Category: Digital Transformation

Digital Transformation, Energy & Infrastructure, Perspectives, Strategy Advisory

Will AI Replace Jobs in Asset Investment Planning and Business Cases?

The rise of artificial intelligence (AI) has brought significant changes to various industries, and asset investment planning and business case development are no exceptions. As AI continues to evolve, there is growing concern that it might replace jobs traditionally performed by humans in these fields. However, the reality is more nuanced. While AI has the potential to automate many tasks, the technology’s effectiveness depends on the quality of data inputs and the structure of the systems it operates within. This article explores how AI is impacting asset investment planning and business cases, what is required to make it work effectively, and the ongoing role of human expertise in managing data, asset information, and SCADA (Supervisory Control and Data Acquisition) systems as inputs.

The Potential of AI in Asset Investment Planning

AI offers several advantages in asset investment planning, particularly in areas where large amounts of data need to be analyzed and complex decisions must be made. Some key benefits include:

  • Automation of Routine Tasks: AI can handle repetitive tasks such as data entry, basic financial analysis, and report generation, freeing up human resources for more strategic activities.
  • Enhanced Predictive Analytics: AI algorithms can analyze historical data to forecast future trends, helping businesses make more informed decisions about asset investments.
  • Improved Risk Management: By continuously monitoring various factors that influence asset performance, AI can identify potential risks early and suggest mitigation strategies.
  • Optimization of Asset Portfolios: AI can evaluate and optimize asset portfolios based on predefined criteria, ensuring that investments align with the company’s overall strategy.

These capabilities make AI a powerful tool in asset investment planning, but they also raise concerns about job displacement. While some roles may be automated, the transition to AI-driven processes requires a new set of skills and expertise.

The Role of Data Entry and Structure in AI

One of the most critical factors determining AI’s success in asset investment planning is the quality and structure of the data it uses. AI systems rely on vast amounts of data to make accurate predictions and recommendations. However, the adage “garbage in, garbage out” holds true—if the input data is flawed, the AI’s outputs will be unreliable.

To ensure that AI systems function effectively, businesses must focus on:

  • Data Integrity: Ensuring that all data entered into AI systems is accurate, complete, and up-to-date. This requires robust data governance practices and regular audits to catch and correct errors.
  • Standardization of Data: For AI to process data efficiently, it needs to be standardized across the organization. This means establishing common formats, units of measurement, and data entry protocols.
  • Data Integration: AI systems often need to pull data from multiple sources, including enterprise resource planning (ERP) systems, financial databases, and external market data feeds. Ensuring seamless integration between these sources is crucial for accurate AI analysis.

While AI can automate some aspects of data management, human oversight is essential to maintain data quality and structure. This means that jobs in data management are likely to evolve rather than disappear, with a greater focus on ensuring that AI has the right inputs to work with.

Managing Asset Information

In asset investment planning, detailed and accurate asset information is crucial. AI systems can analyze asset performance data, predict maintenance needs, and optimize investment decisions. However, managing asset information is a complex task that involves several key activities:

  • Asset Lifecycle Management: Keeping track of an asset from its acquisition to its disposal, including maintenance schedules, performance metrics, and financial data.
  • Condition Monitoring: Continuously monitoring the condition of assets using sensors and other technologies to predict when maintenance is needed.
  • Data Accuracy: Ensuring that all asset information is accurate and up-to-date, which is critical for AI-driven decision-making.

AI can assist in managing asset information by automating data collection and analysis, but human expertise is still needed to interpret the results and make strategic decisions. For example, while AI can predict when an asset might fail, human engineers are often required to validate these predictions and decide on the best course of action.

The Importance of SCADA Systems

SCADA systems play a vital role in asset investment planning, particularly in industries such as utilities, manufacturing, and transportation. These systems collect real-time data from assets and infrastructure, providing valuable inputs for AI analysis.

However, the integration of SCADA systems with AI requires careful planning and management:

  • Data Collection: SCADA systems generate vast amounts of data, which can be overwhelming without proper filtering and processing. AI can help by identifying the most relevant data points for analysis.
  • Real-Time Monitoring: AI can analyze SCADA data in real-time, allowing for immediate responses to issues such as equipment failures or safety hazards.
  • Predictive Maintenance: By analyzing SCADA data, AI can predict when equipment is likely to fail and recommend maintenance before a breakdown occurs.

