Hitachi Energy – Hitachi announces historic $1 billion USD manufacturing investment to power America’s energy future through production of critical grid infrastructure

EMR Analysis

More information on Hitachi Ltd.:  https://www.hitachi.com +Through its Social Innovation Business (SIB) that brings together IT, OT(Operational Technology) and products, Hitachi contributes to a harmonized society where the environment, wellbeing, and economic growth are in balance. Hitachi operates globally in four sectors – Digital Systems & Services, Energy, Mobility, and Connective Industries – and the Strategic SIB Business Unit for new growth businesses. With Lumada at its core, Hitachi generates value from integrating data, technology and domain knowledge to solve customer and social challenges. Revenues for FY2024 (ended March 31, 2025) totaled 9,783.3 billion yen, with 618 consolidated subsidiaries and approximately 280,000 employees worldwide.

More information on Toshiaki Higashihara (Executive Chairman, Hitachi Ltd.): https://www.hitachi.com/corporate/about/officers/index.html#toshiaki-higashihara 

More information on Toshiaki Tokunaga (President & Chief Executive Officer, Hitachi Ltd.): https://www.hitachi.com/New/cnews/month/2024/12/f_241216.pdf + https://www.linkedin.com/in/toshiaki-tokunaga-7113381aa/ 

 

More information on Hitachi Energy by Hitachi Ltd.: See the full profile on EMR Executive Services

More information on Andreas Schierenbeck (Senior Vice President and Executive Officer, Head of Energy Business, Hitachi, Ltd. + Chief Executive Officer, Hitachi Energy Ltd.): See the full profile on EMR Executive Services

More information on Ismo Haka (Chief Financial Officer and Executive Vice President, Hitachi Energy, Hitachi Energy Ltd.): See the full profile on EMR Executive Services

 

 

 

More information on Donald J. Trump (45th President, USA + 47th President, USA): https://www.whitehouse.gov/administration/donald-j-trump/  + https://www.donaldjtrump.com/

 

More information on Glen Youngkin (Governor, Virginia, USA): https://www.governor.virginia.gov/ + https://www.linkedin.com/in/glenn-youngkin/ 

 

More information on the White House AI Action Plan: https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf + In July 2025, President Trump took decisive steps toward achieving this goal duringhis first days in office by signing Executive Order 14179, “Removing Barriers to American Leadership in Artificial Intelligence,” calling for America to retain dominance in this global race and directing the creation of an AI Action Plan.

America’s AI Action Plan has three pillars: innovation, infrastructure, and international diplomacy and security. The United States needs to innovate faster and more comprehensively than our competitors in the development and distribution of new AI technology across every field, and dismantle unnecessary regulatory barriers that hinder the private sector in doing so. 

We need to establish American AI—from our advanced semiconductors to our models to our applications—as the gold standard for AI worldwide and ensure our allies are building on American technology.

Several principles cut across each of these three pillars. First, American workers are central to the Trump Administration’s AI policy. Second, our AI systems must be free from ideological bias and be designed to pursue objective truth rather than social engineering agendas when users seek factual information or analysis. Finally, we must prevent our advanced technologies from being misused or stolen by malicious actors as well as monitor for emerging and unforeseen risks from AI.

 

More information on US Department of Energy (DOE): https://www.energy.gov/ + The U.S. Department of Energy’s mission is to ensure America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions.

More information on Chris Wright (Secretary of Energy, US Department of Energy (DOE), United States of America): https://www.energy.gov/our-leadership-offices + https://www.linkedin.com/in/chris-wright-b8370a17b/ 

 

More information on the US Department of the Interior, USA: https://www.doi.gov/ + The U.S. Department of the Interior protects and manages the Nation’s natural resources and cultural heritage; provides scientific and other information about those resources; and honors its trust responsibilities or special commitments to American Indians, Alaska Natives, Native Hawaiians, and affiliated Island Communities.

More information on Doug Burgum (55th Secretary, the US Department of the Interior, USA): https://www.doi.gov/secretary-doug-burgum + https://www.linkedin.com/in/doug-burgum-03019111/ 

 

More information on the US National Energy Dominance Council: https://www.whitehouse.gov/presidential-actions/2025/02/establishing-the-national-energy-dominance-council/ + The National Energy Dominance Council is a U.S. White House body established by President Donald Trump in February 2025 to advise the President on achieving “energy dominance” by increasing energy production and supply chains.

