The certificate, issued by Polskie Sieci Elektroenergetyczne (PSE), which serves as the Energy Market Information Operator (OIRE), confirms that the solution meets technical and business communication requirements.

Connecting CSIRE with enterprise systems

ConnectPoint has developed NK-CSIRE – a tool that enables energy companies to connect their applications to the Central Energy Market Information System (CSIRE). NK-CSIRE manages communication with CSIRE in accordance with OIRE requirements and transmits the retrieved data to the company’s internal systems, such as billing systems.

In the first phase, ConnectPoint focused on the needs of electricity suppliers. NK-CSIRE automatically retrieves metering data from CSIRE and transfers it to billing systems, where it is used to bill customers.

This connection links data exchange to the company’s day-to-day operations. Information from CSIRE can flow into existing business applications and support the processes in which it is used.

A common standard for data exchange in the energy market

CSIRE is a nationwide Polish system for collecting, processing, and sharing electricity market information. It includes data on electricity consumption points (PPE), contracts, metering data, and information needed to support market processes. PSE is responsible for its operation.

The system introduces uniform rules for exchanging information among electricity suppliers, distribution system operators, and other authorized market participants. It is intended to improve customer service, speed up supplier switching, and provide access to consistent data.

CSIRE began operating on July 1, 2025. The deadline for market participants required to use the system to connect was October 19, 2026. For energy companies, implementation means adapting both their software and the way they handle business processes to the new rules for information exchange.

Scope of PSE certification

The certification process for NK-CSIRE version 1.0.0 covered all technical tests and scenarios required for the electricity supplier (SE) market role.

Certification enables ConnectPoint to develop integration services for electricity suppliers using CSIRE.

As a physical sender, ConnectPoint can communicate with CSIRE on behalf of electricity suppliers. Suppliers do not need to build or certify their own IT solutions: they can use a solution that has passed PSE certification.

Implementation for electricity suppliers

Connecting to CSIRE requires reliable communication with the central system and the use of the data received in the company’s processes. NK-CSIRE is designed to support electricity suppliers in both tasks.

The solution provides:

• communication validated through certification for the electricity supplier role
• automatic retrieval of metering data and transfer to billing systems
• integration with existing applications so that CSIRE data is available in the systems used by the company
• less manual data handling when transferring data between systems.

The scope of further integrations will expand as new features are added to the solution. This will allow communication with CSIRE to be linked to broader support for billing, contracts, and relationships with electricity customers.

Development plans and additional market roles

ConnectPoint plans to develop NK-CSIRE as an integration tool for energy market participants. One area of development is integration with customer care systems, including contract management and electricity supplier switching. This will link information exchange with CSIRE to tasks performed directly in customer service applications.

The company also plans to obtain certification for additional market roles: distribution system operator (OSD), balance responsible party (POB), and authorized user.

ConnectPoint provides a broad range of services to multi-energy companies, and integrating systems with CSIRE is another significant step in its product development.

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In our previous articles -one outlining how BESS installations work and the markets they operate in, and another covering grid services and energy trading – we explored the design of battery energy storage systems, their markets, and the platforms needed to manage these installations. This time, let’s look at a less visible part of the picture: data. At first glance, it seems straightforward. If a BESS has a specified power rating and energy capacity, and its control system displays the battery’s state of charge, we should know how much energy is still available. Well, not necessarily.

From the storage owner’s perspective, what matters most is not the energy capacity on the nameplate, but how much energy can actually be delivered to the grid over a given period, at what power level, and within warranty conditions. For the system operator, the question is whether that energy will be available exactly when needed. These two perspectives meet in the analysis of measurement, operational, historical, and market data. The quality of that data determines whether we use the BESS’s actual capabilities or merely manage a more or less accurate approximation of them.

Battery state of charge: A seemingly simple indicator

Let’s start with the best-known parameter: SoC (State of Charge). In simple terms, it expresses the battery’s charge level as a percentage of an assumed reference capacity. When the operator’s dashboard shows 60%, we intuitively read it like a car’s fuel gauge or a phone’s battery indicator: we still have 60% left. But the meaning is not quite as clear-cut. You cannot simply insert a probe into a battery and measure its remaining energy the way you measure the liquid level in a tank. In addition, for applications such as grid services, the actual amount of energy the system can absorb or deliver is much more useful (in MWh rather than as a percentage).

A typical BMS (Battery Management System) estimates SoC using voltage, current, and temperature measurements together with appropriate models. One method is coulomb counting, which integrates the current flowing into and out of the battery. It works reasonably well, but measurements have errors, and those errors can accumulate. Voltage-based correction also has limitations, particularly with today’s widely used lithium iron phosphate (LFP) cells. Their voltage curve is relatively flat across a broad charge range, making errors easy to introduce. A small voltage difference therefore does not always provide a clear answer about how much energy remains in a cell.

ACCURE, an independent battery analytics company, reports that its collected data shows state-of-charge estimation errors in LFP installations can reach approximately ±15%, and can be even greater in some cases. Physics-based models and advanced data analytics can reduce this error to below ±2%. However, this often involves a “BMS reset” procedure requiring a complete battery discharge and recharge cycle, which may not be feasible within the battery’s operating schedule.

SoC, SoH, and the energy actually available

Now for the next piece of the puzzle. The battery’s state of charge (SoC) does not tell us its available capacity. Cells age over time, and their ability to store energy changes. One indicator used to describe this is SoH (State of Health), which reflects the battery’s condition. The extent of this degradation is often addressed in the agreement with the supplier. Even if two modules display the same 60% SoC, they may not contain the same amount of energy. One may have a lower actual capacity, while another may be operating at an unfavorable temperature or have a power limit imposed by protective functions or an upcoming balancing cycle.

There are also system-wide constraints: the permitted SoC operating window, PCS conversion efficiency, auxiliary power for cooling and heating, transformer limits, and the availability of individual containers and modules. As a result, SoE (State of Energy), an estimate of the energy that can actually be delivered, increasingly appears alongside SoC. For energy trading, the key question is how many MWh the system can deliver to the point of interconnection, over what period, and at what power level – in other words, how much a trader can commit to the energy or balancing services market.

Of course, SoE is not a single, universal device reading. It must be calculated using the battery’s condition, efficiency, technical constraints, and current BESS operating schedule. The availability of a system prepared for a one-hour discharge will differ from that of an asset that must also maintain a reserve for balancing services, while staying within warranty limits on cycle counts and temperatures. That is why data on energy, power, and constraints should form a single, consistent asset model rather than reside in separate applications and spreadsheets. This availability is often defined in the warranty agreement using a formula such as:

Lost hours: (unplanned downtime + planned downtime + maintenance downtime + hours with external constraints)

A BESS management platform should monitor this value according to its contractual definition and provide reporting to support discussions with the supplier if availability measured using the formula differs substantially from the stated figure. These stated figures for actual availability – that is, annual operating time without technical constraints – often exceed 99%.

What are five percentage points worth in a BESS?

Suppose we have a BESS facility with a power rating of 100 MW and a reference energy capacity of 200 MWh. Assume the system displays 50% SoC, but an estimation error means the actual state of charge is five percentage points higher. For this example, we simplify the relationship between SoC and energy to a linear one, disregarding capacity variation and minimum SoC limits. The difference is then 200 MWh × 5% = 10 MWh of energy stored in the batteries.

