Recording of Tuesday, May 06, 2025 | The smarter E Europe Conferences 2025 | Conference Program | Language: English | Duration: 14:43 .
Florian Oest from P3 automotive GmbH presents a comparative analysis of edge and cloud-based Home Energy Management Systems (HEMS). Cloud HEMS solutions, while having higher latency, are cost-effective for basic applications due to lower capital expenditure. Conversely, edge devices offer real-time control with reduced latency but involve complex initial setups. Strategic partnerships in local edge systems enhance readiness for future technologies like V2X or V2G. Cost optimization drives the adoption of these technologies as seen in intelligent EV charging and solar-powered heat pumps examples. A benchmark study indicates that although cloud solutions have longer reaction times, they achieve high self-sufficiency rates compared to some faster-responding edge counterparts which may disrupt self-sufficiency. The energy management market has evolved through phases: initially dominated by edge-based solutions focusing on PV consumption until 2019; followed by cloud-first applications offering tariff-optimized features; now blending both approaches with strategic collaborations enhancing capabilities across domains. As demand grows, scaling is expected to favor cloud-only HEMS due to their accessible entry point before integrating advanced functionalities through combined models.
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Florian Oest
Consultant
P3 automotive GmbH
Germany
Battery storage systems consist of three main components: battery (including BMS), conversion unit (inverter), and controller (EMS). The EMS was used to simply calculate target values for the inverter, based on power meter readings, and to monitor inverters and battery's functionality. Today, it also controls or optimizes other devices in a local energy system, such as EV chargers and heat pumps. Modern EMSs optimize assets for multiple use cases (e.g., solar self-consumption, grid usage, dynamic tariffs). Learn more about the latest trends, including the use of AI in energy management systems for battery storage.
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