The United States stands at a critical juncture, confronting a surge in electricity demand driven by the rapid expansion of data centers and the broader electrification of its economy. This demand spike coincides with a worldwide imperative to transition toward cleaner energy sources. However, a complex and at times contradictory web of federal policies is creating significant headwinds. While the Inflation Reduction Act (IRA) offers powerful incentives to build a domestic clean energy supply chain, a concurrent strategy of imposing steep tariffs on imported components, particularly from China, is creating a policy paradox. This report will analyze how these conflicting measures, intended to foster long-term industrial strength, are raising the immediate cost of the cheapest sources of new power—solar, wind, and batteries—thereby threatening to increase electricity prices and delay the nation’s ability to meet the urgent power needs of data centers and a newly electrified society.

The Conflicting Signals of US Energy Policy

The current U.S. approach to the energy sector is characterized by two powerful but opposing policy levers: punitive tariffs and conditional incentives. This creates a volatile and uncertain environment for developers of renewable energy and storage projects.

The Tariff Wall Against Clean Energy Components

The U.S. has enacted a series of escalating tariffs, primarily under Section 301 of the Trade Act of 1974, targeting a wide range of Chinese goods essential for the energy transition. Lithium-ion batteries, a cornerstone technology for both electric vehicles (EVs) and grid stability, have been a primary focus. In 2024, the tariff on Chinese EV lithium-ion batteries rose from 7.5% to 25%. For non-EV batteries, such as those used in grid-scale storage systems, tariffs are also slated to increase to 25% by 2026. These duties are compounded by additional levies, leading to combined tariff rates on grid batteries of approximately 65%, with projections they could exceed 80%.

The immediate consequence of this tariff wall is a sharp increase in the price of these components in the U.S. market. This directly drives up the capital expenditures for renewable energy projects, complicating deal structures and introducing new financial risks. Because the U.S. battery energy storage system (BESS) industry is heavily reliant on Chinese imports, these tariffs have a particularly disruptive effect, leading to project delays and investment uncertainty.

The Inflation Reduction Act’s Conditional Incentives

In contrast to the punitive nature of tariffs, the 2022 Inflation Reduction Act (IRA) was designed to catalyze a domestic clean energy manufacturing renaissance through substantial subsidies. The Section 45X Advanced Manufacturing Production Credit, for instance, offers lucrative tax credits for domestically produced battery components, including $35 per kilowatt-hour (kWh) for battery cells and $10/kWh for battery modules.

However, these powerful incentives come with significant strings attached. To qualify for consumer tax credits like the $7,500 Clean Vehicle Credit, products must meet stringent sourcing requirements for battery components and critical minerals. Crucially, the IRA includes a “Foreign Entity of Concern” (FEOC) exclusion rule, which, starting in 2024, disqualifies any vehicle containing battery components from entities in China, Russia, Iran, or North Korea from receiving the credit.

This creates a policy paradox. The federal government is simultaneously subsidizing the clean energy industry while taxing its most critical and cost-effective inputs. For a project developer, this means navigating a landscape where the benefits of IRA credits may be partially or wholly negated by the increased costs imposed by tariffs. This dynamic forces companies to re-evaluate their supply chains, seek alternative suppliers that are often more expensive or have limited capacity, and contend with significant investment uncertainty.

The Direct Impact on Clean Power Costs

While the global trend for clean energy technologies has been one of rapidly falling costs, U.S. policy is creating a notable divergence, artificially inflating the price of the very technologies needed to decarbonize the power grid affordably.

The Rising Cost of Grid-Scale Battery Storage

Grid-scale battery storage is essential for a modern, reliable power grid. It solves the intermittency problem of wind and solar power by storing excess energy and dispatching it when needed, thereby enhancing grid stability. Lithium-ion batteries, particularly the Lithium Iron Phosphate (LFP) chemistry, have become the preferred choice for these applications due to their high efficiency and the fact that costs have declined 80-90% over th past ten years. .

However, U.S. tariffs are directly countering this deflationary trend. With the U.S. power industry facing an average tariff rate of 38% on electrical equipment, the cost of deploying BESS has risen significantly, deterring investment. This is especially damaging given that the cost of battery packs, which had been falling dramatically for over a decade, is a primary driver of the economic viability of storage projects. While technological advancements continue to push global battery prices down, U.S. trade policy is forcing domestic project costs in the opposite direction, slowing the deployment of this critical grid-balancing technology.

