Energy management in industry: How to Maximize your Systems Performance

In the past the energy system was silo-ed and divided into the market players for generation, conversion and consumption with a unidirectional energy flow. Meanwhile consumers (and among them also industrial manufacturers) got into the role of producers and renewable energy generation became an important share in the total energy mix (as direct consequence of lower LCoE*).

ESG and other sustainability criteria are motivating companies to erect their own generation facilities and / or be acknowledgeable about their energy supply chain. Volatility in energy prices and the recent scarcity of certain primary energy sources (like gas) is forcing consumers to significantly revisit their energy demand.

In energy-intense industries, throttling production facilities are currently desperate scenarios to respond to partly uncontrollable energy cost increases. Therefore, energy efficiency needs to be understood as a comprehensive and integrative task nurtured with data.

In this article I’ll share my thoughts how to design a smart energy management system, that is based on holistic exploration of data along the entire energy supply chain and allows for intelligent decision making based on adequate data and software models.

What is Energy Management and what if energy management becomes intelligent?

Energy management is a complex process that must be understood comprehensively by industrial consumers to obtain the best possible and efficient energy supply, for which the different stages of energy generation, conversion, recovery and storage need all to be taken into account .

Primary energy is generated from a variety of sources such as coal, oil, renewables, nuclear and natural gas. Primary energy conversion involves transforming these sources into usable forms of energy such as heat, electricity, gas and fuels. Energy storage allows for the capture and stabilization of excess energy during off-peak hours. The different use cases for energy include transportation, electricity and gas demand as well as industrial process heating and cooling.

The main categories of energy management are power optimization, demand response, and storage optimization.

• Power Optimization: Power optimization can be used to ensure that the energy consumed is as close as possible to the energy produced. This generally involves using an energy management system, or EMS, to analyze data from all aspects of an organization’s power system to identify opportunities for optimization.
• Demand Response: Demand response can be used to reduce peak demand on the power grid. It involves collecting data from end-use customers to manage demand, and then offering this demand to the energy system operator to control the system and reduce the overall power demand.
• Storage Optimization: In many companies, energy storage is a key element of energy management. Energy storage is used to manage the variability of renewable energy and to provide back-up power generation in the event of an electricity outage of to save energy surplus generated by renewable energy sources.

A smart energy management system allows for data flows being captured by sensors and other meters to obtain the energy consumption of physical components. Interfaces, computer aided tools and other software based systems are built on top for consumption assessment and analysis, simulation and prediction. With further algorithm and scenario based computation to backtest predictive actions, prescriptions for the ultimate applications can be derived to achieve an optimal energy supply chain, that fulfills the set targets for environmental impact and economic sustainability.

Take aways

???Smart energy management requires an understanding of data flows and information processing (about consumption)

???Software tools and advanced computation allows for an intelligent interpretation and thus for predictive actions based on evidencing information

???Applications can then be prescribed in their energy consumption for an overarching target of reducing carbon footprint and / or gain economic viability

How to implement a smart energy management system?

Actionable structure follows strategy - executable strategies for energy management do comprise today a holistic approach that covers the environmental impact and economical viability of the consuming applications based on a continuous improvement evidenced by data from multidisciplinary domains: engineering, energy, technology and finance.

The following key strategies for energy management should be applied across the different areas of energy management.

• Establish a Strategy for Energy Management: Establish a clear strategy for energy management so that all aspects of the business are accounted for and decisions are made in accordance with the company strategy and business objectives. This strategy should be based on a thorough assessment of the company’s current and future energy requirements and demand, as well as its current and future energy supply options. The main target is to ensure and sustain the economic viability of the company’s energy supply chain.
• Build a Data-driven Model that supports important data flows executing the energy management strategy and ensures operability with other systems involved in the energy supply chain. In particular considering adequate spatial and time resolution in the data sources are an important guarantor that data is being processed with a high accuracy to gain important insights for future projections in usage and economics.
• Engage and Train Employees: Learning and education specifically for understanding and interpreting the multidisciplinary data ensures important input into the decision making process.
• Optimize Power Usage: Optimize the power usage of critical equipment by identifying opportunities for power optimization, such as the use of suboptimal equipment, unnecessary equipment or unneeded sub-systems.
• Monitor the Effectiveness of Power Optimization: Monitor the effectiveness of power optimization techniques, such as sub-metering, in order to ensure that the power optimization solution is meeting its objectives.
• Reduce Energy Costs: Reduce energy costs by using demand response and other demand-side management techniques, such as using smart appliances or using remote power monitoring, and using electricity storage to generate power during low-cost periods.

