.

[0112] From start time 10 to end time 11, dispatch engine 13 controls charging stations 3. Still, charging stations 3 also permanently send data to forecast engine 15, which loop-controls dispatch engine 13 and adjusts the control of charging points 2. For instance, if a local user expresses an urgent need for energy at a particular charging point, said charging point is prioritised. As a result, it consumes energy from the grid 1 despite the current flexibility period.

[0113] Another example is the failure of storage means, which were previously requisitioned to deliver energy to the grid 1 during the peak load 12. Forecast engine 15 also adjusts the controlling of charging points 2 to meet the flexibility need previously agreed upon with the third party.

[0114] As shown in, the regular operation of charging stations 3 resumes after the flexibility period, and the planned load schedule is followed.

[0115] illustrates another example where the distributed energy resources are individual electric heaters 18 (for clarity, only one is represented on the figure, next to the buildings) of individual property heating systems 19.

[0116] Before the flexibility need period, electric heaters 18 are controlled to follow the planned load schedule globally. In practice, they operate according to the usual expectations of their owners.

[0117] During the flexibility need, most heaters are upheld or shut down so that grid 1 feeding the heaters provides a greater energy capacity to the electrical network. After the flexibility period, normal operations resume, and the heaters function according to the usual expectations of their owners.

[0118] represents the internal components of the system according to the invention. Components delimited by broken lines are not parts of the system.

[0119] A personal computer 20 and a server 21, belonging to a third party, represent the third party itself. They are not parts of the system.

[0120] A broken line circles several bubbles 22. They represent distributed energy resources that do not belong to the system (charging points and heaters in the previous examples).

[0121] In this example, the system comprises a booking engine 14, a forecast engine 15 and a dispatch engine 13.

[0122] The booking engine 14 comprises a booking computer-readable medium 23. Instructions are encoded on the booking computer-readable medium 23.

[0123] The booking engine 14 also comprises booking processors 24 connected to the booking computer-readable medium 23. The instructions, when executed by the processes, perform operations illustrated in:

[0124] - interfacing with a third-party agent,

[0125] - receiving and storing a flexibility request 30a provided by the third party's agent,

[0126] - checking the stored flexibility with a flexibility schedule 30b received from the forecast engine 15,

[0127] - in case of feasibility, triggering the controlling of the distributed energy resources by the dispatch engine 13 according to the flexibility schedule 30b,

[0128] - otherwise, declining the flexibility request.

[0129] To that purpose, the forecast engine 15 comprises storage means 25 for storing a set of different subsets 26 of computer-program codes 27. Each one of the computer-program codes 27 implements several subsets of algorithms. Moreover, each subset of algorithms is adapted to a specific type of flexibility request.

[0130] The forecast engine 15 also comprises a forecast computer-readable medium 28. The forecast computer-readable medium 28 has instructions included thereon. The forecast engine 15 also comprises forecast processors 29 connected to the forecast computer-readable medium 28 and configured to, when executing the instructions, perform operations illustrated in:

[0131] - receiving a flexibility request 30a from the booking engine 14,

[0132] - selecting a subset of algorithms adapted to the type of flexibility request 30a and reading the subset 26 of computer-program codes 27 implementing a set selected subset of algorithms in said storage means 25,

[0133] - loading a subset 26 of computer-program codes 27 in the computer-readable,

[0134] - running the loaded subset 26 of computer-program codes 27,

[0135] - delivering to the booking engine 14 a flexibility schedule 30b adapted to the flexibility request 30a.

[0136] In, the steps carried out by the booking engine 14 are represented by blocks:

[0137] - interfacing 31 step: the booking engine 14 exchanges data with a third party's agent. The agent can be a human being or a machine. During this step, the booking engine 14 is interfacing with the third party's agent to help the agent decide which request is the most beneficial to the network,

[0138] - booking reception step 32: the booking engine 14 receives an appropriate flexibility request 30a from the third party's agent. The booking engine 14 stores the flexibility request in the booking computer-readable medium 23,

[0139] - checking step 33: the booking engine 14 checks the feasibility of the stored flexibility request with a flexibility schedule 30b received from the forecast engine 15. Suppose the flexibility request 30a qualifies compared to the flexibility schedule 30b at triggering step 34. In that case, the booking engine 14 triggers the controlling of the distributed energy resources by the dispatch engine 13 according to the flexibility schedule 30b.