The Ripple Effect on Solar and Wind Projects

The cost pressures extend beyond batteries. Import tariffs are driving up capital expenditures for solar panels and wind turbines as well, complicating the economics of new renewable energy projects. Globally, wind and solar represent the cheapest sources of new electricity generation and are expected to provide 70-90% of all new power in the next 5 years. New grid power in the US was about 93% renewable in 2024. By artificially inflating their costs in the U.S., these policies blunt their competitive edge and slow the pace of their deployment. The result is a more expensive energy transition, where the cost savings that should be realized from adopting cheaper renewable sources are instead eroded by trade policy.

Consequences: Project Delays and Unmet Power Demand

The combination of higher costs and supply chain disruptions is creating a bottleneck in the deployment of new clean power resources. This bottleneck comes at the worst possible time, as new sources of electricity demand, particularly from data centers, are placing unprecedented strain on the nation’s grid. While current policies are pushing fossil power, no new coal plants will be built and the cost and schedule for new natural gas power plants has increased substantially with increased costs for steam and gas turbines and a shortage if engineering, procurement, and construction (EPC) manpower to build them.

The Data Center and Electrification Dilemma

The boom in artificial intelligence and cloud computing is fueling a massive build-out of data centers, which have immense and unrelenting power requirements. This, combined with the general electrification of transport and buildings, is creating a surge in new power demand that many utilities are struggling to meet. Clean energy, particularly solar-plus-storage projects, is the ideal solution to quickly power these new loads without increasing emissions. While recent government support for nuclear power is a longer-term option and while firms like Meta, Google, Amazon, and Microsoft have entered into alliances with new SMR and advanced reactor suppliers, new nuclear power will take a long time to get on-line and it is highly likely that new unproven reactors will have delays and cost increases.

However, U.S. policy is hindering this solution. The reliance of data centers on lithium-ion batteries for backup power and grid services means that tariffs are directly increasing their construction costs by mid-to-high single digits. More broadly, the delays and cost increases for utility-scale solar and battery projects make it harder for utilities to bring new, clean generation online in time to meet requests for new data center connections. This could force delays in the tech sector’s expansion or, perversely, lead to a greater reliance on fossil fuel “peaker” plants to meet the demand.

The impact on broader electrification is also significant. Tariffs on batteries and other components are contributing to a 10% or more increase in the price of EVs for American consumers, hindering the transition away from internal combustion engines. The complexity of the IRA’s sourcing rules further limits which vehicles qualify for consumer credits, acting as another drag on adoption.

Supply Chain Disruption and Canceled Projects

The strategic goal of reshoring the battery supply chain is a long-term endeavor. In the short-to-medium term, the primary effect of the current policy mix is disruption. Forced to seek alternatives to the dominant Chinese supply chain, U.S. companies face a market with a limited number of global suppliers and insufficient domestic capacity.

This disruption has tangible consequences. Between 2024 and 2025, canceled battery projects in the U.S. amounted to an estimated $9.5 billion, while new project announcements totaled only $1.175 billion. This investment chill, driven by cost uncertainty and supply chain instability, directly translates to a slower build-out of the manufacturing capacity and energy infrastructure needed for the transition.

Conclusion and Outlook

The United States is pursuing two parallel but conflicting policy goals: the rapid, affordable decarbonization of its economy and the strategic, long-term reshoring of its clean energy supply chain. While the latter is a valid national security and economic objective, the current strategy of combining high tariffs with complex, restrictive incentives is creating a policy paradox that jeopardizes the former.

By raising the cost of solar, wind, and battery storage, these policies are slowing the deployment of the cheapest and cleanest sources of new power. This threatens to inflate electricity prices for consumers and businesses and risks leaving the nation unable to cleanly and affordably meet the surging power demands of data centers and broader electrification. The ultimate success of this strategy will depend on how quickly a cost-competitive domestic supply chain can be established. In the interim, the U.S. faces a period of higher costs, project delays, and a potential slowing of its energy transition, highlighting the profound tension between the urgent need for clean energy deployment and the strategic desire for supply chain security.

