WO2020115099A1

WO2020115099A1 - Non-intrusive method for decentralised management of electricity consumption

Info

Publication number: WO2020115099A1
Application number: PCT/EP2019/083603
Authority: WO
Inventors: Paulin JACQUOT; Nadia OUDJANE; Olivier BEAUDE; Pascal BENCHIMOL; Stéphane GAUBERT
Original assignee: Electricite De France; Institut National De Recherche En Informatique Et En Automatique
Priority date: 2018-12-06
Filing date: 2019-12-04
Publication date: 2020-06-11
Also published as: FR3089710A1; FR3089710B1

Abstract

The present invention relates to a method for the decentralised management of at least one portion of electricity consumption by consumers connected to an electrical distribution network, characterised in that it comprises the steps of: i) establishing in a central coordinator an aggregated optimal profile for all of the considered consumers with an estimate of aggregated constraints, ii) transmitting the aggregated optimal profile to the consumers by the central coordinator, iii) analysing the aggregated optimal profile locally by projection, iv) transmitting by each consumer to the central coordinator the result of the projection established in step iii), v) repeating step i), in order to determine a new aggregated optimal profile based on a new constraint if the central coordinator determines that disaggregation is impossible based on the previous aggregated optimal profile, and then repeating steps ii) to v) until convergence for all consumers.

Description

DESCRIPTION

TITLE OF THE INVENTION: MANAGEMENT METHOD

DECENTRALIZED NON-INTRUSIVE ELECTRICAL CONSUMPTION

GENERAL TECHNICAL AREA

The invention relates to the field of electricity consumption management.

More particularly, it relates to a process and a system for decentralized management of at least a portion of electrical consumption by consumers connected to an electrical distribution network.

STATE OF THE ART

Optimizing consumption flexibilities allows flexibilities to be used by residential consumers (charging electric vehicles, water heaters, washing machines, etc.) in order to reduce peaks in aggregate consumption, or in order to consume at opportune moments (renewable production peaks).

One approach consists in delegating the entire optimization process to a centralized operator (or coordinator) who is responsible for deciding the consumption of each consumer. However, this poses several problems: on the one hand, an important exchange of information must take place between each consumer and the coordinator, each consumer must provide all the information on his consumption possibilities (for example battery capacities, availability and unavailability) ), on the other hand, the resulting problem for the coordinator is very large, and can be difficult or even impossible to solve. A completely decentralized approach consists in letting each consumer act, but in influencing their choices by sending them incentives, for example tariffs (block tariffs). In this way, each consumer can keep his data and constraints and not divulge them to the coordinator. However, the consumption profile resulting from this process is not necessarily close to the “optimal” profile desired by the coordinator.

PRESENTATION OF THE INVENTION

The invention aims to overcome the drawbacks of the prior art described above.

In particular, an object of the invention is to propose a method of coordination between a centralized entity and a large number of local actors (residential electric customers) while making it possible to store the information of said actors at the local level.

The present invention relates according to a first aspect to a method of decentralized management of at least a portion of electrical consumption by consumers connected to an electrical distribution network, characterized in that it comprises the steps consisting in: i) establish at the level of a central coordinator an aggregated optimal profile for all the consumers considered with an estimate of aggregated constraints,

ii) transmit the optimal aggregated profile to consumers by the central coordinator,

iii) locally analyze by projection the optimal aggregate profile received from the central coordinator at the level of each consumer in relation to the specific constraints of the consumer,

iv) transmit by each consumer to the central coordinator the result of the projection established in step iii),

v) repeat step i), to determine a new optimal aggregated profile based on a new constraint if the central coordinator

determines that disaggregation is not possible based on the profile previous aggregated optimal, then repeat steps ii) to v) until convergence for all consumers,

so that the central coordinator proceeds only by processing the aggregated profiles, without taking into account the individual constraints of consumers which remain confidential at the local level of each consumer.

