Method for analysing an electrical energy distribution network aimed at reducing the transportation of reactive energy, and computer program implementing such a method

Abstract

The invention relates to a method for analysing an electrical energy distribution network aimed at reducing the transportation of reactive energy in this network. The method comprises a step (S1) of using map data and economic data to identify at least one overhead line of the network and a plurality of electrical loads associated with an overhead line. The method uses a prediction module able to link a description of an economic activity to a consumption of reactive energy and, in a preparation step (S3), provides a reactive energy compensation means distribution plan.

Description

Method for analyzing an electrical energy distribution network aimed at reducing the transmission of reactive energy and computer program implementing such a method FIELD OF THE INVENTION

[0001] The present invention relates to the field of electrical energy distribution networks, and more particularly to a method for analyzing and optimizing these networks aimed at reducing the transmission of reactive energy. The invention falls within the framework of the intelligent management of energy distribution infrastructures, with the objective of minimizing losses linked to the transmission of reactive energy while improving the stability of the network. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0002] The transmission of reactive energy in electrical power distribution networks is a well-known problem for network operators. Reactive energy, although necessary to maintain voltage in the network, contributes to significant energy losses and can overload existing infrastructure, particularly overhead distribution lines.

[0003] The development of renewable energy is leading to a major transformation of electrical energy distribution networks, notably by reducing the distance between energy production and consumption sites. Unlike traditional power plants, which are often located far from consumption centers, renewable energy sources are often decentralized and installed closer to end users (i.e., the electrical loads to be supplied). This favors the development of low-voltage (less than 1,000 volts) and medium-voltage (between 1,000 volts and 50,000 volts) distribution networks. These networks are more sensitive to reactive power imbalances.

[0004] It is also noted that the compensation of reactive energy is becoming increasingly complex due to the progressive burial of the electrical energy distribution network, this burial being sometimes imposed for aesthetic and reliability reasons. The overhead lines of the distribution network, installed on pylons or poles, are by nature inductive. On the other hand, underground lines, surrounded by insulating materials and buried in the ground, have capacitive characteristics.

[0005] Also, in this context of transition towards a decentralized network, in which buried, semi-buried or overhead lines coexist and dominated by low and medium voltage infrastructures, the management of reactive power becomes a crucial issue to ensure the stability of the network, minimize energy losses, and guarantee a stable quality of power supply for users.

[0006] To address these challenges, reactive energy compensation means are regularly deployed, such as capacitor banks or synchronous compensators. As an illustration, document EP2461454 describes an integrated control system within an electricity distribution network, which aims to optimize the use of renewable energy sources and the compensation of reactive power in order to improve network performance. Document EP3245701 proposes a method for optimizing the consumption of reactive energy in an electrical network by determining the type and configuration of the compensation systems to be installed and by activating these systems, if necessary.

[0007] However, the installation of compensation means is generally opportunistic and based on a partial analysis of network needs, often limited to distribution infrastructures.

[0008] There is now a growing demand for a more global approach to network needs, taking into account its nature and diversity and promoting the installation of compensation means as close as possible to the electrical loads distributed on the network. SUBJECT OF THE INVENTION

[0009] One aim of the invention is to propose a method for analyzing and / or optimizing an electrical energy distribution network providing a response to this need. BRIEF DESCRIPTION OF THE INVENTION

[0010] In order to achieve this aim, the subject of the invention proposes a method for analyzing an electrical energy distribution network aimed at reducing the transport of reactive energy in this network, the analysis method comprising: A step of exploiting cartographic and economic data to locate at least one overhead line of the electrical energy distribution network and a plurality of electrical loads associated with the at least one overhead line, the exploitation step producing a data structure associating with each electrical load, a description of an economic activity linked to this electrical load; A step of processing the data structure, the processing step aiming to complete the data structure to associate with each electrical load a need for reactive energy compensation, the processing step exploiting a prediction module capable of linking a description of an economic activity to a consumption of reactive energy;A step of preparing a distribution plan for reactive energy compensation means from the completed data structure, the distribution plan indicating the electrical loads likely to be associated with reactive energy compensation means in order to reduce the transmission of reactive energy on the at least one overhead line.;

