Siemens – Energy resilience and independence overtake climate confidence, study reveals ahead of COP30

EMR Analysis

More information on Siemens AG: See full profile on EMR Executive Services

More information on Dr. Roland Busch (President and Chief Executive Officer, Siemens AG): See full profile on EMR Executive Services

 

More information on Siemens Smart Infrastructure (SI) by Siemens AG: https://new.siemens.com/global/en/company/about/businesses/smart-infrastructure.html + Siemens Smart Infrastructure (SI) is shaping the market for intelligent, adaptive infrastructure for today and the future. It addresses the pressing challenges of urbanization and climate change by connecting energy systems, buildings, and industries. SI provides customers with a comprehensive end-to-end portfolio from a single source – with products, systems, solutions, and services from the point of power generation all the way to consumption. With an increasingly digitalized ecosystem, it helps customers thrive and communities progress while contributing toward protecting the planet. Siemens Smart Infrastructure has its global headquarters in Zug, Switzerland. As of September 30, 2024, the business had around 78,500 employees worldwide.

More information on Matthias Rebellius (Member of the Managing Board and Chief Executive Officer, Siemens Smart Infrastructure (SI), Siemens AG + Member of the Supervisory Board, Siemens Energy AG): See the full profile on EMR Executive Services

 

More information on the Siemens Infrastructure Transition Monitor 2025 Study by Siemens AG: https://www.siemens.com/global/en/company/insights/infrastructure-transition-monitor-2025.html + Based on a global survey of 1,400 executives and supplemented with in-depth expert interviews, the Siemens Infrastructure Transition Monitor 2025 provides comprehensive insights into the global transformation of infrastructure. This study examines, in three distinct reports, the interconnected pillars of change:

• How the evolution of energy infrastructure – enabled by digital technologies – is driving progess toward a net zero future
• The progress, priorities, and issues in the decarbonization of buildings
• The industrial sector’s progress toward sustainability

 

 

 

More information on COP30 – Climate Change Conference (10th till 21st of November 2025, Belém, Brazil): https://cop30.br/en  + The Conference of the Parties (COP) is the largest global event for discussions and negotiations on climate change. The meeting is held annually, with the presidency rotating among the five regions recognized by the United Nations.

In 2025, Brazil will have the honor of hosting the 30th Conference of the Parties (COP30), which will take place in Belém, Pará. The chosen city will provide the world with a unique platform to discuss climate solutions, firmly rooted in the heart of the Amazon.

As the host country, Brazil is committed to strengthening multilateralism and fostering consensus on global targets to reduce greenhouse gas emissions that contribute to the warming of the planet.

More information on Ambassador André Corrêa do Lago (President, COP30 Brazil + Vice-Minister for Climate, Energy and Environment, Ministry of Foreign Affairs, Brazil): https://cop30.br/en/brazilian-presidency 

 

 

 

More information on The Science Based Targets initiative (SBTi): https://sciencebasedtargets.org/ + The Science Based Targets initiative (SBTi) is a global body enabling businesses to set ambitious emissions reductions targets in line with the latest climate science. It is focused on accelerating companies across the world to halve emissions before 2030 and achieve net-zero emissions before 2050.

The initiative is a collaboration between CDP, the United Nations Global Compact, World Resources Institute (WRI) and the World Wide Fund for Nature (WWF) and one of the We Mean Business Coalition commitments. The SBTi defines and promotes best practice in science-based target setting, offers resources and guidance to reduce barriers to adoption, and independently assesses and approves companies’ targets.

• Defines and promotes best practices in emissions reductions and net-zero targets in line with climate science.
• Provides target setting methods and guidance to companies to set science-based targets in line with the latest climate science.
• Includes a team of experts to provide companies with independent assessment and validation of targets.
• Serves as the lead partner of the Business Ambition for 1.5°C campaign, an urgent call to action from a global coalition of UN agencies, business and industry leaders that mobilizes companies to set net-zero science-based targets in line with a 1.5 degrees C future.

 

More information on Net Zero by 2050 by the United Nations: https://www.un.org/en/climatechange/net-zero-coalition + Put simply, net zero means cutting greenhouse gas emissions to as close to zero as possible, with any remaining emissions re-absorbed from the atmosphere, by oceans and forests for instance.

Currently, the Earth is already about 1.1°C warmer than it was in the late 1800s, and emissions continue to rise. To keep global warming to no more than 1.5°C  – as called for in the Paris Agreement – emissions need to be reduced by 45% by 2030 and reach net zero by 2050.