That is not yet 10 MWh we can sell. If we assume 85% efficiency in transferring this additional energy from the batteries to the settlement metering point, about 8.5 MWh remains. At a hypothetical price of PLN 400/MWh, this represents gross sales of approximately PLN 3,400 in a single event. This is not profit, of course, but rather a missed opportunity: we have not included the cost of earlier charging, degradation costs, fees, market constraints, or whether an opportunity to sell at that price will arise at all. Still, it illustrates why reliable estimates of the asset’s capabilities are worth pursuing.

The situation also works in reverse. If the BMS overestimates SoC and we submit a market offer exceeding the system’s actual energy delivery capability, we risk missing the operating schedule, having to cover energy shortfalls, or incurring contractual penalties. A prudent operator will then build in a larger safety margin using another unit. The problem is that a wide margin protects against errors but may also leave part of a fully functional asset unused. Better data does not eliminate the battery’s physical constraints – it allows us to identify more precisely where those constraints actually lie.

Availability: is the BESS ready, or does it just report that it is?

SoC accuracy is one issue; the technical availability of the entire BESS facility is another. In an ACCURE study published in September 2026 covering more than 50 operating projects, the median availability reported by the installations – the middle value among all measured values – was 96.7%, while median availability measured by the components’ actual response to commands was 94.1%. The difference is 2.6 percentage points. These are results for the study sample and the methodology used, rather than a figure that can be assigned to any storage installation.

Why the discrepancy? A device may be visible on the communications network and have no active critical alarm, yet still fail to reach the requested power level or execute a command within the required time. We therefore need to distinguish “online” status from the ability to perform a specific task. Another question is whether an entire BESS facility is available when one container is offline but the others can still provide some services. There is no simple answer: we need to know how much power and energy are available, for how long, and under what conditions. In our GridConnect platform, we call this dispatch availability planning.

Likewise, at the operational level, collecting alarms and displaying them on a screen is not enough. We need to record command histories, equipment responses, deviations from schedules, power limits, downtime, and maintenance events. It helps if alarm management can be linked to an external service ticketing system. Better still, technicians should receive guidance on what to do based on previous faults of a similar type. Only this combined view allows us to determine whether lost availability stems from an equipment failure, a BMS protective function, a communications problem, or an inaccurate estimate of the installation’s capabilities.

From thousands of signals to one operator decision

As you can imagine, a large BESS facility has an enormous number of variables: cell temperatures and voltages, current measurements, module and container states, PCS parameters, energy meters, protection devices, HVAC systems, medium- and high-voltage equipment, and signals from the local SCADA system. Simply gathering this data in one place does not mean it can support better decisions. An architecture is needed to ensure appropriate data quality, timestamps, and links to specific devices.

The architecture of GridConnect, our modern BESS management platform, has three main areas. The first is the local layer – BMS, PCS, automation, SCADA, and an edge device – where measurements are acquired and critical control tasks are performed. The second is the operational EMS layer, which gives the operator a consistent view of one or more installations, event and availability records, and operating schedules. The third covers analytics and historical data, supporting estimates of capacity, efficiency, and technical condition, availability forecasts, and the delivery of reliable information to trading systems.

Table 1. From technical data to business decisions: An example allocation of BESS platform responsibilities.

Our GridConnect concept, explored at ConnectPoint during work on platforms for large storage installations, calls for precisely this separation of responsibilities. The equipment and integration layer includes local SCADA systems, edge devices, and protocols such as IEC 60870-5-104, MQTT, and OPC UA. At the center are the asset model, operational platform, and data exchange infrastructure. Above them sit a data lakehouse for historical records and integration with applications responsible for trading and optimization. This describes an architectural direction and use cases; it does not claim that every listed function has already been implemented in every such system.

In this architecture, we should not move safety functions or fast control from local controllers to database analytics simply because the database offers a more convenient computing environment. The supervisory platform can provide estimates and schedules, but local protection systems must retain their autonomy. Communications quality is just as important: missing signals, ambiguous timestamps, or a communications outage affecting one asset can quickly turn a good analytical model into one operating on an outdated view of the situation. These scenarios also require a plan.

Forecasting capabilities, not just recording events

It is time to move from current monitoring to the future. When providing services in the energy and balancing markets, knowing how much power a storage system has available at a given second is not enough. We also need to estimate how much will be available in an hour, tomorrow, and over the coming days, accounting for current SoC, planned cycles, temperatures, maintenance outages, and existing commitments. One use case considered in the GridConnect architecture materials is estimating dispatch availability over a longer horizon – a good example of how far today’s requirements extend beyond a simple screen of battery parameters. Given the structure of energy and balancing services markets, reliable estimates covering at least two days are essential.

This analysis involves at least two types of data. The first comes from the storage system itself and describes its technical constraints. The second comes from external sources: operator instructions, forecasts of operating conditions, and required reserves. Only by combining the two can we answer a practical question: can I offer 80 MW and 100 MWh for a specified period without conflicting with another commitment or a battery operating limit?

Comparing the schedule with actual performance is also important. If a trader planned a specific discharge profile but the facility delivered less energy, the difference alone explains little. We need a history of setpoints and actual values, the state of the assets at the time of the event, and the cause of the constraint. This information supports not only settlement of the storage system’s operation, but also improvements to subsequent estimates and verification of the parameters declared by equipment suppliers.

Interestingly, forecasting storage availability is more than an owner’s idea for more effective energy trading. In Poland, PSE also requires relevant data. For JGM scheduling units (storage scheduling units), the operator expects estimates of minimum and maximum power in both the generation and consumption directions (Pmax_gen_est, Pmin_gen_est, Pmax_pob_est, Pmin_pob_est), as well as the maximum energy the storage system can deliver to or draw from the grid (E_max_dysp_up and E_max_dysp_down). This distinction matters because a system may have sufficient power available at a given moment but be unable to sustain it for the entire scheduled period because of its current SoC, temperature, or technical constraints. BPKD-DB (Intraday) schedules have a 15-minute resolution, while BPKD-CR (Real-Time) schedules have a 5-minute resolution, so the forecast must refer to specific points in time rather than just the facility’s overall availability. In addition, key measurements and signals describing current JGM operation are transmitted to the LFC system every 1–2 seconds, allowing us to continuously verify estimates, detect deviations, and adjust subsequent forecasts.

Historical data, degradation, and operating costs

One more issue remains – an important one, although its consequences usually become visible only over time. Battery energy storage systems are assets that wear out with use. Every charge and discharge cycle has consequences that depend on factors such as depth of discharge, temperature, operating power, and cell chemistry. To assess the profitability of future transactions reliably, we cannot consider only the purchase and sale prices of energy. Under the warranty, the supplier specifies how much battery capacity may decline in subsequent years, assuming proper operation by the user.

Historical data lets us track capacity loss, differences between modules, actual round-trip efficiency, the number and characteristics of cycles, and operating time under specific conditions. We can then attempt to estimate the asset wear cost associated with a particular operating strategy and compare it with additional revenue. We must, of course, avoid presenting such an estimate as a falsely precise figure: degradation depends on many factors, and models require periodic calibration and validation against measured data.

For example, two trading strategies may generate similar gross revenue, while one requires more frequent and deeper battery cycles. If our economic model includes only energy settlements, we will treat them as equivalent. Once we add efficiency, warranty conditions, and estimated wear costs, the result may look different. This is precisely where historical analytics, the service system, and operational planning should draw on the same reliable information base. We can determine the “actual cost per cycle.”

