Improved method for multi-level control by strips of an electrical network; Associated computer program.
The method addresses communication sensitivity and suboptimal solutions in multi-level control by using strips to optimize local strategies and globally, enhancing efficiency and optimality in electrical network management.
Patent Information
- Application Number
- FR2023014619
- Authority / Receiving Office
- FR · FR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2023-12-20
- Publication Date
- 2025-11-14
- Estimated Expiration
- 2043-12-20
AI Technical Summary
Existing methods for multi-level control of electrical networks require numerous iterations and are sensitive to communication issues, leading to suboptimal solutions due to the divergence between local and global objectives and the lack of precise cost knowledge.
A method using strips for multi-level control, where each peripheral agent determines an initial control strategy and transmits additional information to a central agent, which then optimizes globally while considering the impact on local costs, allowing for improved coordination and reduced sensitivity to communication disruptions.
The method enhances the efficiency and optimality of multi-level control by independently calculating flexibility margins and optimizing local strategies, reducing complexity and improving the alignment of local and global objectives, thus achieving better network performance.
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Abstract
Description
Title of the invention: Improved method for multi-level control by strips of an electrical network; Associated computer program.
[0001] The present invention relates to the field of optimal control and, more particularly, to the optimal control methods of an electrical network aggregating a plurality of electrical sub-networks distributed over a territory.
[0002] Such a process is implemented in an infrastructure comprising an electrical network and a control system for that electrical network. The electrical network aggregates a plurality of electrical sub-networks. The sub-networks are connected to a common distribution network.
[0003] Electrical subnetworks can be of different kinds, such as electricity production subsystems (photovoltaic panels - PV, wind turbines, thermal groups...), electricity consumption subsystems (industrial buildings, offices, residential houses), or even subsystems combining production and consumption.
[0004] The subnetworks are subject to different constraints from each other, such as the need to ensure a certain production load, the need to respect a series of user preferences, etc.
[0005] Electrical sub-networks can also be complex themselves, by aggregating several electrical components. A sub-network can, for example, be a micro-network made up of several components (battery storage, PV roofs and canopies, electric vehicle charging stations, diesel generators, etc.).
[0006] The control system comprises a plurality of peripheral agents and a central agent, connected to each other via a communication network. While each peripheral agent locally controls an associated electrical subnetwork, the central agent controls the network globally.
[0007] To this end, each peripheral agent determines a local control strategy to meet an internal objective of the associated subsystem, while the central agent determines a global control strategy to meet a global objective of the network, while ensuring coordination of all subsystems.
[0008] However, electrical subsystems have specific internal objectives, such as minimizing economic costs, maximizing user comfort, minimizing greenhouse gas emissions, etc.
[0009] These internal objectives may diverge from the overall objective that one wishes optimize at the network level as a whole, such as minimizing consumption peaks, minimizing CO2 emissions, maximizing flexibility, managing congestion, etc.
[0010] Control methods for such infrastructures based on the "alternating direction method of multipliers" (ADMM) and its variants are known. This is, for example, what is presented in the article by Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) “Distributed optimization and statistical learning via the alternating direction method of multipliers”, Foundations and Trends in Machine Learning, 3(1), 1-122, 23 May 2011, DOE10.1561 / 2200000016, and the article by Nguyen, TL, Tran, QT, Caire, R., & Gavriluta, C., “Agent based distributed optimal power flow using ADMM method”, August 2018, CIRED 2018 conference.
[0011] The ADMM method allows the calculations to be distributed among the peripheral actors, remote and the central actor, at the heart of the network.
[0012] The main drawback of this type of prior art process is that it requires many iterations to reach a solution that is both feasible and globally optimal.
[0013] All these iterations involve numerous exchanges between peripheral actors and the central actor through the communication network.
[0014] This process is therefore very sensitive to the quality of the connection and to latency problems which may occur on this same network and disrupt communications.
[0015] Another prior art method is based on the so-called "band" method. It is presented for example in the article K. Utkarsh, F. Ding, C. Zhao, H. Padullaparti and X. Jin, "A Model-Predictive Hierarchical-Control Framework for Aggregating Residential DERs to Provide Grid Regulation Services", 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2020, pp. 1-5, doi: 10.1109 / ISGT45199.2020.9087773.
[0016] This process does not require iteration. It is therefore much less sensitive to communication problems between actors.
[0017] An embodiment of this process will now be presented in more detail with reference to [Fig.1].
