Method for determining electrical flows within an energy community

Abstract

The present invention relates to a method for determining electrical flows within an energy community, wherein a flow conservation model (MCF) is constructed, at least one electrical flow is measured (MES), a priority level (PIN) is assigned to each flow, and an iterative method is applied to determine the electrical flows by means of a mathematical optimisation method (OPT1, OPTN) applying the flow conservation model. The steps of the iterative method are performed in the order of the assigned priority levels.

Description

[0001] METHOD FOR DETERMINING ELECTRIC FLOWS WITHIN AN ENERGY COMMUNITY

[0002] Technical field

[0003] The present invention relates to the field of managing electrical flows between different components, in particular for managing electrical energy consumption. In particular, the invention relates to determining electrical flows between components of an energy community and an electricity distribution network.

[0004] An energy community is defined as an autonomous entity that brings together shareholders / members who can consume, produce or store energy (such an entity is legally defined in particular in Article 40 of the Energy-Climate Law as well as in the French Energy Code). The members of this community can exchange energy among themselves in order to optimize their consumption according to economic, technical, environmental or social criteria. This community is connected to the electricity distribution network, which makes it possible to supply the energy community when it consumes more energy than it produces, or conversely to supply energy to the electricity distribution network when it produces more energy than it consumes.Such an energy community may, for example, comprise at least one energy production means such as a photovoltaic panel, at least one energy consuming means such as a dwelling or a building, at least one energy storage means such as a battery and at least one connection to an electrical energy distribution network.

[0005] An energy community may include several electricity meters, for example meters provided by the public electricity network operator (for example, the Linky meter deployed in France) of at least one component (called official or certified meters), and additional (unofficial) meters to measure, for example, the production of a photovoltaic (PV) panel or the incoming and outgoing energy of a battery. From these measurements, there may be an infinite number of ways in which energy could have passed between the members of the energy community and the public electricity network if the number of measurements is lower than the number of electrical flows. Some energy transfer scenarios may be very advantageous for some members, and much less so for others.

[0006] Determining the electrical flows between the different components of a power community can be used for power community control, for power community adaptation, etc. Prior art

[0007] The notion of energy community being relatively recent, there is still relatively little work in the scientific literature on the subject.

[0008] In the paper: "F. Tounquet, L. De Vos, I. Abada, I. Kielichowska, and C. Klessmann. Energy communities in the European Union. Revised Final Report of the ASSET Project (Advanced System Studies for Energy Transition), 2019", the authors explore different ways to distribute the benefits of an energy community among stakeholders. To do this, the notion of community stability is introduced along with the notion of marginal value, which describes the marginal cost that a stakeholder gains by being part of a community. An energy community is said to be stable if each stakeholder has a positive marginal value. The authors then introduce a way to distribute the benefits / costs of the community using the Shapley Hart value (described in the paper "S. Hart. Shapley value. In Game theory, pages 210-216. Springer, 1989." which defines a share of the benefits for each stakeholder in the community).

[0009] In the following documents:

[0010] AD Mustika, R. Rigo-Mariani, V. Debusschere, and A. Pachurka. A two-stage management strategy for the optimal operation and billing in an energy community with collective self-consumption. Applied Energy, 310:1 18484, 2022. and

[0011] JE Contreras-Ocana, A. Singh, Y. Bésanger, and F. Wurtz. Integrated planning of a solar / storage collective. IEEE Transactions on Smart Grid, 12(1):215-226, 2020.

[0012] Several strategies for distributing the production of an energy community are presented, ranging from very simple methods to very complex methods combining optimization and game theory. For these strategies, all the electrical flows are determined simultaneously, which complicates the resolution of the problem, particularly for a real-time implementation.

[0013] Furthermore, patent application FR3055048 A1 describes a very specific approach to distributing electrical flows. However, this distribution does not consider an energy storage system. This method cannot be applied to any energy community.

[0014] Furthermore, patent application WO20200709 A1 describes a method for validating a multi-flow (set of flows) of energy allocation. For this method, an energy allocation calculation step is carried out using a mathematical optimization method. However, this method does not precisely detail the mathematical optimization method implemented. In addition, for this method, all the electrical flows seem to be determined simultaneously, which complicates the resolution of the optimization problem, in particular for a real-time implementation.

[0015] Summary of the invention

[0016] The aim of the invention is to accurately determine electrical flows within an energy community, quickly and simply, with limited requirements in computing resources (processors and memory), and while limiting the number of meters. For this purpose, the present invention relates to a method for determining electrical flows within an energy community, in which a flow conversation model is constructed, at least one electrical flow is measured, a priority level is assigned to each flow, and an iterative method is applied to determine the electrical flows by means of a mathematical optimization method applying the flow conservation model. The steps of the iterative method are carried out in the order of the assigned priority levels.The application of an iterative method based on an order of priority allows a simple and rapid convergence towards a determination of the electrical flows (taking into account the different constraints), which allows the application of the process in real time.

