The Complete Guide to Modern Energy Trading & Risk Management (ETRM) in 2025

Executive summary

Energy Trading and Risk Management software, ETRM, is the digital backbone of the modern energy business. It is the system that lets a firm trade, value, risk-manage, schedule, and settle a diverse mix of physical and financial commodities: electricity, natural gas, LNG, crude oil, refined products, coal, carbon allowances, renewable energy certificates, and more. Where a spreadsheet tracks a handful of trades, an ETRM platform governs the entire lifecycle of thousands of them, across commodities, as the operational system of record for the trading floor.

The demand for capable ETRM software has never been higher, and the reason is complexity. Renewable integration has made supply weather-dependent and volatile. Intraday electricity markets and battery storage have compressed the timescale of trading decisions. LNG has knitted regional gas markets into a global one. Carbon and environmental products have become first-class risks. Regulation has grown stricter and reporting more demanding. Each of these forces adds structure and speed that the previous generation of tools, and certainly spreadsheets, cannot handle with the governance and control a trading business requires.

Three shifts in particular have redrawn the map since the last generation of ETRM systems was designed. AI-driven electricity demand is a new and rapidly growing load: the International Energy Agency projects that global data-center electricity consumption will roughly double from about 415 TWh in 2024 to around 945 TWh by 2030, with AI a leading driver, and the U.S. Department of Energy’s 2024 study (produced by Lawrence Berkeley National Laboratory) found data centers already consumed about 4.4% of U.S. electricity in 2023, potentially reaching 6.7% to 12% by 2028 [see references]. Grid congestion is the direct consequence, making locational prices diverge and turning nodal spreads, Financial Transmission Rights, and Congestion Revenue Rights into daily concerns. And battery storage, event-driven trading, and cloud-native, AI-native architecture have together changed what a platform must do: manage a live, granular, multi-asset book in something close to real time. These forces are explored in depth in why AI, data centers, and grid congestion are reshaping ETRM; this guide is the foundation beneath that story.

This guide is a complete, practitioner-level tour of what ETRM software is, what it does across the trade lifecycle, the commodities and modules involved, the architecture that defines a modern platform, how a modern platform is implemented, and how to choose one. It is the cornerstone reference for everything else on this blog, and it links out to the specialized guides that go deeper on each topic. It introduces where Gravitas fits: a cloud-native, API-first ETRM built on a single governed data model, designed to bring enterprise-grade capability to desks that the legacy incumbents priced out. Whether you are a trader, a risk manager, a scheduler, or a CIO evaluating platforms, this guide is written for you.

What is Energy Trading & Risk Management (ETRM)?

ETRM is the enterprise platform that manages the complete lifecycle of energy and commodity trades, from market-data ingestion through trade capture, position management, risk analysis, scheduling, settlement, accounting, and reporting. In a mature operation it is the operational system of record for wholesale trading: the authoritative place where every deal lives and every downstream number originates. The closely related term CTRM (Commodity Trading and Risk Management) covers the same ground with an emphasis on non-energy commodities such as agriculture and metals; in practice the two are used interchangeably, and a good platform handles both. For a fuller treatment, see what ETRM/CTRM is.

What does an ETRM platform actually do? Its responsibilities span the whole desk:

• Market data management, ingesting and governing prices, forward curves, and volatilities as versioned, reproducible data.
• Trade capture, recording every physical and financial deal once, with validation, as the governed record.
• Position management, keeping the live book of what the firm holds, netted and current.
• Portfolio management, organising positions into books and strategies for oversight.
• Risk management, computing VaR, the Greeks, scenarios, and limits on the live book.
• Scheduling & logistics, turning trades into nominated, delivered energy and commodities.
• Settlements, turning delivery into invoiced, reconciled cash.
• Credit risk, tracking counterparty exposure as a live number.
• Accounting integration, posting results to ERP and the general ledger.
• Regulatory reporting, producing compliant datasets from the governed source of truth.
• Business intelligence, analytics and marts over the trading data.
• APIs & integration, exposing every capability so the platform composes into the wider landscape.

