Making the most of your VPP assets in the energy market can sometimes feel like competing in a high-stakes fencing match: you need precise timing, nimble footwork, and a plan for every unpredictable move. And this is needed now more than ever: From single-service pilot projects to multiple overlapping revenue streams, many aggregator setups are reaching their limits as new local congestion markets pop up, intraday volatility spikes, and the latest algorithms constantly reshape scheduling and dispatch.
This wide menu of opportunities demands a technical ability to decide - hour by hour - where to place each unit of flexibility. If you’re locked into a single hard-coded approach, you could miss new revenue streams, especially with intraday arbitrage profits rising and more ancillary services opening up.
In the previous installment of our VPP Agility series, we saw how markets have begun showing real profit potential for flexibility services. Now we look at the architecture behind that success. This second part presents a more flexible “optimization marketplace” mindset, where new modules, specialized algorithms, and evolving forecasting tools plug in without forcing a complete rebuild - so you’re ready for every market shift that comes your way.
The stakes for optimizing a VPP portfolio have never been higher. As soon as one revenue stream becomes popular, it tends to saturate - when too many players pile into a single service, prices fall and margins shrink. Agile VPP operators constantly scan for the next high-value opportunity, whether it’s a new local grid service or a volatile intraday price spike. The risk and reward are two sides of the same coin: those who pivot quickly can capture outsized returns, while those stuck in a single-market mindset may see diminishing profits as conditions change. The biggest returns often accrue to the first movers.
On top of that you have continuously shifting market conditions. Regulatory changes and new market mechanisms appear frequently, and competitors are quick to exploit them. An optimization approach that worked last year might underperform today if, for example, balancing market prices drop or a new entrant floods a niche service with capacity. To stay ahead, VPPs need optimization logic that can dynamically adjust in real-time to where the best rewards are - without exposing the operation to excessive risk. This means not only chasing revenue, but doing so in a controlled way that accounts for uncertainties and avoids overcommitting to any one outcome.
Forecasting accuracy and protecting assets
No optimization can succeed without a solid handle on forecasting. Knowing where to dispatch your flexibility hour by hour is only possible if you have a clear view of expected prices, demand, and asset availability. Better forecasts of market prices and renewable output give you the confidence to position assets in the right market at the right time. Conversely, poor forecasts increase the chance of missed opportunities or costly imbalances. A modern VPP setup therefore invests heavily in advanced forecasting tools, often incorporating machine learning or specialized services to predict price signals and weather-driven and unplanned outage supply/demand swings.
Equally important is protecting the assets that deliver your flexibility. A battery energy storage system (BESS), for example, has a limited number of charge-discharge cycles per day and each cycle has a cost. Aggressively chasing every price spike might yield short-term gains but could also accelerate degradation, eroding asset value and future revenue. Good optimization weighs these factors. Sometimes the algorithm should intentionally skip a marginal arbitrage opportunity to preserve the battery for a bigger move later or simply to prolong its life.
The same goes for demand response assets: Industrial processes or electric vehicles have primary purposes and constraints (like maintaining production or mobility), often backed by third-party agreements or incentive structures. If an algorithm pushes too hard - say, curtailing a factory’s power usage too frequently - it might violate those contracts or reduce the customer’s willingness to participate. Thus, high accuracy in forecasting must be paired with respect for physical and contractual limits. Optimization logic needs to ingest not just market data, but also asset health metrics and any operational or contractual rules, ensuring that the pursuit of profit doesn’t inadvertently harm the underlying business or infrastructure.
Considering time-path dependency
Optimizing across multiple markets is a strategy that only unfolds over time. Some flexible assets such as BESS are energy-limited and exhibit time-path dependency, meaning what you do now affects what you can do later. If you discharge a battery in the morning for a quick win, you might have less energy available for a potentially more lucrative opportunity in the evening. Similarly, an industrial load might only be able to reduce consumption for a certain number of hours per day before it must catch up to maintain production schedules. These kinds of constraints make optimization a multi-period challenge.
This time-path dependency requires the VPP’s algorithms to look ahead and chart an optimal course, not just pick the best immediate action. This is where advanced scheduling techniques come in - for example, algorithms that perform rolling-horizon optimization or scenario analysis to weigh today’s choices against tomorrow’s needs.