[0011] According to other advantageous and non-limiting characteristics of the invention, taken alone or in any technically feasible combination: the reactive energy compensation means are banks of capacitors; the analysis method comprises a preliminary step of learning the prediction module, based on learning data, each learning data item associating a description of an economic activity with historical data of reactive energy consumption.during the operating stage, an overhead line is distinguished from an underground line based on the cartographic and economic data, a line having a given length being considered overhead if it has an unburied portion of the line extending over less than 90% of the given length; during the operating stage, the at least one overhead line identified in the cartographic and economic data is chosen from medium or low voltage lines of the electrical energy distribution network; the data structure is dynamically updated allowing continuous adaptation of the distribution plan of reactive energy compensation means; the economic activity description is a code of a pre-existing classification of economic activity.

[0012] According to another aspect, the invention also relates to a computer program containing instructions adapted to the implementation of each of the steps of the method which has just been described, when the program is executed on a computer. BRIEF DESCRIPTION OF THE FIGURES

[0013] Other characteristics and advantages of the invention will emerge from the detailed description of the invention which follows with reference to the single appended figure which schematically represents the different stages of a method in accordance with the invention. DETAILED DESCRIPTION OF THE INVENTION

[0014] This description relates to a method for analyzing and optimizing an electrical energy distribution network. This method aims to reduce the transmission of reactive energy in this network by proposing a distribution plan for reactive energy compensation means. These means may be capacitor banks, for example, or synchronous compensators.

[0015] The distribution plan specifies the location of the means of reactive energy compensation, at the level of the electrical loads requiring compensation, these loads all being associated with an overhead line of the electrical energy distribution network.

[0016] The reactive energy compensation distribution plan brings an overall benefit to the network by limiting the transit of reactive energy along the power lines that constitute it. It is therefore not just a question of compensating locally, and opportunistically, at the level of a single individual user and for their sole benefit, the production of this energy.

[0017] In the distribution plan, the compensation means are placed "at the end of the line", at the level of the users who are likely to benefit from them and as close as possible to the electrical loads associated with this line.

[0018] It can be provided, within the framework of the present invention, that these means are activated and deactivated according to the instantaneous compensation needs, by a compensation management system, which brings an overall benefit by limiting the transit of reactive energy along the entire length of the power line.

[0019] In addition to reducing Joule effect losses, this limits the load on transformers intended to stabilize the network voltage.

[0020] The analysis and optimization method which is the subject of the present description may take the form of a computer program containing instructions adapted to the implementation of each of the steps of this method, when the program is executed on a computer.

[0021] The method uses freely available cartographic and economic data to identify a network line and the electrical loads associated with it. These electrical loads correspond to network users, these users having an economic activity, a description of which can also be extracted from the cartographic and economic data.

[0022] The intuition that led to the present invention is that it is possible to make a link between an economic activity and the need for reactive energy compensation. As an illustration, the distribution by area of ​​activity of sites with reactive energy consumption from a sample of 141 establishments supplied with medium voltage is presented in the following table.

[0023] Quarrying 4

[0024] Agri-food 5

[0025] Industry 19

[0026] Recycling center 1

[0027] Construction 7

[0028] Commerce 16

[0029] Logistics and warehousing 6

[0030] Hotel chain 6

[0031] Restoration 4

[0032] Miscellaneous 9

[0033] Telecommunications 2

[0034] Bank 5

[0035] Real Estate 7

[0036] R&D 2

[0037] Public administration 25

[0038] High School 4

[0039] Hospitals 13

[0040] Stage 6

[0041] An idea underlying the invention is to prepare a computer prediction module, this prediction module being able to link a description of an economic activity to a consumption of reactive energy.

[0042] As an illustration in the extraction sector, a quarry is characterized by a well-established process: conveyor belt, crusher, crusher, washing pump, etc. This extraction process does not vary according to geographical location and is therefore substantially identical in other regions. The data collected in a particular region can therefore be used to provide predictions in other regions. Additional data can be used to adjust the parameters of the prediction module to estimate the consumption of reactive energy supplied, such as, for example, the age of the site, and / or its surface area or other freely accessible cartographic and economic data.