The defining characteristic of a modern ETRM is that all of this runs on one governed data model, so each function reads and writes the same authoritative record rather than keeping its own copy that must be reconciled. That single idea, explored throughout this guide, is what separates a genuinely modern platform from a legacy one with a new interface.

Why ETRM matters today

The case for serious ETRM software rests on a set of industry forces that have all intensified at once:

• Renewable expansion makes supply weather-dependent and the price curve volatile.
• Intraday electricity markets shrink the timescale of decisions to minutes.
• LNG globalization links regional gas prices into one interconnected market.
• Battery storage introduces continuous, interval-level optimization.
• Carbon markets turn emissions into a first-class price and risk.
• Volatile commodity prices raise the cost of stale or contested numbers.
• Cross-border trading multiplies currencies, calendars, and jurisdictions.
• Regulatory complexity demands auditable, reproducible reporting.
• Real-time risk requirements make overnight batch risk inadequate.
• Digital transformation raises expectations for APIs, cloud, and analytics.

Against this backdrop, the limits of spreadsheets and disconnected applications become acute. Spreadsheets cannot provide governance, there is no reliable audit trail, no version control that survives a deleted tab, no single source of truth. They cannot provide scale, a linked-workbook stack that works for a handful of trades collapses under thousands across commodities. And they cannot provide operational control , every manual re-key between workbooks and systems is a chance for error and a source of reconciliation breaks. As markets grow more complex and faster, the gap between what the business needs and what a spreadsheet can deliver widens into a genuine operational risk. See who this leaves behind, and why it is fixable.

The evolution of trading technology

It helps to see where ETRM sits in a longer arc, because each generation of trading technology solved the limits of the one before it, and the current frontier, the AI-native platform, is the answer to limits that only became acute in the last few years.

The spreadsheet era was manual and siloed, workable for a handful of trades but ungoverned and unscalable. Legacy ETRM brought structure and a system of record, but on batch-oriented, on-premise architectures that are now expensive and slow to change. Cloud ETRM lifted the operational burden and added scale. API-first platforms made the ETRM integrable into the wider enterprise rather than an island. Real-time, event-driven processing replaced overnight batch with a live book. And the AI-native generation grounds intelligence in a governed data model so that forecasting, analytics, and copilots are trustworthy rather than bolted on.

Each of these is a topic in its own right on this blog: cloud-native architecture, API-first design, event-driven processing, and the AI-native future. The important point for this guide is that a genuinely modern ETRM is not any single one of these features; it is all of them resting on one governed data model, which is the theme that runs through everything below.

The complete energy trading lifecycle

The backbone of any ETRM discussion is the trade lifecycle: the path a deal travels from a market signal to settled cash. It runs, in order, through market data, forecasting, trade capture, position management, risk management, scheduling, inventory, settlement, accounting, and reporting. Each stage has a distinct purpose, a distinct set of users, and distinct inputs and outputs, but in a modern platform they are not separate systems. They are functions over one governed model, which is precisely why the deal does not have to be re-keyed or reconciled as it moves.

Market data feeds prices, curves, and volatilities into the system as governed reference data. Forecasting turns that data, plus load and generation signals, into a view of where prices are heading. Trade capture records the resulting deals once, with validation. Position management keeps the live, netted book. Risk management measures the exposure of that book. Scheduling turns positions into delivered energy; inventory tracks the physical stock in tanks and storage. Settlement turns delivery into invoiced cash, accounting posts it to the ledger, and reporting produces the position, P&L, and regulatory views the business and its regulators need.

The common failure of fragmented systems is that each stage lives in its own application, so the trade is re-entered and reconciled at every hand-off, and physical structure, shape, basis, quality, cargo detail , is flattened and lost along the way. The modern alternative captures the trade once and lets it flow straight through, which is the theme the rest of this guide returns to again and again. Each stage has a dedicated deep-dive on this blog: trade capture, position management, P&L, risk and VaR, scheduling, and settlement. Explore the platform and its modules for how the lifecycle runs on one model.

