EP-4049204-B1 - SYSTEM FOR CONFIGURING DEMAND RESPONSE FOR ENERGY GRID ASSETS

EP4049204B1EP 4049204 B1EP4049204 B1EP 4049204B1EP-4049204-B1

Inventors

DIERCKX, Brecht
BRUNELIERE, Renaud
PEETERS, Stef
DELEERSNIJDER, Filip
CLAESSENS, BERT

Dates

Publication Date: 20260506
Application Date: 20201023

Claims (15)

A computer-implemented method of controlling assets connected to an electricity distribution grid, the assets comprising assets arranged to supply electrical energy to and/or consume electrical energy from the grid, wherein at least some of the assets are configurable to adjust energy flow to or from the grid in response to changes in operating conditions of the grid, the method comprising: for each of a plurality of assets to be configured, accessing a mapping, the mapping arranged to: receive an asset configuration as input, the asset configuration comprising configuration data defining how energy flow between the asset and the grid should change in response to changes in one or more operating conditions measured at the asset; and output one or more performance indicators relating to the operation of the asset when operated using the asset configuration; providing inputs to an optimization function, the inputs based on the performance indicators output by the mappings, wherein the optimization function maps the inputs to an optimization metric; performing a search process arranged to alter the optimization metric output by the optimization function by varying asset configurations for one or more of the plurality of assets, wherein the search process is continued until a termination criterion is met; and transmitting asset configurations determined during the search process to one or more asset control devices associated with the assets, to configure the control devices to control energy flow between the assets and the grid in accordance with the asset configurations.
A method according to claim 1, wherein the search process comprises varying one or more asset configurations to alter the performance indicators output by the mappings and thereby change the value of the optimization metric, the varying step preferably iterated until the termination criterion is met.
A method according to claim 1 or 2, wherein the search process comprises optimising the optimisation function with respect to the optimization metric, preferably wherein the asset configurations of the plurality of assets define a set of dimensions of a search space, and wherein the search process comprises performing a gradient descent search in the search space to optimise the output value of the optimisation function.
A method according to any of the preceding claims, wherein the optimisation function is a cost function, the search process comprising minimising the cost function by altering asset configurations.
A method according to any of the preceding claims, wherein the termination criterion comprises one or more of: the optimisation metric attaining a predefined threshold or a locally or globally optimal (e.g. minimal or maximal) value; a maximum number of iterations; a maximum compute time.
A method according to any of the preceding claims, wherein the search process starts with an initial set of asset configurations, the initial configurations comprising one or more of: a current asset configuration of an asset; a default asset configuration for an asset; and a randomly generated asset configuration.
A method according to any of the preceding claims, wherein the mappings comprise machine learning models, optionally neural network models, the method preferably comprising, for one or more of the plurality of assets, training the machine learning model by a process comprising: generating a plurality of asset configurations, the generating optionally comprising randomly selecting the asset configurations; for each asset configuration, simulating the operation of the asset in accordance with the asset configuration, and determining, based on the simulation, one or more performance indicators for the asset configuration; training the machine learning model using a plurality of training samples, each training sample based on an asset configuration and corresponding performance indicators determined for that asset configuration by the simulation; the method preferably comprising repeating one or both of the training of machine learning models and the search process, periodically or in response to a change in the plurality of assets, the change optionally comprising addition, removal of, or a change in operating characteristics of, one or more assets.
A method according to any of the preceding claims, wherein the configuration data defines one or more of: one or more response curves, each response curve defining a required power flow level or power flow change as a function of a given operating condition parameter; required power input or output values or adjustments for the asset for each of a plurality of distinct grid frequency values; wherein the configuration data preferably defines a plurality of response curves each defined with respect to a different operating condition parameter, and/or a plurality of response curves each defined with respect to a different value range of the same operating condition parameter, for example with respect to a different frequency band of a grid frequency parameter.
A method according to any of the preceding claims, wherein the operating conditions comprise one or more parameters relating to a local grid frequency measured at the asset, wherein the frequency parameters optionally comprise at least one of: a local grid frequency measured at the asset; and data derived from the local grid frequency, for example a temporally filtered grid frequency value.
A method according to any of the preceding claims wherein the one or more operating conditions comprise an operating state of the asset or of another asset connected to the grid.
A method according to any of the preceding claims, wherein the search process comprises varying one or more power flow values or adjustments and/or one or more frequency thresholds for power flow adjustments.
A method according to any of the preceding claims, comprising, at a given control device: receiving one or more signals indicative of operating conditions related to operation of the grid or an asset of the grid, optionally comprising a local grid frequency measurement and/or a signal derived from local grid frequency measurements; determining a power flow level for an asset controlled by the control device based on the one or more signals and the asset configuration for the asset, wherein determining the power flow level preferably comprises computing the power flow level based on a response curve defined by the asset configuration, optionally by interpolating a value of the response curve for an operating condition parameter from a set of data points of the curve specified by the asset configuration; and controlling the asset in accordance with the determined power flow level; the method optionally further comprising, at the control device: receiving a plurality of signals indicative of respective operating condition parameters; determining a plurality of power flow adjustments based on the signals, each power flow adjustment derived using a respective response curve defined by the asset configuration, the response curve mapping a respective operating condition parameter to a power flow adjustment; determining a total power flow adjustment based on the plurality of power flow adjustments; and controlling the asset in accordance with the determined total power flow adjustment.
A method according to any of the preceding claims, wherein the performance indicators specify one or more of: one or more measures of the performance of the asset in relation to a required demand response service defined by the asset configuration; availability of an asset to provide the demand response; an energy quantity indicating a total amount of energy supplied or consumed over a given period when providing the demand response; a response time for the asset to achieve a desired energy flow adjustment; a number of operating or charging/discharging cycles for an asset over a given period; a measure of success for delivering the configured demand response; a measure of a cost of providing the demand response service.
A computer system or apparatus having means, optionally comprising one or more processors with associated memory, for performing a method as set out in any of the preceding claims.
A computer-readable medium comprising software code adapted, when executed on one or more data processing devices, to perform a method as set out in any of claims 1 to 13.