[0017] The invention further relates to a method for controlling an energy community, a method for adapting an energy community, and an energy community implementing the method for determining electrical flows according to the invention.

[0018] The invention relates to a method for determining electrical flows within an energy community, said energy community comprising a plurality of components and at least one means for measuring the electrical flow of a component, each component of said energy community being chosen from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network, and said energy community being characterized by a topology, which defines nodes for each component as well as connections between said nodes. For this method, the following steps are implemented: a. Constructing an electrical flow conservation model for each node of said energy community as a function of said connections of said topology of said energy community; b.A priority level is assigned to each electrical flow of said topology, said priority level being determined from a first priority level and at least one higher priority level, each higher level having less priority than a lower level; c. At least one electrical flow is measured at a node by said measuring means; d. The electrical flows belonging to said first priority level are determined by an optimization method which minimizes the sum of the electrical flows as a function of said at least one measured electrical flow and as a function of said electrical flow conservation model at each node; and e. Said optimization method is repeated for each higher priority level to determine the electrical flows, each repetition of the optimization method taking into account the electrical flows determined for each lower priority level.

[0019] According to one embodiment, said optimization method implements a linear programming algorithm.

[0020] Advantageously, when said linear programming algorithm determines a plurality of solutions, said optimization method implements a quadratic programming algorithm.

[0021] Advantageously, said quadratic programming algorithm is constrained by a predefined electric flow distribution rule.

[0022] Preferably, said predefined electrical flow distribution rule is chosen from a weighted distribution, a distribution proportional to electrical consumption or a combination of both.

[0023] According to one implementation, said optimization method is constrained by said measured electrical flux obtained by a certified measuring means.

[0024] According to one aspect, said optimization method is capable of adjusting a measured electrical flux obtained by a non-certified measuring means.

[0025] Furthermore, the invention relates to a method for controlling an energy community, said energy community comprising a plurality of components and at least one means for measuring the electrical flow of a component, each component being chosen from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network. For this method, the following steps are implemented: a. Determining electrical flows within said energy community by means of the method according to one of the preceding characteristics; and b. Controlling at least one component of said energy community as a function of said determined electrical flows.

[0026] Furthermore, the invention relates to a method for adapting an energy community, said energy community comprising a plurality of components and at least one means for measuring the electrical flow of a component, each component being chosen from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network. For this method, the following steps are implemented: a. Electrical flows within said energy community are determined by means of the method according to one of the preceding characteristics; and b. Said energy community is adapted according to said determined electrical flows, by adding or removing at least one component or by modifying at least one component.

[0027] The invention also relates to an energy community comprising a plurality of components and at least one means for measuring the electrical flow of a component, each component being chosen from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network. It comprises computer means for implementing the method for determining electrical flows according to one of the preceding characteristics, and possibly only means for displaying the determined electrical flows.

[0028] Other characteristics and advantages of the methods and the system according to the invention will appear on reading the following description of non-limiting examples of embodiments, with reference to the figures appended and described below.

[0029] List of figures

[0030] Figure 1 illustrates the steps of the method for determining electrical flows according to one embodiment of the invention.

[0031] Figure 2 illustrates the steps of the method for controlling an energy community according to one embodiment of the invention.

[0032] Figure 3 illustrates the steps of the method for adapting an energy community according to an embodiment of the invention. Figure 4 illustrates, for an example, a topology of an energy community.

[0033] Figures 5A to 5C illustrate the iterative steps of determining electrical flows for the example of Figure 4.

[0034] Figure 6 illustrates the energy consumed from the distribution network for the example of Figure 4, the energy consumed being measured by an official meter and determined by the method according to the invention.

[0035] Figure 7 illustrates the energy produced transferred to the distribution network for the example of Figure 4, the energy produced being measured by an official meter and determined by the method according to the invention.

[0036] Figure 8 illustrates the energy flows of different components of the energy community of Figure 4, the energy flows being measured or determined by means of the method according to the invention.

[0037] Figure 9 illustrates the errors between the measured flows and the flows determined by the method according to the invention, for the example of Figure 4.

[0038] Figure 10 illustrates the origins of the energy transferred to the energy community consumer for the example in Figure 4.