[0043] For reasons of simplicity of implementation, the description of economic activity may correspond to a code from a pre-existing classification of economic activity. For example, this description may be the APE (Main Activity Exercised) code taken from the French Activity Nomenclature. As the INSEE website reminds us, this NAF nomenclature is a nomenclature of productive economic activities, mainly developed to facilitate the organization of economic and social information. It has the same structure as the European NACE activity classification, itself derived from the international ISIC classification.

[0044] It is noted that the APE code, preferentially chosen to deploy a process in accordance with the invention, is assigned to any company in France. Registers of this economic data are publicly available, so that it is easy to extract the required information from this economic data (company identifier, address, APE activity code, etc.). It is of course possible to choose data other than the APE code as a description of economic activity for the implementation of the present invention.

[0045] Returning to the description of the prediction module, it is therefore able to predict the reactive energy consumption of a site from the APE code corresponding to the economic activity associated with this site. By "predicting the reactive energy consumption", it should not be understood that this prediction is necessarily and precisely expressed in reactive energy consumed, in reactive energy unit (kilo Volt Ampere Reactive hour - kVARh), which of course depends on its level of activity. It may involve, within the framework of this description and according to certain modes of implementation, providing, in arbitrary unit, a relative quantity aimed at determining whether a site is likely to produce reactive energy. The greater this quantity, the greater the site's need for reactive energy compensation. It will therefore form a preferred site for installing means of compensating for this energy.

[0046] In some implementation modes, however, this prediction can be quantitative and expressed, for example, in relation to the site's electricity consumption. As an illustration, the prediction module can indicate that the reactive energy consumption of a site identified by its APE code corresponds to 40% of its total electrical energy consumption.

[0047] In other implementation modes, the prediction can be expressed quantitatively in units of reactive energy over a given period, for example over a period of one year. For this purpose, the prediction module can rely on historical energy consumption data for a site, to which a relative estimate of reactive energy consumption compared to total energy consumption is applied. Note that these historical consumption data are data that are generally public (for example, in France, these data are prepared and provided by the Energy Network Operators Agency).

[0048] In addition to the economic activity description, the prediction module may include other input elements, such as the geographical location of the site. Advantageously, these other input elements are also available in the form of freely accessible cartographic and / or economic data.

[0049] The prediction module can be configured in different ways, depending on the specific needs and the available data. Preferably, it is configured by a supervised learning process, i.e. from pre-existing training data. The training data associates a description of an economic activity with historical data on reactive energy consumption, allowing the module to build robust and relevant prediction models. As already stated, it can integrate other input elements than the description of economic activity, such as for example the age of the site and / or its surface area.

[0050] More precisely, during the learning phase, each training data consists of a vector characterizing at least one economic activity description (for example the APE code) associated with historical quantitative or qualitative data on reactive energy consumption. The module uses this data to extract a model of underlying relationships between economic activity and reactive energy.

[0051] To improve its prediction capabilities, the module can implement an advanced machine learning technique, such as an artificial neural network. This approach makes it possible to capture complex non-linear relationships that are difficult to model using traditional methods. The neural network is trained using historical data and uses backpropagation algorithms to adjust its internal parameters (synaptic weights) to generalize a reliable relationship from the training data.

[0052] In addition to, or instead of, neural networks, other learning techniques can be used, such as random forests, Bayesian networks, or multivariate regression models, depending on the complexity of the data and the nature of the correlations observed between the different descriptors of economic activity, any other input data, and reactive energy. These techniques are configurable and adjusted according to the required performance, whether in terms of predictive accuracy, speed of execution, or ability to adapt to new data.

[0053] Regardless of the manner in which it has been previously configured in a step prior to the implementation of a method in accordance with the invention, for this implementation, a prediction module is available capable of linking a description of an economic activity to a consumption of reactive energy.

[0054] Continuing the very general description of the present invention, and as reported in the introduction to this application, a distribution plan for reactive energy compensation means is relevant when these means are associated with an overhead line of the electrical energy distribution network, that is to say with a line having an inductive character.

[0055] It is for this reason that the invention proposes to distinguish, in the cartographic and economic data, the overhead lines from the underground lines of the network. In the context of the present description, and preferably, a line having a given length is considered to be "overhead" if an unburied portion of the line extends over at least 90% of its given length. One could naturally choose another discriminating value than the 90% proposed here.