How can we make the most of every percent of BESS battery capacity? First, we need to measure it reliably

There is no single method that guarantees maximum utilization of every BESS. A battery storage system used for price arbitrage and balancing market services will operate differently from an installation maintaining reserves for the operator, or from a BESS behind the meter at a large industrial plant. In every case, however, the starting principle should be the same: move from data collected at the installation to a model of its actual capabilities, and only then to operational and business decisions.

If we do not know where our storage system’s technical capabilities end, we will either take excessive risks or continually maintain an expensive safety margin. This is perhaps the strongest argument for investing in a robust BESS data platform and analytics: it will not replace the cells, converters, or operator, but it will help us make much better use of what the investor has already paid for.

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Assessing data sources and user needs

The first stage is to identify the PLCs, SCADA and DCS systems, meters, databases, and other sources that will feed PI System. This includes reviewing the available protocols, data quality, measurement frequency, and historical data requirements.

Use cases are defined in parallel. These may include monitoring plant operations, reporting KPIs, analyzing failures, improving energy efficiency, or making data available to business systems. This establishes from the outset which data actually provides value to users.

Designing the AVEVA PI System architecture and integrations

The assessment provides the basis for the solution architecture. This covers data acquisition, PI Server configuration, communication between OT and IT zones, and system access rules.

This stage also defines requirements for availability, redundancy, backups, time synchronization, and secure communication. Larger environments need to account for multiple plants and integrations with MES, ERP, CMMS, analytics platforms, or a data lake.

The architecture chosen will affect how easily PI System can be expanded later, so it should account for both the current project and anticipated future uses of the data.

Asset Framework adds context to data

Once data acquisition is up and running, the data can be organized. Asset Framework (AF) makes it possible to model the company’s structure—sites, lines, plant systems, and equipment—and associate the relevant measurements with each asset.

This is an important stage of implementation because historical data from thousands of tags can quickly become difficult to use on its own. The AF model allows a process specialist or engineer to analyze a pump, boiler, or production line together with its operating parameters and performance indicators.

This layer can also include Asset Analytics, KPIs, and Event Frames that describe events such as startups, shutdowns, or operating parameters exceeding their limits.

Visualizations, testing, and go-live

Once the data and asset model are ready, user displays are created. AVEVA PI Vision can present current values, trends, events, and KPIs in the context of specific assets and processes.

Before the system goes live in production, testing verifies data acquisition, data quality, calculations, permissions, and system behavior in situations such as a loss of communication. Users also verify that the displays and reports reflect how they actually work.

Testing is followed by go-live, user training, and handover for ongoing support. PI System can then be expanded to include additional data sources, plant systems, analytics, and integrations.

In practice, a phased approach provides the best control over implementation. The organization starts with specific needs, builds a structured data model, validates it in day-to-day work, and only then extends the solution to other areas.

ConnectPoint supports the entire process, from assessment and architecture design through AVEVA PI System integration and configuration to testing, training, ongoing support, and further development of the environment. We describe the scope of our services and the capabilities of AVEVA solutions in detail on our page dedicated to AVEVA solution integration.

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AVEVA PI System, previously developed as OSIsoft PI System, is an operational data platform designed for industry and energy. Its central component is PI Server, which stores time-series data and links it to equipment and process context.

PI System is often described as an industrial historian, but data archiving is only one of its functions. The system can model plant structures, perform calculations, identify events, display data to users, and share it with other applications, BI systems, and analytics solutions.

How does AVEVA PI System work?

The first step is data acquisition. PI Interfaces, PI Connectors, and AVEVA Adapters communicate with OT sources and send measurements to PI System. Historical data is stored in PI Data Archive, where values are retained along with timestamps and quality information.

Measurement history alone is not enough for efficient analysis. That is why Asset Framework (AF) associates data with specific plants, lines, facilities, and equipment. This allows users to work with assets such as pumps, turbines, or production lines rather than analyzing individual tags with technical names.

The next layer handles data interpretation. Asset Analytics calculates KPIs, efficiency, specific consumption, and other metrics. Event Frames record events such as startups, shutdowns, production batches, and parameter limit violations. Users can then compare entire events and investigate the reasons for differences in process behavior.

Data is made available to users through tools such as AVEVA PI Vision, which lets them create dashboards, trends, and process displays. PI System can also pass information through APIs and integration tools to business systems, data platforms, BI tools, and AI environments.

What is AVEVA PI System used for?

PI System is primarily used where process data comes from multiple sources and requires a shared history and context. Typical applications include production monitoring, energy efficiency analysis, KPI reporting, startup and shutdown analysis, equipment comparisons, and infrastructure condition monitoring.

Data collected and organized in PI System can also support predictive maintenance, data science, and AI models. This is especially important because the effectiveness of these analyses depends not only on the amount of data but also on its quality and context. Temperature or vibration data becomes much more useful when users know which asset it relates to, its operating condition at the time, and what happened to it previously.

The current AVEVA ecosystem also extends this model to the cloud through AVEVA CONNECT, allowing data from on-premises installations to be used in architectures that span multiple plants, centralized analytics, and advanced computing solutions powered by cloud resources (advanced analytics / machine learning).

In practice, AVEVA PI System serves as a layer that connects OT data with the people and systems that need to use it. Its value increases when data is properly integrated, described in Asset Framework, and linked to specific operational questions.

ConnectPoint, an official AVEVA partner, helps companies implement, integrate, and expand the use of this technology. Learn more about AVEVA solutions and ConnectPoint integration services.

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In the previous article in our series, we explained how such a system works. Now let’s take it a step further and look at how BESS can operate in today’s energy market and what roles it can play.

Battery systems are divided into front-of-the-meter (FTM) and behind-the-meter (BTM) systems, depending on where they are installed and the role they are intended to perform. The latter also include residential battery systems (from the operator’s perspective, they are behind the meter, forming part of the electrical installation on the owner’s side, for example that of a homeowner). The figure below helps explain the difference.

Figure 1. a) Front-of-the-meter (FTM) and b) behind-the-meter (BTM) BESS systems.

In industrial settings, BTM BESS systems are mainly used to:

• Maximise self-consumption from renewable energy sources (RES, if the home, plant, company or farm has such sources): When combined with a local photovoltaic installation, a BTM BESS stores surplus energy generated during the day or, for example, at weekends, rather than feeding it back into the grid at unfavourable market prices or simply not generating that energy at all. The stored energy is used when needed, for example in the evening or the following day.
• Reduce peak demand charges (peak shaving): This is one of the key roles in industry. A BTM BESS discharges during periods of the highest (peak) electricity consumption by a company or plant. This reduces the maximum power drawn from the grid (the BESS and the grid jointly supply the plant’s needs, so the amount drawn from the grid is reduced by the amount supplied by the BESS). This can reduce fixed distribution charges or simply keep consumption within the contracted capacity agreed with the distribution system operator, avoiding penalties.
• Manage electricity prices (energy arbitrage/load shifting, for companies that choose electricity purchase contracts with dynamic tariffs): The system charges the batteries when electricity from the grid is cheapest (e.g. during off-peak night-time tariffs or periods of market oversupply) and supplies energy to the facility when electricity prices rise sharply.
• Provide emergency power and business continuity: A BESS operates like an advanced, large-scale UPS system. In the event of an external grid failure, it protects the company against costly production downtime and damage to machinery.
• Compensate for reactive power: Modern BESS systems allow power quality to be controlled and can therefore act as a source or load with specified characteristics. For companies with a significant inductive component (e.g. a large number of electric drives) or capacitive component (e.g. data centres or large facilities), this function is important for reducing electricity costs.