[0039] Description of the embodiments

[0040] The present invention relates to a method for determining electrical flows within an energy community. An energy community is defined as an autonomous entity that brings together components that can consume, produce or store energy. The members of this community can exchange energy in order to optimize their consumption according to economic, environmental or social criteria. This energy community is connected to the electricity distribution network, which makes it possible to supply the energy community when it consumes more energy than it produces, or conversely to supply energy to the electricity distribution network when it produces more energy than it consumes. An electrical flow is a quantity of energy, which can correspond either to:

[0041] - The amount of electrical energy transferred between two components of the energy community,

[0042] - The quantity of electrical energy transferred between the electrical distribution network and a component of the energy community, or The quantity of energy entering and / or leaving a component of the energy community.

[0043] Thus, an energy community, also called an energy system or energy network, may comprise a plurality of components selected from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network. Preferably, the energy community may comprise at least one producing system, one consuming system and a connection to an electrical distribution network. Advantageously, the energy community may comprise at least one producing system, one consuming system, a connection to an electrical distribution network and an energy storage means.

[0044] The energy production system may in particular comprise at least one renewable energy production system (such as at least one photovoltaic panel, at least one wind turbine, a hydraulic power station, a biomass power station), or any other electrical power station, for example a nuclear power station, a gas power station, etc.

[0045] The energy-consuming system may include at least one building (for example, a residential building, an industrial building, or a professional building) and all the electrical equipment integrated into the building (for example, for heating, industrial tools, cooking, IT, etc.), or shared equipment (for example, public lighting, a vehicle charging station), etc.

[0046] An energy storage means may in particular be a battery, a compressed gas storage system, a fuel cell, or any similar means.

[0047] The energy community is characterized by a topology, which defines nodes for each component and connections between said nodes. In other words, the energy community is represented by a network comprising nodes and links between the nodes, the nodes representing the components of the energy community and the links representing the connections between the components. Advantageously, the topology may comprise for each node the type of component among energy producing system, energy consuming system, energy storage means and connection with the energy distribution network.

[0048] In addition, the energy community includes at least one means for measuring the electrical flow of a component, and possibly a means for reconstructing an electrical flow of a component from the measurements. In other words, a measuring means captures an electrical flow leaving and / or entering a component of the energy community. This measuring means is generally called a meter. This measuring means can be certified (official, MID certified for billing) or non-certified. A certified measuring means is a measuring means whose measurement is correct, and which can be used in particular by the electricity supplier for billing. This can be, for example, a Linky meter as deployed in France. A non-certified measuring means is a measuring means whose measurement may be subject to measurement inaccuracies. It can be an informal meter placed on a photovoltaic panel or any other component.

[0049] The method according to the invention implements the following steps:

[0050] 1) Construction of a flux conservation model

[0051] 2) Assigning a priority level

[0052] 3) Measurement of electrical flows

[0053] 4) Determination of electrical flows of the first priority level

[0054] 5) Determination of electrical flows of other priority levels

[0055] The steps may be implemented by computer means, including a computer, a server, or a calculator. Steps 1 and 2 may be implemented once offline, and then steps 3 to 5 may be implemented in real time or for a past period. Steps 1 and 2 may be performed in that order, in reverse order, or simultaneously. The steps will be detailed later in the description.

[0056] Figure 1 illustrates, schematically and in a non-limiting manner, the steps of the method according to one embodiment of the invention. From the topology TOP of the energy community, a flux conservation model MCF is constructed and a priority level NIP is assigned to each electrical flow of the topology. Electrical flows MES are measured by the measuring means. An optimization method OPT 1 is then applied a first time for the first priority level, to determine electrical flows Fe1 as a function of the MES measurements and the flux conservation model MCF. Then, an optimization method OPTN is repeated for each priority level to determine electrical flows FeN, as a function of the MES measurements and the flux conservation model MCF. The repetition (iteration) is indicated by the feedback arrow N=N+1.This repetition of the optimization method takes into account the electrical flows determined for the previous priority levels.

[0057] 1) Construction of a flux conservation model In this step, we build an electric flux conservation model for each node of the energy community according to the topology of the energy community. In other words, for each component of the energy community, we apply a flux conservation model which imposes that the sum of all the flows in this node (outgoing or respectively incoming) is equal to the energy of the component (produced or respectively consumed). This model, which reflects the energy balance of each component, allows a good representativeness of the electrical exchanges within the energy community, ensuring the precise determination of electric flows within the energy community.

[0058] According to an implementation, within the topology and for the construction of the flow conservation model, the storage means can be seen as a node that can be divided into a consumer node and a producer node.

[0059] To implement the electric flux conservation model, we identify, using the topology of the energy community, the set of electric flows in the energy community.

[0060] In the following, we describe an implementation of this model of conservation of electric flux:

[0061] We note F Pi ,cj an electric flow going from a producer system Pi to a consumer system Cj.