The key is flexibility in your software architecture: you might need a specialized module to handle this kind of multi-interval planning, especially for storage-heavy portfolios. Without accounting for path dependency, an operator could inadvertently burn through an asset’s capability too early in the day (or week), leaving money on the table later. The most agile VPP platforms allow operators to plug in these advanced optimization models so that each asset’s schedule is optimized holistically rather than in isolation.
The pros and cons of outsourcing
With the complexity of multi-market operations, many VPP operators consider outsourcing the “brain” of the operation. A growing ecosystem of third-party optimization services and software vendors promises to handle bidding and dispatch decisions for you.
Outsourcing can have clear benefits:
- Quick deployment and expertise: You can tap into specialized knowledge and software without building your own tools from scratch. This is particularly attractive for new entrants who lack in-house data science teams.
- Advanced algorithms out of the box: Vendors often provide state-of-the-art optimization techniques tuned to specific markets (for example, a proprietary algorithm tailored for frequency regulation or intraday trading).
- Lower upfront costs: Instead of a heavy up-front investment, you might pay a subscription or share revenues with an optimization service. This converts fixed costs into variable costs tied to performance, which can be easier to manage in the early stages.
However, there are notable limitations to relying entirely on outside optimization:
- Loss of control and transparency: When a third party is making decisions, you may have limited insight into why certain bids or dispatches are chosen. If market conditions shift or the algorithm misfires, your ability to intervene or adjust is constrained.
- Vendor lock-in: An outsourced solution can bind you to the vendor’s capabilities. If a new revenue stream emerges (say, a novel TSO product or a local flexibility program) and your provider doesn’t support it, you either wait for them to catch up or miss the opportunity. Switching providers later can be as painful as a full system overhaul if the integration is deeply entrenched.
- Potential misaligned incentives: A generic third-party model might not account for your specific risk appetite or asset constraints. For instance, it could cycle your battery more than you’re comfortable with in pursuit of market fees, leaving you with higher degradation costs.
- Long-term cost: What starts as a cost-effective solution can become expensive at scale. Revenue-sharing models, for example, eat into profits; over time, those costs might rival or exceed what you would spend developing your own in-house optimization capabilities.
In practice, many experienced VPP operators use a mix of in-house and external solutions. They might begin with an outsourced algorithm to get off the ground, then gradually bring optimization in-house as they grow. Others use external modules for certain niches while handling core strategy internally. The lesson is to be deliberate about what to outsource, and to ensure you’re not ceding the agility you need to respond quickly to new market developments.
Owning your digital infrastructure
An emerging trend among leading VPPs is to own the digital infrastructure which your optimization solution then uses, while keeping it open to external plug-ins. Rather than using a single integrated end-to-end solution, you adopt a modular approach. Core digital infrastructure handles all data ingestion, transformation, storage, asset/market/TSO communications and controls asset dispatch, while specialized algorithms can connect via standard interfaces. If a new market product arises or a better forecasting service appears, you can slot it in swiftly - no major overhaul is required. You then retain full ownership of your valuable data, transaction and asset performance history, and the decision logic thus preventing any 3rd party supplier lock-in. Additionally by utilizing digital twins of your assets you can run multiple optimization modules and strategies side by side to compare the detailed results.
Although this approach can require more initial technical effort, it really pays off in reductions of lifetime costs and future-proofed commercial agility. You can decide how aggressively to trade, how best to protect your flexibility assets, and when to tap external expertise - all without being stuck on a single vendor’s roadmap. If tomorrow a TSO announces a new real-time reserve market or intraday volatility soars, you can respond instantly by integrating a new optimization module.
Maintaining commercial control and agility
Ultimately, a modular architecture preserves commercial control. It allows you to fine-tune your bids based on risk appetite, market signals, and asset constraints, without sacrificing visibility or waiting for a third party to catch up. By integrating whatever the current best-of-breed algorithms are whenever you need them you stay nimble, maximize profits and protect critical assets from unnecessary wear. In a market as dynamic as flexibility services, that adaptability can be the difference between average and exceptional performance - both now and as new opportunities continue to surface.
By shifting to a modular, open digital infrastructure platform, VPP operators can be permanently ready to profit from new market products, sudden volatility, and evolving TSO rules. This is why in the next installment of our VPP Agility Series, we’ll look into regional TSO requirements and changing regulations shaping aggregator choices - and why a flexible, future-proof architecture gives you an edge in every new market you enter.