These are only some of the potential applications, but they include the most important functions offered by behind-the-meter BESS systems. One of the latest trends is the use of BTM BESS at electric vehicle fast-charging stations. In this case, the main objectives are also usually peak shaving and the ability to charge several cars simultaneously at high power. Such charging does not take long, but the power demand is substantial. BESS can eliminate the need to sign a contract with the operator for the station’s full capacity or enable a station to be built at a location where the required peak power is not available at the connection point. The second argument is price management.

These characteristics make BESS increasingly attractive to many investors. According to the Energy Market Agency (ARE), the total energy storage capacity in Poland reached 8.85 GWh (equivalent to our largest power station in Bełchatów operating at full capacity for almost two hours). According to the agency’s data from August 2026, there are 114,418 electricity storage installations operating in Poland, most of them in prosumer systems (112,465 units). There are 1,948 industrial storage installations (medium-sized, with capacities of 0.05–10 MW) in Poland, which is a considerable number. However, their combined power and energy capacity suggest that these are relatively small installations. The data are presented in the table below.

Summary of the data is presented in the table below.

Table 1. Electricity storage installations in Poland (source: Energy Market Agency S.A.).

The five large energy storage systems mentioned in the ARE report are pumped-storage power stations (including the largest, at Żarnowiec, with 716 MW/3.6 GWh). The small number of BESS systems in this segment may seem surprising, but we should remember that 1 GW/4 GWh of BESS capacity is awaiting grid connection conditions, acceptance procedures and so on in 2026 alone. According to the National Energy and Climate Plan adopted by the Council of Ministers (a document prepared by the Ministry of Energy), Poland is expected to have approximately 6 GW of installed battery storage power capacity by 2030 under the ambitious WAM (With Additional Measures) scenario, presumably involving support mechanisms.

Let us now look at front-of-the-meter (FTM) systems, i.e. large BESS installations that should provide services and enable interaction with both the distribution and transmission systems. Unlike domestic or behind-the-meter BTM systems, FTM installations are not assigned to a specific electricity consumer or factory. Their purpose is to stabilise the electricity system as a whole and generate revenue from energy trading on a large scale.

There are also many operational possibilities here, but let us focus on the key ones:

• Market arbitrage: The storage system charges from the grid when electricity is cheap (e.g. at sunny midday periods thanks to photovoltaics or on windy nights thanks to wind turbines), when prices are attractive and may even be negative. It then feeds energy back into the grid during periods of highest demand (usually in the evening), when prices reach their highest levels.
• Participation in the Capacity Market: The storage owner receives a fixed payment from the grid operator simply for being ready to supply energy at critical moments (so-called stress events), when reserve capacity is needed.
• Provision of ancillary and frequency regulation services (the balancing services market): This is the most technically advanced role and often the most important revenue stream. The storage system responds to grid frequency fluctuations within fractions of a second (FCR and aFRR functions), stabilising grid parameters and preventing blackout-type failures (it is highly likely that the malfunctioning of such mechanisms, combined with several other operator errors, was at the root of the blackout in Spain in April 2025).

How do these operational opportunities work in the market? Market arbitrage seems fairly easy to understand, although from the perspective of control and revenue generation, it is by no means a simple mechanism. The electricity exchange operates in 15-minute intervals, so prices change very dynamically. In addition, every battery charging and discharging cycle brings us closer to the cycle limit specified in the warranty conditions. The number of cycles must therefore be managed sensibly. Making money from energy trading alone is a considerable challenge.

As for securing reserve capacity, this system in Poland is managed by Polskie Sieci Elektroenergetyczne (PSE), under the supervision of the President of the Energy Regulatory Office (URE). The main capacity auctions are held well in advance. In December 2025, the main auction for reserves for 2030 was concluded (Notice No. 1/2026 from the President of the Energy Regulatory Office regarding the announcement of the final results of the main auction for the 2030 delivery year).  Almost 6.9 GW of reserve capacity was contracted across 198 units, including 33 foreign units. The price was PLN 465.02/kW/year.

Supplementary auctions also take place during a given year. For example, an auction for 2027 is scheduled for September 2026. The cost of providing reserves is ultimately passed on to consumers through the so-called capacity charge (visible on electricity bills).

So, if we contracted an entire 100 MW BESS system in the auction for 2030, we would receive PLN 46,502,000 per year. Impressive! But we can no longer win that auction, and there are still more than three years until 2030. The final and, at the time of writing, most important “product” that can be offered on the market is frequency stabilisation services, known as balancing services. As this is a fairly complex mechanism and service, I will devote more attention to it.

Balancing services market

In our first article, we drew attention to the complexity of electricity systems and the need to balance them. The transmission system operator (in Poland, this is straightforward, as there is only one such operator, PSE) continuously monitors the situation, paying particular attention to two parameters: frequency and voltage in the system.

In the simplest terms, when electricity demand begins to rise and no new sources of supply become available, the frequency starts to fall. This can happen for many reasons: large consumers or many smaller consumers may connect to the system, one of the major loads may significantly increase its power consumption, or an important electricity producer may disconnect from the system (e.g. due to a failure or cloud cover over a large photovoltaic farm).

This sensitive mechanism also works in the opposite direction: if there is an oversupply of electricity generation in the system and insufficient demand, the frequency begins to rise.

The operator monitors this frequency very carefully. In Europe, we use 50 Hz, so the operator will respond if the frequency falls to 49.99 Hz or exceeds 50.01 Hz. The operator will use the full contracted capacity when the frequency falls below 49.8 Hz or rises above 50.2 Hz. In the context of this article and BESS services, voltage is not quite as important (locally, it indicates the reactive power balance and the condition of the transmission network).

How can the transmission system operator protect against system destabilisation? The answer is simple: reserves must be secured. This means guaranteeing reserve capacity for the provision of balancing services, rather than simply for electricity generation. It involves maintaining a dynamic balance in the system by securing reserves both “upwards” (having units ready to supply additional energy when there is a shortage) and “downwards” (the ability to reduce generation or use surplus energy for charging). This is not a cheap process. Every year, PSE spends more than PLN 2 billion on it. The operator specifies whether an upward or downward service is required and which particular service is needed. There are several such services: FCR, aFRR, mFRR and RR.

These stand for:

• Frequency Containment Reserve (FCR): Also known as primary frequency control.
• Automatic Frequency Restoration Reserve (aFRR): Also known as secondary frequency control.
• Directly activated manual Frequency Restoration Reserve (mFRRd): Also known as tertiary frequency control.
• Replacement Reserve (RR): Each of these services is provided separately in the upward and downward directions.

Their most important characteristics are summarised below.

Table 2. Characteristics of different balancing mechanisms.

As we can see, different balancing services must support the operator not only by providing contracted capacity but also by responding within a specified time.

