Forecasting of power demand plays an essential role in the power sector, as it provides the basis for making decisions in system planning and operations. However, forecasting is influenced by various meteorological and socio-economic factors, which can lead to a mismatch between Actual vs Projected demand. Underestimating demand results in supply shortages and forced power outages, with serious consequences for productivity and economic growth. Overestimating demand can lead to overinvestment in generation capacity, possible financial distress, and, ultimately, higher electricity prices. Presently, DISCOMs use traditional standard methods of demand forecasting by projecting day to day demand based on the past data manually which consumes a lot of time and inputs. Various factors like weather, usage patterns etc. impact the load profile and the relationship is non-linear and complex. Leveraging AI/ ML by using various data points like weather forecasts, consumer consumption patterns, peak demand etc. as input, can help Discoms enhance their data processing techniques, reduce manual intervention as well as accurately forecast demand on a near real-time basis.
TANGEDCO, TSNPDCL, PSPCL, MP Poorv Kshetra Vidyut Vitaran Co. Ltd, Jabalpur, UHBVNL
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AI/ML drive by IoT/Smart device installed on these assets may be leveraged to gather critical performance data, which can be useful in predicting the asset health, variations from design performance, response under varying system demand etc. It can enable timely identification of components vulnerable to failure based on the historical data sets and system generated triggers can be sent to the concerned filed personnel’s to carry out predictive maintenance. Unmanned aerial systems (UASs) can also be adopted as a part of AI/ML for driving predictive failure analytics to ensure robust and resilient electricity infrastructure, via image and video analytics.
TSNPDCL
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Aggregate Technical and Commercial (AT&C) losses is one of the crucial KPIs for a Discom and ensuring timely and accurate computation of the same can help Discoms identify various high-loss pockets and take decisive actions focused on loss reduction. AT&C loss is a combination of technical loss (Network losses) & commercial loss (Inefficiency in collection/billing/Metering + Theft). The conventional approach of loss reduction across discoms, have enabled them to achieve some level of AT&C loss reduction, with further reduction necessitating exploration of advanced technological interventions
TSSPDCL, JVVNL, PSPCL, KESCO, MVVNL , MP Paschim Kshetra Vidyut Vitran Co. Ltd. Indore
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With power distribution shifting into a more consumer-centric business, it is crucial for Discoms to prioritize consumers based on a multitude of parameters, like consumer category, payment schedules, consumption patterns, etc. so as to drive customized and consumer-friendly initiatives like loyalty programs, rebates/ cashbacks etc. Consumer services are impacted with many issues like long on-hold time, incorrect tagging of complaints to officer/locations, long resolution time, non-optimum complaint handling procedure, manual intervention etc. Leveraging data analytics coupled with AI/ML technologies can enable a predefined system response preparedness based on historical data sets of consumer complaints. It can record and evolve by learning from the key aspects of consumer queries and generate appropriate responses. AI enabled systematic assistance like Chatbots can also be leveraged to reduce manual intervention and readily address consumer grievances and aid in gaining consumer’s trust and forging a lasting relationship.
MP Madhya Kshetra Vidyut Vitaran Co. Ltd. HPSEBL
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AI/ML can be leveraged to analyze the historical data and automate the loss computation process and also enabling timely identification of high loss pockets for the Discoms to drive targeted initiatives. It can enable data driven approach for timely identification of consumers, geographies based on historical data patterns and using a multitude of factors like consumer payment pattern, frequency of payment, geographical and seasonal demand trends, variation in sales volumes etc. to devise targeted revenue enhancement programs and simultaneously monitor overall recovery process at a field level via an intelligent and dynamic dashboard
MP Poorv Kshetra Vidyut Vitaran Co. Ltd, Jabalpur
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Lately, the Renewable Energy (RE) policies have given a push to consumers to install RE generation assets to leverage various benefits like reduced monthly energy bills, compensation for feeding the utility system, increased power reliability with reduced dependence on utility network. This would ultimately enable cut energy waste, lower energy costs, and facilitate and accelerate the use of clean renewable energy sources in power system. This diversification and decentralization of energy production, along with the advent of new technologies and changing demand patterns, create complex challenges for power distribution utilities. Currently the absence of advanced technologies like AI/ML to develop learning pattern from decentralized generation sources, limits the Discoms ability to plan the upstream network across these sources. AI/ML backed with data of consumers RE generation, internal consumption, weather factors, upstream Utility network capacity/loading can help utilities re-design overall tariff structures, enhanced incentive to consumers, better plan power procurement, upstream NTW capacity design etc
TANGEDCO, MVVNL
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With increased infrastructure density and complexity, backed by persistent investment across power distribution sector, it is crucial for Discoms to ensure adequate asset/system maintenance through their lifecycle to enhance their longevity. A healthy and resilient electricity distribution system will enable lowering the system downtime, increase system reliability, endure threats from natural disasters etc. The predominant corrective maintenance approach and limited use of technology across the established maintenance frameworks limit the desired improvement level in distribution network. The end power user connected to Distribution transformers (DT) are often rendered without electricity, due to faults occurring at the DT level due to reasons not just limited to overloading, wear and corrosion, power surges, moistures etc.
UHBVNL, PGVCL
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Power purchase cost accounts for ~80% of the total costs borne by the Discoms. Optimizing the same has been an area of concern for the Discoms as it factors in a multitude of historic and projected data points like demand, tied-up capacity, energy and fixed charges, must-run plants etc. AI/ML can be leveraged by using historic data as well as forecasts to automate the power purchase cost optimization process and formulate a framework to repurpose existing power purchase portfolio to identify high-cost or underutilized low-cost plants can help Discoms reduce power purchase costs, which in turn brings down electricity tariffs..
UPPCL
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The increased infrastructure density possesses challenge of not only to monitor the increased number of scattered electrical components but also the risk from natural vegetation. With the assets across a large geographical area, the Discoms have limited capacity to ensure manually maintaining a comprehensive health cards for the critical component like Power transformers, Distribution transformers, Circuit breakers, lightening arrestors etc. The power lines passing through dense vegetative areas with long tree branches make them vulnerable to faults which lead to power outages or momentary power interruptions.
TSNPDCL, UHBVNL
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There are numerous challenges that the Discom faces, having wider operational and financial impact, these may or may not have direct impact on the some of the key performance parameters like AT&C loss levels, power reliability, infrastructure health etc, but have a wider implication on the overall performance of DISCOMs, accordingly it is vital to address these challenges via technology driven innovative solutions. Accordingly, under this category the applicants can apply on a long-term basis wherein anyone interested can apply.