[0018] The piloting method 100 comprises three successive steps.
[0019] In a first step 110, each peripheral agent 10; (i integer between 1 and n, the number of peripheral agents) determines its optimal internal steering strategy, its optimal external steering strategy, and optimal global flexibility margins.
[0020] For example, if we consider a sub-network composed of a battery, a building, and a PV roof, the internal control strategy is the detail of the operating points of each of the components of the sub-network (battery, building, PV roof) at the desired time step (minutes by minutes, hours by hours, etc.).
[0021] The external control strategy is the control strategy at the point of connection of the subnetwork to the common distribution network, on the same time step.
[0022] The flexibility margins of each of the components are aggregated over each time step to determine overall flexibility margins at the connection point.
[0023] This information results from the solution by the peripheral agent 10 of a local optimization problem as described below. This local optimization problem consists of two sets of variables, x and z, defined as follows:
[0024] - x; represents the internal variables of the i-th subnetwork, such as the Battery charge / discharge power at each time step, PV production power at each time step, building consumption power...
[0025] - Z; represents the external variables for the ith subnetwork, such as the injected power or power withdrawn at the point of connection to the distribution network, for each time step.
[0026] This distinction between internal and external variables makes it possible to separate private information, which is processed locally, from public information, which is shared with the outside, in this case the central actor playing the role of network coordinator.
[0027] The optimization algorithm used in the prior art at the local level makes it possible to calculate both the optimal operating point, i.e. the optimal value of each internal variable and each external value, as well as the lower and upper bounds for each external variable of the micronetwork, the interval between the lower and upper bounds defining the margin of flexibility on the corresponding external variable.
[0028] The variables x; and z; are therefore divided into three groups:
[0029] - first group:
[0030] xl: the internal control variables of each component of the subnetwork i, corresponding to a locally calculated internal operating trajectory (hence the index "1");
[0031] zj: the external control variables of subnetwork i, corresponding to an external operating trajectory calculated locally (hence the index "1");
[0032] - second group:
[0033] xy ; the lower control bounds of each component of the subnetwork i, corresponding to a lower internal flexibility trajectory, calculated locally;
[0034] zf: the lower control bounds of subnetwork i, corresponding to a lower external flexibility trajectory, calculated locally;
[0035] - third group:
[0036] xf: the upper control bounds of each component of the subnetwork i, corresponding to a higher internal flexibility trajectory, calculated locally;
[0037] zf: the upper control bounds of subnetwork i, corresponding to a higher external flexibility trajectory, calculated locally.
[0038] Furthermore, the trajectories must respect a number of constraints, particularly physical ones. For example, it is impossible to store more energy in a battery than its capacity, to exceed a predefined charging or discharging power, to disregard power balance equations, etc.
[0039] All these constraints are described, in matrix form, as follows:
[0040] Apq -i- B;z, = c,
[0041] A,xf + B,zf = Cj
[0042] AjXV+BjZ^Ci
[0043] This constraint matrix allows filtering of feasible solutions by the subnetwork (or trajectories), which respect the constraints, from non-feasible solutions, which do not respect the constraints.
[0044] Finally, to select the optimal trajectory from the set of possible trajectories, a cost function must be defined. For example, and without loss of generality, the following cost function is minimized:
[0045] fx (Xj) + fz () + aflex(zè - z^)
[0046] where fx is a partial cost function taking into account only internal variables, fz is a partial cost function taking into account only external variables, and ariex is a predefined coefficient.
[0047] The cost function described here makes it possible to optimize the trajectory and, at the same time, to determine upward and downward flexibility trajectories by seeking to maximize the gap between the upper and lower trajectories.
[0048] The local optimization problem that each peripheral agent 10 of a subnetwork must solve can therefore be summarized as follows: / x ' x^1 * X^* \ ( f / L' ' L = argmin 1 x(x;) + fz(¾) + an«(zf ~ zD ViA'4Zi / xt,Zi s. t AjXj + B,z( = c, Ajxf 4- Bjzf = q AjX'' 4- BjZj7 = q
[0049] where the result of the optimization gives a set of operating points, that is to say, specific values for the variables and bounds. These optimal values are marked with an asterisk (*).