[0056] For the same reasons of efficiency, the invention proposes to preferentially focus on medium voltage or low voltage lines.

[0057] Another idea underlying the invention is to use mapping data from the public electricity network to locate an overhead line of interest and to install, if necessary, reactive energy compensation means on the most suitable sites associated with this line. These most suitable sites are those of users, operating an electrical load associated with the analyzed line, who have the most obvious need for reactive energy compensation. It is noted that the sites associated with the analyzed line can also be located in the mapping data of the electricity network or inferred from this mapping data. It is also noted that this mapping data makes it possible to identify the location of the sites along the line, and therefore the energy transmission distance from its place of production.Losses, and in particular losses due to the Joule effect, are directly linked to this transport distance, so that this information can be used to preferentially seek compensation for sites for which this distance is relatively significant.

[0058] Naturally, within the framework of the invention, it will be possible to use several data sources to collect the cartographic and economic data necessary for the envisaged processing. Data relating to the location of power lines can, for example, be extracted from a first database, and data relating to the location of sites and their respective economic activities extracted from a second database, distinct from the first. This information can be cross-referenced and, on the basis of the geographical proximity of a site and a line, the assumption can be made that this site is indeed electrically connected to the line in question.

[0059] With reference to the, we now proceed to a detailed description of the different stages of the process of analysis and optimization of the electrical energy distribution network aimed at reducing the transport of reactive energy in this network.

[0060] During a first step S1, called "exploitation", the cartographic and economic data that have been collected are used to identify: at least one overhead line of the electrical energy distribution network; a plurality of electrical loads associated with at least one overhead line.

[0061] As previously indicated, this exploitation step may lead to cross-referencing data from separate databases. It may be assisted by an operator, for example to designate from among several overhead power lines the line or plurality of lines that will be the subject of analysis during the following steps of the process.

[0062] Importantly, during the S1 exploitation stage, overhead lines are distinguished from underground lines based on cartographic and economic data because, to be relevant, the analysis is carried out on overhead lines.

[0063] Also importantly, the overhead line identified in the cartographic and economic data is chosen from medium or low voltage lines of the electrical power distribution network.

[0064] This exploitation step S1 leads to the production of a data structure associating with each electrical load associated with the overhead line analyzed, a description of an economic activity linked to this electrical load. This data structure can be presented in any suitable form, for example in the form of a list, a table, a collection of vectors, or even in the form of a collection of pointers, as is perfectly understood by the person skilled in the art.

[0065] As already mentioned, this data structure may include other information extracted from the databases or derived from cartographic and economic databases. In particular, it is possible to supplement the structure, for each entry, with the surface area of ​​the corresponding site, its age, the transport distance separating the electrical load from the energy production site, etc.

[0066] In a second step S2 called “processing”, which follows the first step S1, the data structure provided by this first step S1 is processed, using the prediction module presented in a previous passage of this description.

[0067] The processing step S2 aims to complete the data structure and associate, with each electrical load of a site, a reactive energy compensation requirement. To this end, the prediction module is applied to the structure data, and this structure is completed, for each electrical load of a site, with the compensation requirement returned by this module.

[0068] As already presented, this compensation requirement can be provided in the form of qualitative information (according to a predetermined requirement scale), in a relative quantitative form (such as a % of the electrical energy consumed by the site's electrical load), or even in an absolute quantitative form. In the latter case, the prediction module can rely on a database containing a history of electrical energy consumption.

[0069] In a following step S3, called "preparation", the distribution plan for reactive energy compensation means is prepared from the data structure completed during the processing step. As presented above, this distribution plan indicates the electrical loads likely to be associated with reactive energy compensation means in order to reduce the transmission of reactive energy on the at least one overhead line.

[0070] In its simplest implementation mode, this preparation step can thus identify in the data structure produced at the end of processing step S2, the entries associated with the most significant reactive energy compensation needs. The distribution plan is then composed of a list of sites where the identified electrical loads are located, and the capacity of the compensation means to be respectively implemented on these sites.

[0071] Other more complex implementation modes are of course possible. We can thus take into account the distribution distance associated with each entry of the data structure, to estimate the Joule losses occurring in the analyzed line, and to search for the electrical loads which, once equipped with compensation means, will make it possible to reduce the reactive energy transport on the at least one overhead line as much as possible.