More information on Jarrod Agen (Executive Director, White House National Energy Dominance Council, USA): https://www.linkedin.com/in/jarrodagen/ 

 

More information on Mark Warner (Senator, USA): https://www.warner.senate.gov/public/ + https://www.congress.gov/member/mark-warner/W000805 

 

More information on Tim Kaine (Senator, USA): https://www.kaine.senate.gov/ + https://www.congress.gov/member/timothy-kaine/K000384 

 

More information on John McGuire (Congressman, USA): https://mcguire.house.gov/ + https://www.congress.gov/member/john-mcguire/M001239 + https://www.linkedin.com/in/john-j-mcguire/ 

 

More information on the Commonwealth’s Congressional Delegation, Virginia, USA): https://www.bluebook.virginia.gov/legislative-branch/virginia-congressional-delegation/ 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• Grid, Microgrids, DERs and DERM’s:
  • Grid / Power Grid:
    • The power grid is a network for delivering electricity to consumers. The power grid includes generator stations, transmission lines and towers, and individual consumer distribution lines.
      • The grid constantly balances the supply and demand for the energy that powers everything from industry to household appliances.
      • Electric grids perform three major functions: power generation, transmission, and distribution.
  • Microgrid:
    • Small-scale power grid that can operate independently or collaboratively with other small power grids. The practice of using microgrids is known as distributed, dispersed, decentralized, district or embedded energy production.
  • Smart Grid:
    • Any electrical grid + IT at all levels.
  • Micro Grid:
    • Group of interconnected loads and DERs (Distributed Energy Resources) within a clearly defined electrical and geographical boundaries witch acts as a single controllable entity with respect to the main grid.
  • Distributed Energy Resources (DERs): 
    • Small-scale electricity supply (typically in the range of 3 kW to 50 MW) or demand resources that are interconnected to the electric grid. They are power generation resources and are usually located close to load centers, and can be used individually or in aggregate to provide value to the grid.
      • Common examples of DERs include rooftop solar PV units, natural gas turbines, microturbines, wind turbines, biomass generators, fuel cells, tri-generation units, battery storage, electric vehicles (EV) and EV chargers, and demand response applications.
  • Distributed Energy Resources Management Systems (DERMS):
    • Platforms which helps mostly distribution system operators (DSO) manage their grids that are mainly based on distributed energy resources (DER).
      • DERMS are used by utilities and other energy companies to aggregate a large energy load for participation in the demand response market. DERMS can be defined in many ways, depending on the use case and underlying energy asset.

 

 

• Substation:
  • A power station is where the power is generated. A substation is a critical part of an electrical generation, transmission, and distribution system, where power is split apart, transformed, and distributed further into the grid.
  • Substations contain the specialist equipment that allows the voltage of electricity to be transformed (or ‘switched’). The voltage is stepped up or down through pieces of equipment called transformers, which sit within a substation’s site.
  • Substations typically include:
    • Transformers: The core components for voltage transformation.
    • Circuit Breakers: To isolate and protect equipment.
    • Switchgear: For controlling and protecting the flow of electricity.
    • Shunt Reactors (sometimes): Used to improve system stability.
    • Other equipment: Measuring instruments, control panels, etc.

 