The method allows a step of "generation" of a new constraint in the case where the disaggregation of the global profile is impossible. From the disaggregation results, the method thus makes it possible to calculate a constraint which systematically improves the aggregated profile during the following calculation. Thus, the calculation process stops after a small number of iterations.

In addition, the number of consumers that can be considered is relatively large (over a thousand) since we rely on local calculations which can be performed in a distributed manner.

Advantageously, but optionally, the system according to the invention can also comprise at least one of the following characteristics:

- step iii) is established by implementing an algorithm of alternating projections;

step i) is carried out by seeking an optimization, in particular by seeking a solution to a minimization problem, for example a minimization of production costs or a minimization of purchase of electrical energy on the market ;

step i) is carried out by defining aggregated profiles for all the consumers considered over a defined time horizon, for example 24 hours, and for periods of time dividing this time horizon, for example for each half hour of the 24 hour period;

- the initial step i) is carried out by taking into account initial constraints including a constraint of overall satisfaction of aggregate demand for all consumers over the time horizon defined and aggregate capacity constraints for each time period including lower and upper bounds for aggregate consumption for each time period;

- step i) is carried out also taking into account a maximum capacity for each period of time;

step iii) of local analysis at the level of each consumer is carried out on the basis of the consumption constraints of each respective consumer comprising a global energy demand respective to each consumer over the defined time horizon, as well as a minimum and maximum power for each time period;

- step v) is operated by favoring the processing of overproduction periods corresponding to periods for which for at least one consumer the profile generated by the central coordinator is strictly greater than its maximum possible consumption;

the portion of electrical consumption managed corresponds to a flexible portion of electrical consumption, for example the charge of an electric vehicle or of a water heater;

each consumer divides his profile into several parts at the end of step iii) and sends a part to other consumers, preferably to each consumer considered, each consumer summing up all the parts received from other consumers before operating the transmission to the coordinator in step iv); and

each consumer cuts his profile into N parts corresponding to N-1 vectors drawn at random plus a vector equal to the difference of his profile minus the first N-1 parts.

The present invention also relates according to a second aspect to a system for decentralized management of a portion of electrical consumption by consumers connected to an electrical distribution network, characterized in that it comprises:

. a central coordinator

. consumer posts and . means of communication between the central coordinator and the consumer stations,

in which the central coordinator is adapted to establish an aggregated optimal profile for all the consumers considered with an estimate of aggregated constraints, transmit the aggregated optimal profile to consumers and repeat these steps by adding a new constraint to each iteration as long as the profiles have not been validated by consumers, and each consumer is adapted to locally analyze by projection the aggregated optimal profile received from the central coordinator in relation to its specific constraints and transmit to the central coordinator the result of the projection,

each consumer station includes an intelligent or communicating counter, for example of the LINKY type. The counter is configured

- a module for defining a time profile of predefined electrical consumption, for one or more uses, and for a range

defined time and

- means configured to specify, automatically or

manually, the need for energy over a given time range, as well as to specify, automatically or manually,

minimum and maximum electrical powers to be observed;

it includes at least 1000 consumer stations; and

the portion of electrical consumption managed corresponds to a flexible portion of electrical consumption, for example the charge of an electric vehicle or a water heater.

The system according to the invention allows the coordinator to establish an optimal profile with an estimate of the "aggregated" constraints. Then a “disaggregation” stage takes place: this stage is carried out by an exchange of information between the coordinator and each user, without the latter revealing his constraints. Thus, private information is kept by each user, and the coordinator avoids the resolution of large-scale problems, since he is only interested in the "aggregated" profile.

DESCRIPTION OF THE FIGURES

Other characteristics, aims and advantages of the present invention will appear on reading the detailed description which follows, with regard to the appended figures, given by way of non-limiting examples and in which:

[Fig. 1]

FIG. 1 schematically represents a decentralized management system according to the invention;

[Fig. 2]

FIG. 2 schematically represents the main steps of a decentralized management method implemented by the decentralized management system;

[Fig. 3a]

[Fig. 3b]

FIGS. 3a and 3b represent, by way of illustrative example of implementation of the decentralized management method, data of global energy demands (kWh), of upper bounds on powers per hour (kWh) and of minimum loading power (kW), and coefficients of the price functions; and

[Fig. 4]

FIG. 4 schematically represents the main steps of the decentralized management method implemented by the system with the data of FIGS. 3a and 3b.