While AI can enhance the capabilities of SCADA systems, human operators are still needed to oversee the process and ensure that the AI’s recommendations are implemented correctly. Additionally, the design and maintenance of SCADA systems themselves remain critical tasks that require specialized skills.

The Ongoing Role of Human Expertise

Despite the potential for AI to automate many tasks in asset investment planning and business case development, human expertise remains essential. AI systems are tools that require proper setup, management, and interpretation to be effective. The following areas will continue to require human involvement:

  • Strategic Decision-Making: While AI can provide data-driven insights, the final decision on asset investments and business cases will likely remain in the hands of human managers, who can weigh factors such as company culture, market conditions, and ethical considerations.
  • Oversight and Validation: AI systems need to be regularly monitored to ensure they are functioning correctly. Human oversight is crucial to catch any errors or biases in the AI’s outputs and to adjust the system as needed.
  • Data Management: As mentioned earlier, maintaining data quality and structure is critical for AI to function effectively. This requires ongoing human effort to manage and clean data, ensure standardization, and integrate different data sources.
  • SCADA and Asset Information Management: The management of SCADA systems and asset information will continue to be a vital role, requiring both technical expertise and strategic insight.

AI is poised to significantly impact asset investment planning and business cases, automating routine tasks, enhancing predictive analytics, and improving risk management. However, its effectiveness depends on the quality of data inputs, the structure of data management systems, and the integration of SCADA and asset information.

Rather than replacing human jobs entirely, AI is likely to change the nature of work in these fields, with a greater emphasis on data management, system oversight, and strategic decision-making. For businesses to fully realize the benefits of AI, they must invest in the right tools, train their workforce to manage and interpret AI outputs, and ensure that their data is accurate, standardized, and well-integrated.

The future of asset investment planning and business case development will be a collaboration between AI and human expertise, each playing a crucial role in driving better outcomes for businesses.

Digital Transformation

What is Distribution Automation?

Distribution automation (DA) refers to the integration of advanced technology into the electrical distribution system to enhance its efficiency, reliability, and flexibility. It involves the use of communication systems, sensors, and control devices to automate the operation of the electrical grid, allowing for real-time monitoring, control, and optimization of the distribution network. The primary goal of DA is to improve service quality, reduce operational costs, and increase the resilience of the electrical distribution system.

Key Components of Distribution Automation

  • Smart Meters
    • Provide real-time data on energy consumption
    • Enable remote reading and two-way communication
    • Facilitate dynamic pricing and demand response programs
  • Remote Terminal Units (RTUs)
    • Collect data from various sensors and devices
    • Communicate with central control systems
    • Control local equipment based on commands from the control center
  • Intelligent Electronic Devices (IEDs)
    • Monitor and control electrical parameters
    • Provide protection, automation, and monitoring functions
    • Enhance grid reliability and efficiency
  • Communication Networks
    • Ensure seamless data transfer between devices and control centers
    • Utilize technologies such as fiber optics, wireless, and power line communication
    • Support real-time data exchange and remote control
  • Advanced Distribution Management Systems (ADMS)
    • Integrate data from various sources for holistic grid management
    • Provide tools for outage management, voltage control, and load balancing
    • Enhance situational awareness and decision-making capabilities

Benefits of Distribution Automation

  • Improved Reliability
    • Faster detection and isolation of faults
    • Reduced outage durations
    • Enhanced fault location and service restoration
  • Operational Efficiency
    • Lower operational costs through automation
    • Reduced need for manual intervention
    • Optimized asset utilization and maintenance
  • Enhanced Customer Service
    • Real-time monitoring and quicker response to issues
    • Improved power quality and reliability
    • Better customer engagement through detailed usage insights
  • Energy Efficiency
    • Improved voltage regulation and loss reduction
    • Integration of renewable energy sources
    • Support for demand response and energy management programs
  • Scalability and Flexibility
    • Easier integration of new technologies and systems
    • Adaptability to changing grid conditions and demands
    • Support for distributed generation and microgrids