• Transformers (Power Transformers, Distribution Transformers, Traction Transformers, HVDC Converters, Solid State Transformers (SST), Rectifier Transformers):
  • A transformer is a passive electrical device that transfers electrical energy from one electrical circuit to another, or to multiple circuits. It can be classified into three types based on voltage change:
    • Step-up: Increases voltage and decreases current.
    • Step-down: Decreases voltage and increases current.
    • Isolation: Provides electrical isolation without changing the voltage.
  • Distribution vs. Power Transformers:
    • Power Transformers: These are used in high-voltage transmission networks for both stepping up and stepping down applications (e.g., 400 kV, 200 kV). They are generally rated above 200 MVA and are designed for maximum efficiency at or near full load.
    • Distribution Transformers: These are used in lower-voltage distribution networks to connect to end-users (e.g., 11 kV, 440V, 230V). They are generally rated less than 200 MVA and are designed for maximum efficiency at 60-70% of their rated load, as they operate at a load less than full load. They perform the final voltage transformation for household and commercial use.
  • Specialized Transformers:
    • Traction Transformers: These are special transformers used in railway systems to step down high-voltage AC power from the overhead catenary to the required voltage for the train’s traction system. They are typically medium-frequency transformers with ratings ranging from 25 kVA to 25 MVA.
    • HVDC Converter Transformers: These are used in HVDC stations. The transformer steps up the generated AC voltages to the required level before it is rectified into DC for long-distance transmission.
    • Solid State Transformers (SSTs): Also known as power electronic transformers (PETs) or intelligent universal transformers (IUTs), these are AC-AC converters that can replace conventional transformers. SSTs use power electronic converters in conjunction with a high-frequency transformer, which allows for smaller size and weight.
    • Rectifier Transformers: These transformers provide an AC output that is then converted into DC by a rectifier. Their design helps to ensure that the resulting DC is as smooth and stable as possible. They are used in industrial processes that require large amounts of DC power.

 

 

• Shunt Reactor:
  • Shunt reactors (SRs) are used in high-voltage energy transmission systems to control the voltage during load variations.
  • A shunt reactor is a device that absorbs reactive power, thereby stabilizing the voltage and increasing the energy efficiency of the system. It is the most compact device commonly used for reactive power compensation in long high-voltage transmission lines and in cable systems.
  • A shunt reactor can be directly connected to the power line or to a tertiary winding of a three-winding transformer. The shunt reactor can be permanently connected or switched via a circuit breaker. Unlike a power transformer, a shunt reactor typically has only one winding per phase.

 

 

• Extra Low-Voltage (ELV):
  • Extra-Low Voltage (ELV) is defined as a voltage of 50V or less (AC RMS), or 120V or less (ripple-free DC).
• Low-Voltage (LV):
  • The International Electrotechnical Commission (IEC) defines Low Voltage (LV) for supply systems as voltage in the range 50–1000 V AC or 120–1500 V DC.
• Medium-Voltage (MV):
  • Medium Voltage (MV) is a voltage class that typically falls between low voltage and high voltage, with a common range being from 1 kV to 35 kV. In some contexts, this range can extend higher, up to 69 kV.
• High-Voltage (HV):
  • The International Electrotechnical Commission define high voltage as above 1000 V for alternating current, and at least 1500 V for direct current.
• Super High-Voltage or Extra High-Voltage (EHV): 
  • Super High-Voltage or Extra High-Voltage (EHV) is the voltage class used for long-distance bulk power transmission. The range for EHV systems is typically from 230 kV to 800 kV.
• Ultra High-Voltage (UHV): 
  • Ultra High-Voltage (UHV) is the highest voltage class used in electrical transmission, defined as a voltage of 1000 kV or greater.

 

 

• AI – Artificial Intelligence:
  • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
  • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but several, including Python, R and Java, are popular.
  • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
  • Machine Learning (ML):
    • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • ML algorithms use historical data as input to predict new output values.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
  • Deep Learning (DL):
    • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Computer Vision (CV):
    • Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
  • Machine Vision (MV):
    • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
    • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
    • Computer Vision systems can gain valuable information from images, videos, and other visuals, whereas Machine Vision systems rely on the image captured by the system’s camera. Another difference is that Computer Vision systems are commonly used to extract and use as much data as possible about an object.
  • Generative AI (GenAI):
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.

 

• Edge AI Technology:
  • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
  • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
  • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
• Multimodal Intelligence and Agents:
  • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
  • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
  • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
• Small Language Models (SLM) and Large Language Models (LLM):
  • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
  • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
  • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
• Agentic AI:
  • Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
  • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
  • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
• High-Density AI: 
  • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.

 

 

• Cloud Computing:
  • Cloud computing is a general term for anything that involves delivering hosted services over the internet. It is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center.