[0050] The process 100 continues with a step 120, carried out by the central agent 12, following the reception, via a communication network, of the values of the external variables of each of the subnetworks i:
[0051] (z^ zf, zf)
[0052] The central agent 12, by solving a global optimization problem (hence the index "g"), determines new external operating trajectories determined globally for each of the subnetworks:
[0053] (zf zf, ...,zr)
[0054] These trajectories must be achievable by all sub-networks, i.e., they must remain within the communicated operating margins:
[0055] z^* < z^' < zf*
[0056] It is also possible that the aggregation of the local external operating trajectories of the different subnetworks must respect another set of constraints.
[0057] This is the case, for example, when the central agent seeks to position the infrastructure on flexibility markets by participating, for example, in frequency reserve services to balance an electrical network, or to respond to network congestion problems.
[0058] These other constraints can be written as:
[0059] £.DlZi = h
[0060] Or, more succinctly:
[0061] Dz = h
[0062] Since the overall objective of the central agent differs from the local objectives of each of the peripheral agents, the cost function associated with the overall optimization problem is different. It is denoted:
[0063] g'(z)
[0064] Step 120 therefore corresponds to solving the following optimization problem: (zf,..., z»', ..., z®*) = argmin g'(z) Z s. t Dz = h zC < Z; < zf*
[0065] After determining new operating points for each of the subnetworks i, the central agent 12 communicates the new strategies zf to each of them.
[0066] Then, in a step 130, each of the peripheral agents 10 receives, via the communication network, the globally optimized operating point for its external variables: zf*.
[0067] The objective here is therefore to optimize the internal control strategy of each of the components of the micronetwork i while respecting the overall trajectory as new constraint imposed by the central agent.
[0068] Thus, the set of constraints is no longer the same as in step 110:
[0069] A^ + = Cj
[0070] But becomes:
[0071] Aixi = ci-Bizf
[0072] The global trajectory z$* is now imposed. It is therefore no longer a variable over which the local agent can have an influence.
[0073] The cost function is also simplified, since it now only involves minimizing the contribution of the internal variables:
[0074] minf^Xj)
[0075] Step 130 therefore corresponds to solving the following second local optimization problem: X”' = argmin fx(Xj) XI s. t AjXj = Cj - 13^
[0076] The main drawback of this process is the lack of optimal overall control with knowledge of the internal costs that this may represent.
[0077] During step 120, the central agent decides on an overall trajectory and distributes it so as to move away from the local optimal trajectory in the same way for all sub-networks.
[0078] However, for two different subnetworks, deviating from the local optimal trajectory does not have the same cost. For one site, this may represent a slight degradation in its internal operating cost, while for a second site, it may represent a very large degradation in its internal operating cost.
[0079] The aim of the invention is therefore to propose an improved multilevel control method using strips to address this problem.
[0080] To this end, the invention relates to a computer-implemented method for multi-level control, using strips, of an infrastructure comprising an electrical network and an electrical network control system. The electrical network aggregates a plurality of electrical subnetworks, the electrical subnetworks being connected to a common distribution network. The control system comprises a plurality of peripheral agents and a central agent. The central agent is connected to the peripheral agents by a communication network. Each peripheral agent is associated with a single electrical subnetwork to locally control the operation of the associated electrical subnetwork, while the central agent controls the operation of the electrical network globally. The method comprises the steps of: determining, by each peripheral agent, an initial control strategy by performing an initial operation. local timing, by implementing an ith local cost function, on a set of control variables of the ith electrical subnetwork, said set of variables comprising, on the one hand, internal variables xi5 associated with components of the ith electrical subnetwork, and, on the other hand, external variables z; associated with the connection point of the ith electrical subnetwork to the distribution network; transmission, to the central agent, of a first plurality of information comprising, for each electrical subnetwork: the local optimal values of the external variables z|*; the local optimal lower bound values of the external variables zy*; and the optimal upper bound values of the external variables of the subnetwork; determin mination, by the central agent, of a global control strategy by performing a global optimization on all external variables z; of the different electrical sub-networks; transmission, by the central agent, of a second plurality of information containing, for each electrical sub-network, the global optimal values of the external variables z?*; and, determination, by each peripheral agent, of a second
[0081]
[0082] control strategy by performing a second local optimization under constraint of the global optimal values of the external variables on the local variables x;, the process being characterized in that the first plurality of information includes, in addition, additional information, the additional information including, for each electrical subnetwork: a value of the ith local cost function on the local optimal values of the internal variables xpJ; a value of the ith local cost function on the local optimal values of the lower bounds of the internal variables f^(xV*);ct a value of the ith local cost function on the local optimal values of the upper bounds of the internal variables xa*), and in that the determination, by the central agent, of a globally optimized steering strategy takes into account the additional information to distribute the contribution of each subnetwork to the globally optimized steering strategy by taking into account, for each subnetwork, a deviation of an ith approximate cost on the first locally optimized steering strategy between the local optimal values of the external variables and the global optimal values of the external variables, the ith approximate cost deriving from the additional information transmitted by the ith peripheral agent. ; According to other advantageous aspects of the invention, the method comprises one or more of the following features, taken individually or in all technically possible combinations: - the i-th cost approximation is a piecewise linear function with a lower segment connecting the point with coordinates zy* and xy* to the point with coordinates z!* and xl*, and an upper segment connecting the point with coordinates zî* fj, ( x]*) ct 'c P°int of coordinates z^* and ( xa* j.