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While the historical “War of the Currents” between Thomas Edison and Nikola Tesla seemingly settled on alternating current power dominance, the energy transition created a close relationship between alternating current and direct current power. We’re witnessing a surge in DC power systems and the need for efficient conversion to and from AC and DC. This is fueled by several factors:

• Solar photovoltaics: The largest source of new grid power, solar panels inherently produce DC electricity.
• Renewable Energy Transmission: Remote generation sites like offshore wind farms often utilize high-voltage direct current transmission for efficient long-distance power delivery.
• Energy Storage: Grid-scale batteries and electric vehicles store energy in DC form, requiring conversion for grid interaction.
• Hydrogen Production: The emerging green hydrogen sector relies on electrolysis, a process powered by DC electricity.
• Data centers, electric vehicle charging, and heat pumps are the major new loads for the power grid with new innovations in DC power networks. Supply chains increasingly rely or generate direct current.

Supply Chain Management and the Energy Transition

It is no secret that supply chains rely on fossil fuels. The transition to renewable energy and the adoption of sustainable practices are now essential for reducing environmental impact, ensuring regulatory compliance, and maintaining competitiveness. The logistics sector alone, a small portion of total supply chain emissions, is a significant contributor to greenhouse gas emissions. Road freight alone accounts for approximately 7% of global CO2 emissions, with maritime and air transport further amplifying the environmental burden.

The future of last mile is electric vehicles. Transitioning to EVs can also benefit from government subsidies and tax incentives, accelerating adoption. Companies like DHL and Amazon are already setting benchmarks by integrating EVs into their logistics operations.

And warehouses and factories increasingly utilize solar panels. Warehouses and factories can integrate solar panels and wind turbines to lower energy costs and carbon footprints. Facilities powered by renewable energy is beginning to attract environmentally conscious clients and stakeholders. Further retrofitting existing infrastructure with energy-efficient technologies not only further enhances sustainability efforts, it is often not that difficult.

The leading supply chain software solutions are cloud solutions, requiring data center power. Further, the future of supply chain management will be based on generative AI. GenAI is a major consumre4r of energy.

PCS Systems that Connect to The Grid Need New Features

This rise in devices that produce and consume DC power has created a booming market for Power Conversion Systems (PCS). According to an ARC market report, the PCS market (excluding transformers) is valued at $41 billion and ARC projects this market to grow at a compound annual growth rate (CAGR) of about 23 percent. This highlights the increasing importance of efficient and reliable PCS equipment. While it’s true that inverters and rectifiers that make PCS, are fundamentally manipulating analog waveforms (voltages and currents) with power transistors, modern PCS systems are far from purely analog and are highly integrated with the digital world.

To handle the complexities of the changing energy landscape, PCS (Power Conversion Systems) are evolving with increasingly sophisticated features. The rise of renewable energy sources presents new challenges for grid stability and reliability, as these power sources are inherently intermittent. Utilities are demanding PCS systems that not only seamlessly integrate renewable power but also enhance grid performance and safety.

This means PCS systems are now tasked with a wider range of responsibilities:

• Grid Stability: Regulating grid frequency, maintaining voltage and power factor stability. Power factor stability is the ability of a power system to maintain a steady state after being subjected to disturbances. A common industry standard generator power factor rating is 80%, meaning these loads can use 80% of the generator’s power supply.
• Power Quality: Inverters can minimize or even filter harmonic distortion to ensure clean and reliable power supply. Harmonic distortion is the presence of frequencies in the output of a device that are not present in the input signal. Harmonic distortion can lead to accidents such as facility overheating or equipment damage.
• Adaptability: Handling diverse generation sources and managing new load profiles from electric vehicles, data centers, and heat pump-based heating, ventilation, and air conditioning systems.

The evolution of PCS technology is crucial for the successful transition to a more sustainable and resilient energy system. By providing advanced functionalities and grid support capabilities, PCS systems are enabling greater penetration of renewable energy sources while maintaining the reliability and stability of the power grid.

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The energy transition is driven by the goal to decarbonize the energy used for transportation, buildings, and industry. Lacking a low-cost way of removing greenhouse gases from the air, the solution is to electrify as much as possible with clean power. In 1990, coal accounted for 54.6 percent of the total electricity generated in the US. This number steadily decreased over the years, to less than 17 percent in 2023. While the electric grid decarbonization has already made progress, the proportion of power generated by renewables must grow substantially to achieve clean power.