[0140] - declination step 35: otherwise, declines the flexibility request 30a.

[0141] In, the steps carried out by the forecast engine 15 are represented by blocks.

[0142] - forecast reception step 36: the forecast engine 15 receives the flexibility request 30a from the booking engine 14,

[0143] - forecast selection step 37: to better perform the flexibility request treatment, the forecast engine 15 selects a subset of computer-program code, adapted to the type of flexibility request 30a, from the set of available subsets 26 of computer-program codes 27 stored in the storage means 25,

[0144] - loading step 38: the forecast engine 15 loads said subset 26 of computer-program codes 27 in the forecast computer-readable medium 28, and runs the loaded subset 26 of computer-program codes 27,

[0145] - delivery step 39: the forecast engine 15 delivers to the booking engine 14 the flexibility schedule 30b adapted to the flexibility request 30a, enabling the booking engine 14 to qualify or decline the flexibility request 30a.

[0146] shows fours examples of subsets 26 of computer programs that the invention can handle to manage several types of flexibility request:

[0147] - instance_a subset 40 contains three computer programs 41 that can deliver a flexible schedule on a specific market. In addition, an instance_a tag 42 is attached to the subset to make it selectable if a flexibility request of such a type is expressed.

[0148] - instance_b subset 43 contains five computer programs 44 that can deliver a flexibility schedule for reducing the imbalance settlement price. An instance_b tag 45 is attached to the subset to make it selectable in case a flexibility request of such a type is expressed.

[0149] - instance_c subset 46 contains four computer programs 47 that can deliver a flexibility schedule limiting the asset management to a predetermined type of assets. An instance_c tag 48 is attached to the subset to make it selectable in case a flexibility request of such a type is expressed.

[0150] - instance_d subset 49 contains two computer programs 50 that can limit the request to predetermined steering of predetermined assets, such as the known V1G vs V2G for electric vehicle charging stations 3. An instance_d tag 51 is attached to the subset to make it selectable in case a flexibility request of such a type is expressed.

[0151] Possible requests are classified and stored in advance associated with the contract ID signed by the requester.

[0152] Only requests the type of which is known and predefined are accepted and can be considered. Last-minute requests are not allowed unless for an urgent stop.

[0153] In, a block diagram shows the steps carried out by the forecast engine 15 when selecting a subset of computer-program codes 27.

[0154] - agent step 52: the storage engine reads the agent's identity, which has been previously authenticated.

[0155] - contract step 53: the storage engine gets the contracts IDs of contracts that are linked to the agent.

[0156] - type step 54: the storage engine gets the type of request from the flexibility request.

[0157] - filtering step 55: the storage engine filters out subsets 26 of algorithms meeting criteria set by the two preceding steps.

[0158] - pointing step 56: the storage engine delivers a pointer to the appropriate subset of algorithms.Examples

[0159] One embodiment of the invention is exemplified by vehicle-charging stations.

[0160] In this example, available adjustment mechanisms are the NEBEF market and the contract pricing option of off-peak / peak hours.

[0161] The distributed electric resources are the charging stations.

[0162] In a preliminary step, one carries out a classification and an estimation of charging needs and connection times of electric vehicle users at the charging points. This enables an estimate of how much the charging points can be deleted without affecting end-users' charging sessions.

[0163] In a first step, five actions are taken:

[0164] a. Estimation of the aggregate consumption of the terminals at the minute scale for day D, with a probability envelope for this value. The industry allows the actual value to be lower than the predicted value 20% of the time; a single value is then extracted from the probability envelope. This value is calculated based on historical terminal data, weather forecasts and calendar data.