[0083] - the overall optimization is written: (zf',..., .....z®') = argmin g(zs) zi
[0084] global optimization being carried out while respecting a set of global constraints including the constraint:
[0085] zV* < Zj < z^with 8 a global cost function translating the global objective.
[0086] - the overall cost function g includes a term associating the deviation of the i-th cost approximate (F,) for each subnetwork.
[0087] - the determination, by each peripheral agent, of a first strategy of Piloting by performing an initial local optimization consists of:
[0088] + solve locally a local optimization problem allowing the determination of the Locally optimized control strategy for the micronetwork: (x**,Zja)= argmin fr / x,; z,) Xj.Zi st Cj(Xj; Zj) - 0
[0089] with f1 the ith local cost function translating the local objective to be optimized while respecting a set of local constraints Q, and the optimal local values of the internal variables;
[0090] + solving two local optimization problems to determine, on the on the one hand, the upper flexibility limit and, on the other hand, the lower flexibility limit, for the previously determined locally optimal microgrid control strategy, i.e. for the lower limit: - argmin «^(z?' - z^) st z?) < (¾(xl"; z|*)) + P*
[0091] and for the upper bound: (xf'\zf')= argmin «^(zf - ZjV) KJ.Zj St Cj(xf;zf) = O
[0092] where Cty]ex, «i7. [5V, Cttflex' ct [3A are predefined coefficients, x^ the local optimal values of the lower bounds of the internal variables and x^* the local optimal values of the upper bounds of the internal variables.
[0093] - the determination, by each peripheral agent, of a second steering strategy Locally optimized under the constraint of the global optimal values of the external variables, it can be written as: xf' = argmin t? (xO Xj st Cj(xj; zf ) = 0
[0094] where f1 is the ith local cost function translating the local objective to be optimized while respecting a set of local constraints Q and x^ are the local optimal values of the internal variables of the second locally optimized steering strategy.
[0095] - the method further comprising a final step of controlling each component of the nth subnetwork in accordance with the second steering strategy.
[0096] - The process is iterated for each time step.
[0097] - the different iterations of the process allowing the approximate i-th cost to be specified.
[0098] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement a control method as defined above.
[0099] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which:
[0100] [Fig-1] [Fig.1] is a schematic representation of a multi-control method levels by strips according to the state of the art;
[0101] [Fig.2] [Fig.2] is a schematic block representation of a system for the implementation of a multi-level control method using strips according to the prior art or according to the invention;
[0102] [Fig.3] [Fig.3] is a schematic representation of a multi-control method levels by strips according to the invention; and,
[0103] [Fig.4] [Fig.4] is a graph illustrating the consideration of the cost of flexibility for overall optimization.
[0104] The method according to the invention is a multi-level control method using strips, with local control for each of the micronetworks and centralized global control for the coordination of the different micronetworks.
[0105] This process is implemented in an infrastructure which will now be presented with reference to [Fig.2].
[0106] The infrastructure 1 comprises an electrical network 2 and a control system 3 for the electrical network 2.
[0107] The electrical network 2 aggregates a plurality of electrical subnetworks 4;, where i is an integer index between 1 and n, n being the number of subnetworks constituting network 2.
[0108] For example, the 4i micronetwork groups together several components, such as:
[0109] • a PV 61 power plant with a peak capacity of 100 MW;
[0110] • a residential building 7i of type B1, part of whose charges include heating / air conditioning can be adjusted up or down;
[0111] • an 8i charging station for long-range electric vehicles (possibility of to offset the load), with some fast charging needs.