[0062] We also denote by adj(Pi) the set of consumer systems having a flow coming from the producer system Pi, and by adj(Ci) the set of producer systems having a flow going to the consumer system Ci.

[0063] For each producing system, we can write the following equation:

[0064] With pi the amount of electrical energy produced by the producing system i, which can be measured or deduced from the measurements.

[0065] For each consuming system, we can write the following equation: With ci the amount of energy consumed by the consumer system i, which can be measured or deduced from the measurements.

[0066] We can write the set of equations in an expression of the form to form a flux conservation model:

[0067] With x a vector comprising all possible energy flows, and matrix A and vector B can be constructed to satisfy the previous equations.

[0068] Since the electric flows are formulated from a producing system to a consuming system, we can write the following inequality: x > 0.

[0069] 2) Assigning a priority level

[0070] In this step, a priority level is assigned to each electrical flow in the topology. The priority level is determined within a classification that includes a first priority level and at least one higher priority level, each higher level being less priority than a lower level (the notions of higher and lower are linked to the priority number: the first priority level is a level lower than the second priority level, which itself is lower than the third priority level). The priority level allows certain flows to be prioritized over others. In other words, the priority level reflects the fact that certain electrical flows can take electrical energy that is available while other electrical flows can make do with the surplus.

[0071] For example, electrical energy produced by a producer system is intended first to supply energy to a consumer system, and if not all of the energy is consumed, to supply energy to an energy storage means. For this example, the flow of energy from the producer system to the consumer system is of first priority, and the flow of energy from the producer system to the energy storage means is of second priority.

[0072] Advantageously, the priority level may be predetermined, for example by means of a power community control system, based on a control strategy applied to the power community. Such a control strategy may include maximizing the use of energy produced by a generating system within the power community, maximizing the use of the energy storage system, maximizing the supply of energy to the distribution network, maximizing the consumption of renewable energy, maximizing a proximity criterion (a generating system will supply electrical energy first to the geographically closest components), etc. or any combination of these strategies.

[0073] Flow measurement

[0074] In this step, at least one electrical flow is measured at a node by the electrical flow measurement means. In other words, an electrical flow entering or leaving a component of the energy community is measured. For example, this may be an electrical flow supplied by the electrical distribution network, or an electrical flow produced by a producer system (this is the quantity pi defined above for the flux conservation model) or an electrical flow consumed by a consumer system (this is the quantity ci defined above by the flux conservation model).

[0075] If the measuring means is certified (official), then the measurement is called a "certified measurement" or "official measurement", and if the measuring means is not certified (unofficial), then the measurement is called an "uncertified measurement" or "unofficial measurement".

[0076] Determination of electric flows of the first level of

[0077] In this step, electric flows belonging to the first priority level (determined in step 2) are determined by an optimization method that minimizes the electric flows based on the electric flow measurements from step 3 and by implementing the flux conservation model constructed in step 1. In other words, the electric flows belonging to the first priority level are determined by a minimization of a function obtained by the flux conservation model, the minimization being constrained by the measurements.

[0078] According to one embodiment, the optimization method can implement a linear programming algorithm, and / or a quadratic programming algorithm. These algorithms allow for the determination of electrical flows in an accurate, robust and rapid manner. Linear programming, also called linear optimization, is a method for obtaining the best result in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming (also called mathematical optimization). More formally, linear programming is a technique for optimizing a linear objective function, subject to linear equality and inequality constraints.Its feasible region is a convex polytope, which is a set defined as the intersection of a finite number of half-spaces, each of which is defined by a linear inequality. Its objective function is a real-valued affine (linear) function defined on this polyhedron. A linear programming algorithm finds a point on the polytope where this function has the smallest (or largest) value, if such a point exists. Quadratic programming is a process for solving certain mathematical optimization problems involving quadratic functions. More precisely, one seeks to optimize (minimize or maximize) a multivariate quadratic function subject to linear constraints on the variables. Quadratic programming is a type of nonlinear programming.

[0079] According to one implementation of the invention, the optimization method may implement a linear programming algorithm. In addition, in the case where the linear programming algorithm determines a plurality of solutions, the optimization method may implement a quadratic programming algorithm. Thus, the quadratic programming method is only applied to the most complex optimizations, which promotes speed, and limits the processors and computer memory required to determine the electrical flows. Thus, the method can be applied in real time.

[0080] According to one aspect of the invention, when the optimization method implements a quadratic programming algorithm, this quadratic programming algorithm can be constrained by a predefined rule for distributing electrical flows. Such a rule for distributing electrical flows translates for components of the same priority level how the electrical flows are distributed between these components. For example, such a rule can be applied when the energy community comprises a producer system and two consumer systems of the same priority level; it is then a question of distributing the energy produced by the producer system between the two consumer systems. The distribution rule can be chosen in particular from a weighted distribution, a distribution proportional to the electrical consumption, or a combination of the two.