DETAILED DESCRIPTION OF AT LEAST ONE EMBODIMENT OF THE INVENTION One embodiment of the invention relates to a method for decentralized management of at least a portion of electrical consumption by consumers connected to an electrical distribution network.

This process is implemented by a decentralized management system for part of the electrical consumption by consumers connected to an electrical distribution network. The portion of electrical consumption managed corresponds to a flexible portion of electrical consumption, for example the charge of an electric vehicle or a water heater.

With reference to FIG. 1, there is illustrated such a decentralized management system 1. This comprises a central coordinator 11 (or centralized entity, or aggregator), consumer stations 12 associated with consumers or agents, and means 13 between the central coordinator 11 and the consumer stations 12.

More specifically, a consumer station 12 comprises an intelligent or communicating meter, for example of the LINKY type, for specifying, automatically or manually, the need for energy over a given time range, as well as for specifying, automatically or manually, powers minimum and maximum electrical requirements.

A consumer station 12 also includes a module making it possible to define a predefined electrical consumption time profile, for one or more uses, and over a defined time range. Each consumer thus has a system capable of automatically receiving and sending information, this which makes it possible to set up the process described below.

With reference to FIG. 2, it illustrates the stages of the process implemented by the decentralized management system 1. In a step E10, the central coordinator 11 proceeds to the centralized calculation of the aggregate consumption profile. This calculation is the solution to a minimization problem (for example minimization of production or purchase costs on the market). We denote by C (p) the cost function associated with the centralized problem, where (p: = pi, ..., PH) is the aggregated profile, time period by time period, over all the time periods considered H (for example the 48 half-hours over a whole day time horizon). The problem solved is:

- minimize C (p) under constraints {initial constraints} (1)

- {constraints generated by steps E30 (described below)}.

The central coordinator 11 obtains a solution to this problem, for example via an optimization solver. The initial constraints are:

- a constraint of overall satisfaction of aggregate demand:

å _h eHPh = åneNEn WHERE N denotes all consumers and En, the overall consumer demand n for energy over all the time periods considered H. For example, if agent n offers recharging of a battery of electric vehicle up to 30 kWh, we will have En = 30 kWh. In practice, the number of agents N to be checked is, for example, of the order of a hundred or a thousand;

- aggregated capacity constraints for each time period h, [Math. 1]

D _h £ V _h £ D _h , where D _h , D _h

are respectively lower and upper bounds on the aggregate consumption for the period h;

- potentially other operational constraints of the central coordinator 11 on the aggregated profile p: for example, the central coordinator 11 may be subject to a maximum capacity k _h for each time period p _h £ k _h .

In a step E20 carried out by the central coordinator 11, a disaggregation is carried out for example with an algorithm of "alternating projections" (cf. IL.G. Gubin, BT Polyak, and EV Raik. "The method of projections for finding the common point of convex sets. USSR Computationai Mathematics and Mathematical Physics ", 7 (6): 1 - 24, 1967 and algorithm 1 below). The consumption constraints of each consumer n are modeled by an overall energy demand En, over all time periods, as well as by a minimum and maximum power for each period h, that is to say that a profile consumption x _n , must be overall:

[Math.

We also want the calculated disaggregated profile to correspond to the aggregated profile p _h calculated during the previous step E10, that is to say that it is in the set:

[Math. 3]

Algorithm 1 of alternate projection aaents-aaréaateur Initialization y ⁽⁰⁾ , k = 0, stoi, s ¥ nv <<stoi

[1] As long as lly ^Ck) -y ^Ck_1) ll> stoi, (not convergence) do

[2] for each neN agent, do

[3] local calculation of the profile x _n ^{(k + 1)} <- Pxn (y _n ^(k) ) by projection on the admissible space of n

[4] send x _n ^{(k + 1)} is then to the aggregator

[5] end for

[6] centralized calculation of

[Math.