[0112] The micronetwork 4; includes several components, such as:
[0113] • a PV power plant 6; of 50 MW peak;
[0114] • a stationary storage system 7; with a capacity of 5 MWh and a power of + / - 1MW during charging / discharging;
[0115] • a charging station 8; for electric vehicles.
[0116] The 4n micronetwork includes several components such as:
[0117] • a stationary storage system 6n with a capacity of 10 MWh and a power of + / - 1 MW during charging / discharging;
[0118] • a 7n diesel generator, of 5 MW;
[0119] • an industrial building 8n with a production requiring large needs and a need for continuous power supply.
[0120] The subnetworks 4; are connected to a common distribution network 5. The latter may, for example, itself be connected to another network, such as a grid network.
[0121] The control system 3 comprises a plurality of peripheral agents 10; and a central agent 12.
[0122] The central agent 12 is connected to the peripheral agents 10 by a suitable communication network 14. The network 14 is, for example, an IP communication network, such as the Internet. The central agent 12 and each peripheral agent 10 are thus in bidirectional communication.
[0123] Each peripheral agent 10 equips a single electrical subnetwork 4 to locally control the operation of that subnetwork. Each peripheral agent 10 optimizes the micronetwork's operating variables at the local level.
[0124] Each peripheral agent 10 consists of at least one computer suitably programmed for implementing the method according to the invention. This computer includes, for example, a memory and a processor associated with the memory. The memory stores code instructions which, when executed by the processor, contribute to the implementation of the method according to the invention.
[0125] The central agent 12 controls the overall operation of the entire network 2. The central agent 12 acts as coordinator. The central agent 12 consists of at least one computer suitably programmed for implementing the method according to the invention. This computer includes, for example, a memory and a processor associated with the memory. The memory stores code instructions which, when it are executed by the processor, participate in the implementation of the process according to the invention.
[0126] The central agent 12 is, for example, a service hosted in a cloud computing architecture. The central agent 12 can advantageously offer significantly greater computing power than the local agents 10. This makes it possible to offer flexible network management services that require substantial computing power.
[0127] Fig. 3 schematically represents the process 200 according to the invention.
[0128] In general, process 200 is an improvement of process 100 in the state of the technique. We therefore repeat in what follows the notations introduced above for process 100, in particular the notations for internal and external variables, cost functions, constraints and optimal values calculated for variables and bounds.
[0129] Each peripheral agent 10 calculates and transmits additional information to the central agent 12.
[0130] This additional information corresponds to assessments of the degradation of the local objective considered during the local optimization, in particular for each of the two flexibility margins, respectively upper and lower.
[0131] According to process 200, each peripheral agent 10; first performs a first step 210.
[0132] Step 210 comprises a first sub-step 212, followed by a second sub-step 214.
[0133] The first substep 212 consists of solving a first local optimization problem locally. This problem is simpler than the one solved in step 110 of process 100.
[0134] Indeed, we now seek to determine only the locally optimized control strategy for the micronetwork 4;. This can be written in a condensed form: = argmin fXxi) + fz(zi) Xi,Z; st AjX; -f BjZj = c;
[0135] More generally, the ith local cost function is written:
[0136] f^Zj)
[0137] and the constraints are written:
[0138] C^zj^O
[0139] In this first substep 212, there is no calculation of flexibility margin. These will be calculated locally and separately in the second substep 214 next.
[0140] The advantage of separating the optimization of local, internal and external variables into several steps lies in the fact that the optimal trajectory thus calculated is then no longer influenced by the calculation of the flexibility bounds.
[0141] Indeed, in the state of the art, depending on the coefficients aiicx chosen for the partial cost function, solving the calculation of the flexibility bounds and the optimal trajectory in the same optimization problem can, in some cases, lead to degraded optimal solutions which tend to maximize the flexibility margins at the expense of the optimal trajectory.
[0142] The solution proposed here does not suffer from this disadvantage since the optimal trajectory and the margins of flexibility are treated independently and sequentially.
[0143] The control strategy calculated at the end of the first sub-step 212 is therefore the most optimal possible control strategy for the micronetwork 4 under consideration. It leads to the determination of the values xl* and z|*.
[0144] Then, the second substep 214 consists of solving, again locally, two optimization problems allowing us to determine, on the one hand, the upper flexibility bound and, on the other hand, the lower flexibility bound, for the locally optimal microgrid control strategy determined in 212.