The front office

The front office is where trades originate. Its people, traders, originators, structurers, and quantitative analysts, take positions, price deals, and manage the book against the market. What they need from an ETRM is speed and clarity: fast trade capture, accurate pricing, live position visibility, and market data they can trust.

The core front-office modules are trade capture and the deal blotter, pricing and forward-curve construction, option pricing, and live position monitoring. A trade a front-office user captures should be instantly visible to valuation and risk, not because a batch pushed it there overnight, but because it is the same record those functions already read.

A concrete example makes it real. A power forward might be captured as: commodity electricity, market ERCOT, volume 100 MW, price $54.20/MWh, delivery January 2026. In a modern platform, capturing that trade immediately updates the live position, feeds real-time valuation against the current ERCOT curve, and flows into risk, all without re-keying. The trader sees the effect of the trade on the book at once, which is exactly what a fast market demands.

The middle office

The middle office owns risk and control. Its job is to make sure the firm understands and stays within its exposure, and to explain the P&L the front office generates. Its users watch market risk, credit risk, and compliance, and they need numbers that reflect the book as it actually is, right now.

The middle-office toolkit includes Value at Risk and Expected Shortfall, stress testing and scenario analysis, P&L and its attribution, exposure monitoring, position limits, and compliance checks. A typical risk dashboard surfaces VaR, today’s P&L, exposure by commodity, credit utilization, open positions, and limit usage, all at a glance.

The critical requirement is that these run on the live book, on the same governed model as valuation. A VaR or limit computed on an overnight snapshot is managing yesterday’s risk; a limit that checks a stale position is theatre. When risk and valuation share a model, the risk you see is the risk you hold, and limits actually bind. This is the difference between a control function that controls and one that merely reports after the fact.

The back office

The back office turns trades into delivered commodities and settled cash. It is the least glamorous part of the lifecycle and often the most expensive when it goes wrong, because this is where re-keying and reconciliation breaks concentrate.

Back-office work spans confirmations, scheduling and nominations, inventory, invoicing, settlements, accounting, and integration with ERP systems such as SAP and Oracle. Each of these depends on accurate trade data, and each is a place where a fragmented landscape produces disputes: a confirmation that does not match, a nomination built from stale data, an invoice that disagrees with the counterparty.

On a single governed model, the back office reads the same trades the front office booked, so reconciliation happens by construction rather than by effort. Settlement flows from delivery, delivery flows from scheduling, and scheduling flows from the captured trade, one record, one history, from deal to cash. This is where straight-through processing pays off most visibly, in reclaimed hours and eliminated disputes.

Commodities supported

A modern multi-commodity platform handles the full breadth of energy and commodity products, physical and financial, on one book. The table below shows typical coverage, and the key point is that positions across all of these net and aggregate on a single model rather than living in per-commodity silos.

Each commodity brings its own structure, and a platform has to model each faithfully rather than flattening them into a common shape: power is half-hourly and shaped; gas carries basis and storage; LNG trades as cargoes; oil lives in differentials; agriculture turns on quality and basis; and metals net exchange against physical warrant. See all commodities for the specifics.

Modern power markets: what an ETRM must handle

Power deserves special attention, because the modern electricity market is where ETRM complexity concentrates and where legacy systems most often fall short. A platform that treats power as just another commodity with a monthly price misses most of what actually drives value and risk on a power desk.

The concepts a modern power ETRM must model directly include:

• Locational marginal pricing (LMP), the price of power at a specific node, reflecting energy, congestion, and losses, so the same megawatt-hour is worth different amounts at different points on the grid.
• Financial Transmission Rights (FTRs) and Congestion Revenue Rights (CRRs), the instruments desks use to hedge or take positions on congestion between nodes.
• Ancillary services markets, regulation, reserves, and frequency response, that pay for grid reliability alongside energy.
• Capacity markets, which pay for firm capacity to be available, a separate value stream from energy itself.
• Congestion trading, expressing a view on nodal spreads directly as the grid becomes more constrained.
• Battery optimization, dispatching storage at interval granularity against price, state of charge, and cycle limits to capture spread and congestion value.
• Demand response, treating flexible load as a dispatchable resource.
• Virtual power plants (VPPs), aggregating distributed batteries, solar, and flexible load into a single dispatchable, locational resource.

