Grid Analytics

Being able to measure low voltage levels opens the door to a better understanding of grid behaviour and optimization of a variety of DSO processes. To do this, we must change data into information and information into knowledge. Mycroft Mind has cracked this! Big data processing techniques and machine learning algorithms specifically designed for smart grid data processing are the key.

We help utilities in areas

Detection and prioritization of technical
and non-technical losses

Being able to effectively identify and eliminate technical and non-technical losses (NTL) is often closely related to the profitability of a DSO. Smart metering can help a lot but is only one of a number of data sources needed.

An effective solution for non-technical losses combines data from smart meters, industrial meters, substations and predictions of reasonable technical losses. The solution takes the risk profile of each consumption point into account and estimates the probability and volume of non-technical losses.

The result is a prioritized list of NTL events, which helps DSOs route field workers to those situations, which are obvious and whose resolution will have a long term effect on DSO profitability.

Recognition and assessment of voltage problems

Smart meters and measurements taken at substations can be used to monitor voltage proactively and to detect, classify and prioritise problems. Our solution can distinguish between unimportant random voltage drops and peaks and those worsening trends over a longer period, which can lead to consumer complaints and to penalties for the DSO.

The effective processing of voltage problems includes the identification of those factors which have a particular bearing on the problem, examples being production from nearby photovoltaics or ambient outside temperatures. The correct identification of clusters of voltage problems is also very important, as in this way we can distinguish between an isolated problem at a single consumption point, a problem with a feeder or a problem at a high voltage level being experienced at the same time at neighbouring substations.

Detection and prioritization of phase asymmetry

Phase asymmetry needs to be kept within reasonable limits. With new photovoltaic sources being connected to the grid, often to just one phase, asymmetry is on the increase. Measurements from substations should therefore be systematically processed to automatically detect potentially unacceptable asymmetry variances and to distinguish between random small problems and problems which repeat and become worse.

Recognizing how phases are connected

Phases at a consumption point were typically connected randomly; utilities did not have a record of how this had been done. Having this information is of course important if a utility needs to solve an asymmetry issue or operate a single phase PLC communication. We have invented and proved a software algorithm, which can estimate from smart metering measurements how the phases at a given connection point are connected.

Optimizing phase connection

Our optimization algorithm will advise how phases at selected consumption points should be reconnected to keep asymmetry problems under control.

Recognition of consumption type

Consumption profiles taken every 15 minutes or hourly from smart meters can be used to estimate the types of consumption at a given consumption point. Being able to recognise electrical heating, air-conditioning, hot water boilers or EV charging activities can help reveal the potential for consumption flexibility in the grid and can lead to a better understanding of consumer behaviour.

Tariff utilization analyses

Data from smart meters allows consumption point behaviour to be analysed and compared with the tariff system in force. This data can form the basis of recommendations for changes in tariffs for certain consumers and can also identify customers misusing the existing tariff system.

Energy consumption predictions

Smart metering opens the door for precise predictions for consumption by individual consumption point, by groups of consumption points, by feeders and by substation. Accurate consumption predictions are a basis for other advanced features such as demand side response.

Flexibility detection and prediction

Using consumption type recognition we are able to analyse consumption flexibility in the grid. We are able to predict the degree of flexibility in the grid at any given time and how individual consumption points are likely to react to responses affecting demand such as tariff switching or dynamic pricing.

GridMind

Software tool for creating complex models and simulations of AMM/ smart grid infrastructures.

Key Characteristics:

  • Powerful tool for modelling large infrastructures and simulate their behaviour
  • General model of smart metering and smart grid infrastructures
  • Simulation build on discrete events methods
  • Export of simulation results by visualisation tool QlikSense

Contains specific modules for different communication technologies:

  • IP communication over mobile networks (GPRS, LTE)
  • Models of PLC physical communication layer (PRIME, G3, BPL, S-FSK) developed in cooperation with Czech Technical University in Prague and Brno University of Technology
  • DLMS protocol

Why we should use GridMind?

Software helps us to understand the design concept and related applications during pilot or roll-outs projects for substation automation solutions for the smart grid.

  • Helps to identify problematic locations
  • Helps to find optimal configuration of reading and maintenance processes
  • Helps to identify communication requirements to perform data integration

 

GridMind use case

Identify areas in low voltage topology, where results in field are significantly worse than in model – noise sources, cable joints. Software helps to estimate communication capacity for each substation and configure collection system to collect as much measurements as possible – not only billing data but also technical data (voltage profiles) in following steps

  • Import/generate low voltage distribution grid topology
  • Select specific communication technology (GSM, RF, PRIME, G3, BPL, S-FSK)
  • Define behaviour models of smart meters and data concentrators
  • Define application scenarios (readings, sending TOU tables, firmware upgrades, …)
  • Evaluate expected readout rate, command delivery reliability in individual locations

DeepGrid - Fibre

Software for optimal planning of fiber optic infrastructure construction in accordance with the renewal and development of the distribution grid. The result in savings 4-11% compared to the manual plan creation using just GIS data.

DSO objectives

  • to connect selected substations with minimal costs
  • use connection redundancy to make communication network more robust
  • to choose routes which can „by the way“ connect bonus substations
  • to choose routes which have lower risks of delays due to obstructions of land owners
  • to choose routes which can have a potential to provide connectivity to customers

Deepgrid-Fibre functionalities

  • optimisation algorithm which counts with expected line reconstructions periods, different prices of cables/overhead lines and obstruction risk profiles
  • it processes data about grid topology and grid reconstruction plans
  • it computes optimal fibre grid infrastructure plan and export it as a GIS layer and reports

DeepGrid - Fibre Length vs Price

By using DeepGrid-Fibre we saved on just one optical route 80.000 €.