[0081] According to one aspect of the invention, the optimization method may be constrained by the measured electrical flux obtained by an official (certified) measuring means. In other words, when the measured electrical flux is certified, this measurement serves as a constraint for the optimization method. In this way, the determined electrical fluxes are consistent with the measurement at the measurement location.

[0082] Additionally or alternatively, when the measured electric flux is obtained by a non-certified measuring means, this measurement can serve as a constraint for the optimization method. In other words, this non-certified measurement serves as a constraint for the optimization method. In this way, the determined electric fluxes are consistent with the measurement at the measurement location. Alternatively, when the measured electric flux is obtained by a non-certified measuring means, this measurement can be adjusted by the optimization method. Thus, an additional degree of freedom can be left to the optimization method, while respecting as much as possible what was measured, to maintain good representativeness of the determined electric fluxes.

[0083] A non-limiting example of carrying out the determination of magnetic fluxes is described in the remainder of the description.

[0084] By applying the flux conservation model at each node belonging to the first priority level, we can solve the following optimization problem:

[0085] With N the number of energy flows, Xi the energy flows.

[0086] This problem can be solved using a standard linear programming algorithm.

[0087] The above problem may not have solutions if the measurements are inconsistent with each other, and this is very likely if the measurements are all independent of each other, due to measurement errors. The measurements, denoted by y, correspond to sums of certain energy flows and can be related to the electric flow vector x by a matrix M in the following way:

[0088] We denote y as the vector of actual measured and certified flows. We denote A as the matrix that allows us to select the certified measurements. We can then add the following constraint to the optimization problem:

[0089] \'l \Mf t

[0090] To overcome the case where the inconsistency of the measurements does not allow the following two constraints to be respected (conservation of the flux and constraint of the measurements): and We can consider a vector u of the fluxes entering and leaving the energy community and B a matrix such that:

[0091] This equation then becomes the new energy conservation equation. We can note that u > 0. We can optionally add a constraint to prohibit the possibility of having an energy storage device that discharges and charges simultaneously. We can then formulate the optimization problem as:

[0092] We define E (p)as an identity matrix cut so as to keep only the electric flux lines determined at the lower priority levels. We also define z (p-1) a vector of the electric flows determined at the lower priority levels. And we can write:

[0093] This formula ensures that the electric flux values ​​determined for the lower priority levels are preserved in x. This matrix and vector are used in step 5. In step 4, for the first priority level, no electric flux was previously determined.

[0094] The optimization problem can then be written:

[0095] With L (p)T x is the cost function in the linear problem, x (p)the set of electrical flows of priority level p and p indicates the optimization problem that can be solved by a linear programming algorithm of priority level p. This problem is solved using a linear programming algorithm. If this problem has a unique solution, in particular if there is a number of authorized flows in accordance with the number of available measurements, the unique solution defines the electrical flows of priority level p and we go directly to the next step with priority level p+1 (step 5). If this problem has an infinity of solutions, in particular if there are too many authorized flows compared to the number of available measurements (the problem is underdefined), then we apply a quadratic programming algorithm.The quadratic programming algorithm allows to determine a single solution that ensures the proper functioning of the energy community, that is to say without choosing a solution from among the infinite number of solutions that can advantage certain components of the energy community and disadvantage others.

[0096] In this case, a distribution rule can be applied, in particular from one of the following three rules:

[0097] 1. A weighted distribution rule, which distributes energy according to predetermined weights for each electrical flow of a priority level p. We denote by e Pi cj the distribution weighting for flow F Pi ,cj and we can write constraints of the optimization:

[0098] With adjp(X) the set of components that have a flow coming from or going to component X at priority level p (if this component is a consumer system or a producer system), Cs the consumption measurement on the energy component

[0099] 2. A proportional distribution rule (also called prorata), which distributes energy in proportion to the consumption of each of the consuming systems, we can write a optimization constraint

[0100] With Cs the consumption measure on the energy component, 3. A combination of the two previous rules, for which we can write the constraint of the following optimization:

[0101] These constraints can be reformulated as follows:

[0102] / / ' -■' ■ H

[0103] With H (p)a matrix formed for the priority level p from the constraints formulated above. We can then write the optimization problem as follows:

[0104] L (p)T x is the cost function in the linear problem and P denotes the optimization problem that can be solved by a linear programming algorithm of priority level p.

[0105] This problem can be solved by a quadratic programming algorithm. Advantageously, to be sure that the linear and quadratic objectives do not compete, we can determine a first solution by means of the linear programming algorithm denoted by x* and we can write the problem solved by the quadratic programming algorithm as follows:

[0106] The solution to this problem defines the electric flows of priority level p and we move directly to the next step with priority level p+1 (step 5).