[0145] Thus, for calculating the lower bound on the external variables zî, the optimization problem to be solved is written: 04'*« zf) = argmin a^eH(zj5s - z^) « St AiX? + BjzX = es 't <1 1
[0146] And, for calculating the upper bound on the external variables, the optimization problem to be solved is written: ( = argmm - zp) st Ajxf + BjZp = Cj feW) + W) + fFOD)+ PC
[0147] where the coefficients Ot£nex and Ct^flex can be identical or different for the upper and lower bounds and / or from one subnetwork to another.
[0148] The additional constraint allows the bounds to be determined with control of the acceptable degradation of the optimality value. This control is achieved through the choice of the coefficients 0^ and pv, on the one hand, and the coefficients of and [jA, on the other hand.
[0149] This improvement over process 100 makes it possible to limit the flexibility limits on values of the cost function which are locally acceptable (degradation of the comfort of users of a building for example).
[0150] Separating the calculation of the bounds into two sub-problems also allows the solution process to be parallelized, in addition to reducing the complexity of the optimization problem, which saves time in solution and memory space required for calculations, which is an important advantage for deployment in a small local computing unit, such as the peripheral agent 10;.
[0151] Following steps 212 and 214, each local agent 10 transmits (step 215) the following information to the central agent 12, via the communication network 14:
[0152] - the optimal control strategy for the external variables zf;
[0153] - the optimal driving strategy for the lower flexibility bound of the variables external z^¥; and,
[0154] - the optimal driving strategy for the upper flexibility bound of the variables external z^,
[0155] but also the following additional information:
[0156] - the cost of the local optimal steering strategy on the internal variables f^x^ )
[0157] - the cost of the optimal local steering strategy for the flexibility terminal in lesser of the internal variables f(xy*j ; and,
[0158] - the cost of the optimal local control strategy for the flexibility terminal su external variables
[0159] Alternatively, a local agent 10; can also transmit the cost of the local optimal steering strategy on the external variables and the associated flexibility bounds: ZI*), , and f^zA*) • However, the central agent knows gen ralement the cost fi for each of the external variables of the subnets, since these external variables are public and correspond to connection points on the common distribution network 5.
[0160] Step 220, performed by the central agent 12, then consists of solving a global optimization problem, with a global cost function § representing the overall objective to be optimized, and respecting all the constraints, including a new constraint on the flexibility bounds. The global optimization problem to be solved is written as follows: (zj z['J.....z®") = argmin g(z) Z, s. t Dz = h zf < Zj < zf
[0161] This optimization problem solves two problems at once:
[0162] - the first problem is to determine an overall optimal piloting trajectory for the whole of system 2. This trajectory has the new constraint of having to be within the envelope defined by all the flexibility margins of the micronetworks 10;
[0163] zf^z^zf*
[0164] - the second problem is to distribute this overall piloting trajectory between the micro-networks, so as to determine, for each of the micro-networks 10;, its participation in this overall trajectory.
[0165] Solving this second problem is crucial because it requires making the right choices, that is, deciding which microgrid should increase / decrease its production / consumption, by how much, and when. The additional cost information provided by each microgrid is then used to guide this allocation.
[0166] To do this, the additional information allows us to construct an approximate cost Fj for each subnetwork 10;. This is a piecewise linear function.
[0167] As for example shown in Figure 4, in a simple embodiment the ith approximate cost F; consists of two segments.
[0168] A first lower straight segment connects the points with coordinates (zy* ^(xf )) and xPp, on the one hand, and a second upper straight segment Mf connects the points with coordinates (z| ' ))cl j ), on the other hand.
[0169] At the minimum of the approximation, we have the relation = f (x1^ ct 'cs maximum of the approximation are given for the allowed flexibility margins.
[0170] This approximate cost makes it possible to evaluate the impact of the deviation from the optimal trajectory on the internal cost of the i-th subnetwork 4; between the local optimal values of the external variables zF and the global optimal values of the external variables zU.
[0171] The use of such an approximation makes it possible to order the flexibilities of the micronetworks and to pass on the deviation calculated by taking into account the overall objective to the micronetworks for which this deviation has the minimum impact on the internal objective.
[0172] For this, the cost function & can be divided into two parts
[0173] g = GlobalCost(z) +£.IntemalCost(zi)
[0174] With a first part, GlobalCost, corresponding to the gain from the coordinated participation of all 4 sub-networks; to the global strategy, as a revenue gain for participation in remunerative flexibility services.