These are not edge cases; they are the substance of a modern power desk, and they are increasingly interlinked as AI-driven demand and grid congestion reshape the market. A platform has to model them on the same governed book as everything else, so that a battery’s congestion value, an FTR hedge, and the physical position it relates to all net and risk-manage together. This is explored in the power trading lifecycle, battery energy storage trading, and how AI, data centers, and grid congestion are reshaping ETRM. Modeling these correctly is one of the clearest lines between a platform built for today’s power market and one retrofitting a simpler past.

Reference data: the foundation underneath everything

Behind every trade, position, and settlement is reference data, and in a complex nodal, multi-asset market the reference-data model is not a background detail but the connective tissue that makes everything else possible. It is, quietly, one of the biggest architectural differentiators between platforms.

A modern ETRM models a clean, governed hierarchy that includes:

• Nodes, the specific pricing points on the grid where LMP is set.
• Hubs and zones, the aggregated trading points that liquidity concentrates around.
• Delivery calendars and ISO calendars, the market-specific definitions of trading days, holidays, and delivery periods.
• Peak and off-peak definitions, which vary by market and determine how blocks are priced and shaped.
• Transmission paths, the routes and constraints that connect nodes and drive congestion.
• Curve hierarchies, the structured relationships between benchmark curves, basis, and derived curves that valuation depends on.
• Instruments, products, and contracts, each with its full specification, tied to the locations and calendars above.

When this hierarchy is governed and consistent, a nodal position carries its delivery point as first-class data, congestion exposure becomes visible and risk-managed rather than implicit, and every downstream number ties back to a single, clean definition. When it is fragmented, or worse, maintained in spreadsheets, the same node is spelled three different ways, curves do not reconcile, and reporting becomes a reconciliation exercise. This is why reference data is a structural differentiator, not plumbing, and it is covered in depth in master data management for energy trading and data lineage and governance.

Core modules of a modern ETRM

An ETRM platform is a set of modules, each serving a stage of the lifecycle. In a modern system they are not separate products bolted together but coherent capabilities over one model. The essential modules are:

• Trade capture, multi-asset physical and financial entry with validation and enrichment on the way in.
• Market data, governed, versioned prices, curves, and volatilities, reproducible as-of any date.
• Reference data, the hierarchy of commodities, products, instruments, locations, and contracts that everything else depends on.
• Valuation, real-time mark-to-market on live curves.
• Position management, the live, netted book across commodities.
• Risk, VaR, Greeks, scenarios, and limits on live positions.
• Scheduling, nominations and physical operations for delivered commodities.
• Inventory, physical stock in tanks, storage, and warehouses as position and risk.
• Credit, live counterparty exposure and limits.
• Settlements, invoicing, matching, and dispute resolution to cash.
• Reporting, position, P&L, and regulatory reporting from one source of truth.
• Workflow & documents, approvals, confirmations, and document management.
• BI & analytics, governed marts for reporting and data science.
• APIs, every capability exposed as a governed service.

For each module the pattern is the same: it takes governed inputs, performs its function on the shared model, and produces outputs that the next stage reads directly. Explore the module set for how each one works in depth.

Modern ETRM architecture

What makes an ETRM “modern” is not its feature list but its architecture. A contemporary platform is layered: users interact through a web UI; the UI talks to REST APIs; the APIs sit over business services; those services drive the trade, risk, scheduling, and settlement engines; and all of it persists to a governed database on elastic cloud infrastructure. Crucially, the same APIs that serve the UI are available to integrations, so nothing is locked behind a screen.

The diagram captures the defining idea: market data, forecasting, trade capture, the position engine, valuation, risk, scheduling, settlement, analytics, and the AI copilot are not separate systems but functions over one governed data model. Because every function reads and writes the same authoritative record, positions reconcile with risk, risk reconciles with settlement, and analytics and AI reason over the same truth, without the reconciliation tax that fragmented architectures pay at every hand-off. This is developed in full in data architecture for enterprise ETRM.