More than 140 countries, including the biggest polluters – China, the United States, India and the European Union – have set a net-zero target, covering about 88% of global emissions. More than 9,000 companies, over 1000 cities, more than 1000 educational institutions, and over 600 financial institutions have joined the Race to Zero, pledging to take rigorous, immediate action to halve global emissions by 2030.

 

More information on Net Zero by 2050 by the Science Based Targets initiative (SBTi): https://sciencebasedtargets.org/net-zero + The SBTi’s Corporate Net-Zero Standard is the world’s only framework for corporate net-zero target setting in line with climate science. It includes the guidance, criteria, and recommendations companies need to set science-based net-zero targets consistent with limiting global temperature rise to 1.5°C.

UN vs. SBTi: 

• UN targets nations, while SBTi focuses on companies. UN sets a broad goal, while SBTI provides a detailed framework for target setting.
• Both aim to achieve net zero emissions and limit warming to 1.5°C. The UN sets the overall direction, and SBTi helps businesses translate that goal into actionable plans.

Key components of the Corporate Net-Zero Standard:

1. Near-term targets: Rapid, deep cuts to direct and indirect value-chain emissions must be the overarching priority for companies. Companies must set near-term science-based targets to roughly halve emission before 2030. This is the most effective, scientifically-sound way of limiting global temperature rise to 1.5°C.
2. Long-term targets: Companies must set long-term science-based targets. Companies must cut all possible – usually more than 90% – of emissions before 2050.
3. Neutralize residual emissions: After a company has achieved its long-term target and cut emissions by more than 90%, it must use permanent carbon removal and storage to counterbalance the final 10% or more of residual emissions that cannot be eliminated. A company is only considered to have reached net-zero when it has achieved its long-term science-based target and neutralized any residual emissions.
4. Beyond Value Chain Mitigation (BVCM): Businesses should invest now in actions to reduce and remove emissions outside of their value chains in addition to near- and long-term science-based targets.

