Energy value chain: The energy and utility industry typically comprises power plants generating electricity that is transmitted over long-distance transmission lines and then finally provided over distribution lines to residences and businesses.
The energy and utility industry is undergoing a large-scale transformation through such technologies as predictive analysis. Grids are getting smarter by the day with the help of predictive analysis. Electric power sources are getting cleaner and customers have more choices to receive power. One of the technological drivers that has impacted this is the emergence of Big Data and analytics which play a pivotal role in the industry. Let me try to address and explain each of the valuable components.
Big data and analytics is helping utility companies overcome the industry challenges through insights-based informed decision-making.
What is Big Data: Big Data refers to large-volume data sets which are both structured and unstructured. This data swamps businesses daily. It’s not the amount of data which is important; rather, what matters is what companies do with the data. These large data sets can be analyzed for insights that lead to better and informed decision-making and predictive analysis so that organizations can achieve strategic business objectives.
What is analytics: Analytics uses various techniques like mathematics, statistics, predictive modeling, predictive analysis, and machine-learning to find meaningful patterns in large data sets.
How these are disrupting the industry:
Energy and utility organizations apply smart technology to their landscape, including sensors, cloud computing technologies, wireless, power planning, and network communication. These produce large data sets on a continuous basis which gets collected over a period of time. For example, a utility company, using smart meters and power, can collect around three petabytes of data every 15 minutes for a year for about one million households.
If we start expanding intelligent devices like sensors and thermostats, we are talking of large-volume data sets being generated across power generation to transmission to distribution and then to consumers via substations. Businesses across the utility industry are facing a lot of challenges to draw insights out of this valuable data and conduct power planning.
Source of Big Data and the Value Derived:
In an energy and utility company, there are various sources of Big Data, including grid equipment, smart meters, weather data, measurements from power systems, GIS data, storm data, and data related to asset management. These companies are using this data to bring in operational efficiencies, reduce costs, lower carbon emissions, and manage energy demand for end consumers.
Power generation planning: Utility companies can optimize their power planning and generation using analytics. There are two key decision-making processes in power generation – power planning and dispatching of the economic load. Once we gather all the data from multiple sources, there are multiple models run on top of that data to arrive at power planning. By economic load dispatch, we mean matching energy demand with the optimal power supply from the grid over a specific time frame.
Efficient and accurate forecasting: Data analytics helps in accurately forecasting the energy consumption which plays a pivotal role in the generation and thus, dynamic pricing. Similarly, it plays an important role in forecasting the power generation, especially for renewable energy sources which include solar as well as wind, which gets impacted due to changing weather conditions. This all gets taken care by doing predictive analysis on all the data taken from weather systems.
Site selection: The integration of all the data, be it energy production, energy consumption, GIS, and weather data like wind direction, temperature, humidity, atmospheric pressure, cloud, and wind speed can support the sites selection where renewable power generation devices have to be installed. This improves energy efficiency as well as power output and brings in a lot of efficiencies. GIS data equally plays an important role. It includes geographical information data from satellite data or LiDAR (light detection and ranging) that helps in spatial (three dimensional) planning.
Asset management: The industry has asset-intensive units. Companies regularly face a lot of asset management-related challenges; for example, asset operations, asset monitoring, sharing of resources, asset maintenance, asset procurement, inventory management, etc. Utility companies can achieve efficiency based on insights drawn from the analytics.
Energy efficiency: Data coming from smart meters, asset operations, business policies, and weather data can be integrated and analyzed over a period of time which helps in designing electrical devices with energy-efficiency parameters, thus reducing power requirements. Energy efficiency plays an important role to reduce carbon emissions. This also includes various other issues like equipment efﬁciency issues and problems in insulation, as well as improvements in operational areas. This way, companies can forecast their energy consumption and predict energy savings.
Dynamic Energy Management in Smart Grids
Big Data & Analytics enables dynamic energy management in Smart grids. Smart grids enable a two-way flow of data and power between consumers as well as suppliers. This helps in the optimization of power in terms of reliability, energy efficiency, and power sustainability. This way energy consumers and energy producers take a more active role in the electricity market and thus, management of dynamic energy. Effective dynamic power management depends very much on load forecasting and production of renewables. This calls for a need for intelligent methods and solutions, including machine algorithms for analysis of large data volumes generated by the enormous number of smart meters. Therefore, efficient data network management, robust data analytics, high-performance computing, and cloud computing technology are critical for optimized smart grid operation.
Model Failure Probability
We can use machine learning algorithm to predict the failure model of a distribution feeder element using all the data inputs like SCADA events, GIS data, power quality data, maintenance, and inspection work. By adopting this model, we can improve the quality of service and reduce operational cost on maintenance. Advanced analytics of this caliber should help operators bring their costs down in the range of 8-11% in medium-voltage distribution grids and 16-20% in high voltage distribution grids.
There are a lot of changes happening in the energy and utility industry and companies continue to evolve each day. There are still many companies out there who do not have this technology implemented fully and are not able to reap the benefits. Utilities need to invest into three major analytics areas: enterprise analytics which includes real-time predictive analytics and situational awareness followed by grid analytics that comprises asset management, outage management, mobile workforce, etc., and then finally consumer analytics which includes consumer-buying patterns, social media integration, and power tariffs. In order to harvest the true benefits of Big data and analytics, utility companies need to continuously invest in this field to get insights out of the data for informed decision-making and thus, overcome the challenge that this industry brings each day.