Cost-efficient use of systems and processes
Rising grid fees, volatile energy markets, and the integration of renewable energies require new strategies in operational energy management to keep energy costs under control. In this article, we show you how you can optimize your energy costs and benefit from other advantages through peak load management, consumption forecasts, and the automated control of flexible systems.
Grid fees represent a significant cost factor for industrial and commercial enterprises. It can be assumed that these costs will continue to rise due to increasing investments in grid infrastructure. The cost development will affect not only the energy price component of grid fees, but also the capacity price.
For many companies, the continuous management of peak loads represents a major challenge. These can arise, for example, when several machines are started up simultaneously, energy-intensive processes are suddenly ramped up, or new, power-intensive systems such as electric charging stations are integrated. Another issue in the future will be the electrification of heat generation.
The resulting peak loads lead to high energy costs for companies due to increasing power prices, which are avoidable.
Systematic peak load management can help here. Approaches include, for example:
“The combination of peak load management with self-generated photovoltaic
Arne Grein
power and battery storage leads to significantly higher financial benefits
than the singular creation of PV power at the site.”
Head of Energy Markets, ÖKOTEC EnergY management
The combination of peak load management with photovoltaic systems and battery storage is particularly promising from an economic perspective. Grid consumption is optimized through the predictive charging or discharging of the battery and the targeted planning of additional flexible systems with continuous monitoring and forecasting of PV creation. Additional benefits: Not only are peak loads reduced efficiently, but the costs for grid usage fees and grid feed-in at negative prices are also reduced, and PV shutdowns are avoided.
A structured approach is recommended for successful implementation:
The basis and success factor for implementation is the selection of a digital solution that can effectively and dynamically map complex energy systems and, if necessary, high-load time windows. In addition, the software should be flexibly expandable in order to meet future requirements.
Practical examples show that the investments usually pay for themselves within a few years thanks to reduced energy costs. Depending on the previous peak loads and the usable shutdown capacities, payback periods of less than a year are also possible.
We recommend that companies that do not currently benefit from individual grid fees address the issue of peak load management in the short term.
Cooling processes and the storage of food in compliance with legally prescribed temperature requirements play a key role at the site of food processing company Ornua.
The cooling requirements and the efficiency of the cooling system were weather-dependent and caused peak loads. EnEffCo® made it possible to reduce these peak loads.
The second key approach for companies to reduce energy costs is to create energy demand forecasts in order to plan energy consumption precisely, forecast energy costs, and make these available to the energy supplier. To do this, energy management and energy purchasing must work closely together in the procurement process.
The management of balancing groups and energy costs is becoming increasingly complex. For example, energy suppliers must balance deviations between forecast and actual consumption across various markets – from the long-term futures market to short-term balancing energy. Balancing energy prices in particular can fluctuate wildly, with peak prices of up to €15,000/MWh (as of May 2025). This volatility leads to significant cost risks, which are ultimately passed on to end consumers.
Reliable energy forecasting enables energy suppliers to reduce the risk of balancing energy costs. The resulting cost savings can be passed on to end consumers on a pro rata basis. The amount of savings that can be achieved depends on the achievable forecast quality, energy consumption, and the energy supplier.
The forecasts, which are made at 15-minute intervals, are automatically transmitted to the energy supplier on a daily basis for the next 2-7 days using suitable software. Compared to energy suppliers, companies have much better opportunities to make energy forecasts due to the data available at the production site.
Head of Energy Markets
„Active schedule management minimizes the costs and risk of deviations for energy suppliers. Companies can also benefit from these savings.“
Various factors must be taken into account when developing accurate forecasting models. These include the following influencing parameters:
Simple reference-based methods or complex machine learning approaches can be used to generate forecasts. The following factors are particularly crucial for the quality of the forecasts:
Jack Link’s is the global market leader for branded protein snacks with 3,500 employees worldwide. ÖKOTEC examined the forecast and evaluation of flexibilities in plant utilization based on two CHP units with combined heat storage and two gas boilers.