Under that layering sit the design choices that define a modern system: microservices that scale independently, event-driven processing so a booked trade immediately triggers downstream reactions rather than waiting for a batch, streaming of market data and events, containerization and orchestration for resilience and elastic scale, CI/CD for frequent low-risk releases, and API-first integration throughout. These are covered individually in API-first ETRM and streaming market data with Kafka.

The contrast with legacy architecture is stark. Legacy ETRM systems were typically built as monoliths with batch processing at their core and limited, awkward integration points. Retrofitting real-time dashboards or APIs onto that foundation produces a faster face on a slow engine. A modern platform is real-time and open because it was designed that way from the start, which is exactly the bet behind the Gravitas platform.

Cloud-native ETRM

Cloud-native is the deployment model that makes modern ETRM economics work. Rather than provisioning and maintaining on-premises hardware sized for peak load, a cloud-native platform runs as elastic, often multi-tenant SaaS, scaling with demand and billing for what is used.

The technical ingredients are specific and now well established: containers (Docker-style packaging) orchestrated by Kubernetes for resilience and elastic scale; event streaming through a backbone such as Kafka so market data and trade events flow in real time; autoscaling to absorb volatile intraday volume without over-provisioning; multi-region deployment for availability and, where required, data residency; and zero-downtime deployment through rolling releases and CI/CD, so the platform is updated continuously without maintenance windows. The business benefits follow directly: lower infrastructure cost, because there is no over-provisioned hardware; faster releases, because deployment is continuous; improved resilience, through built-in redundancy; simplified upgrades, because the platform is maintained centrally rather than through painful on-premises migrations; and global accessibility for distributed teams.

Cloud-native and modular architecture is the defining trend in modern ETRM, and for good reason: it is what lets a platform deliver enterprise capability without the enterprise-scale infrastructure, implementation, and headcount that legacy on-premises systems demand. For firms with data-residency or regulatory requirements, a good platform still offers private-cloud or on-premises deployment, the point is that security and residency should shape the deployment model, not force a compromise on capability. The full comparison is in cloud-native vs on-prem ETRM; see also how Gravitas approaches security and deployment.

AI in modern ETRM

AI is the most-discussed and most over-claimed theme in trading technology. Used well, it augments decision-making in specific, concrete places; used carelessly, it produces confident but wrong output on ungoverned data. The firms getting value treat it as a set of tools grounded in a governed model, not a headline.

The genuinely useful applications include demand and price forecasting, trade recommendations and portfolio optimization, anomaly detection for unusual trades and settlement breaks, counterparty risk scoring, intelligent scheduling, AI copilots, natural-language assistants that answer questions over the live book, and document extraction that reduces manual re-keying from confirmations and contracts. Copilots in particular typically use retrieval-augmented generation (RAG), so the assistant answers from the firm’s own governed trading data rather than from the model’s training alone, which is what keeps its answers grounded and current.

The unifying requirement is governance, and in a regulated, high-stakes domain it is non-negotiable. Responsible AI in ETRM rests on grounded AI (reasoning over governed data), explainability (every answer traceable to the data behind it), model governance (models versioned and monitored for drift), prompt governance (controlled, auditable prompts), human approval for consequential actions, and an AI audit trail so every AI-assisted action is logged and reviewable. Pricing, risk limits, and regulatory numbers must rest on transparent, explainable, and fully auditable methods with a person accountable, while AI assists around them, and the platform should never trade autonomously. This is covered across how AI is transforming trading desks, building AI copilots, and the AI-native future. See also how Gravitas approaches AI, grounded in governed data, honest about where judgment must lead.

Build vs buy

Firms evaluating trading technology inevitably face the build-versus-buy question. The honest answer is usually to buy the lifecycle and build only the genuinely differentiating pieces, and an API-first platform lets a firm do exactly that.