Of course, the operator also purchases these services separately, for specific periods and with an indication of the upward or downward direction (except for FCR, which is a symmetrical service). Four-hour time slots are standard, although there is already discussion about moving towards the electricity market’s 15-minute intervals. Due to the technological limitations of other solutions, BESS systems have an important role to play here. They are fast enough to provide FCR and aFRR services (the remaining balancing services can be provided by conventional resources and renewable energy sources). They also allow energy to flow in both directions, enabling both upward and downward regulation.

Current prices for providing aFRR services are approximately PLN 180/MW/h for upward regulation and approximately PLN 140/MW/h for downward regulation. Therefore, if I win an auction to provide upward aFRR services for four hours, using a 100 MW BESS system, I can expect to receive PLN 56,000. This applies regardless of whether the operator actually uses that capacity, because the operator has reserved it. This is known as a “capacity” service, securing balancing capacity. If the operator uses this capacity, as the service provider I will issue another invoice, this time for the energy itself. I must obtain the price for that energy on the balancing energy market, where I am required to submit an offer for the contracted capacity that I previously secured. I have become (or rather, my BESS system has become, and has been assigned the relevant identifier) a Balancing Service Provider (DUB).

Of course, entering this market is not easy. I must complete a complex qualification process, integrate with the operator’s systems and meet other requirements.

How does the balancing services market work?

How does this market work in practice? Let’s start by explaining who a DUB is. According to the definition on the PSE website: “A Balancing Service Provider (DUB) is an electricity market participant that supplies balancing energy and maintains balancing capacity for the Transmission System Operator.” If we are a DUB, we can therefore participate in auctions for providing balancing services to the transmission system operator (PSE), offering our available capacity as capacity reserved for balancing services. The market itself operates through auctions. PSE holds auctions on its balancing services platform, separately for “capacity”, meaning the reservation of capacity itself, and for the energy that may be needed if the operator decides to use it. Once I have qualified as a DUB, I can submit an offer on the capacity market for a specific time slot (at least one hour), a specific balancing unit (one of my BESS systems that has already received its identifier as a DUB) and a specified rate per reserved MW/h.

The operator forecasts its requirements for balancing services and selects the most advantageous offers.

Below is a graphical example of an auction result, followed by an explanation.

Figure 2. Example of the outcome of an auction for balancing services.

In our example, four bidders participated in an auction for balancing services, including me as a BESS operator qualified as a DUB. The operator needs to reserve the balancing capacity indicated by the dashed line for upward aFRR services (more power in the system). Each auction participant (DUB) proposed a price per megawatt for each hour of available balancing capacity reserved during that time slot. The unit is therefore PLN/MW/h. The operator closed the auction. The offer from provider DUB 1 was successful. My offer, as a DUB, was somewhere in the middle of the price range, and provider DUB 3 also qualified. We will all receive an offer to reserve our declared capacity for the declared period at the rate offered by provider DUB 3 (pay-as-cleared). DUB 3 will receive an offer, but not for the full capacity it offered, as this is not necessary. Bidder DUB 4 will earn nothing today for capacity reservation alone because its price was too high. However, if I have won the auction (as in our example), I must reserve that capacity for the required service during the specified time slot and be ready to provide the service. The operator has the right to request it if the situation in the system requires it.

This means that I will have to submit an offer on the balancing energy market, specifying a price for each MWh. If the operator requests this capacity and I fail to fulfil my obligation (because I do not submit an energy offer or ultimately fail to deliver the energy for another reason), penalties will be imposed. To summarise this rather complex mechanism: I participate in an auction to secure capacity (a “capacity” reservation priced in PLN/MW/h). If my offer is among the successful bids, I receive the capacity reservation rate at which the auction cleared. I must then submit an offer for the balancing energy itself (if it is ultimately needed, priced in PLN/MWh delivered), at a rate that I determine myself. The operator will again close the auction, selecting the final accepted offer in that auction and settling all bidders at that rate. I can therefore use my price per MWh to influence the likelihood of my offer actually being activated and, for example, limit the number of charging and discharging cycles of my system.

Once I have won a capacity auction and there is a need to use my energy at the proposed price per MWh, it is better to fulfil the obligation partially than not at all (for example, because of technical difficulties, which will also need to be explained). Even if I am forced to provide the service using another resource I own, rather than the one specified in my offer, I will have to explain this. In the case of mFRR and RR services, conventional generating units and renewable energy sources usually offer prices attractive enough that competing with BESS systems in these markets currently makes little sense.

As the number of distributed energy sources grows, so does the need to digitalise energy systems. The BESS battery system itself requires access to large amounts of real-time data and an intelligent operational platform that not only supports submitting different offers in different markets (energy and balancing markets) but also enables forecasting of the power and availability of my installation for different services in the future. A good BESS management platform (often referred to as an Energy Management System, or EMS) will also support the use of technological innovations such as advanced reporting, availability estimation, automatic execution of operating schedules, predictive maintenance management and artificial intelligence. It is also necessary to build an infrastructure capable of handling huge volumes of data, including historical information on energy consumption, the number of cycles and the condition of battery cells, in order to make more accurate forecasts of installation availability and monitor compliance with warranty conditions.

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When I talk to customers about AVEVA PI System, I rarely start with the number of tags, server versions, or data collector architecture. I’m much more interested in the answer to one question: What would you like to know about your facility that you currently can’t find out in just a few minutes?

The answers are similar across very different industries. How much energy do we consume per ton of product, and what causes the differences between shifts? Which pumps are operating outside their optimal range? How long did boiler startups take in the last quarter, and what parameters distinguished the shortest from the longest? What deviations preceded equipment failures?

The data needed for these analyses often already exists. It can be found in PLC controllers, SCADA and DCS systems, and, in many companies, in years of archived PI data. The problem begins when users see thousands of time series but lack a consistent model showing which equipment and processes they belong to, under what conditions they were recorded, and how they should be interpreted.

In AVEVA’s current portfolio, PI System remains an actively developed environment for operational data management. The vendor now also offers it as part of AVEVA PI Data Infrastructure, which extends the PI architecture to support data management from the edge, through on-premises installations, to CONNECT services. For organizations already using PI Server, this means they can continue developing their existing environment and making data available through hybrid architectures.

Data archiving is one layer of a well-designed PI System

PI System is often associated with a historian because PI Data Archive is responsible for storing time-series data. In a real industrial environment, however, archiving is part of a broader data flow that includes data acquisition, storage, contextualization, calculations, visualization, and exchange with other applications.

AVEVA’s current portfolio clearly illustrates this division:

PI Interfaces, PI Connectors, and AVEVA Adapters remain part of AVEVA’s current data acquisition offering, just as PI Vision, PI DataLink, and PI Integrator for Business Analytics remain available for working with and using data.

During data acquisition and archiving, settings that determine which values are actually passed on are particularly important. In traditional PI architectures, exception reporting is primarily associated with the data acquisition layer, while compression reduces the number of events stored in the archive. These mechanisms must be configured according to the characteristics of each signal. Excessive tolerance can reduce the visibility of brief changes that may be important for later analysis, while overly conservative settings lead to storing large volumes of data that provide no additional information. However, the biggest change I usually see in how PI System is used occurs at a higher level: data context.

Asset Framework turns a list of tags into a facility model

Knowing that a temperature was 87°C at a given moment has limited value without knowing what was being measured. The same value could occur during normal equipment operation, startup, reduced load, or a period when the machine should be shut down.
Interpreting this information requires context: the equipment, operating state, load, recipe or product, nominal parameters, and the behavior of the same asset under comparable conditions.