Intelligent software solutions support the creation and automated monitoring of forecasts as well as communication with the energy supplier.
In order to fully exploit the potential and ensure the economic efficiency of the actions, close coordination with the energy supplier should be established in advance.
Furthermore, exchange and cooperation between different areas of the company, such as purchasing, production, and maintenance, is a key success factor for implementation.
With the increasing feed-in of fluctuating renewable energies and the decline in conventional power plant output, flexibility options offer the opportunity to benefit from the increasing volatility in the energy system.
The increasing complexity of operational management, dynamic energy markets, and regulatory requirements pose major challenges for companies. Electricity prices on the day-ahead market today show a high dependence on renewable electricity generation. The average difference between high and low prices (spread) will be over €120/MWh in 2025 – and rising (as of June 2025). These price fluctuations must be used intelligently.
There is a wide range of potential for flexibility in industry and commerce: from production processes to pumping and ventilation systems to refrigeration and compressed air generation. New technologies such as electrolysers, battery storage and Charging Infrastructure also offer flexibility options. The challenge is to intelligently enhance connectivity of this system and control it in a targeted manner.
Several steps are necessary to implement successful flexible energy management:
The cost-effectiveness of the various flexibility options varies significantly: For example, battery storage systems with investment costs of €450/kWh require an amortization period of around five years, while existing flexible loads such as cold storage facilities or CHP plants can generate revenues of €30,000 to €50,000 per megawatt hour of capacity per year without additional investment, meaning they can be amortized within a year (as of June 2025).
Intelligent software solutions enable the automated control of flexible systems, taking into account electricity price signals and operational restrictions. The digital twin maps the system and continuously optimizes its use. This greatly simplifies integration into existing processes and communication with energy suppliers.
Digitalization opens up enormous opportunities for increasing efficiency. In this interview, Prof. Dr. Hans-Rüdiger Lange, Head of Product Development and Product Manager EnEffCo® at ÖKOTEC, talks about how the digital optimization of systems and processes can be achieved and why the EnEffCo® software is a decisive success factor for energy management and production in the future.
Energy management decision-makers should promptly identify potential for flexibility within their own companies and tap into it using modern, digital management systems. Investing in these areas and digital solutions will pay off in the form of lower energy costs and improved competitiveness.
The three approaches presented offer companies concrete opportunities to significantly reduce their energy costs while contributing to the energy transition. Combining these strategies with digital solutions enables particularly effective implementation and can be approached step by step:
Benefit from our many years of expertise and start optimizing your energy management digitally now.
As an experienced consulting firm, ÖKOTEC supports you in identifying and tapping into your individual potential and successfully implementing it, taking into account location-specific requirements.
Our energy monitoring software EnEffCo® enables automated control and optimization of “smart operations” systems—from peak load reduction to profitable Flexibility Operations.
Launched as a research and development project, EnEffCo® is now the software solution of choice at over 220 production sites and 1,500 commercial locations in 35 countries, including Mercedes Benz, BMW, Berliner Stadtreinigung, Deutsche Rockwool, Duo Plast, Ineos, Innospec, N-Ergie, Ornua, Speira, Veolia, and Wela-Plast.
Head of Digital Analytics & Energy Markets
Senior Sales Manager
This year’s EnEffCo® Academy provided an exclusive platform for managing directors and energy managers to gain in-depth insights into the latest developments and research projects in the field of energy efficiency and digitalization.
EnEffCo® connects systems and processes for smart operations. In this interview, Dr. Hans Rüdiger Lange explains how companies can leverage AI and digital solutions to realize savings potential and strengthen their competitiveness.
Power prices for electricity have been pointing in one direction in particular since 2024: upwards. This increases the incentive for all companies that have not agreed individual grid fees to reduce their peak loads in order to save costs.