• Data Centers:
  • A data center is a facility that centralizes an organization’s shared IT operations and equipment for the purposes of storing, processing, and disseminating data and applications. Because they house an organization’s most critical and proprietary assets, data centers are vital to the continuity of daily operations.
• Hyperscale Data Centers:
  • The clue is in the name: hyperscale data centers are massive facilities built by companies with vast data processing and storage needs. These firms may derive their income directly from the applications or websites the equipment supports, or sell technology management services to third parties.
• White Space and Grey Space in Data Centers:
  • White space in a data center refers to the area where IT equipment is placed. It typically houses servers, storage, network gear, and racks.
  • Gray space, on the other hand, is the area where the back-end infrastructure is located. This space is essential for supporting the IT equipment and includes areas for switchgear, UPS, transformers, chillers, and generators.
• Edge Computing:
  • Edge computing is a form of computing that is done on site or near a particular data source, minimizing the need for data to be processed in a remote data center.
  • Edge computing can enable more effective city traffic management. Examples of this include optimising bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in future, managing autonomous car flows.
  • An edge device is any piece of hardware that controls data flow at the boundary between two networks. Edge devices fulfill a variety of roles, depending on what type of device they are, but they essentially serve as network entry — or exit — points.
  • There are five main types of edge computing devices: IoT sensors, smart cameras, uCPE equipment, servers and processors. IoT sensors, smart cameras and uCPE equipment will reside on the customer premises, whereas servers and processors will reside in an edge computing data centre.
  • In service-based industries such as the finance and e-commerce sector, edge computing devices also have roles to play. In this case, a smart phone, laptop, or tablet becomes the edge computing device.
  • Edge Devices:
    • Edge devices encompass a broad range of device types, including sensors, actuators and other endpoints, as well as IoT gateways. Within a local area network (LAN), switches in the access layer — that is, those connecting end-user devices to the aggregation layer — are sometimes called edge switches.

• HPC (Hight-Performance Computing):
  • Practice of aggregating computing resources to gain performance greater than that of a single workstation, server, or computer. HPC can take the form of custom-built supercomputers or groups of individual computers called clusters.

 

 

• Supply Chain: 
  • Network of all the individuals, organizations, resources, activities and technology involved in the creation and sale of a product. A supply chain encompasses everything from the delivery of source materials from the supplier to the manufacturer through to its eventual delivery to the end user.
  • At the most fundamental level, Supply Chain Management (SCM) is management of the flow of goods, data, and finances related to a product or service, from the procurement of raw materials to the delivery of the product at its final destination.

 

 

• Information Technology (IT) & Operational Technology (OT):
  • Information Technology (IT): 
    • Refers to anything related to computer technology, including hardware and software. Your email, for example, falls under the IT umbrella. IT forms the technological backbone of most organizations and companies by managing data, communications, and business processes. These devices and programs have little autonomy and are updated frequently.
  • Operational Technology (OT): 
    • Refers to the hardware and software used to change, monitor, or control physical devices, processes, and events within a company or organization. This form of technology is most commonly used in industrial settings, where these systems are engineered for safety, reliability, and precision control. An example of OT includes SCADA (Supervisory Control and Data Acquisition).
  • => The main difference between OT and IT devices:  OT devices control the physical world, while IT systems manage data.

 

 

• Transmission and Distribution (T&D):
  • Vital components of the electricity supply chain, ensuring that electricity reaches consumers safely and reliably.
    • Transmission:
      • Transmission is the first vital component of the electricity supply chain.
      • High-voltage electricity: Electricity is generated at power plants at relatively low voltages. To transport it efficiently over long distances, the voltage is increased significantly using transformers.
      • Long distances: High-voltage transmission lines, often carried on tall towers, transport bulk electricity from power plants to substations located closer to cities and towns.
      • Minimizing losses: Transmitting electricity at high voltage reduces energy loss during transportation.
    • Distribution
      • Distribution is the final stage of the electricity supply chain, bringing power to the end user.
      • Lower voltage electricity: At substations, the high-voltage electricity is reduced to lower, safer levels suitable for homes and businesses.
      • Local delivery: Distribution lines, typically the ones you see along streets, carry electricity from substations to individual customers.
      • Final stage: Distribution is the final step in the electricity delivery process, bringing power directly to homes and businesses for everyday use.