[0165] b. Based on the results of the pre-study, a value is estimated for the drop in power per 30-minute slot (duration of the NEBEF slots and our load shedding) which will not impact user charging sessions beyond 4 kWh lost (power drop generally ~20-25%).

[0166] c. Calculation of the erasable power per 30-minute slot (predicted power × power reduction factor).

[0167] d. Based on external predictions of market prices (for NEBEF) and the supply contract (for off-peak / peak hours), regulatory constraints, control of production, and predictions of available shedding power, estimate the gains for each 30-minute shedding period.

[0168] e. Proposal of the 3 most profitable time slots over the day for the business (NEBEF and off-peak / peak hours combined).

[0169] In a second step, a decision has to be made by the requester on the day before expected shedding. To help, the system provides a support:

[0170] a. Presentation of the selected slots and their characteristics to the business via a user interface, enhanced by an estimate of the robustness of the predictions (based on real-time data from the bollards, business maintenance tickets and weather predictions). Fine-tuning of the time slot terminals to take account of the observed response time of the terminals in real time.

[0171] b. Selection of slots to be activated by the business.

[0172] In a third step, the booking takes place on the day before the flexibility day. In practice, this means:

[0173] a. In the case of NEBEF, send the corresponding offers on the market (nothing to do for off-peak / peak hours optimization).

[0174] b. Return of offers to the market and consolidation of slots to be activated on D-Day.

[0175] In a fourth step, dispatch is carried out on the flexibility day. The dispatch engine performs the real-time dispatch and control of clearing orders to individual assets to meet aggregate flexibility requirements.

[0176] Then, in a fifth step, a debrief takes place. A calculation is done of KPIs and a presentation is made of a debriefing dashboard of past load shedding to the business. In particular, the energy, the power erased, the expected financial gains, the number of users affected, and the quality of the predictions made in step 1 are highlighted.

[0177] The invention is not limited to disclosed embodiments.

[0178]

[0179] 1 . . . Grid

[0180] 2 . . . Charging points

[0181] 3 . . . Charging stations

[0182] 4 . . . Loop

[0183] 5 . . . Lower part

[0184] 6 . . . Upper part

[0185] 7 . . . Graph

[0186] 8 . . . Planned load profile

[0187] 9 . . . Power plant

[0188] 10 . . . Start time

[0189] 11 . . . End time

[0190] 12 . . . Peak load

[0191] 13 . . . Dispatch engine

[0192] 14 . . . Booking engine

[0193] 15 . . . Forecast engine

[0194] 16 . . . Data collection network

[0195] 17 . . . Actual electricity load

[0196] 18 . . . Electric heaters

[0197] 19 . . . Individual property heating systems

[0198] 20 . . . Personal computer

[0199] 21 . . . Server

[0200] 22 . . . Bubbles

[0201] 23 . . . Booking computer-readable medium

[0202] 24 . . . Booking processors

[0203] 25 . . . Storage means

[0204] 26 . . . Subset of computer-program codes

[0205] 27 . . . Computer-program codes

[0206] 28 . . . Forecast computer-readable medium

[0207] 29 . . . Forecast processors

[0208] 30a . . . Flexibility request

[0209] 30b . . . Flexibility schedule

[0210] 31 . . . Interfacing step

[0211] 32 . . . Booking reception step

[0212] 33 . . . Checking step

[0213] 34 . . . Triggering step

[0214] 35 . . . Declination step

[0215] 36 . . . Forecast reception step

[0216] 37 . . . Forecast selection step

[0217] 38 . . . Loading step

[0218] 39 . . . Delivery step

[0219] 40 . . . Instance_a subset

[0220] 41 . . . Three computer programs

[0221] 42 . . . Instance_a tag

[0222] 43 . . . Instance_b subset

[0223] 44 . . . Five computer programs

[0224] 45 . . . Instance_b tag

[0225] 46 . . . Instance_c subset

[0226] 47 . . . Four computer programs

[0227] 48 . . . Instance_c tag

[0228] 49 . . . Instance_d subset

[0229] 50 . . . Two computer programs

[0230] 51 . . . Instance_d tag

[0231] 52 . . . Agent step

[0232] 53 . . . Contract step

[0233] 54 . . . Type step

[0234] 55 . . . Filtering step

[0235] 56 . . . Pointing step

[0236]