There are many multi-faceted management decisions and operational plans that need to be made by RTOs (Regional Transmission Operators), ISOs (Independent System Operators), regulators, and utilities of all sizes. There are financial and reliability benefits that are driving “behind the meter” (BTM) generation and storage which is adding to the complexity. Municipal power companies, rural cooperatives, commercial, agricultural, and residential customers are increasingly generating power and interacting with the larger power grid in new ways. There are many opportunities and risks for suppliers of hardware, software, and services to the electric power industry.

The next decade will see solar and wind generation as the dominant source of new power, further displacing dispatchable fossil power plants. The US installed about 33 GW of Solar PV in 2023 which is up from 21GW in 2022, but the Chinese PV installations were a staggering 216.9 GW eclipsing its record of 87.4 GW from the previous year as seen in the chart below. In a single year, China installed more PV power than the US has installed in its entire history. Grid and transmission operators in the US have a very large interconnection queue as they plan how to integrate renewable power and FERC order 2023 is pushing to reduce this queue which can delay interconnections by up to five years. One might ask how China managed the integration of this much power in a single year.

Technologies such as biofuels, geothermal, and tidal power will play a relatively smaller role. In the short term the grid will retain the existing hydro and nuclear base load, which will provide about 25 percent of the grid power. Wind and solar will eventually provide nearly half of all US grid power by 2050.

This is a complex problem, and solutions will vary by geographical region, weather conditions, the specific characteristics of the local and regional generation mix, and by the anticipated new electric loads such as EV charging, heat pumps, and eventually green hydrogen production at scale. The key grid operator themes at ISO New England below resemble issues across the US, Europe, and Asia:

Managing Risk

Existing reliability risks during extreme weather will be amplified by increasing restrictions on carbon emissions and a prevalence of limited energy resources (gas/renewables). There are limits to how much risk can be mitigated through the market. New England is learning from the response to extreme weather events in other regions.

Transmission Expansion

More transmission will be needed to interconnect and deliver large scale renewable energy to meet state policy goals (separate from reliability needs).

Adapting the Market Design

Electricity markets are adapting to encourage energy storage, clean peak power, demand response, with changing circumstances and policy objectives.

Planners must model the weather, utility generation assets, behind the meter solar generation and storage, new customer load profiles due to EV charging, new heat pump HVAC systems, the load from green hydrogen electrolyzers, how smart meters are evolving, how customers will behave with time-of-day pricing and how effective aggregators forming virtual power plants will be to achieve useful levels of demand response.

Modeling weather and human behavior has uncertainty and risks. Renewables will create significant gaps that must be predicted, and multiple measures must be put into place to ensure these gaps are filled and the grid is reliable as the energy transition progresses. In the short-term gas peaking plants and to a lesser extent coal fired plants are filling these gaps.

This means grid planners must model and simulate a progression of scenarios and each scenario will have a range of actions to reduce risks. Understanding the characteristics of generation, energy storage, the electrical distribution system, regulations, and the changing load profiles is essential to develop plans that will keep power costs low and power reliability high. This report will provide an update on these various technologies and the solutions that utilities have already deployed, are planning to deploy, and the technical and administrative obstacles ahead.

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The reason that microgrids are growing faster than larger grids comes down to economics. In many cases microgrids are now bankable investments due to high power costs, low renewable and battery costs, and the value of improving power resilience.

The option of using local power generation from a microgrid for supplying your electric power can alleviate the risk of power outages when your main grid fails. The utility that manages the main grid may be able to buy excess solar generation and may provide attractive power purchase options if the microgrid has a battery that can provide ancillary services, like frequency regulation, peak shaving, voltage control, power factor control, black start, or demand response. The main utility may also benefit by buying non-carbon emitting power, which can help the utility meet portfolio requirements. According to Wood Mackenzie, the US microgrid market is expected to grow at a compound annual growth rate (CAGR) of 19% from 2022 to 2027. In contrast, the US utility grid is expected to grow at a CAGR of only 2% over the same period.

The cost of grid power has been increasing.

Over the past 5 years, the cost of power in the US increased about 26% to 15.8 cents per kW*hr, Germany 20% to 27.5 cents per kW*hr, and China about 13% to 8.5 cents per kW*hr (with Japan at 27.5 and India at 12.5 cents).

