Sigenergy AI has successfully integrated with dynamic tariffs in Poland, helping a business achieve up to 48% savings on electricity costs. This not only marks a significant milestone in reducing electricity expenses for businesses but also demonstrates an innovative example of intelligent energy management.

In this article, we explore a project collaborating with Gaia Solar and how SigenStor and Sigen AI help businesses reduce energy bills in C&I settings.

Background

Sigenergy partnered with Gaia Solar to implement the SigenStor energy storage solution at a 2,000-square-meter office and warehouse complex in Teolin, Łódź, Poland. The solution included a 32 kWh SigenStor energy storage unit and Energy Gateway.

The client operated the SigenStor system in maximum self-consumption mode, using PV generation to power the building, and storing excess energy in the battery.

On October 1 this year, Gaia Solar switched the system to dynamic tariff pricing via Poland’s TGE Day-Ahead Market (RDN). This dynamic tariff system adjusts electricity prices in real-time based on supply and demand. The pricing is lower during off-peak hours and higher during peak times, helping businesses optimize their energy usage with technologies like PV generation and energy storage. The dynamic tariff is changing hourly, with plans to move to 15-minute intervals in 2025 in Poland, making it even more complex.

The Purpose and Implementation of Dynamic Tariff

The core concept behind dynamic tariffs is to optimize electricity purchases and storage operations in response to market fluctuations. However, the real-time nature of dynamic pricing presents challenges in effectively managing these changes.

To better navigate the complexities of dynamic tariffs, Gaia Solar turned to the mySigen App, Sigenergy’s smart energy management platform powered by AI. Through the mySigen App, the building's energy management is fully controlled by Sigen AI, a smart tool that leverages machine learning algorithms and big data analysis. Sigen AI optimizes battery charging and discharging schedules by considering factors such as electricity price fluctuations, weather patterns, building load variations, and energy usage predictions, ensuring maximum efficiency and cost savings.

Performance Comparison and Analysis

Teolin, situated at a relatively high latitude (approximately 52.4°N), experiences a significant decrease in sunlight intensity during the autumn and winter months. In September, as the weather cooled and sunlight diminished, we found that the battery often couldn't reach a full charge and discharge cycle, resulting in lower utilization. Full charge and discharge only occurred on days with optimal weather conditions.

Below is the electricity data for September 2024, when fixed pricing was still in effect:

• Total grid power purchase: 1,527 kWh
• Total cost: 1,065.85 PLN (pre-tax)
• Average price per kWh: 0.698 PLN

Below is the electricity data for October 2024, after Gaia switched to dynamic tariff pricing:

• Total grid power purchase: 3,863 kWh^[1]
• Total cost: 1,556.50 PLN (pre-tax)
• Average price per kWh: 0.403 PLN

Several key changes were observed in the energy strategy for October when Sigen AI was put into action:

• The battery successfully completed at least one full charge and discharge cycle per day, optimizing energy storage and usage.
• The system prioritized charging the battery when electricity prices were low and discharging it when prices were high.

With dynamic tariffs, Sigen AI adapted its strategy in real-time. Even when PV generation was sufficient, the system would use grid electricity when prices were low (sometimes even negative prices) and store PV generation. When electricity prices were high, the battery would discharge to power the load. Any surplus energy was sold to the grid at higher prices.

Despite a dramatic increase in electricity consumption (2,336 kWh more than in September) mainly because of the activation of heat pump and shorter daylight hours, the electricity bill increased by only 46%. Had the system continued to operate in maximum self-consumption mode in October, the electricity bill would have been 2,696.37 PLN. However, with dynamic pricing and Sigen AI, the bill only amounted to 1,556.50 PLN, resulting in savings of over 48%.

In terms of energy sold to the grid, 272 kWh was sold in September at the fixed price of 0.22262 PLN per kWh, whereas in October, 739 kWh was sold at an average price of 0.46325 PLN per kWh, more than doubling the price per kWh.

AI-empowered After-Sales Service

The mySigen App goes beyond energy management by integrating GPT-4 to offer a more intelligent customer experience. It functions as a virtual assistant, providing quick system setup, instant information queries, and natural language q&a to address common issues.

As the system gathers more user data, it continuously improves, reducing response times and providing 24/7 support for frequent service inquiries.

Conclusion

This collaboration between Sigenergy and Gaia Solar demonstrates the significant savings and operational benefits achievable through dynamic pricing and AI-powered energy management. Businesses seeking similar solutions are encouraged to reach out to Sigenergy for more information.