Asset Framework, or AF, serves as the operational data model within PI Server. It makes it possible to represent a structure familiar to plant personnel: company, location, department, production line, equipment, and components. The hierarchy can be adapted to the asset model used by the organization, including structures similar to ISA-95 if the company uses that classification approach.

Element templates are particularly useful. A centrifugal pump template can define a common set of attributes: flow rate, suction and discharge pressure, temperatures, power consumption, operating state, nominal parameters, and calculated values. Dynamic attributes can reference specific PI Points, while some information may come from other sources or be the result of calculations.

Once a model has been created for a particular equipment class, it can be applied to additional assets. This means that KPI definitions, analyses, and data presentation methods do not have to be recreated separately for every pump, compressor, or heat exchanger. AVEVA identifies Asset Framework hierarchies, models, and templates as mechanisms for organizing and contextualizing data in PI Server. From the user’s perspective, the difference is significant. A process engineer begins working with an asset called “Pump P-101” instead of a list of tags whose names are primarily familiar to automation engineers. Maintenance, production, and energy management teams can use the same equipment and KPI definitions, reducing the creation of parallel spreadsheets containing different versions of the same calculations.

Asset Analytics and Event Frames enable event-based process analysis

The next step is to move from observing individual values to analyzing what is happening to a piece of equipment or a process.
Asset Analytics enables streaming calculations on PI Server data and recalculation of historical data. This makes it possible to create KPIs such as specific energy consumption, availability, efficiency, cycle counts, and equipment operating time.

A definition included in an AF template can be applied to an entire equipment class, ensuring consistency in how the KPI is calculated. AVEVA’s current product documentation continues to describe the ability to create template-based analyses and backfill historical data. Event Frames introduce another way of looking at a process. They make it possible to record a period with a defined start, end, and set of associated data.

This could be a startup, shutdown, production batch, parameter exceedance, protective system activation, or a period of operation in a specific state.

A practical example

If every boiler startup is recorded as an Event Frame, hundreds of startups can be compared by duration, fuel consumption, temperatures, pressures, and other process parameters. The analysis no longer requires manually selecting time ranges on a trend chart. Instead, users work with a set of comparable events and can examine the conditions that occurred during the best-performing and most problematic startups.

AVEVA describes Event Frames as a mechanism for identifying and recording recurring events with start and end times, such as batches, shifts, startups, or downtime.

At this point, process history begins to answer operational questions. Users can analyze the number of events, their duration, the conditions under which they occurred, and the relationships between them, rather than reviewing dozens of separate trend charts.

Data quality determines the reliability of advanced analytics

PI environments that have been operating for many years regularly experience problems that are not always obvious during routine trend reviews. These include tags without up-to-date descriptions or units, incorrectly configured exception and compression settings, frozen values from disconnected sensors, incorrect quality states, timestamp offsets, and multiple points representing the same measurement.

Such problems become much more visible when data begins feeding automated KPIs, comparisons across multiple pieces of equipment, or analytical models. A mathematically correct calculation performed on an incorrect signal still produces an incorrect result.

This is particularly important in Predictive Maintenance and machine learning. For supervised models, labeled examples of events and failures can provide highly valuable training data. Anomaly detection methods can also work without large numbers of labeled failures, but they still require representative data from normal operations, the correct operational context, and subsequent validation of results by experts.

Event Frames can play an additional role here. If an organization consistently records downtime, alarms, maintenance work, startups, and other events, it obtains structured time intervals that can be used when preparing and validating analyses. PI System itself provides data, context, and calculation capabilities. Advanced predictive applications may use additional tools. AVEVA Predictive Analytics remains a separate AVEVA product integrated with PI System, offering capabilities such as anomaly detection and estimating the time until a potential failure. PI data can also be made available to custom models and external analytics platforms.

This is why it pays to assess data quality before evaluating the model itself when starting an AI project. If a process engineer, automation engineer, and business user interpret the same tag differently, the problem lies in the data and context layer.

PI System data can be shared without rebuilding the existing environment

Mature PI System implementations often reach a point where data is also needed outside the OT environment: in business reporting, custom applications, data lakes, AI systems, CMMS, ERP, or cloud solutions. AVEVA continues to develop several ways to provide this access.

PI Integrator for Business Analytics prepares PI Server data for use in BI tools and advanced analytics. It allows data to be selected, cleaned, and structured into tabular datasets while preserving the context provided by PI Server. The product remains part of AVEVA’s current portfolio.

Custom applications can use PI Web API, AF SDK, and PI SQL. PI Web API provides access to time-series data, asset metadata, and events through REST. AF SDK allows .NET applications to work directly with the Asset Framework model, while PI SQL makes it possible to query PI data using mechanisms familiar from relational databases.

AVEVA continues to list all these technologies as current PI System developer technologies.

CONNECT data services are playing an increasingly important role in hybrid architectures. AVEVA’s current terminology uses the name CONNECT data services; the older name AVEVA Data Hub is best avoided in new materials. The service integrates natively with PI System and is designed to make contextualized operational data available to cloud applications, analytics, and data science tools.

This allows organizations to develop their existing PI System environments incrementally. Data retained at the facility can continue supporting the daily work of operators and engineers, while selected information can be made available to business systems or cloud services in accordance with the organization’s IT/OT architecture.

PI System development should start with a specific operational problem

If PI System has been operating within a company for several years, the starting point should be to assess the existing environment and select a use case whose results can be verified.

In practice, this process can be divided into four stages:

1.  Environment assessment. Review component versions and support status, the data acquisition architecture, mechanisms ensuring continuous data transmission, point settings, the quality of descriptions, and the extent to which Asset Framework is being used.
2. Problem selection. The team defines a specific operational question, such as energy consumption per unit of production, downtime analysis, startup monitoring, or the behavior of a critical equipment class.
3. Model and analytics development. AF structures, templates, calculations, Event Frames, and PI Vision views are created to the extent required by the use case.
4. Validation and expansion. Users verify whether the information obtained helps them make decisions. Only after the approach has been validated is it extended to additional equipment, production lines, or locations.

This approach limits the scope of the initial stage while making it possible to determine whether the organization’s problem can actually be solved using data already available in PI System.

In the projects we deliver, we very often find that a company already has a significant portion of the data it needs. Most of the work involves organizing that data, linking it to the appropriate assets, and building logic that describes the process in a way its users can understand. For me, this is precisely where AVEVA PI System demonstrates its greatest value. Process history begins helping process engineers, operators, maintenance teams, energy specialists, and managers answer questions that previously required manually gathering information from multiple sources.

If your company uses PI System and wants to assess how effectively its environment is being utilized, a good starting point is one specific operational question: What information about our facility do we need today that, despite having the data, we still cannot obtain quickly enough?

Check out our AVEVA solutions.