There are several reasons for the increased costs of power:

• The cost of fuel, such as natural gas and coal, has been rising in recent years. This is due to several factors, including the Ukrainian war, increasing demand for energy, and the limited availability of fossil fuels.
• The power grid in many countries is aging and needs to be updated to support more distributed power. This is leading to increased costs for maintenance and repairs.
• The demand for electricity is increasing as the world economies grow, putting a strain on the power grid and leading to higher prices.
• Government policies, such as carbon taxes and renewable portfolio standards, have affected the cost of electricity.
• Weather events, such as heat spells, hurricanes, wildfires, and floods can stress or damage the power grid and lead to higher prices.

The cost of PV power and grid batteries has been declining.

According to the Solar Energy Industries Association (SEIA), the average cost of a US commercial PV system has fallen by about 60% since 2017.

In a microgrid configuration, diesel power is usually too expensive to export to the grid, but excess PV power can often be sold to the grid, and with a microgrid battery that power may be fed into the grid when the grid needs it and is willing to pay a premium for it.

The cost of PV power has been dropping.

According to Wood Mackenzie, the US microgrid market is expected to grow at a compound annual growth rate (CAGR) of 19% from 2022 to 2027. In contrast, the US utility grid is expected to grow at a CAGR of only 2% over the same period.

According to the National Renewable Energy Laboratory (NREL), the average cost of a US grid-scale battery has fallen by about 75% since 2017.

The declining cost of lithium batteries can be attributed to the EV industry which created the supply chain and manufacturing scale need to reduce mining and manufacturing costs. In 2022, lithium nickel manganese cobalt oxide (NMC) remained the dominant battery chemistry with a market share of 60%, followed by the lower cost lithium iron phosphate (LFP) with a share of just under 30%. In recent years, alternatives to Li-ion batteries have been emerging, notably sodium-ion (Na-ion). This battery chemistry has the dual advantage of relying on lower cost materials than Li-ion, leading to cheaper batteries, and of completely avoiding the need for critical minerals. The Na-ion battery developed by China’s CATL is estimated to cost 30% less than an LFP battery. Battery costs will continue to decline, improving the economic case for microgrids. A sufficiently large lithium battery in a microgrid can shift power to the peak evening load for export or may be able to last through the night to service a local load during longer power failures. The solar battery combination can greatly reduce the need for generator operation and reduce fuel costs.

The economic toll of power failures has increased in Industry, commercial and especially residential sectors.

According to a study by the Ponemon Institute, the average cost of a power failure to an industrial customer in the US was $1.9 million in 2022, up from $1.2 million in 2017. In the US the average cost of a power failure to a commercial customer was $500,000 in 2022, up from $300,000 in 2017. And the average cost of a power failure to a residential customer was $10,000 in 2022, up from $5,000 in 2017. The ability to provide local power cheaper than grid power and avoid the cost of power failures is why the US leads microgrid growth, followed by Europe and Asia.

Large utilities can act as brokers as they understand the issues of connecting and managing microgrids.  In some cases, large utilities may also own or operate microgrids themselves but microgrids owners are growing fastest at universities, military sites, healthcare facilities, commercial building complexes, power coops, municipal power companies, rural communities, and locations subject to power outages from weather events. There are a growing number of companies that are providing Energy as a Service (EaaS) where they will finance, design, build, own, and operate the microgrid. Utilities have always provided Energy as a Service, but now customers have the option of a local microgrid and even residential customers have found PV systems with batteries, (nanogrids) attractive. ARC will be publishing an update to their market study on “Microgrid Automation” in Q3 this year.

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ARC recently attended the August 2021 New England Public Power Association (NEPPA) conference, where Gordon Van Welie, President & CEO of grid operator ISO New England (ISO-NE) described the challenges to maintain reliable, low-cost power, while meeting the urgent goals to decarbonize electric power. Gordon mentioned how obsolete regulations and policies are preventing the electric grid from efficiently making the right investments in the needed generation, transmission, and distribution assets. According to Gordon, unless new electric markets are developed, rolling blackouts may come to New England grid customers. It should be noted that this is a class of risk not generally considered in North America by supply chain professionals. But, if it can occur in New England, it can probably occur across the entire United States.

According to Gordon, the MOPR (Minimum Operating Price Rule) that protects legacy fossil-fueled power plants from competition needs to be abandoned and replaced with the new ANOPR (Advance Notice of Proposed Rulemaking). The FERC (Federal Energy Regulatory Commission) needs to enable grid operators to create new capacity-based markets. New England has a wholesale energy market, ancillary service markets, and a forward capacity market, but new market structures and rules are needed. Studies done by NESCOE (New England States Committee on Electricity) provided comments on new market designs to FERC.