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• Carbon Dioxide (CO2):
  • The primary greenhouse gas emitted through human activities. Carbon dioxide enters the atmosphere through burning fossil fuels (coal, natural gas, and oil), solid waste, trees and other biological materials, and also as a result of certain chemical reactions (e.g., manufacture of cement). Carbon dioxide is removed from the atmosphere (or “sequestered”) when it is absorbed by plants as part of the biological carbon cycle.
• Biogenic Carbon Dioxide (CO2):
  • Biogenic Carbon Dioxide (CO2) and Carbon Dioxide (CO2) are the same molecule. Scientists differentiate between biogenic carbon (that which is absorbed, stored and emitted by organic matter like soil, trees, plants and grasses) and non-biogenic carbon (that found in all other sources, most notably in fossil fuels like oil, coal and gas).
• CO2e (Carbon Dioxide Equivalent):
  • CO2e means “carbon dioxide equivalent”. In layman’s terms, CO2e is a measurement of the total greenhouse gases emitted, expressed in terms of the equivalent measurement of carbon dioxide. On the other hand, CO2 only measures carbon emissions and does not account for any other greenhouse gases.
  • A carbon dioxide equivalent or CO2 equivalent, abbreviated as CO2-eq is a metric measure used to compare the emissions from various greenhouse gases on the basis of their global-warming potential (GWP), by converting amounts of other gases to the equivalent amount of carbon dioxide with the same global warming potential.
    • Carbon dioxide equivalents are commonly expressed as million metric tonnes of carbon dioxide equivalents, abbreviated as MMTCDE.
    • The carbon dioxide equivalent for a gas is derived by multiplying the tonnes of the gas by the associated GWP: MMTCDE = (million metric tonnes of a gas) * (GWP of the gas).
    • For example, the GWP for methane is 25 and for nitrous oxide 298. This means that emissions of 1 million metric tonnes of methane and nitrous oxide respectively is equivalent to emissions of 25 and 298 million metric tonnes of carbon dioxide.
• Carbon Footprint:
  • There is no universally agreed definition of what a carbon footprint is.
  • A carbon footprint is generally understood to be the total amount of greenhouse gas (GHG) emissions that are directly or indirectly caused by an individual, organization, product, or service. These emissions are typically measured in tonnes of carbon dioxide equivalent (CO2e).
  • In 2009, the Greenhouse Gas Protocol (GHG Protocol) published a standard for calculating and reporting corporate carbon footprints. This standard is widely accepted by businesses and other organizations around the world. The GHG Protocol defines a carbon footprint as “the total set of greenhouse gas emissions caused by an organization, directly and indirectly, through its own operations and the value chain.”
• Decarbonization:
  • Reduction of carbon dioxide emissions through the use of low carbon power sources, and achieving a lower output of greenhouse gases into the atmosphere.
• Carbon Credits or Carbon Offsets:
  • Permits that allow the owner to emit a certain amount of carbon dioxide or other greenhouse gases. One credit permits the emission of one ton of carbon dioxide or the equivalent in other greenhouse gases.
  • The carbon credit is half of a so-called cap-and-trade program. Companies that pollute are awarded credits that allow them to continue to pollute up to a certain limit, which is reduced periodically. Meanwhile, the company may sell any unneeded credits to another company that needs them. Private companies are thus doubly incentivized to reduce greenhouse emissions. First, they must spend money on extra credits if their emissions exceed the cap. Second, they can make money by reducing their emissions and selling their excess allowances.
• Carbon Capture and Storage (CCS) – Carbon Capture, Utilisation and Storage (CCUS):
  • CCS involves the capture of carbon dioxide (CO2) emissions from industrial processes. This carbon is then transported from where it was produced, via ship or in a pipeline, and stored deep underground in geological formations.
  • CCS projects typically target 90 percent efficiency, meaning that 90 percent of the carbon dioxide from the power plant will be captured and stored.
  • CCUS adds the utilization aspect, where the captured CO2 is used as a new product or raw material.
• Carbon Dioxide Removal (CDR) or Durable Carbon Removal: 
  • Carbon Dioxide Removal encompasses approaches and methods for removing CO2 from the atmosphere and then storing it permanently in underground geological formations, in biomass, oceanic reservoirs or long-lived products in order to achieve negative emissions.
  • Direct Air Capture (DAC): 
    • Technologies that extract CO2 directly from the atmosphere at any location, unlike carbon capture which is generally carried out at the point of emissions, such as a steel plant.
    • Constraints like costs and energy requirements as well as the potential for pollution make DAC a less desirable option for CO2 reduction. Its larger land footprint when compared to other mitigation strategies like carbon capture and storage systems (CCS) also put it at a disadvantage.
  • Direct Air Capture and Storage (DACCS):
    • Climate technology that removes carbon dioxide (CO2) directly from the ambient atmosphere using large fans and chemical processes to bind with the CO2.
  • Bioenergy with Carbon Capture and Storage (BECCS):
    • Negative emissions technology that captures carbon dioxide (CO2) from biomass used for energy production and stores it permanently. Plants absorb CO2 from the atmosphere as they grow (photosynthesis), and BECCS interrupts the cycle by capturing this biogenic CO2 during the energy conversion process—burning, fermentation, etc.—instead of letting it re-enter the atmosphere.
  • Enhanced Rock Weathering (ERW):
    • Carbon dioxide removal (CDR) technique that accelerates the natural process of rock weathering by grinding silicate rocks into dust and spreading it on land, typically agricultural fields. This process uses rainwater to convert atmospheric carbon dioxide into mineral carbonates, which are then stored long-term in soils, groundwater, and oceans.
• Limits of Carbon Dioxide Storage: 
  • Carbon storage is not endless; the Earth’s capacity for permanently storing vast amounts of captured carbon, particularly in geological formations, is limited, potentially reaching a critical limit of 1,460 gigatonnes at around 2200, though storage durations vary significantly depending on the method, from decades for some biological methods to potentially millions of years for others like mineralization. While some methods offer very long-term storage, the sheer volume needed to meet climate targets requires scaling up storage significantly beyond current capacity, raising concerns about the available volume over time.

 

