• In search of ways to reduce power loss

    In search of ways to reduce power loss

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Avatar de autor Manuel Rodríguez Urbina
Published 22 de May de 2018

Every year, electricity companies work hard to establish how much power is lost from their networks, which – according to the latest figures – amounts to over €150m in Spain alone.

A range of different algorithms and methods are used to pinpoint where the losses occur, which mean that they can then be measured and categorised.

When we talk about energy losses, we can categorise them into four basic groups:

 1.- Technical losses: these are the energy losses which occur due to elements within the network itself – such as cables and transformers – that are involved in generating, transporting, distributing and transforming energy. Measuring this type of loss requires accurate digitisation of the network, which for historical reasons is not always possible, and effective monitoring of the different energy magnitudes. If either of these components is not in place, significant investment will be necessary.

 2.- Network consumption: this is the energy used by the network for management and monitoring purposes, such as pumping stations and support services, which are not identified as consumption per se. These are usually classed as technical losses, meaning that actual data is skewed.

 3.- Energy lost in the contracting, metering and billing processes themselves: these losses are ultimately not invoiced to customers, as they are the result of things such as inaccurate meter readings, insufficient contract estimations without measurements, and unbilled consumption. Like network consumption, these are usually classed as technical losses.

 4.- Non-technical losses: this is unauthorised energy consumption, which is commonly known as fraud.

By using the latest consulting and software development technology, we are now capable of substantially mitigating this issue:

1.- Identifying and calculating technical losses. Properly understanding technical network losses enables us to reduce them, as mechanisms can be added to do things such as correctly balance loads and check conductors, circuit balance and transformer shutdown. Although this may lead to a number of costs, not only is the recovery of this initial investment guaranteed, but the expenditure will set the system in good stead for the future. At Ayesa, we work with large-scale monitoring and design condition estimation models for various network points in which this monitoring is not possible or which require comparisons to be made, meaning we can identify situations in which network stability is under threat and/or energy loss hotspots in real time with information which, much like hourly loss measurements, up until now would have been inconceivable.

2.- Network consumption detection. Locating consumption points throughout the network and correctly classifying them avoids this being mistakenly classed as energy loss. Here at Ayesa we use processes to mine and analyse network data, identifying and reducing the number of points, and in doing so reducing losses.

3.- Improving metering and billing processes. Identifying specific processes within the company which lead to this loss and reducing the extent to which it occurs is a solution that is both effective and long-lasting. At Ayesa we successfully provide consultancy services in which we analyse business processes and identify, measure and propose solutions to improve them. 

 4.- Locating non-technical losses. In the past, fraud was detected by selecting network areas according to aspects of their contracts and carrying out expensive inspection campaigns in the field with a low success rate, resulting in a poor return on investment and therefore a method that was rather more discouraging that it was effective. In recent years, with the incorporation of large-scale data mining technology, abnormal behaviour patterns can be identified in specific sub-networks or sources, which reduces the scope of inspections and in doing so increases their success rate. At Ayesa, we use Big Data technology and carry out hourly energy balance measurements to conduct micro- and macro-analysis of network sections, measuring losses per section and categorising them by risk of fraud, thereby ultimately increasing the success rate of our inspections.

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