[0107] Thus, the optimization method makes it possible to determine the electrical flows belonging to the first level of prioritization.

[0108] 5) Determination of electrical flows of other priority levels

[0109] In this step, the optimization method implemented in step 4 is repeated for each priority level, and at each iteration, the optimization method takes into account the electrical flows determined for the lower priority level(s) (for example, for the second priority level, the optimization method takes into account the electrical flows determined for the first priority level, and for the third priority level, the optimization method takes into account the electrical flows determined for the first priority level and the second priority level). Each iteration makes it possible to determine electrical flows of the priority level considered, while retaining the electrical flows determined for the lower priority levels. Thus, the method implements an iterative method which determines the electrical flows per priority level.The application of an iterative method based on an order of priority allows a simple and rapid convergence towards a determination of the electrical flows (taking into account the different constraints), which allows the application of the process in real time.

[0110] For the embodiment implementing the optimization problems exemplified in ' and F step 4, the problems can be solved f ~ iteratively by increasing the priority level p, the quadratic programming algorithm can be applied only if the linear programming algorithm determines an infinity of solutions.

[0111] Furthermore, the invention relates to a method for controlling an energy community, in which the following steps are implemented: a) determining electrical flows within the energy community by means of the method for determining electrical flows according to any one of the variants or any one of the combinations of variants described above; and b) controlling at least one component of the energy community according to the determined electrical flows.

[0112] Figure 2 illustrates, schematically and in a non-limiting manner, the steps of the method according to an embodiment of the control method. The steps identical to Figure 1 are not re-described. The method further comprises a step of controlling CON the energy community according to the determined electrical flows FeN. For example, if the energy consumption exceeds the demand, certain components can be started in order to increase the punctual energy production. Conversely, if the energy production exceeds the consumption, the surplus energy produced can be used to store energy, for example in batteries or in compressed air energy storage means or by storing water in hydroelectric dams. Advantageously, the control step can in particular implement at least one action among the following actions:

[0113] - At least one energy storage means is controlled to store and / or restore energy according to the determined electrical flows, for example by storing energy in an energy storage means if the electrical flows from a production system are supplied to the electricity distribution network while the energy storage means is not fully charged,

[0114] - At least one production system is controlled according to the determined electrical flows, for example by increasing the production of a production system if the production system is not at full load, and if the electricity distribution network is in demand, i.e. if there is at least one non-zero electrical flow coming from the electricity distribution network,

[0115] At least one consumer system is controlled according to the determined electrical flows, for example by reducing the consumption of a consumer system if the electricity distribution network is in demand, i.e. there is at least one non-zero electrical flow coming from the electricity distribution network, etc.

[0116] Furthermore, the invention relates to a method for adapting an energy community, for which the following steps are implemented: a) determining electrical flows within the energy community by means of the method for determining electrical flows according to any one of the variants or any one of the combinations of variants described above; and b) adapting the energy community according to the determined electrical flows.

[0117] Figure 3 illustrates, schematically and in a non-limiting manner, the steps of the method according to an embodiment of the control method. The steps identical to Figure 1 are not re-described. The method further comprises a step of adaptation ADA of the energy community according to the determined electrical flows FeN.

[0118] Advantageously, the adaptation step can implement at least one action from among the following actions: a) At least one producing system is added to produce more energy, for example if the electricity distribution network is in demand, i.e. there is at least one non-zero electrical flow from the electricity distribution network, b) At least one energy storage means is added to store more energy, for example if part of the energy produced is distributed to the electricity distribution network, i.e. there is at least one non-zero electrical flow from a producing system to the electricity distribution network, c) At least one energy storage means is modified to store more energy, if it is not adapted to the incoming or outgoing electrical flows, for example by replacing an energy storage means with another energy storage system with a greater storage capacity,d) At least one connection is added between two components of the energy community, for example between a producing system and an energy storage means, in particular if there is a non-zero electrical flow from a producing system to the electrical distribution network while an energy storage means is not charged by this producing system, e) A connection between two components is modified, for example if a connection is not sized for the electrical flow between these two components, etc.,

[0119] The invention also relates to an energy community which comprises a plurality of components selected from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network. In addition, the energy community comprises computing means, such as a computer, a server or a calculator, for implementing the steps of the method for determining electrical flows according to any one of the preceding variants, or any one of the combinations of the preceding variants.

[0120] Preferably, the energy community may comprise at least one producer system, one consumer system and a connection to an electrical distribution network. Advantageously, the energy community may comprise at least one producer system, one consumer system, a connection to an electrical distribution network and an energy storage means.