Applications of Distribution Automation

  • Fault Detection, Isolation, and Service Restoration (FDIR)
    • Automated detection and isolation of faults
    • Minimized outage impact through quick restoration
    • Enhanced reliability and reduced downtime
  • Volt/VAR Optimization (VVO)
    • Optimized voltage levels and reactive power
    • Improved energy efficiency and reduced losses
    • Better voltage stability and power quality
  • Demand Response (DR)
    • Real-time management of energy consumption
    • Reduced peak demand and associated costs
    • Enhanced grid stability and efficiency
  • Distributed Energy Resource (DER) Integration
    • Seamless integration of solar, wind, and other renewables
    • Improved grid stability and reliability
    • Support for a cleaner and more sustainable energy mix
  • Advanced Metering Infrastructure (AMI)
    • Real-time monitoring of energy usage
    • Enhanced billing accuracy and customer engagement
    • Support for energy-saving programs and dynamic pricing

Challenges in Implementing Distribution Automation

  • High Initial Investment
    • Significant upfront costs for technology and infrastructure
    • Long-term return on investment considerations
    • Funding and financial planning challenges
  • Cybersecurity Concerns
    • Increased risk of cyber-attacks and data breaches
    • Need for robust security measures and protocols
    • Ongoing management of security threats and vulnerabilities
  • Integration with Legacy Systems
    • Compatibility issues with existing infrastructure
    • Need for phased implementation and transition strategies
    • Balancing old and new technologies during the upgrade process
  • Regulatory and Policy Hurdles
    • Compliance with evolving regulations and standards
    • Coordination with government and regulatory bodies
    • Navigating policy changes and incentives
  • Skill and Knowledge Gaps
    • Need for specialized skills and training for personnel
    • Keeping pace with rapidly evolving technology
    • Managing workforce transition and development

Future Trends in Distribution Automation

  • Increased Use of Artificial Intelligence and Machine Learning
    • Enhanced predictive maintenance and fault detection
    • Improved decision-making through data analytics
    • Automation of complex grid management tasks
  • Integration of Internet of Things (IoT)
    • Greater connectivity and data exchange between devices
    • Improved real-time monitoring and control
    • Enhanced overall grid intelligence and responsiveness
  • Expansion of Renewable Energy Integration
    • Greater reliance on distributed energy resources
    • Improved management of intermittent renewable sources
    • Support for a more sustainable and resilient grid
  • Development of Microgrids and Smart Grids
    • Enhanced local control and independence
    • Improved resilience and reliability at the community level
    • Integration of advanced technologies for optimized performance
  • Evolution of Grid Management Practices
    • Transition to more proactive and predictive management
    • Greater emphasis on sustainability and efficiency
    • Adoption of holistic and integrated grid management solutions

Distribution automation is revolutionizing the way electrical distribution systems operate, offering numerous benefits such as improved reliability, operational efficiency, and enhanced customer service. While challenges remain in terms of investment, cybersecurity, and integration, the future trends point towards even greater advancements with the use of artificial intelligence, IoT, and renewable energy integration. As the technology continues to evolve,

Digital Transformation, Perspectives

‘Private LTE’​ for Smart Grids: Yes! (but no…?)

A pipe dream years ago, ‘Private LTE’ is growing in utility circles and a buzz at the #Distributech conference I attended in February 2019.

Utilities have a range of current and emerging use cases and smart grid applications to meet customer service objectives and to remain relevant with new services.

These needs are currently enabled by ‘stacking’ private, proprietary narrowband wireless networks for priority ‘mission critical’ voice and data services, and complementing with commercial telecommunications carrier services.

If all of these services are aggregated, including Push-to-Talk’ voice and smart metering, an upgradable industry-standard wireless broadband solution can meet the needs of tomorrow.

The solution would be designed and operated to meet the reliability and security requirements of the most demanding ‘mission critical’ utility use cases.

However, this is contingent on access to valuable spectrum.

US emergency services with similar ‘mission critical’ reliability and security needs are migrating to ‘FirstNet’ mobile broadband, on federal provision of 700 MHz spectrum.