(w _h ) h

[7] centralized calculation of

[Math. 5] [8] sending to each agent n of the profile y _n ^{(k + 1)}

[9] k <- k + 1

[10] End while

[11] If | | x ( ^k ) -y ( ^k ) | | <scvg then

[12] returns Possible degradation x ^* = x ^(k)

[13] Otherwise

[14] returns Impossible degradation x ^* = x ^(k) y ^* = y ^(k)

[15] end if

Algorithm 1 describes this disaggregation step. In the description, "local calculation" refers to a calculation carried out at the level of each agent's household (by an electronic device connected to its communicating counter), while "centralized calculation" refers to a calculation carried out by the centralized entity .

The algorithm allows a decentralized and iterative calculation, using a two-way communication means 13 between each consumer station 12 and the central coordinator 11.

At each step k and until convergence:

(i) each consumer station 12 calculates locally, the result of the projection of y _n on the set X _n (line [3]). Thus it does not reveal its constraints and information (line [2]);

(ii) the result x _n of this projection is returned to the central coordinator 11 for recalculation (line [4]);

(iii) the central coordinator 11 calculates new profiles (y _n ) _n by projection on its constraints (lines [6-7]);

(iv) the result of its calculation is then sent to each consumer station 12 which receives its corresponding profile y _n (line [8]). Algorithm 1 converges in the following sense:

- either disaggregation is possible, ie there exists for each consumer n a consumption profile x ^* _n verifying its constraints (2) and such that the aggregated profile p = å _n eN x ^* _n verifies the constraints of the aggregator (1). In this case the algorithm converges to such a solution;

- or disaggregation is impossible: there is no profile verifying both the constraints of each consumer 1 and the constraints of the central coordinator 11. In this case, the algorithm will provide two types of profiles: on the one hand profiles (x * _n ) n which verify the constraints of consumers (2), and on the other hand profiles (y ^* _n ) _n such as the aggregated profile å _n y * n verify the constraints of the central coordinator 11 (1 ).

In the latter case, the profiles returned x ^* and y ^* are used to calculate the new constraint in a step E30.

Thus, in this step E30, the new constraint is generated, in the case where disaggregation is impossible, is based on the calculated profiles (x ^* _n ) _n, and (y ^* _n ) _n described in the step E20.

For this, the central coordinator 11 first calculates a set of time periods Ho which correspond to hours of

"overproduction" This set is calculated as follows: if, for one of the consumers, the profile y ^* _n , _h at hour h is strictly greater than its maximum possible consumption

[Math. 6]

% n, h described by its constraints (2). The whole is therefore written:

[Math. 7]

The calculation of this set is done directly from the profile x ^* returned by the algorithm 1. Once Ho calculated, the central coordinator 11 calculates the aggregate quantity A: = åheHoå _n eNX ^* _{n, h} , and integrates the inequality constraint on the aggregate profile to the problem (1): åheHoph £ A.

Variant of the disaggregation calculation based on the multi-stakeholder calculation

In step E20 described above, during algorithm 1, the consumer stations 12 must communicate to the central coordinator 11 their calculated profile x ^(k) . The central coordinator 11 therefore has access to the consumption profile x ^* _n of each agent n. The use of a multi-stakeholder calculation method (see AC Yao. 'How to generate and exchange secrets'. In 27th Annual Symposium on Foundations of Computer Science (sfcs 1986), pages 162-167, Oct 1986.) makes it possible to avoid this by performing step (b) according to algorithm 2 described below.

Here, each agent "cuts" his profile into N parts (N — 1 vectors drawn at random, plus a last vector equal to the difference of his profile minus the first N - 1 parts) online [4]. Each agent then receives a share of the profile of each of the other agents, which reveals nothing about these profiles, then sums the shares received before sending this to the central coordinator 11 (line 9).