[0121] According to an implementation of the invention, the energy community may further comprise means for displaying the determined electrical flows. Advantageously, the display means may be included in the computer means. Alternatively, the display means may be linked to at least one component of the energy community. According to one embodiment, the energy community may comprise at least one means for controlling at least one component of the energy community, for controlling a component of the energy community as a function of the determined electrical flows.

[0122] The characteristics and advantages of the method according to the invention will appear more clearly on reading the application example below.

[0123] This example concerns an energy community of a professional building, which has a production system using photovoltaic panels, a consumer system (the building), a battery to store energy, and a connection to the electricity distribution network. For this example, the battery is controlled in real time by an energy management algorithm, requiring it to charge or discharge a certain amount of energy at different times.

[0124] This example is applied to measurements taken between November 21 and 26, 2021. The measurements are sampled at 10 minutes. The energy community's measurement resources include:

[0125] - A certified meter (Linky) for the building, arranged upstream of the production system, the battery and the building,

[0126] - An uncertified meter at the terminals of the photovoltaic panels, and

[0127] - An uncertified meter at the battery terminals.

[0128] In addition, a measurement of the energy consumed by the building can be reconstructed from the measurements of the three meters.

[0129] Figure 4 illustrates, schematically and in a non-limiting manner, the topology of the energy community. The energy community is connected to the distribution network RES by means of connections 0. The photovoltaic panels (producer system) 147 provide a quantity P1 of electrical energy. The building 146 (consumer system) consumes a quantity C1 of electrical energy. The battery 148 is illustrated by an element 148C for charging the battery which has a consumption Pc1, and by an element 148D for discharging the battery which provides an energy Pd1. The node 145 represents a device which provides the interface between the consumer system, the producer system, the storage means and the electrical distribution network. It can be an electrical cabinet, in which everything is wired, and which allows the switching of energy flows. The arrows illustrate the connections between the different components of the energy community.

[0130] This topology is assigned three priority levels:

[0131] - A first level of priority: o Electric flow from the production system 147 to the building 146, A second level of priority: o Electric flow from the production system 147 to the battery 148, o Electric flow from the battery 148 to the building 146, A third level of priority: o Electric flow from the distribution network 0 to the building 146, o Electric flow from the distribution network 0 to the battery 148, and o Electric flow from the production system 147 to the distribution network 0.

[0132] The iterative method for determining the electrical flows by priority level according to the invention is then applied. Figure 5 illustrates the order of determination of the electrical flows. In this figure, the dotted arrows correspond to the electrical flows of higher priority level not considered at this stage, the arrows in thin continuous lines correspond to the electrical flows of the priority level considered, and the arrows in thick continuous lines correspond to the electrical flows of lower priority level(s) and which are fixed at this stage. The energy community comprises a producer system 147 supplying energy P1, a building 146 consuming energy C1, a battery 148 (represented by a load 148C consuming energy Pc1 and a discharge 148D supplying energy Pd1), a connection to the distribution network RES. Figure 5A illustrates the first step for the electrical flows of the first priority level.Figure 5B illustrates the second step for electrical flows of the second priority level. Figure 5C illustrates the third step for electrical flows of the third priority level.

[0133] For the example, we apply the optimization method as illustrated in steps 4 and 5, the optimization method being constrained by the three measurements: the certified measurement and the uncertified measurements. The optimization method makes it possible to determine the different electrical flows within the energy community.

[0134] Figure 6 illustrates the energy flow Ef in kWh leaving the electricity distribution network as a function of the date. Figure 6 represents this measured electrical flow and this electrical flow determined by the method according to the invention. The two curves are superimposed, given that the measurement of this electrical flow results from a certified measurement which is a constraint of the optimization method. Figure 7 illustrates the energy flow Ef in kWh entering the electricity distribution network as a function of the date. Figure 7 represents this measured electrical flow and this electrical flow determined by the method according to the invention. The two curves are superimposed, given that the measurement of this electrical flow results from a certified measurement which is a constraint of the optimization method.

[0135] Figure 8 illustrates different electrical flows Ef in kWh as a function of the date. Curve F146m corresponds to the measured (in this case reconstructed) electrical flow of the building. Curve F146e corresponds to the electrical flow determined by the building optimization method. Curve F147m corresponds to the measured electrical flow of the photovoltaic panels. Curve F147e corresponds to the electrical flow determined by the photovoltaic panel optimization method. Curve F148Cm corresponds to the measured battery charging electrical flow. Curve F148Ce corresponds to the battery charging electrical flow determined by the method according to the invention. Curve F148Dm corresponds to the measured battery discharging electrical flow. Curve F148De corresponds to the battery discharging electrical flow determined by the method according to the invention.The curve F146m corresponds to the measured electrical flow consumed by the building and the curve F146e corresponds to the electrical flow consumed by the building determined by the method according to the invention. The two curves F147m and F147e are superimposed, given that the flow measurement of the photovoltaic panels is a constraint of the optimization method. Similarly, the two curves F148Cm and F148Ce as well as the two curves F148Dm and F148De are superimposed, given that the electric flow measurement of the battery is a constraint of the optimization method. In addition, the curves F146e and F146m are very close to each other.