Take aways

???Smart Energy Management is data and software driven; only then all aspects for optimization are captured: environmental, economic and machine performance

???Raising sensitivity through education allows for a continuous improvement throughout strategy execution for a smart energy management

Data-driven Decisions to raise energy efficiency

A smart energy management system requires a data and software driven - technology rich - model to gain a deep understanding about the energy, information and trade flows in a multi-carrier energy system with:

• Primary energy sources
• Conversion, recovery and storage
• Final energy output
• End user sectors with their demands

Digitalization of this sector goes along with understanding the data underlying in the energy, information and trade flow to process the information and distill insights for further optimization and initiation of predictive and / or prescriptive actions.

Therefore, some key decisions in energy management are related to the use of data.

What Data to collect: A technology rich model collects data in different dimensions, i.e. data about energy, information and trade flows. Input data covering different domains regarding machinery, energy consumption and economic values needs to be processed in computation modules for tracking machinery / asset activity, stock and capacity, e.g.:

• Historic production data with load profiles
• End-use demand, product lifetimes, process yields, recycling and reuse rates
• Energy and raw material intensities, energy prices, CAPEX & OPEX, CO2 emissions reduction trajectory
• Historic and planned capacity, lifetimes, refurbishments

How and when to Collect Data: With sensors, meters and other devices that could supply data over an IP address, data collection is at easing reach. Spatial and time resolution need to be harmonized for consistent interpretation in further appliances to distill useful insights and predict / project consumption, material stocks saturation, scrap availability and production as examples.

What to Do With the Data: With analytical capabilities simulations can be run to design scenarios for projections and their backtesting with actual data. A further step is then the prescription of actions related to automation, machine controls and 3D printing to name a few. Based on a stepwise approach following the set energy management strategy an architecture for automated decision support can be developed to empower energy consumers.

Take aways

???Digitalization of the energy sector means the consistent collection of data about energy, information and trade flows in a multi-carrier system

???The benefits with digital demand-side end-use efficiency and flexibility are improved energy system cost efficiencies and an empowerment of energy consumers

What does it mean to your organization?

Energy has become more volatile, and the market is not always able to supply the amount of energy that consumers need at affordable costs. This has caused customers to scramble for cheaper sources of energy and has brought new challenges to the energy industry. The continued development of renewable energy sources like solar and wind has created new challenges such as grid instability and limited supply.

Energy management tools allow customers to make informed decisions about their energy use. Energy management is the process of optimizing energy consumption in an organization to reach desired results. It is an approach to managing energy that involves accurate measurements, optimization, and monitoring to avoid wasting energy and resources.

The overall goal of energy management is to reduce operating costs while increasing overall productivity and quality. Energy management deals with the various aspects of the energy sector including power generation, power distribution, and energy storage.

Smart energy management allows for an intelligent interpretation of data along energy, information and trade flow to build an architecture for automated decision support about the energy supply chain in the organization.

In our work at /LD7 we are using an energy management blueprint to help our customers identify the main fields for action in their organizations and digitalize in a targeted way. Using agile frameworks allows us to increment the features development and expansion of an initial MVP in the digitalization of the energy supply chain in a company.

Feel free to leave a DM for starting the conversation and discuss the options for your individual challenge.

*LCoE - Levelized Cost of Energy calculates the present value of the total cost of building and operating a power plant over an assumed lifetime - it measures lifetime costs divided by energy production. A good reference for an overview is Lazard's yearly analysis.