• Global Warming: 
  • Global warming is the long-term heating of Earth’s climate system observed since the pre-industrial period (between 1850 and 1900) due to human activities, primarily fossil fuel burning, which increases heat-trapping greenhouse gas levels in Earth’s atmosphere.
• Global Warming Potential (GWP): 
  • The heat absorbed by any greenhouse gas in the atmosphere, as a multiple of the heat that would be absorbed by the same mass of carbon dioxide (CO2). GWP is 1 for CO2. For other gases it depends on the gas and the time frame.
  • Carbon dioxide equivalent (CO2e or CO2eq or CO2-e) is calculated from GWP. For any gas, it is the mass of CO2 which would warm the earth as much as the mass of that gas. Thus it provides a common scale for measuring the climate effects of different gases. It is calculated as GWP times mass of the other gas. For example, if a gas has GWP of 100, two tonnes of the gas have CO2e of 200 tonnes.
  • GWP was developed to allow comparisons of the global warming impacts of different gases.
• Greenhouse Gas (GHG):
  • A greenhouse gas is any gaseous compound in the atmosphere that is capable of absorbing infrared radiation, thereby trapping and holding heat in the atmosphere. By increasing the heat in the atmosphere, greenhouse gases are responsible for the greenhouse effect, which ultimately leads to global warming.
  • The main gases responsible for the greenhouse effect include carbon dioxide, methane, nitrous oxide, and water vapor (which all occur naturally), and fluorinated gases (which are synthetic).

• GHG Protocol Corporate Standard Scope 1, 2 and 3: https://ghgprotocol.org/ + The GHG Protocol Corporate Accounting and Reporting Standard provides requirements and guidance for companies and other organizations preparing a corporate-level GHG emissions inventory. Scope 1 and 2 are typically mandatory for companies that are required to report their emissions by national or regional regulations. The GHG Protocol itself is a voluntary standard.
  • Scope 1: Direct emissions:
    • Direct emissions from company-owned and controlled resources. In other words, emissions are released into the atmosphere as a direct result of a set of activities, at a firm level. It is divided into four categories:
      • Stationary combustion (e.g from fuels, heating sources). All fuels that produce GHG emissions must be included in scope 1.
      • Mobile combustion is all vehicles owned or controlled by a firm, burning fuel (e.g. cars, vans, trucks). The increasing use of “electric” vehicles (EVs), means that some of the organisation’s fleets could fall into Scope 2 emissions.
      • Fugitive emissions are leaks from greenhouse gases (e.g. refrigeration, air conditioning units). It is important to note that refrigerant gases are a thousand times more dangerous than CO2 emissions. Companies are encouraged to report these emissions.
      • Process emissions are released during industrial processes, and on-site manufacturing (e.g. production of CO2 during cement manufacturing, factory fumes, chemicals).
  • Scope 2: Indirect emissions – owned:
    • Indirect emissions from the generation of purchased energy, from a utility provider. In other words, all GHG emissions released in the atmosphere, from the consumption of purchased electricity, steam, heat and cooling. For most organisations, electricity will be the unique source of scope 2 emissions. Simply stated, the energy consumed falls into two scopes: Scope 2 covers the electricity consumed by the end-user. Scope 3 covers the energy used by the utilities during transmission and distribution (T&D losses).
  • Scope 3: Indirect emissions – not owned:
    • Indirect emissions – not included in scope 2 – that occur in the value chain of the reporting company, including both upstream and downstream emissions. In other words, emissions are linked to the company’s operations. According to the GHG protocol, scope 3 emissions are separated into 15 categories.

 

 

 

• 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.

 

 

• 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.
  • 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.
  • 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.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • 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) :
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs. While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.

 

 

• 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.

 

 

• Energy Storage System (ESS):
  • An energy storage system, often abbreviated as ESS, is a device or group of devices assembled together, capable of storing energy in order to supply electrical energy at a later time. An energy storage system consists of three main components:
    • a power conversion system, which transforms electrical energy into another form of energy and vice versa;
    • a storage unit, which stores the converted energy;
    • a control system, which manages the energy flow between the converter and the storage unit.
• Battery Energy Storage System (BESS):
  • A BESS is an energy storage system (ESS) that captures energy from different sources, accumulates this energy, and stores it in rechargeable batteries for later use.

• Hybridized Energy Storage System (HESS):
  • Combines two or more energy storage technologies in a single system to leverage their complementary strengths, improving overall performance, efficiency, and lifespan compared to using a single storage technology. An energy storage system must be reactive and flexible depending on demand which can vary considerably. As a result, within a fit for purpose HESS system there are storage components dedicated to “high power” demand such as supercapacitors and others dedicated to “high energy” demand such as batteries.
• Distributed Energy Storage Systems (DESS):
  • Distributed Energy Storage Systems (DESS) are energy storage devices deployed at multiple locations across an electrical grid rather than in one large, centralized facility. These systems, which can be as small as a home battery or as large as a utility substation system, store excess energy generated during low-demand periods or from renewable sources like solar and wind. They then release that energy when demand is high or renewable supply is low, which improves grid stability, resilience, and efficiency.