Greener grids

How can energy intensive industries, such as mining, meet sustainability goals? ABB is evolving its industrial grid design toolbox to enable automatic analysis of various load, supply, and potential fault scenarios in the industrial power grid, thereby assisting engineers, designers, and operators in making design and operational decisions. 

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_{Boda, boda.li@de.abb.com
Chen Song, chen.song@de.abb.com
Theresa Loss, theresa.loss@
de.abb.com
Matthias Biskoping, matthias.biskoping@de.abb.com
Jan Schlake, jan-christoph.schlake@de.abb.com
ABB Corporate Research Mannheim, Germany}

_{Maryam Sharifi, maryam.sharifi@ se.abb.com
Fredrik Ljungberg, fredrik.ljungberg@ se.abb.com
Stefan Thorburn, stefan.thorburn@ se.abb.com
ABB Corporate Research Vaesteras, Sweden}  

_{Nic Beutler, nic.beutler@ch.abb.com
Process Industries, Baden-Daettwil, Switzerland} 

By merging detailed models of consumption, equipment and operations with historical data on renewable energy, ABB´s mine electrification framework, ABB eMine™, efficiently optimizes the integration of renewable power sources into industrial settings – particularly in the mining industry. 

To reach worldwide sustainability targets, the integration of renewables is of paramount importance. This is particularly critical in industries such as manufacturing, mining, hydrogen production, and data centers, which are known for their substantial energy needs. Transitioning these industries to operations based on renewable energy is not only about meeting their high energy demands but also about aligning these efforts with further targets such as carbon emission reductions and sustainability. The availability of renewables at any given industrial plant varies significantly based on daily and seasonal variabilities. This means that each installation must be individually designed to meet the specific needs of its industry. Current estimates foresee renewable sources supplying 45 to 50 percent of global electricity generation by 2030, and between 65 and 85 percent by 2050 [1].
However, integrating renewable energy into the industrial power grids of these industries presents a set of unique challenges, because each industry exhibits complex operational dynamics that lead to intricate load changes. The variabilities of renewable energy sources further escalate this complexity, making the analysis and design of power grids challenging. Moreover, to ensure the smooth operation of any given industrial site, there must always be a balance between multiple energy sources and the overall energy-consuming system →01. In almost all cases this requires a continuous power supply all year round. In addition to the challenges on supplies and loads posed by intermittency, an increasing number of applications utilize non-linear equipment, which adversely affects power quality through harmonics and power factor issues. As a result, poor power quality impacts asset health and performance, resulting in asset failure, diminished or halted production, and eventually plant financial losses.

To address these challenges, industrial power grids must efficiently and reliably handle a mix of conventional and renewable energy sources and accommodate diverse operational scenarios ranging from steady state to transient conditions. Furthermore, the scarcity of skilled professionals in many industrials domains, combined with the imperative to integrate user requirements early in industrial power grid development, emphasizes the urgent need for innovative and efficient design solutions. This need is further compounded by the absence of automated solutions and the lack of accessible and useful data, which significantly delays the incorporation of renewables into systems. The resulting prolonged integration periods can lead to substantial additional costs, delays and intensified challenges associated with renewable energy adoption. 

Detailed simulation models and advanced framework

In today’s industrial power grids, understanding the intricate dynamics of equipment energy consumption is essential for renewable energy integration, increasing operational efficiency, and reducing costs. To better support the grid design and corresponding analysis, ABB sets out to provide an in-depth analysis of energy consumption in industrial settings by precisely modelling different assets. These models can capture the unique profiles from diverse stationary systems, such as motors and charging stations, as well as from mobile assets such as trucks.

ABB also considers how dynamic changes in plant operations affect models. For this purpose, a sufficiently generic modeling framework is used to capture physical effects across different time frames. Additionally, industrial plants often operate assets from various manufacturers, and because data and expertise are valuable, it is crucial to keep the models’ implementation details confidential. To tackle these issues, ABB uses the interoperability standardized Functional Mockup Interface (FMI) concept, allowing for the combination of models from different manufacturers. Functional Mockup Units are simulation model files conforming to the FMI standard, comprising only compiled code. This allows the protection of model integrity as well as the intellectual property of sources. The models and simulation framework facilitate the creation of load profiles for grid design and seamless integration into the grid analysis module.

Automated industrial power grid design

With these challenges in mind, ABB has developed an automated power grid design and analysis framework that is specifically tailored for modern industrial power grids and is focused on the challenges encountered during renewable energy integration. As illustrated in →02, this solution leverages advanced optimization theory and thorough analysis of power grid conditions to streamline the design process, enhance operational efficiency and reduce the costs associated with integrating renewable energy.

Indeed, the integration of renewable energy is the essential ingredient in achieving truly sustainable systems. However, as many users have discovered, integrating renewable energy into industrial energy systems often necessitates upgrades or modifications to the existing power grid to accommodate complicated load dynamics and renewable energy fluctuations. ABB addresses this challenge by developing a comprehensive automation of grid design solutions tool suite. This innovative approach improves the grid layout design process by incorporating an adaptive feedback loop informed by dynamic analysis results. 