Presentations at the NEPPA meeting were consistent in their message that the utilities industry needs new regulations and policies to plan and build the right assets to keep cost low and reliability high. For example, former FERC commissioner Neil Chatterjee, a Kentucky Republican who was known as Mitch McConnell’s “Coal Guy” has changed priorities now that he has retired. Neil’s August 30th twitter post indicates he is supporting putting a price on carbon and joining the Climate Leadership Council (CLC) and Americans for Carbon Dividends. Economists and several of the large integrated oil companies like Shell, BP, and Total support putting a price on carbon as the most effective way of fairly reducing CO2 emissions across the whole energy industry including electric power, transportation, and building HVAC. Chatterjee is attempting to bring Republicans to the table to address our climate crisis as putting a price on carbon is politically difficult and it remains to be seen how this will unfold.

Key Problems for New England’s Electric Grid

New England is transitioning to a power system with heavy penetration of renewable energy resources to meet state environmental objectives. This means New England is transitioning away from generating resources with on-site fuel storage.

• New England is already natural gas constrained in winter months. Since 2013, roughly 7,000 MW of generation have retired or announced plans for retirement in the coming years.
• FERC rejected the ISO-NE Energy Security Improvements (ESI) proposal. New options are needed.
• Flexible resources will be needed to balance the variability of renewable energy.
• The market design needs to ensure we can attract and retain the resources needed throughout the clean energy transition.
• New England, as a region, needs to evaluate the amount of risk it can live with for extreme weather events, whether to mitigate those risks, by how much, and by whom.

It is projected that New England will see a winter peak as homes transition to electric heating. In late December 2017, and early January 2018, New England temperatures fell well below the winter average temperatures and stayed at that level for a two-week period. New weather patterns with less on-site energy storage increases the risk of power outages. New England has two pumped-storage hydro facilities that have operated since the 1970s. These resources can supply up to 1,800 MW of power within 10 minutes for up to 7 hours, but New England could use on-demand power for a duration of one or two weeks. To handle cold weather events, ISO-NE is developing plans for controlled outages and will work with the distribution companies and state regulators, to ensure the ability of distribution companies to implement controlled outages.

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The often-massive storage tanks and extensive auxiliary equipment and instrumentation represent expensive assets.  These allow for surges in volume to support the transition of batch crude oil receipts into batch finished product shipments while the refinery operates continuously.

Receiving/distribution terminals are associated with refinery off-sites that receive gasoline and distillates from refineries via pipelines, rail cars, barges, and/or marine carriers. Such terminals may blend ethanol or butane, into raw gasoline from the refinery and typically have truck loading operations to distribute the petroleum products to gas stations and fuel oil companies.

Operational Flexibility Is Key

A key goal of tank farm automation in refinery off-sites is to support overall operational flexibility. This enables safe operation within operational constraints. The ability to accommodate a low-cost crude purchase or deliver a finished, on-spec product when needed provides financial benefits for both refinery operations and supply chain management.

Operational flexibility allows the refinery to run with fewer changes to the refinery unit operations. Alternatively, it allows receipts and shipments to continue with minimal disruption when refinery unit operations are upset, such as an unexpected shutdown of a process unit like a hydrocracker.

The refinery runs continuously to process crude into rundown or blend component tanks. The rundown tanks in the tank farm provide surge capacity to meet batch blending and batch product shipments. Operational flexibility is improved if the refinery has many tanks with a large volume to maximize surge capacity.  Given that refineries and terminals have a specific number and configuration of tanks, the problem to be solved is how to use those tanks most effectively.  Operational flexibility provides oil traders with more options to buy the right crude oil at the right prices and to sell specific products to meet constantly changing customer requests.

Oil Movements Are Complex

Of course, tank farms must also be safe and environmentally compliant.  It is important to plan feasible movements, line up the piping, and execute the movement without delay or risk of product contamination.  With best practices, the topology of the piping system is precisely known and understood by the tank farm automation system. The status of the piping system, block valves, control valves, and all pumps must be known. Supply chain planning must consider any leaking valves or defective pumps, as well as other simultaneous material movements that could conflict with the lineup path of other movements.