Description

The present invention relates to systems and methods for controlling assets supplying energy to, or consuming energy from, an electricity distribution grid to adjust energy flow to/from the grid, for example in response to grid frequency fluctuations. Electricity suppliers and grid operators implement a variety of energy management techniques at industrial sites or residences, as well as in the distribution grid and transmission grid. Grid operators find it increasingly challenging to manage aspects of their respective energy grids such as balancing electricity supply with demand and responding to frequency shifts in the electrical grid. In general, a grid operator may mandate behaviour of (or provide financial incentives for) energy producers or energy consumers in order to ensure a stable and responsive electrical grid. For example, a grid operator may buy regulation capacity from industrial consumers and/or producers of power. A consumer or producer offering such a service will receive the mandate to reduce or increase their power consumption when required by the grid operator in order to maintain stability and quality of the grid. There may be a specific requirement that a reduction or increase in power consumption must be stable for a relatively long period of time, or that any such reduction or increase occurs rapidly. Importantly, a grid operator desires to manage assets that consume or supply electrical energy (e.g. electrical loads or generators) at the portfolio level rather than at the individual asset level. A fast response time can be particularly important for an electricity grid operator. A grid operator is expected to keep the frequency of the power offered on the grid stable (typically 60 Hz in the United States and 50 Hz in Europe), but it can be challenging to keep the grid frequency within an allowable margin. For example, if a power plant is shut down unexpectedly, a large amount of power is suddenly unavailable (demand exceeds supply) and the frequency on the grid will decrease. Similarly, the frequency on the grid will drop if large industrial loads come online and supply is slow to meet that demand. If the frequency of the grid decreases, the frequency can be brought to its reference level by reducing power consumption on the grid or by increasing the supply (or a combination of both). However, it can be challenging to mandate a reduction in power consumption from among a diverse collection of industrial or residential consumers. Perhaps more importantly, it can also be very difficult to achieve a reduction in power consumption as quickly as a grid operator seeks to achieve it - typically on the order of seconds (or even faster), rather than on the order of minutes. A centralized management system may not be able to detect the deviation, schedule a reduction in power, and deliver the schedules to the industrial loads reliably in that short amount of time. The reverse can happen as well. When supply is larger than demand, as happens for instance in case of under-forecast of renewable power production, the frequency can rise above its reference level (50 Hz or 60Hz). This can be offset either by decreasing the power production or by increasing the power consumption (or a combination of both). Prior art techniques include using a simple binary switch at a load that will switch off the entire load when the switch detects that the frequency of the power has dropped to a certain level (e.g., the load is switched off when the frequency drops to 49.9 Hz). However, this is a static technique in which the switch is an isolated hardware device that is locked into always switching off the load at a particular frequency; such a device might rigidly switch off the load in such a fashion for many months or years without taking other information into account. This technique also works unilaterally, in the sense that it does not allow the local operational managers to refuse requests for power activation based on operational or business constraints. Moreover, this technique is performed at the load level and does not benefit from any portfolio optimization. More recently, techniques have been developed that utilize portfolio optimization to improve response by using a combination of energy consuming and/or energy producing assets to implement a combined demand response. WO 2015/059544, describes an energy management system that allows a grid operator to manage a portfolio of energy loads at the aggregate portfolio level, while responding rapidly and reliably to changes in grid characteristics. A hybrid approach is used in which a central site, based upon a mandate by a grid operator to reduce (or increase) power according to frequency deviations within a frequency band, determines the optimal frequency triggers at which each load within a portfolio should reduce (or increase) power. Symbolically, loads are "stacked" within this frequency band in order to optimize the global droop response of the port