The right lens is not just cost and speed but the ongoing commitments each path creates. Building means owning the roadmap, the upgrades, the operational cost, and the technical debt indefinitely, and building the in-house expertise to sustain all of it. Buying a configuration-driven, API-first platform inherits an advancing vendor roadmap, central upgrades, an implementation-partner ecosystem, and vendor-managed technical debt, while still letting the firm extend through configuration and APIs where it genuinely needs something bespoke. Building a full ETRM from scratch means recreating decades of accumulated lifecycle logic, a slow, expensive, high-risk undertaking that ties up scarce engineering talent on undifferentiated plumbing. That is why buying the lifecycle and building only the genuinely differentiating pieces is the pragmatic middle path most desks should take.

Implementing a modern ETRM: the roadmap

Choosing a platform is only half the decision; a credible evaluation also considers how it will be implemented, because the best platform delivers no value until it is live and adopted. A typical ETRM implementation runs through a defined sequence of phases.

Discovery defines requirements and scope against the firm’s actual trade lifecycle. Reference data setup establishes the governed hierarchy of commodities, nodes, calendars, and curves that everything else depends on, often the most underestimated phase. Integration connects the platform to market data, ERP, and downstream systems through its APIs. A parallel run operates the new platform alongside the existing one to confirm they agree before cutover. User acceptance testing (UAT) validates the platform against real workflows with the people who will use it. Production go-live is the controlled switch to the new system. And hypercare provides intensive support through the first weeks so early issues are resolved quickly and confidence builds.

A modern, cloud-native, API-first platform makes each of these phases more manageable, cleaner data migration onto a governed model, integration through real APIs rather than fragile point-to-point connections, and central upgrades that remove the painful migration projects legacy systems impose. The full treatment is in how to migrate to a new ETRM platform and ETRM implementation best practices.

Implementation realities

A realistic view of the practical work matters as much as the roadmap, and it builds credibility with anyone who has lived through a system change. Several considerations deserve explicit attention.

• Data migration, mapping, moving, validating, and reconciling trades, positions, and history from the existing system against the source.
• Master data cleansing, deduplicating and correcting counterparties, instruments, and reference data before it enters the new platform, since migrating dirty data simply relocates the problem.
• Parallel runs, operating old and new together to confirm they agree before relying on the new one.
• Historical positions, confirming that migrated positions and P&L history tie out to the legacy book so the opening balance is trusted.
• User adoption, involving traders, risk, and operations staff early so the platform reflects real workflows and is used well rather than worked around.
• Change management, communicating, training, and supporting the transition so the organization moves with the technology.

None of these should be underestimated, and a platform whose architecture eases them, a governed data model that makes migration and reconciliation cleaner, real APIs that simplify integration, matters as much as its feature set. A migration handled this way, phased, parallel-run, with rigorous data reconciliation and genuine change management, is a controlled transition rather than a leap of faith.

How to choose an ETRM platform

Selecting an ETRM is a consequential, multi-year decision, and the best evaluations anchor in the firm’s own trade lifecycle rather than a generic feature grid. Work through a structured checklist, and insist on seeing each capability on your own commodities and trades, not curated demo data.

• Trade capture, does it handle your physical and financial products, with the structure intact?
• Risk, VaR, Greeks, scenarios, and limits on the live book, in real time?
• Scheduling, nomination and physical operations at the right granularity?
• Settlements, invoicing and reconciliation that ties out by construction?
• APIs, is every capability available as a governed service?
• Reporting, position, P&L, and regulatory reporting from one source?
• Performance, does it stay real-time under your volume?
• Security & cloud readiness, authentication, encryption, residency, deployment options?
• AI capabilities, grounded in governed data, or bolted on?
• Multi-commodity support, do positions net and aggregate across commodities?
• Scalability, does it grow with your desk without re-platforming?
• Vendor roadmap, is the platform advancing where the market is heading?
• Total cost of ownership, including reconciliation, change lead time, and integration, not just licence.

The architectural questions, one model or many, real-time or batch, configured or coded, predict total cost of ownership far better than any feature count, because they determine whether the platform keeps working as your desk grows and changes. For a practitioner framework, see the Knowledge Center and how Gravitas is scoped and priced.