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For decades, the power system operated in a relatively straightforward way. On one side were utility-scale electricity generators – power plants and combined heat and power plants of various types and sizes – and on the other were consumers, also of various types, from large industrial plants through medium-sized industry and large commercial, office and hotel facilities to individual consumers and households. Most power plants and CHP plants were conventional facilities, powered by energy generated through the combustion of fossil fuels. As long as CO2 emissions were not strictly monitored, fuel supplies at these plants were full. There were also no other significant technical barriers, so their output was very stable and predictable. The energy equation was simple: electricity generation met the immediate need for consumption – generation = consumption. One side of the energy equation generated and sold electricity in a stable and predictable manner, while the other consumed and purchased it somewhat less steadily, but still quite predictably. Generators and transmission system operators carried the full responsibility for ensuring stable system operation by maintaining the balance between supply and demand. Most of the time, this worked, although there were exceptions, although sometimes it did not, hence the “power supply restriction levels” known from older movies and publications, when generators were unable to keep up with the needs of large consumers and had to persuade them to reduce their demand. According to the Institute of Environmental Protection, in 1990 electricity generation in Poland came from:

• coal: approximately 76.8%,
• oil: 14.8%,
• natural gas: 8.9%,
• biofuels and renewable energy sources: 3.9%.

Figure 1. Balance in the historical power system – generation balances consumption. Figure generated using artificial intelligence.

Power system: changes have arrived

Today the situation is completely different, as can be seen from the profiles and types of electricity generators reported for August 2026 by the PSE portal. (Polish Power Grid Company portal – system data, viewed on August 26, 2026, rounded to one decimal place.)

• Coal, oil and gas combined: 42.4%
• Hydropower plants: 0.3%
• Onshore wind farms: 4.3%
• Offshore wind farms: 0.2%
• Photovoltaics: 52.8%

It is not only the generation profile that has changed. Consumer behavior has also changed significantly, with many consumers becoming local generators themselves – producer + consumer = prosumer – and energy now flowing in different directions rather than only from generators to consumers. The system has grown, the number of sources has increased, and their nature has become less stable. The behavior of wind and photovoltaic power plants can be forecast when good weather forecasts are available, but it is also obvious that photovoltaic installations will not generate electricity after sunset, while on bright, windless days wind farms cannot be relied upon. In this situation, maintaining the balance between supply and demand, and consequently ensuring stable system operation, becomes much more difficult.

Figure 2. A fragile balance in a complex energy market. Figure generated using artificial intelligence.

When trying to balance supply and demand in a market, a good storage facility can be extremely useful. If a farmer produces too many apples and has no immediate buyer, they look for a good warehouse where the apples can be stored until demand increases. This way, the farmer does not lose either the product or the money. When a customer decides they want apples, the apples are still available. Of course, storage comes at a cost.

The energy market works in a similar way. There are storage systems designed for electrical energy, and energy can be stored in many different ways:

• in potential energy, for example by lifting a large weight when there is excess energy and lowering it to generate energy when there is a shortage; often that weight is water, as in pumped-storage power plants,
• in kinetic energy, by accelerating a mass when there is excess energy and then recovering that energy by slowing it down; this is how kinetic energy storage systems, also known as flywheels, operate,
• in a certain sense, energy can also be stored by heating a material, although converting it back into electricity later is difficult,
• in the energy of chemical bonds, either in elements such as hydrogen and fuel cells, or, much more commonly, in components known as batteries.

This last mechanism is the most widespread. For years, we have stored electricity this way to start vehicles using batteries, although some of us may still remember manual starters in motorcycles and cars, as well as to power portable devices, from flashlights, calculators and radios to today’s smartphones, tablets, watches, headphones and portable speakers. Since around 2009, with the first generation Nissan Leaf, batteries have also been used to power electric vehicles, today covering almost every type of vehicle, from passenger cars and vans to buses, trucks and specialty vehicles. Batteries and accumulators vary and are based on different electrochemical processes and material technologies. Lead-acid batteries remain the most widespread and, in various forms, still dominate in applications such as cars. However, it is now widely understood that other technologies dominate portable devices and electric vehicles because of their much better energy-storage capacity relative to size and weight. These include NMC and LFP, while Na-ion and solid-state electrolyte technologies are increasingly being discussed. Over the past decade, batteries have significantly improved their technological parameters, including the number of charging cycles, thermal characteristics and availability in different forms and shapes. At the same time, their prices have steadily fallen, making them an attractive alternative for building large systems capable of operating together with power systems.

Figure 3. Energy storage systems supporting stability in a complex energy market. Figure generated using artificial intelligence.

It is not difficult to see how such an energy storage system can greatly support grid stability. Put simply, it can quickly absorb excess energy by charging, and when the system is short of energy, it can discharge it. In our example with the scales shown in the figure above, it acts as a kind of shock absorber, both for generators and consumers. As a result, operators and participants in the electricity market gain a new tool: a Battery Energy Storage System, or BESS.

What is BESS really?

As the name suggests, a BESS is a system, not a single device or even simply a collection of devices. There is no single market standard for the construction of a BESS, but these installations, often referred to as battery parks, have many common components. Depending on the design, these may differ in power, appearance, location and role. Let’s begin with the simplified arrangement shown in the figure below.

Figure 4. Simplified BESS park. Figure generated using artificial intelligence.

The main components of most BESS parks are containers. These usually contain battery modules installed in battery racks. The modules often resemble slide-in cartridges with electrical and communication terminals on the front panel. Inside are battery cell packs and a BMS, or Battery Management System, responsible for managing and protecting the local pack and communicating with the outside world. It is worth becoming familiar with this terminology, because the terms rack, module and cell are often confused or used interchangeably, which can lead to misunderstandings. Packs inside modules, as well as the modules themselves, are connected in series and in parallel to achieve the required output voltage and total current.

Figure 5. Container, rack, module and cell – terminology used in battery systems. Figure generated using artificial intelligence.

Batteries are components that store direct-current energy. A typical cylindrical household battery, just like a battery module, has clearly marked positive and negative terminals – direct current, or DC. The power grid, on the other hand, uses alternating current – a sinusoidally varying current, or AC. To connect the two, a device is needed to convert the positive-negative DC supply into a 50 Hz sine wave. This is the role of PCS, or Power Conversion Systems. During charging, such a power electronics converter draws energy from the three-phase AC grid and, operating as a controlled rectifier, uses it to charge the cells in the modules. When the battery is discharging, the process is reversed. The system draws direct current from the battery and converts it into alternating current while operating as an inverter. From a technical perspective, this is a four-quadrant power electronics converter. In modern converters, power quality on both the DC and AC sides is very high, and converter efficiency often exceeds 98%. Typically, a single PCS serves an entire group of battery modules.

The next step is to combine the energy from individual PCUs into larger groups and match the output voltages of the inverters to the voltage levels typically used in the power system in a given region. A typical PCU is therefore followed by a transformer, often with two primary windings to which between one and several PCUs are connected. The secondary sides of these transformers are connected to switchgear, often at medium voltage. This is an old but still commonly used term; in Poland, typical voltage levels are 15 kV and 20 kV. Depending on the system capacity, this transformer will be connected through switchgear to the local power grid or, in the case of large BESS parks, the voltage level will be increased once again using a high-voltage transformer. Naturally, measuring equipment and systems are present at many points throughout this complex installation, on both the DC and AC sides. There are also electricity meters between the park and the power grid for final settlement purposes.

Additional systems at the BESS park

The park is supplemented by several additional systems. A significant role in the park’s internal energy balance is played by the heating, ventilation and air conditioning system, or HVAC.