The management of piping flow paths involves booking or reserving pumps, valves, and piping segments for the duration of a movement. Planning a series of product movements can result in many feasible solutions with many simultaneous movement paths. The particular pumps, flowmeters, valves, and pipe segments selected will place limits on the flowrates for feasible paths.  This can be a critical decision that impacts the duration of a movement.  A blending operation, for example, involves lining up multiple rundown tanks to a blend header that flows into a product storage tank.  Selecting a small pump or a small flowmeter for the alkylate blending component might limit the blend rate and extend the time required to fill the product tank. This could impact future oil movements by tying up the needed equipment. This is but one of the many complex situations that can arise for oil movement and storage. Many technology solutions are available for these types of problems. These include the following.

Tank Gauging Systems

Clearly, knowing the inventory in a refinery tank farm in real time is a fundamental requirement for effective automation. There are various technologies for level measurement and many will include “strapping tables” used to convert tank level to stored volume using a look up table. Tanks expand and distort when filled. Temperature compensation is often also needed to ensure accurate volumetric measurements.

Available tank gauging options include:

• Radar/microwave
• Hydrostatic, D/P head-based level
• Capacitance
• Ultrasonic
• Buoyancy float (servo or tape)
• Float Inductive
• Nuclear
• Dip tube with gas flow back pressure
• Level switches (D/P, capacitance, float…)

ARC has developed a selection guide that can help users select the appropriate tank gauging systems for their varied applications.

Tank Information Systems

Tank information systems (TIS) combine tank level/volume information with product quality information that might be computed, measured with analyzers, or determined by periodic sampling with lab analysis.  The TIS could include a graphical user interface and likely provides connectivity to other automation functions.  The TIS provides a useful real-time database to support other tank farm applications.

Movement Monitoring and Yield Tracking Applications

Movement monitoring and material balance yield tracking applications can display all the active movements with alarms for issues and keep records for all movement histories.  Mass balance checks can help ensure that reduced volumes in the source tanks match both flow metering and the volume gain at the destination tank.  Flowrate metering can be adjusted using accepted practices to compensate to standard conditions.  Movements can be tracked against customer orders.

Path Management/Movement Automation

Path management typically employs procedural automation to facilitate movement line ups, start the flow, and stop the movement when completed (or if it must be interrupted).  The application may involve booking specific equipment and requires a complete understanding of the entire tank farm piping network. Without instrumented pumps, valves, hoses, and other assets, path management cannot be automated, requiring confirmation that manual procedures are accomplished. For example, some hose connections may be required to start an oil movement, but the operator must first confirm that the proper hose connections are made.

Blend Ratio Control

Refinery tank farms routinely have gasoline and distillate blending systems.  At a minimum, a refinery will execute a simple ratio control scheme in which selected components are combined into a blend header before being delivered to a product storage tank or sent to a pipeline.  During this blend movement, the blend components are maintained in a volumetric ratio based on flow measurements. In a typical arrangement, the flows ramp up to a blend rate, continue at the blend rate until the blend approaches the end, then flows ramp down to a trickle before the blend stops.  Various schemes can handle a situation in which one or more components cannot keep up the required flow, which would prevent the desired blend ratio from being achieved.  For example, “pacing” control could be used to slow down the blend rate to maintain the desired blend ratio.

Blend Optimization

Simple blend ratio control does not respond to upsets in product quality or errors in estimating the component tank qualities used to compute the blending ratios.  Various implementations use online analyzers at the blend header for feedback control to adjust the blend ratio during the blending process.  This helps ensure the blended product meets all product quality specifications.  Some configurations compute the values of some or all blend properties in the product tank as it is filling, based on analyzers connected to the blend header.  With optimization, the blend can be optimized for the lowest cost of components, or an alternate goal like minimum deviation from starting ratio.

In either case, the analyzer-based control system attempts to fill a product tank that will meet product specs.  An off-spec product tank is problematic as it may require splitting the tank into two volumes with a patch (reblend) for each. At best, it ties up tankage and delays shippable product. The blend optimization problem typically extends into the refinery operation to keep a proper inventory of blend components and ensure the refinery itself is operated at optimal targets within all operational constraints. Producing the most profitable blended products when needed has significant impact on supply chain management and refinery profitability.