_{¹ TCO: Total cost of ownership
² KPI: Key Performance Indicator
³ Power quality criteria: Power quality criteria refer to the set of standards and guidelines used to evaluate the stability and reliability of the electrical power. These criteria cover various aspects of power supply, including voltage/frequency stability, power supply continuity, harmonic distortion, power factor, etc. High-quality power is essential for the smooth operation of industrial equipment and for reducing the risk of operational disruptions.}

The process begins with collecting data and user-specific requirements, including key performance indicators such as building costs and carbon emissions. This are used to generate an optimal power grid layout that takes elements such as voltage ratings and cable selection into account. This layout is achieved by solving an optimization model that adheres to grid codes and static operational safety margins. Optimized grid design results can then be read by current mainstream power system analysis software. The system automatically generates various simulation and operational scenarios for transient behavior analysis, such as switching and faults. In addition, the feedback loop adjusts the design through iterations based on transient analysis, thus refining its layout solutions.

The method’s advantages are manifold and significant. Compared to the traditional approach, it can dramatically reduce design time, thus streamlining processes. In addition, the combination of steady-state and dynamic transient analyses ensures high safety and accuracy, guaranteeing that the grid layout meets diverse operational requirements under various scenarios. The adaptive feedback loop further reinforces reliability, satisfying both static and dynamic safety criteria. Importantly, the method’s design is reusable and scalable, making it applicable to a range of industries such as mining, marine and port operations, as well as hydrogen production plants.

Industrial power grid performance optimization with automatic transient analysis

Today, the prevailing methodologies employed for the dynamic analysis of power grids predominantly entail the construction of a single-line diagram (SLD) within the relevant simulation tool, followed by the execution of simulations to examine grid behavior. However, there are significant drawbacks to this approach, as it takes a long time to manually set up grids – a process that can lead to human error – especially in cases with many grid components.

To avoid such drawbacks, a comprehensive approach for executing grid analysis is needed – one that includes automatic model creation and modifications to the grid setup. With this in mind, the analytical approach developed by ABB for power grid analysis delves into transient grid dynamics. Initially, the grid layout design results are imported as inputs of the simulation. All pertinent grid information, encompassing the type and electrical characteristics of each grid component, as well as the topological structure, resides within a data exchange universal format file as a bidirectional interface designed specifically for data transfer among applications.

This file format is universally supported by both the grid layout design and grid analysis modules, facilitating a seamless transfer of data without necessitating manual implementations of the grid. This aspect is particularly important when dealing with extensive grids or those undergoing structural changes due to topology modifications.

This initial phase marks the inception of the first step of automatic grid analysis, which offers notable advantages. After importing grid attributes, a master grid aligned with an SLD is automatically generated. Clone grids, modified from the master grid by altering component parameters or setups, are created with various dynamic asset models for transient behavior studies. In addition, the required dynamic simulation events, including load energization via switching and fault events, are generated and executed automatically. Analyses based on simulation results are then looped back into the grid design module, enabling the generation of a revised design if needed. This iterative process highlights the integration of analytical insights and automation, fostering an efficient and informed approach to power grid stability analysis. This automated open interface opens the door to optimizing grid operation and analysis.

This process involves sweeping through component parameter ranges and different operation scenarios to verify grid stability and optimize for a target variable, such as operational costs.

Most importantly, the techno-economical calculation functionality in this proposed simulation module enables investigation into grid expansion strategies, power quality assessments, and service interruptions, all of which further incorporate optimization decision criteria for resource allocation and profitability.

Applying grid design & analysis solutions to mining industries

The mining and mineral processing industry offers an ideal application area for ABB’s solution. Facing high energy demand and significant pushback due to its environmental impact, this industry urgently needs to integrate renewable energy sources such as solar and wind power to mitigate greenhouse gas emissions. Given the complex and dynamic power requirements of mining equipment, particularly the accelerated adoption of electric trucks and the associated infrastructure for stationary (battery charging), and dynamic energy transfer (vehicle propulsion), the complexity of elerictrical grid design and analysis in this area cannot be underestimated.

Yet, the absence of effective automated solutions hampers the smooth integration of renewable energy, thus slowing down the journey toward sustainability. ABB eMine™ answers this challenge by evolving its approach for mining grid design in such a way as to be able to efficiently address the related complexities and significantly contribute to more sustainable and effective industrial practices. Additionally, by utilizing the automated grid analysis method, various load, supply, and potential fault scenarios can automatically be analyzed in the power grid, thereby assisting engineers, designers, and operators in making design and operational decisions. This is accomplished by constructing detailed energy consumption and simulation models for a range of mining equipment. By merging this with historical data on renewable energy data →03, ABB can efficiently optimize power grid solutions for the integration of renewable energy.

Advanced analysis of this type offers optimal configuration solutions for mining grids. These solutions, informed by the renewable capability design and equipment energy consumption analyses, serve as essential inputs for the subsequent phase of dynamic behavior verification, thus ensuring system reliably without compromising efficiency.

In addition, the grid analysis approach automatically evaluates various operating scenarios, empowering mine operators with critical insights for informed decision-making. The solution will further form the basis and provide potential for developing advanced features in other mining areas, such as optimizing the sizing of battery storage systems and electric truck fleets.

All in all, by leveraging ABB’s process automation mining expertise, its industrial grid and analysis solutions enhance the offering around ABB eMine™ and support informed investment decisions, thus pointing the way to improved decarbonization and cost-efficient operations.

_{Reference
[1] Mckinsey & Co. Global Energy Perspective 2023. Available: https://www.mckinsey. com/industries/oiland-gas/our-insights/ global-energy-perspective-2023 [Accessed February 17, 2024].}

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