Investor-owned Utilities (IOU) do not (and in my opinion should not) have such a luxury.

Licensed spectrum can be acquired in competition with telcos; an expensive proposition. As such, the telecommunications industry has progressed technology to manage the precious spectrum asset including roaming, carrier aggregation, network slicing, spectrum arbitrage, prioritization, preemption and Virtual Network Operator (VNO).

Any IOU submission to a regulator for capital recovery for a broadband spectrum and network is likely (and rightly so) to raise red flags.

Forward-thinking utilities can demonstrate prudency and sweeten the deal by bringing telcos millions of subscribers and offsetting the cost by bartering access to their valuable Transmission and Distribution assets: overhead structures, conduit, easements and land.

To summarize, is there a telecommunications platform available to enable the smart grid?

  • Yes! Access to a industry-standard wireless, mobile broadband technology can meet all utility requirements
  • Yes! The telecommunications solution can be designed to meet stringent ‘mission critical’ reliability and security needs, while sharing valuable spectrum
  • Yes! With collaboration, asset arbitrage and/or recycling, broadband solutions and services can be accessed at a lower TCO (compared to today’s suite)
  • But no, the ownership model does not need to be private.

And this, for utilities, will be a new world of IT/OT convergence….

Service management rather than asset management!

Please leave a comment, share or connect, I’d be interested in your thoughts.

Digital Transformation, Perspectives, Strategy Advisory

Grid Modernization – Justified

Many business cases for Grid Modernization (formerly known as Smart Grid) have focused on the technology and the direct and indirect benefits, generally a long bullet point list which sounds good, but is not usually presented as a compelling argument focused on outcomes.

The following contains a storyboard to use to develop a compelling Grid Modernization business case.

Context

The Distribution System Operator (DSO) is traditionally a monopoly with only one way for customers to access a reliable supply – the electricity network. Both customer behaviour and the regulated revenue stream were dependable and predictable.

However, the industry is experiencing increasing:

  • total energy consumption
  • regulatory scrutiny and WACC pressure
  • customer price sensitivity
  • accessibility of technological disruption and change (distributed energy generation, storage and vehicles) and;
  • changing customer behaviour (participation in energy markets)

Disruptive technology

  1. Distributed generation
    1. reduced total demand
    2. maintain investment for peak demand
    3. increase investment for power quality management and protection
  2. Energy storage
    1. potential reduced customers
    2. reduced total demand as generating customers will store and consume rather than export
    3. increase investment for power quality management and protection
  3. Electric vehicles
    1. transient load / demand (peakier peak)
    2. maintain investment for peak demand
    3. increase investment for power quality management and protection

Enterprise Risk

For the DSO, adoption and proliferation of these disruptive technologies will lead to:

  • an under-utilised or orphaned asset
    • ongoing interest repayments for sunk capital investment
    • potentially operating costs > regulated revenue
  • reduced regulated revenue
  • perceived barriers to changing customer behaviour and risk to current and projected regulated asset base

Objective

The objective is to:

  • maintain a safe and reliable energy transport service at the lowest price (and increase shareholder Return on Investment).

AND/OR

  • provide new services at the lowest price (and increase shareholder Return on Investment) such as distribution system operations (frequency control, load balancing), telecommunications or services

Asset Management Strategy

To meet the objective by investing in the minimum required asset to meet forecast demand, safety and service standards and increase existing asset utilisation (i.e. balance supply / load, smooth profile).

  1. Maintain network assets risk to ‘as low as reasonably practicable’
    1. Operational asset management
    2. Digital transformation / operational technology
  2. Influence customer behaviour
    1. Time of use tariff
    2. Demand management
    3. Seek alternative arrangements with customers (encourage distributed generation and energy storage)
  3. Improve network flexibility
    1. Increase application of advanced monitoring, interconnection, protection, automation and control systems
    2. Increase access to dispatchable energy storage
  4. Actively pursue alternative unregulated revenue streams i.e. telecomms, EPCM services, gas/water merger/acquisition

This is to be supported by ongoing corporate and asset strategies for continuous improvement (improve productivity / efficiency).

Digital Transformation, Perspectives, Strategy Advisory

What is Operational Technology?