Claims

System for responding to flexibility needsto be expressed by third parties regarding an electrical power grid, having a dispatch engine (13) able to control distributed energy resources according to a flexibility schedule (30b), a booking engine (14), a forecasting engine (15), all connected for exchanging data,characterized in that the booking engine (14) comprises a computer-readable medium with instructions encoded thereon and processors connected to the medium and configured to, when executing the instructions, perform operations of:- interfacing with a third party's agent,- receiving and storing a flexibility request provided by the third party's agent,- checking the feasibility of the stored flexibility request with a flexibility schedule (30b) received from the forecast engine (15),- in case of feasibility, triggering the controlling of the distributed energy resources by the dispatch engine (13) according to the flexibility schedule (30b),- otherwise, declining the flexibility requestand the forecast engine (15) comprises storage means (25) for storing a set of different subsets of computer-program codes (27) implementing different subsets (26) of algorithms, each subset (26) of algorithms being adapted to at least one specific adjustment mechanism and at least one specific type of distributed energy resources, a computer-readable medium with instructions encoded thereon and processors connected to the medium and configured to, when executing the instructions, perform operations of:- receiving a flexibility request from the booking engine (14), associated with at least one adjustment mechanism,- selecting a subset (26) of algorithms adapted to the at least one adjustment mechanism associated with the flexibility request (30a) and adapted to a type of distributed energy resources, and reading the subset of computer-program codes implementing said selected subset (26) of algorithms in said storage means (25),- loading said subset of computer-program codes (27) in the computer-readable medium,- running the loaded subset of computer-program codes (27),- delivering to the booking engine (14) a flexibility schedule (30b) adapted to the request.System according to claim 1, wherein the booking engine (14) is configured to carry out any one or several of these actions:- send flexibility forecasts to the third party,- allow the requester to ask to stop the flexibility commands on existing assets urgently,- allow the requester to bid / nominate assets on relevant markets.System according to any one of claims 1 and 2, wherein the set of algorithms is a library organized so as to gather the algorithms in subsets (26) each dedicated to at least one adjustment mechanism and at least one type of distributed energy resources.System according to claim 3, wherein a decision tree filters out algorithms based on tags used as selection criteria.System according to any one of claims 1 to 4, wherein the forecast engine (15) is configured to take into account anyone or several of these parameters:- market constraints,- unexpected events, such as maintenance, failures, accidents,- risk predefined as acceptable by the requester,- frequency, timestep and horizon of the forecasts can be customized.System according to any one of claims 1 to 5, wherein successive versions of the algorithms are stored for later use.System according to any one of claims 1 to 6, wherein an instant reporting is sent back to the booking engine (14) to help discuss with the third party's agent.System according to any one of claims 1 to 7, wherein the dispatch engine (13) is configured to dispatch the flexibility to a portfolio of individual assets or a subpart of it.System according to any one of claims 1 to 8, comprising a database containing data used by the forecast engine (15).System according to claim 9, wherein the database is structured so as to store:- resources' metadata such as hierarchy between resources or geographic addresses, or- flexibility constraints such as activation time, delay, maximum tolerated activation, and activation costs, or- dynamic states such as communication status, telemetries, time series, and user needs, or- user-expressed constraints such as electric vehicle mobility, heating schedule, and request constraints retrieved through tailored means, or- unexpected events, such as electricity cuts or information system-related incidents, or- technical, functional, contractual and / or regulatory constraints, or- weather forecast, or- market price, or- CO2 content of the power generated or saved by the resources.System according to any one of claims 1 to 10, wherein comprises a reporting module configured to carry out any one or several of these actions:- gather all the required data from various other in-house software and databases and produce various aggregated outputs as needed;- allow retroaction on flexibility commands.Methodfor responding to flexibility needs to be expressed by third parties regarding an electrical power grid, characterised in that it comprises steps defined in any one of claims 1 to 11.

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