[0175] The second part of the cost function S, InternalCost(Zj), corresponds to the estimation of internal costs, using the piecewise linear approximation, such as that in Figure 4, for each global steering trajectory zi, of a subnetwork 4j, described previously:
[0176] internalCostCZj) = F^zJ -F / z*)
[0177] This leads to the global optimization of the external variables zp*, each of these values corresponding to a local cost p.^8^-
[0178] Finally, in step 225, the central agent 12 transmits to each of the peripheral agents 10;, the new operating point z£*.
[0179] Then, each of the peripheral agents 10; implements step 230.
[0180] This is identical to step 130 of process 100 of the prior art.
[0181] Thus, each micronetwork 4 calculates its local optimal control strategy in such a way as to follow the imposed global optimal control strategy: xp' = argmîn fsCxi) HAS s. t AjXj q BjZ® '
[0182] The peripheral agent 10; then controls the various components of the electrical subnetwork 4;, using the xp* as a control instruction.
[0183] The process 200 is iterated for the next time step.
[0184] Alternatively, process 200 can be iterated for the same time step, to get closer to the optimal global control solution.
[0185] This variant allows, at each iteration, to refine, for each of the micronetworks, the approximation of the cost of a deviation between local trajectory and global trajectory.
[0186] This more precise information allows the coordinator to refine the optimization of the overall trajectory and the participation of each micro-network in this overall strategy.
[0187] It is necessary to emphasize that the local objective of a subnetwork may differ from the local objective of another subnetwork and, above all, from the overall objective of the network as a whole. Thus, the cost functions, in particular the internal partial functions f^, the external partial functions f*, and the margin partial functions (coefficients a[lex]), bear the index i of the subnetwork that uses them.
[0188] The form of these partial functions can also depend on time in order to favor certain time steps of the time horizon, in particular the time steps for which Flexibility is important. In this way, the calculated flexibility is not constant across all time steps but can vary over time. It is therefore more advantageous to have greater flexibility when electricity prices are high and, conversely, less flexibility when electricity prices are low. This can also lead to improved operation of the electrical grid by taking into account the physical constraints of the network, such as line congestion, power flow limitations, etc.
[0189] Thus, according to the invention, the network coordinator has additional information enabling him to distribute the achievement of the overall objective among the different sub-networks.
[0190] It should be noted that if a microgrid indicates a significant margin of flexibility, this does not provide information on the impact of the deviation on the local optimal control strategy and on the degradation of its own local objective.
[0191] With the invention, the degradation of the local objective can be controlled so as to limit it.
[0192] The particular embodiment presented in detail above has the additional advantage of parallelization possibilities (during step 214 of process 200) of the calculations by reducing the complexity of the problems dealt with, particularly in the first local optimization steps.
[0193] In the particular embodiment described in detail above, the functionalities offered by the agents, whether peripheral or central, are in the form of software, or a software component, executable by the processor of the associated computer. This software, or computer program, is also capable of being stored on a computer-readable medium (not shown). The computer-readable medium is, for example, a medium capable of storing electronic instructions and being connected to a bus of a computer system. By way of example, the readable medium is an optical disc, a magneto-optical disc, ROM, RAM, any type of non-volatile memory (e.g., FLASH or NVRAM), or a magnetic card. A computer program comprising software instructions is then stored on the readable medium.
[0194] The process according to the invention has numerous applications, such as:
[0195] - aggregating several sites (micronetworks) which have capacities and power limited, to position the resulting coordinated network in flexibility markets.
[0196] - to control a distribution network with PV production and consumption local consumption is encouraged to promote local use and minimize the backflow of active power onto the transmission network. This also improves the stability of the transmission network and overall network management.