Batteries heat up during intensive operation, as every smartphone owner knows. As a result, battery modules require cooling on hot days. On the other hand, batteries cannot operate, particularly at full power, at very low temperatures and therefore require heating. These requirements are handled by the park’s HVAC system, which can reach an impressive scale and require substantial amounts of power. Safety systems are also required, covering both electrical safety, with numerous protection systems of different types at various points within the BESS park, and fire protection. Different manufacturers use different technologies, but installations include numerous sensors for smoke, fire and temperature, as well as fire suppression systems and fire barriers. Technical spacing is also maintained, both for internal logistics, since these are neither lightweight nor easy-to-transport components, and for safety reasons, as well as to provide easy access to key components within the park.

As can easily be imagined, such a facility generates enormous amounts of data: measurement data, communications data, information about equipment status and condition, and a very large number of electrical measurements. Managing such a complex system is a highly responsible and demanding task, which makes having a trusted partner particularly important. At the park level, management is handled by park controllers, which often perform the role of an EMS, or Energy Management System, which we discuss in a separate article.

A well-designed and properly managed BESS requires not only the right technology, but also a partner capable of combining electrical engineering, automation, data and IT systems into a single functioning ecosystem. At ConnectPoint, we deliver projects of a similar nature and, based on our international experience, we see enormous potential in the management of BESS systems.

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We were also joined by the event partners – ASTOR and Agencja Rynku Energii and CIRE.pl, who worked with us to create a space for conversations, meetings and knowledge exchange.

Topics important for industry, energy and utilities

The first day of ConnectPoint Insights 2026 was devoted to three main areas: Manufacturing, Energy Efficiency and Energy & Utilities. Representatives of, among others, Basell Orlen Polyolefins, Twinings, Veolia ESCO, PGE GiEK and PSE shared their experiences related to the use of production data, digitalisation, predictive maintenance, energy efficiency and infrastructure management. The common denominator of all the presentations was the practical use of data to improve the management of production, energy and processes, and of course AI.

After the conference sessions, it was time for dinner, networking and less formal conversations. This is an important element of ConnectPoint Insights, allowing participants to continue discussions started during the presentations and exchange experiences in a more relaxed atmosphere. We also had an artistic part here – a performance by Teatr Los Fuegos, which presented a spectacular light show combining dance, juggling and acrobatics with fairy-tale colours, fluorescent body painting and impressive light props.

The second day had a workshop format – our team presented a practical approach to using Smart RDM, process data, IIoT and OPC UA communication, showing how to combine data from different industrial systems into one coherent environment.

Nearly 100 people in one place

Nearly 100 people took part in this year’s edition of ConnectPoint Insights. They represented different industries, companies and organisations – from production and maintenance, through energy, automation, OT and IT, to data, digitalisation and enterprise management.

This diversity is one of the event’s greatest strengths. The same technologies and similar problems often look completely different from the perspective of a manufacturing plant, an energy company or an infrastructure operator. Meeting in one place makes it possible to compare these experiences and look at your own projects from a different perspective.

ConnectPoint Insights has become our tradition

ConnectPoint Insights has now become a permanent fixture in the events calendar – ours, but, to our delight, also that of our clients. Although each edition focuses on different market and technology challenges, we consistently emphasise practical project experience, conversations with experts and space for knowledge exchange.

We would like to thank nearly 100 participants, speakers, partners and everyone involved in organising the event.

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The scope of both certifications covers the design, development, implementation, and maintenance of IT solutions and systems, as well as IT consulting services supporting the automation of business processes in the industrial, energy, and infrastructure sectors.

ISO 9001 at ConnectPoint

ISO 9001 is one of the world’s most widely recognized standards for quality management systems. It defines requirements that help organizations design, deliver, and continuously improve their processes in a structured, measurable, and customer-focused way.

In practice, a quality management system compliant with ISO 9001 includes, among other things:

• clearly defined roles, responsibilities, and processes,
• monitoring the quality of services delivered,
• managing risks and opportunities,
• analyzing customer needs and expectations,
• responding to nonconformities and implementing corrective actions,
• regularly evaluating process effectiveness,
• continuously improving the organization.

Achieving ISO 9001:2015 certification confirms that ConnectPoint GmbH applies a systematic approach to quality management when designing, implementing, and maintaining IT solutions, as well as when providing consulting services.

The certificate was issued on 3 August 2026 and is valid for three years.

ConnectPoint GmbH_ISO 9001_certificate_2026_en

ISO/IEC 27001 at ConnectPoint

ISO/IEC 27001 is an international standard that defines the requirements for an Information Security Management System (ISMS).

The standard helps organizations identify and mitigate risks related to information processing while protecting the:

• confidentiality of information by ensuring that it is accessible only to authorized persons,
• integrity of information by protecting it against unauthorized modification or loss,
• availability of information and systems by ensuring that they can be accessed when needed.

An ISO/IEC 27001-compliant management system goes far beyond technical security measures. It also covers policies, procedures, employee responsibilities, access control, incident management, supplier relationships, business continuity, and regular risk assessments.

The Polish company within the ConnectPoint Group completed a similar certification process earlier. ISO/IEC 27001 is particularly important in the energy, utilities, and IT services sectors, where data protection and system reliability are fundamental to building trust between service providers and their clients.

ConnectPoint GmbH’s certificate was issued in accordance with the current version of the standard, ISO/IEC 27001:2022. It has been valid since 4 August 2026 and is valid for three years. The certificate also refers to the Statement of Applicability (SoA), version 1, dated 1 June 2026.

ConnectPoint GmbH_ISO 27001_certificate_2026_en

ISO Certifications bring tangible benefits to clients and partners

Obtaining ISO 9001 and ISO/IEC 27001 certifications confirm that service quality and information security at ConnectPoint GmbH are managed in a consistent and documented manner and are subject to regular independent assessment.

For our clients and partners, this primarily means:

• Greater predictability in service delivery

Clearly defined processes, responsibilities, and control mechanisms help maintain consistent standards throughout every stage of cooperation, from requirements analysis and solution design to implementation, maintenance, and further development.

• Systematic risk management

Risks related to quality, information security, and service continuity are identified, assessed, and appropriately managed. As a result, decisions are not based solely on reacting to individual issues, but on a structured risk management process.

• Protection of entrusted information

ISO/IEC 27001 confirms the use of a controlled and structured approach to protecting client data, project information, technical documentation, and access to IT systems and environments.

• Regular audits and continuous improvement

Certification is not a one-time activity. Maintaining the certifications requires internal audits, management reviews, monitoring of process effectiveness, and regular independent surveillance audits.

• Easier cooperation in regulated sectors

These certifications are particularly important for organizations operating in the energy, industrial, and infrastructure sectors. In these industries, requirements related to quality, information security, supplier control, and risk management are often an integral part of procurement processes and project delivery.

• Greater transparency and credibility

Certificates issued by an independent certification body provide external confirmation that ConnectPoint GmbH’s processes have been assessed for compliance with the requirements of internationally recognized standards.

Quality and security now formally established

Implementing these two management systems at ConnectPoint GmbH formalizes standards that had already been applied in the delivery of our services. To demonstrate compliance with the requirements and achieve ISO certification, the company coordinated several key areas, including organizing documentation, defining responsibilities, conducting risk assessments, reviewing security measures, standardizing service delivery processes, and preparing the organization for audits.

Achieving these certifications does not mark the end of the process of continuously raising standards within ConnectPoint GmbH and other ConnectPoint companies. Instead, it represents another important step in improving the way we design solutions, deliver projects, and protect our clients’ information.

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