Operational Technology (OT) is the electronic and computing equipment used for the Real-Time protection, operation and management of the assets, infrastructure and networks. It is characterised by its mission and business critical use, industrial digital electronic technologies and computing nature, installation practices and technical skillsets.

Operational Technology includes:

  • Real-Time Systems (RTS) including computing hardware such as servers, storage, firewalls and peripherals, operating systems, databases, middleware, software applications and associated data cabling, racks, licenses and configurations. Real-Time software applications include the Distribution Management System (DMS), Outage Management System (OMS), Energy Management System (EMS), Communications Network Management System (CNMS), Plant Information (PI) data historian and Market Management System (MMS)
  • Supervisory Control and Data Acquisition (SCADA, also known as automation, process control, instrumentation, control systems) systems including Front End Processors (FEP), Remote Terminal Units (RTU), Programmable Logic Controller (PLC), hardwired Input-Output (I/O) collection, Human-Machine Interface (HMI), Front-End Processors (FEP), terminal servers, manufacturer specific controllers and associated transducers, small wiring, embedded software, firmware and configuration
  • Digital protection and control equipment including all digital and numeric relays, controllers and associated transducers, small wiring, firmware and configuration
  • Digital metering, instrumentation and monitoring equipment and associated transducers, small wiring, firmware and configuration
  • Telecommunications and networking including all:
    • Electronics (active and passive) such as Radio transceivers, receivers, transmitters, multiplexors, switches, routers, modems, handsets, firewalls, rectifiers and associated small wiring, cabling, firmware and configuration
    • Physical infrastructure including telecommunications structures (towers, masts, monopoles, lightning protection, Earthing and antennas), Power Line Carrier (PLC) line traps, self-contained mounting (shelters, racks, kiosks), optic fibre (fibre, conduit and pits), data cabling (copper pilot and coaxial cables)
    • Associated contracts for frequency spectrum licensing, co-site sharing, dark fibre leases and utility data carrier services
  • Configuration, testing and troubleshooting equipment and tools including Personal Computers (PC), spectrum analysers, oscilloscopes, multimeters, Optical Time-Domain Reflectometer (OTDR), application or manufacturer specific test and simulation equipment and associated software, firmware and configuration

Operational Technology does not include:

  • Near Real-Time Systems including computing environments, software applications and associated licenses and configurations. Near Real-Time applications including the corporate Geographic Information System (GIS) and Mobile Workforce Management (MWM)
  • Corporate Information Technology (IT) systems including computing hardware such as servers, storage, firewalls and peripherals and operating systems (OS), databases, middleware, software applications with associated data cabling, racks, licenses and configurations. Corporate IT applications include the Enterprise Resource Planner (ERP) or Asset Management System (AMS), Meter Business System (MBS), Customer Relationship Management (CRM)
  • Power electronics[1] including:
    • Power electronic control systems including Static Synchronous Compensators (STATCOM) and Rapid Earth Fault Current Limiters (REFCL)
    • Electro-mechanical protection systems including electro-mechanical relays, Metering, Current Transformers (CT), Voltage Transformers (VT) and Circuit Breakers (CB) and associated transducers, small wiring, test equipment and configuration
    • Electro-mechanical instrumentation and control systems including High-Tension mimic panels and analogue meters
    • Direct Current (DC) systems (Uninterrupted Power Supplies, chargers and batteries)
    • Alternating Current (AC) power systems (Generators, Transmission and Distribution networks)
  • Environmental management systems including security systems, air-conditioning, weather stations and fire alarm systems
  • Land, property and physical security such as buildings, fences, gates, access roads and land leases

[1] In other industries power electronics and energy storage systems could be considered OT, however for a Distribution System Operators’ (DSO) area of expertise it is considered as Transmission and/or Distribution infrastructure

Digital Transformation, Perspectives, Strategy Advisory

Power to the People!

The Distribution System Operator (DSO) is traditionally a monopoly with only one way for customers to access a reliable supply – the electricity network. Both customer behaviour and the regulated revenue stream were dependable and predictable for the shareholder.