[0072] Other constraints can be added in this distribution plan preparation step, such as sites or types of sites (identified for example by their APE code) that we do not want to equip with compensation means or that have a compensation limit. These may be sites or types of sites for which the quality of the energy delivered is critical, and which could be degraded by the presence of compensation means. For example, we could impose that sites of the "data center" type (data center, according to the established Anglo-Saxon expression) be excluded from processing, during this distribution plan preparation step.

[0073] More generally, it can be expected that the distribution plan will recommend a type of compensation means best suited to the nature of the site (capacitor banks or synchronous compensators for example), or even a particular model. During this preparation stage, an installation and / or operating cost can also be associated with the compensation means proposed by the distribution plan.

[0074] The preparation stage can then establish a distribution plan aimed at separately or jointly optimizing the overall cost of installation and / or operation of the compensation means (overall cost which we are of course seeking to minimize) and the losses avoided, in particular Joule losses, by the installation of the compensation means (losses avoided which we are of course seeking to maximize).

[0075] We therefore understand that, in a general approach, this step of preparing the distribution plan can implement complex processing of the data structure, for example multi-criteria optimization.

[0076] In addition to the distribution plan, the preparation stage can provide other data, such as the losses avoided through the installation of compensation means for the proposed distribution plan, or the cost associated with the deployment of this plan and / or its operation.

[0077] The analysis process can be implemented punctually to develop a distribution plan for reactive energy compensation means, in order to plan its deployment. In this case, the execution of the process is followed by a step of installing the compensation means at the level of the electrical loads recommended by the distribution plan.

[0078] But it is also possible to dynamically update the data structure and thus allow for continuous adaptation of the distribution plan for reactive energy compensation means. In this way, network performance can be optimized over time and in response to changes made to the network and its uses.

[0079] Of course, the invention is not limited to the embodiments described and variant embodiments can be made without departing from the scope of the invention as defined by the claims.

[0080] Thus, during and at the end of each of the process steps, the data being processed can be displayed in text, graph or mixed form, in order to allow the user to become aware of this data. In certain implementation modes, this user can interact with the data presented, for example to add or eliminate a site of an electrical load or choose the power line to be analyzed, if the cartographic data proposes analyzing several.

Claims

Method for analyzing an electrical energy distribution network aimed at reducing the transport of reactive energy in this network, the analysis method comprising: A step (S1) of exploiting cartographic and economic data to locate at least one overhead line of the electrical energy distribution network and a plurality of electrical loads associated with the at least one overhead line, the exploitation step producing a data structure associating with each electrical load, a description of an economic activity linked to this electrical load; A step (S2) of processing the data structure, the processing step aiming to complete the data structure to associate with each electrical load a need for reactive energy compensation, the processing step exploiting a prediction module capable of linking a description of an economic activity to a consumption of reactive energy;A step (S3) of preparing a distribution plan for reactive energy compensation means from the completed data structure, the distribution plan indicating the electrical loads likely to be associated with reactive energy compensation means with a view to reducing the transport of reactive energy on the at least one overhead line.; Analysis method according to the preceding claim in which the reactive energy compensation means are capacitor banks. Analysis method according to one of the preceding claims comprising a preliminary step of learning the prediction module, based on learning data, each learning data associating a description of an economic activity with historical data of reactive energy consumption. Analysis method according to one of the preceding claims in which during the exploitation step (S1), an overhead line is distinguished from an underground line from the cartographic and economic data, a line having a given length being considered overhead if an unburied portion of the line extends over at least 90% of the given length. Analysis method according to one of the preceding claims in which during the exploitation step (S1), the at least one overhead line identified in the cartographic and economic data is chosen from medium or low voltage lines of the electrical energy distribution network. Analysis method according to one of the preceding claims in which the data structure is dynamically updated allowing continuous adaptation of the distribution plan of reactive energy compensation means. Analysis method according to one of the preceding claims in which the economic activity description is a code of a pre-existing classification of economic activity. Computer program containing instructions adapted to the implementation of each of the steps of a method according to one of their claims 1 to 7, when the program is executed on a computer.

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