The central coordinator 11 then calculates the vector of the "corrections" (wh) heH to be applied over each time period (line [11]). This amounts to carrying out the projection on space P as in algorithm 1, using the fact that å _n x _n = ånOn always corresponds to the aggregated profile. The calculation of y (line 13), which involves the disaggregated profiles x _n , is done this time locally with the corrections (wh) so as to preserve the confidentiality of the profiles x _n .

If disaggregation is impossible, the alternating projections converge as before towards two profiles x and y. The new constraint is then also added confidentially, using the same principle. In particular, the lines [20-22] of algorithm 2 (described below), which correspond to step E30, allow the addition of the new constraint on the master problem (1) without the agents ever having to communicate their disaggregated profile (x _n ) n simply by using the (wh) h and (On) calculated previously.

Algorithm 2 Alternated Projections Secured agents - aerator

Start with

[Math. 8]

[1] As long as llyn ^ -yn ^ ^ ll ^ toi,, for at least one agent neN (not convergence) do

[2] for each neN agent

[3] local calculation of the profile x _n ^(k) <- px _n (y _n ^(k) ) by projection on the admissible space of n

[4] Generate Nl random vectors of size H: (s _n , i ^(k) , s _n, 2 ^(k) , ..., s _n , Ni ^(k) ), define

[5] Send part s _{n, i} ^(k) to agent i

[6] end for

[7] for each neN agent

[8] receive shares Si, _n ^(k) from other agents i¹n

[9] local calculation of the sum profile o _n ^(k) = s _n , n ^(k) + åi * nSi, _n ^(k) and sending of o _n ^(k) to the aggregator

[10] end for

[11] centralized calculation of

[Math. 10]

and sending of each agent of Wh ^(k) heH

[12] for each neN agent

[13] local calculation of

[

[14] end for

[15] k <- k + 1

[16] end while

[17] if llx ^(k) -y ^(k) ll £ scvg then

[18] returns Possible degradation, final profile x _n * = Xn ^(k)

[19] otherwise

[20] Define

[Math. 12]

(fc)

H _{0 <} - [he H 0 <w h. }

[21] Centralized calculation of

[Math. 13]

[22] Returns Impossible degradation, constrained addition

[Math. 14]

[23] end if

Structural example

Numerical simulations of the process are presented by implementing the process in Python language. We offer here a concrete example where we apply the process described with N = 4 agents optimizing the recharging of their electric vehicle. Here, N is taken small to facilitate the understanding of this example and to explain the steps of the method. In the same way, we consider H = 10 time periods, which symbolize for example the hours of a night from 10 p.m. to 8 a.m. the next day.

In this example, we consider that there are periods when the vehicle is not available because it is used (late evening and early morning mainly, except for agent 1 which has a use between 5h and 7h) . This is modeled by a higher constraint

[Math. 15] ^x n, t— 0

in Figure 3a. The other upper limits can, for example, be induced by the loading capacity of the vehicle and by the power subscribed by the user and the rest of the consumption of his household.

The lower bounds x _{n, t} considered in this example are simpler: the vehicle of each agent, when loaded, requires a minimum power noted p _n ^min and whose values are given in Table 1. Thus when the vehicle n is not available for load, x _{n, t} is taken equal to 0.0 and, in other cases, it is taken equal to p _n ^min .

The price functions for each time period, involved in the master problem (1), are taken as quadratic functions C _h (p _h ) = a _h P _h + b _h P _h ² , (in €) whose coefficients a _h , bh are expressed in FIG. 3b.

The method according to the invention then converges into 4 iterations (steps E10 and E20 / E20 '), and therefore three generations and additions of new aggregated constraints (step E30).

With reference to FIG. 4, the various calculation steps are detailed during the application of the method proposed in this example. In particular, it is necessary to add three additional constraints (three steps E30) to the aggregated constraints initially present.