However, the industry is experiencing increasing:

  • total energy consumption
  • regulatory scrutiny and WACC pressure
  • customer price sensitivity
  • accessibility of technological disruption and change (distributed energy generation, storage and vehicles) and;
  • changing customer behaviour (prosumer participation in energy markets)

Disruptive technology

  1. Distributed generation
    1. reduced total demand
    2. maintain investment for peak demand
    3. increase investment for power quality management and protection
  2. Energy storage
    1. potential reduced customers
    2. reduced total demand as generating customers will store and consume rather than export
    3. increase investment for power quality management and protection
  3. Electric vehicles
    1. transient load / demand (peakier peak)
    2. maintain investment for peak demand
    3. increase investment for power quality management and protection

For the DSO, adoption and proliferation of these disruptive technologies will lead to:

  • an under-utilised or orphaned asset
    • ongoing interest repayments for sunk capital investment
    • potentially operating costs > regulated revenue
  • reduced regulated revenue
  • perceived barriers to changing customer barrier and risk to current and projected regulated asset base

DSO Objectives

The DSO objective is to maintain a safe and reliable service at the lowest price (and increase shareholder Return on Investment).

DSO Strategies

To meet the objective by investing in the minimum required asset to meet forecast demand, safety and customer service standards and increase existing asset utilisation (i.e. balance supply / load, smooth profile).

  1. Maintain network assets risk to ‘as low as reasonably practicable’
    1. Operational asset management
    2. Digital transformation / operational technology (OT)
  2. Influence customer behaviour
    1. Time of use tariff
    2. Demand management
    3. Seek alternative arrangements with customers (encourage distributed generation and energy storage)
  3. Improve network flexibility
    1. Increase application of advanced monitoring, interconnection, protection, automation and control systems
    2. Increase access to dispatchable energy storage
  4. Actively pursue alternative unregulated revenue streams i.e. telecommunications, EPCM services, gas/water merger/acquisition

This is to be supported by ongoing corporate and asset strategies for continuous improvement (improve productivity / efficiency) to minimize operating overhead.

Digital Transformation, Perspectives, Strategy Advisory

(Digital) Technology at the Distribution System Operator (DSO)

The Distribution System Operator’s (DSO) purpose is to connect customers with electricity. This valued and essential service is achieved via an electrical infrastructure that is:

  • Pervasive and accessible across geographic extremes
  • Inherent high risk due to electrical hazards
  • Asset and capital expenditure intensive
  • Requires a skilled workforce
  • Experiencing changing customer behaviour and price sensitivity due to technological advances
  • Essential for Energy trading
  • Increasing community reliance for modern society
  • Capable of additional revenue streams eg telecommunications, engineering & design services

To meet and manage customer service objectives of improving safety, reliable and affordable; the electrical infrastructure and workforce has increasingly leveraged digital technology.

Digital technology is a broad term for all electronic, power electronic, computing and software systems used to provide, process, transform, protect, distribute and transfer electrical infrastructure, corporate and customer data.

Current State

The DSO’s digital technology portfolio includes Information Technology (IT) and Operational Technology (OT) including disciplines central SCADA Computing Systems, Geographic Information Systems (GIS), Metering Systems, Telecommunications Systems and both Substation and Pole Mounted Protection, Control and Automation systems.

A majority of the technology and information is core to the achievement of Western Power’s current and future customer service objectives, unique to an electricity utility and key to development of the energy industry.

Currently at most DSOs, technology is service orientated and grows inefficiently in response to specific problems or through the initiative of individuals.

This leads to two risks to the achievement of enterprise objectives!

Issues / Risks

  1. Undiscovered, islanded or under-utilised technology and information leading to underutilisation of infrastructure assets, constrained capital and skilled workforce
  2. Duplicated / disjointed technology frameworks, skillsets, processes leading to inefficiency, employee disengagement and current/emerging network risk

Vision

<Insert DSO’s Vision – Technology or otherwise>

Proposed Strategy

  1. Identify and establish a representative technology leadership group
  2. Provide a single accessible/consistent/agile/clear governance/framework
  3. Review and revise asset strategies, engineering standards and operations processes
  4. Re-prioritise the investment portfolio, including divestiture of commodity business units
  5. etc