Why organizations replace legacy ETRM systems

Most ETRM replacement projects are driven by the same recurring frustrations. Recognising them early is the best signal that a modernization is due.

• End-of-life technology that is costly to run and hard to hire for.
• Slow, expensive implementations for even modest changes.
• High operating costs dominated by reconciliation and maintenance.
• Limited APIs that make the system a silo.
• Batch-only processing that cannot deliver real-time risk.
• Poor UI/UX that slows every user.
• Difficult upgrades that firms defer until they are stuck.
• Weak cloud support that blocks elastic scale.
• Limited support for renewables, batteries, and carbon, the fastest-growing parts of the market.
• Fragmented data that makes every number provisional.

Organizations modernize to escape these constraints, to gain cloud deployment, renewable and battery support, real-time analytics, and AI-ready workflows on a foundation that will not need replacing again in a few years. Many discover that the incumbents they are leaving were only ever affordable for the largest houses in the first place, which is why cloud-native platforms have opened the market to desks previously priced out. See why firms replace Endur, Allegro, and RightAngle and where ETRM is heading.

Introducing Gravitas

Gravitas is a cloud-native, API-first ETRM/CTRM platform built for the market as it is now. Rather than compare feature-by-feature against incumbents, it is easiest to describe by its measurable capabilities, each of which follows from the single architectural decision to run everything on one governed data model.

• Cloud-native architecture that scales with volatile volume.
• API-first integration so every capability composes into your landscape.
• Modular deployment, managed SaaS, private cloud, or on-premises.
• Multi-commodity support across power, gas, LNG, oil, ags, metals, and environmental products.
• Physical and financial trading that nets on one book.
• Real-time position management and valuation.
• Advanced risk analytics, VaR, Greeks, scenarios, and limits on the live book.
• Scheduling and logistics for physical delivery.
• Settlement automation that reconciles by construction.
• A modern, hierarchical reference-data model that represents nodal, multi-asset markets faithfully.
• An open reporting framework and governed analytical marts.
• An AI-ready foundation, governed data first, so AI is reliable.

Crucially, all of this is delivered at economics that fit desks the incumbents priced out. If you want to see it on your own trades, request a demo, the fastest way to judge any platform is to watch your own commodities and workflows run on it end to end.

A CIO and trading-leader checklist

For technology and trading leaders, the decision reduces to a short set of questions that predict long-term fit far better than any feature grid. Use this as a starting checklist when evaluating platforms.

The unifying test is simple: does the platform give the desk one governed, real-time, granular view of its book that both people and AI can act on and audit? A leader who evaluates against that question, rather than a feature checklist, is far more likely to choose a platform that still fits the business in five years. The deeper treatments are in selecting the right ETRM platform and the ultimate buyer’s guide.

Sources and further reading

The market context in this guide draws on public analysis from energy agencies, national laboratories, market operators, and industry bodies. The figures cited are as reported by those sources at the time of writing; readers should consult the originals for the latest data and full methodology.

International Energy Agency, Electricity 2026 and Energy and AI (iea.org/reports/electricity-2026; iea.org/reports/energy-and-ai), on electricity demand growth and data-center consumption roughly doubling toward 2030.

U.S. Department of Energy / Lawrence Berkeley National Laboratory, 2024 United States Data Center Energy Usage Report (LBNL-2001637) (eta.lbl.gov), on U.S. data-center electricity use and its projected growth to 2028.

U.S. Energy Information Administration (EIA) Short-Term Energy Outlook (eia.gov), on U.S. electricity demand; U.S. Federal Energy Regulatory Commission (FERC) and independent system operators / regional transmission organizations, PJM, ERCOT, CAISO, and Europe’s ENTSO-E, on market structure, interconnection queues, and congestion (ferc.gov).

Electric Power Research Institute (EPRI), research on data-center load growth and grid impact (epri.com).

These references support the market backdrop; the platform and architecture views in this guide are Gravitas’s own. Citations are provided for transparency and do not imply endorsement by the cited organizations. For the deeper analysis of how these forces reshape trading technology, see why AI, data centers, and grid congestion are reshaping ETRM software in 2026.