[0136] Figure 9 illustrates the errors of the electrical flows eEF in kWh as a function of the date. The errors are the difference between the measured value and the value determined by the method according to the invention. Curve E146 corresponds to the error for the electrical flow of the building. Curve E147 corresponds to the error for the electrical flow of the producing system. Curve E148C corresponds to the error for the electrical flow of the battery under charge and curve E148D corresponds to the error for the electrical flow of the battery under discharge. Curves E147, E148C and E148D are zero since the associated measurements are constraints of the optimization method. The error for the electrical flow of the building E146 varies little, the determination of this flow by means of the method according to the invention allows a good estimation of this flow.Thus, the method according to the invention respects the constraints well, and allows a precise determination of the electrical flow of the building, in coherence with the reconstructed value determined directly from the measurements.

[0137] Figure 10 is a curve illustrating the origin of the energy flow to the building Ef in kWh as a function of the date. Curve PO concerns the energy flow from the electrical network, curve P147 concerns the energy flow from the photovoltaic panels, and curve P148D concerns the energy flow from the battery discharge. Thus, the method according to the invention makes it possible to determine at any time the electrical flows within an energy community.

Claims

Claims 1. Method for determining electrical flows within an energy community, said energy community comprising a plurality of components and at least one means for measuring the electrical flow of a component, each component of said energy community being chosen from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network, and said energy community being characterized by a topology (TOP), which defines nodes for each component as well as connections between said nodes, characterized in that the following steps are implemented: a. An electrical flow conservation model (MCF) is constructed for each node of said energy community as a function of said connections of said topology (TOP) of said energy community; b.A priority level (NIP) is assigned to each electrical flow of said topology (TOP), said priority level being determined from a first priority level and at least one higher priority level, each higher level having less priority than a lower level; c. At least one electrical flow is measured (MES) at a node by said measuring means; d. The electrical flows belonging to said first priority level (Fe1) are determined by an optimization method (OPT1) which minimizes the sum of the electrical flows as a function of said at least one measured electrical flow and as a function of said electrical flow conservation model at each node; and e. Said optimization method is repeated for each higher priority level (OPTN) to determine the electrical flows (FeN), each repetition of the optimization method taking into account the electrical flows determined for each lower priority level.

2. Method according to claim 1, wherein said optimization method (OPT1, OPTN) implements a linear programming algorithm.

3. The method of claim 2, wherein when said linear programming algorithm determines a plurality of solutions, said optimization method implements a quadratic programming algorithm.

4. The method of claim 3, wherein said quadratic programming algorithm is constrained by a predefined electrical flow distribution rule.

5. Method according to claim 4, wherein said predefined electrical flow distribution rule is chosen from a weighted distribution, a distribution proportional to electrical consumption or a combination of both.

6. Method according to one of the preceding claims, in which said optimization method (OPT 1, OPTN) is constrained by said measured electrical flux obtained by a certified measuring means.

7. Method according to one of the preceding claims, in which said optimization method (OPT, OPTN) is capable of adjusting a measured electrical flow obtained by a non-certified measuring means.

8. Method for controlling an energy community, said energy community comprising a plurality of components and at least one means for measuring the electrical flow of a component, each component being chosen from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network, characterized in that the following steps are implemented: a. Electrical flows (FeN) are determined within said energy community by means of the method according to one of the preceding claims; and b. At least one component of said energy community is controlled (CON) as a function of said determined electrical flows (FeN).

9. Method for adapting an energy community, said energy community comprising a plurality of components and at least one means for measuring the electrical flow of a component, each component being chosen from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network, characterized in that the following steps are implemented: a. Electrical flows (FeN) are determined within said energy community by means of the method according to one of claims 1 to 7; and b. Said energy community is adapted (ADA) according to said determined electrical flows (FeN), by adding or removing at least one component or by modifying at least one component.

10. Energy community comprising a plurality of components and at least one means for measuring the electrical flow of a component, each component being chosen from an energy producing system, an energy consuming system, an energy storage means and a connection to an electrical distribution network, characterized in that it comprises computer means for implementing the method for determining electrical flows according to one of claims 1 to 7, and possibly means for displaying the determined electrical flows.

Images