Claims
Demands
1. A computer-implemented method (200) for multi-level strip control of an infrastructure (1) comprising an electrical network (2) and an electrical network control system (3), the electrical network (2) aggregating a plurality of electrical subnetworks (4), the electrical subnetworks (4) being connected to a common distribution network (5), and the control system (3) comprising a plurality of peripheral agents (10) and a central agent (12), the central agent (12) being connected to the peripheral agents (10) by a communication network (14), each peripheral agent (10) being associated with a single electrical subnetwork (4) to locally control the operation of the associated electrical subnetwork, and the central agent (12) globally controlling the operation of the electrical network (2), the method comprising the steps of: - determination, by each peripheral agent (10;), of a first control strategy by carrying out a first local optimization, by implementing an th local cost function, on a set of control variables of the th electrical subnetwork (4;), said set of variables comprising, on the one hand, internal variables xi5 associated with components of the th electrical subnetwork, and, on the other hand, external variables z; associated with the connection point of the th electrical subnetwork to the distribution network (5); - transmission, by each peripheral agent (10;) to the central agent (12), of a first plurality of information comprising, for each electrical subnetwork (4;): the local optimal values of the external variables z-*; the local optimal values of lower bounds of the external variables zV*; and the optimal values of upper bounds of the external variables z^* of the subnetwork; - determination, by the central agent (12), of an overall control strategy by carrying out a global optimization on all external variables z; of the different electrical sub-networks (4;); - transmission, by the central agent (12) to each peripheral agent (10; ), of a second plurality of information including, for each electrical subnetwork (4; ), the overall optimal values of the external variables zp* ; and, - determination, by each peripheral agent (10;), of a second steering strategy by performing a second local optimization, under constraint of the global optimal values of the external variables (z?*), on the internal variables x;, the process being characterized in that the first plurality of information includes, in addition, additional information, the additional information comprising, for each electrical subnetwork (4;): a value of the ith local cost function on the local optimal values of the internal variables XP); a value of the ith local cost function on the local optimal values of the lower bounds of the internal variables xy* j; and a value of the ith local cost function on the local optimal values of the upper bounds of the internal variables f ( xa* j, and in that the determination, by the central agent (12), of a globally optimized control strategy takes into account the additional information to distribute the contribution of each subnetwork (4;) to the globally optimized steering strategy taking into account, for each subnetwork (4;), a deviation of an th approximate cost (F,) on the first locally optimized steering strategy between the local optimal values of the external variables (zp and the global optimal values of the external variables (zp), the th approximate cost deriving from the additional information transmitted by the th peripheral agent (10i).;
2. A method according to claim 1, wherein the i-th cost approximation is a piecewise linear function comprising a lower segment connecting the point with coordinates zp and xy* j and the point with coordinates zh and xlp and an upper segment connecting the point with coordinates zp; f ( xl* j and the point with coordinates zp and p ( xa* ).
3. A method according to claim 1 or claim 2, wherein the global optimization is written: (zp, ..., zp..., Z®') = argmin g(zs) 2i the global optimization being done respecting a set of global constraints including the constraint: Zp < Z, < zp with § a global cost function reflecting the global objective.
4. A method according to claim 3, wherein the overall cost function g includes a term associating the deviation of the ith approximate cost (F;) for each subnetwork (4;).
5. A method according to any one of the preceding claims, wherein the determination, by each peripheral agent (10;), of a first control strategy by performing a first local optimization consists of: - solve (212) locally a local optimization problem allowing the determination of the locally optimized control strategy for the micronetwork: ( = argmin Zj) st QCxp Zj) = 0 with f the ith local cost function translating the local objective to be optimized while respecting a set of local constraints Q, and x|* the local optimal values of the internal variables; - solve (214) two local optimization problems allowing us to determine, on the one hand, the upper flexibility bound and, on the other hand, the lower flexibility bound, for the locally optimal microgrid control strategy previously determined, i.e. for the lower bound: (xf,zn = argmin «^(zf - z5v) St = 0 and for the upper limit: ) = argmin - z^) Xi,Zi St C|(xp; z[v) = 0 ft(xoZL) af (ft(xhzF)) + PC where Otj^iex, aï, pv, dffiex, aC and pA are predefined coefficients, xV* the local optimal values of the lower bounds of the internal variables and X^ the local optimal values of the upper bounds of the internal variables.
6. A method according to any one of the preceding claims, wherein the determination, by each peripheral agent (10i), of a second locally optimized control strategy subject to the global optimal values of the external variables z^, is written: = argmin fl (x.) Xi st Cj(xj; z®') = 0 where f1 is the ith local cost function translating the local objective to be optimized while respecting a set of local constraints Q and x?¥ are the local optimal values of the internal variables of the second locally optimized steering strategy.
7. A method according to any one of the preceding claims, comprising a final step of controlling each component of the ith subnetwork in accordance with the second control strategy.
8. A method according to any one of the preceding claims, the method being iterated for each time step.
9. A method according to any one of the preceding claims, the method being iterated at the current time step, the different iterations allowing the ith approximate cost to be specified.
10. A computer program comprising software instructions which, when executed by a computer, implement a method according to any one of the preceding claims.