Since blending can involve a collection of online analyzers, these analyzers must be managed to ensure they deliver reliable and accurate results. This requires periodic calibration and validation of streaming real-time data.

Oil Tank Storage and Movement Best Practices

A top-performing oil movement system requires many different functions to work together smoothly.  Compared to typical refinery units, tank farm automation touches more business organizations and requires frequent coordination between the operations group, supply chain and trading group, and refinery scheduling and planning groups.

ARC recommends that installing high-quality (ideally, intelligent) field instrumentation, especially tank gauging, is a good starting point. Since product quality of crude oil impacts refinery operation and finished product quality must meet shipment specifications; high-quality, well-calibrated online and laboratory analyzers are also essential.

It is important to clearly identify roles and responsibilities that are consistent with the software integration of business and operational applications.  As an example, the planning group may queue up a sequence of gasoline blends for the next 30 days.  Operations may execute a planned blend, but might be forced to deviate from the recipe due to unforeseen refinery upsets.  This, in turn, may require the blend planner to edit future blends in the queue.

Clearly, it’s critical for all personnel to be working from accurate and timely data with a single version of the truth.

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This contrasts with the “data lake” concept in which all imaginable data is simply collected and modern database programming tools like Hadoop are used to organize the data. It’s not hard to imagine that the data lake concept could collect a veritable junkyard of data in the supply chain. For example: imagine digitizing old plant and warehouse photos; scanning old and new plant design documents; and collecting old and new versions of operating procedures and the control and safety system configurations; collecting production records, raw material purchases, records on shipment transactions, product quality measurements from the lab, and the real-time streaming data from the plant control system.  Now, connect to local weather databases; sprinkle in some OSHA, EPA, and local zoning regulations; and don’t forget those old VHS training videos.

Basically, the above data collection is disordered and largely unstructured, has high entropy, and would take work (programming) to make it ordered. Surely there are ways to organize and extract useful information from this junkyard of data. Buried deep in this data, you could potentially find the justification to replace a specific piece of factory equipment that limits production rate or that is responsible for off-spec product.

Just because we have a massive junkyard of data in a data lake does not mean we have the right data to answer questions like: “How do I improve production rate, reduce maintenance costs, or improve product quality in my factory”. Machine learning with neural networks is a promising technology for extracting useful models for data, but typically needs very specific data that is not likely to be found in a data lake.

An IEEE article points out that, in many cases, you need more data than your big data “junkyard” can provide.  In this example, the article’s author (Jacques Mattheij) decided to build a factory that sorted tons of mixed Lego blocks he could buy at low cost into sorted Lego blocks he could sell for nearly four times as much. Perhaps he was motivated by the technical challenge more than the business opportunity. The basic idea is to use a USB camera connected to a PC that monitors a conveyor belt. The PC needs to identify each Lego block and use puffs of air to blow parts off the belt into sorted bins that contain a specific part type. The hard problem was to train a machine learning algorithm to correctly identify each unique Lego part. Considering there are more than 1,000 types of Lego blocks, rather than photograph each unique type of Lego block from multiple angles and label each photograph, Jacques found a faster way to train his neural model. Jacques started by creating training sets of some Lego types, but as he used early models to identify mixed Lego types he would correct the many mislabeled parts and use these new labelled photos to further train his model. The next test had even less mislabeled parts, which were easier to correct. This procedure continued until the rate of incorrect identifications became very small.

Organizing the data collection for a specific need (like production reports) is a good fit for the data warehouse concept and established relational database technology. Creating data lakes with massive amounts of data does add considerable flexibility for the types of analysis that can be done. With some programming effort, you could use the data from the lake to build the types of business intelligence applications that have been done with data warehouse concepts, but from a programming point of view you would need a good map of all the data you have, and how to access it with a query. It is possible to use data in warehouses and lakes for machine learning applications, but in many cases the data you will need is just not there.

The conclusion here is that data warehouses, data lakes, and even the real-time and historical supply chain data are all great sources of data to make useful reports, regressions, and discoveries that can help drive business efficiency. However, even the biggest piles of data will not contain all the information needed to build most machine learning applications that can bring automation to the next level. The Lego example shows that to create a useful machine learning application required the creation of new data needed to train the neural networks and some of this data was created during the development process. Successful applications of data analytics and machine learning technology work best when there is a clear vision of a problem to be solved and modern tools are deployed in a thoughtful manner.

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