After the third added constraint, the new solution calculated by the centralized problem (step E10 performed for the 4th time) results in a profile (p _h ) _h e _H whose disaggregation is possible.

Claims

1. Method for decentralized management of at least part of electrical consumption by consumers connected to an electrical distribution network, characterized in that it comprises the steps consisting in:

i) establish at the level of a central coordinator an optimal aggregate profile for all consumers considered with an estimate of aggregated constraints,

determines that disaggregation is impossible on the basis of the previous aggregated optimal profile, then repeat steps ii) to v) until convergence for all consumers,

2. Method according to claim 1, characterized in that step iii) is established by implementing an alternating projection algorithm.

3. Method according to one of claims 1 or 2, characterized in that step i) is carried out by search for an optimization, in particular by search for a solution to a minimization problem, for example a minimization production costs or minimizing the purchase of electrical energy on the market.

4. Method according to one of claims 1 to 3, characterized in that step i) is operated by defining aggregated profiles for all of the consumers considered over a defined time horizon, for example 24 hours, and for periods of time dividing this time horizon, for example for each half hour of the 24 hour period.

5. Method according to claim 4, characterized in that the initial step i) is operated by taking into account initial constraints comprising a constraint of overall satisfaction of the aggregate demand for all consumers over the defined time horizon and aggregated capacity constraints for each time period including lower and upper bounds for aggregate consumption for each time period.

6. Method according to claim 5, characterized in that step i) is carried out also taking into account a maximum capacity for each period of time.

7. Method according to one of claims 4 to 6, characterized in that step iii) of local analysis at the level of each consumer is carried out on the basis of the consumption constraints of each respective consumer comprising a global demand for respective energy of each consumer over the defined time horizon, as well as a minimum and maximum power for each period of time.

8. Method according to one of claims 4 to 7, characterized in that step v) is operated by favoring the treatment of overproduction periods corresponding to periods for which for at least one consumer the profile generated by the central coordinator is strictly greater than its maximum possible consumption.

9. Method according to one of claims 1 to 8, characterized in that the portion of electrical consumption managed corresponds to a flexible portion of electrical consumption, for example the charge of an electric vehicle or a water heater.

10. Method according to one of claims 1 to 9, characterized in that each consumer cuts his profile into several parts to the outcome of step iii) and addresses a part to other consumers, preferably to each consumer considered, each consumer summing up all the parts received from other consumers before transmitting to the coordinator in step iv) .

11. Method according to claim 10, characterized in that each consumer cuts his profile into N parts corresponding to N-l vectors drawn randomly plus a vector equal to the difference of his profile reduced by the N-l first parts.

12. Decentralized management system for part of

electrical consumption by consumers connected to an electrical distribution network, characterized in that it comprises:

. a central coordinator

. consumer posts and

. means of communication between the central coordinator and the consumer stations,

in which the central coordinator is adapted to establish an optimal aggregated profile for all the consumers considered with an estimate of aggregated constraints, transmit the optimal aggregated profile to consumers and repeat these steps by adding a new constraint to each iteration as long as the profiles have not been validated by consumers, and each consumer is adapted to locally analyze by projection the aggregated optimal profile received from the central coordinator in relation to its specific constraints and to transmit to the central coordinator the result of the projection, so that the coordinator central only proceeds by processing the aggregated profiles, without taking into account the individual constraints of consumers which remain confidential at the local level of each consumer.

13. System according to claim 12, characterized in that each consumer station comprises an intelligent or communicating counter, for example of the LINKY type. The counter is configured:

. a module for defining a consumption time profile predefined electrical, for one or more uses, and over a defined time range and

means configured to specify, automatically or

manually, the need for energy over a given time range, as well as to specify, automatically or manually, minimum and maximum electrical powers to be observed.

14. System according to one of claims 12 or 13, characterized in that it comprises at least 1000 consumer stations.

15. System according to one of claims 12 to 14, characterized in that the portion of electrical consumption managed corresponds to a flexible portion of electrical consumption, for example the charge of an electric vehicle or a water heater.