# Autonomous Cyber-Physical Systems Enabling Smart Positive Energy Districts
## 3 Emerging Technologies Enabling Positive Energy Districts
### Edge Computing  
Edge computing is utilized for growing computing and networking needs. It enables real-time processing and analysis, and reduces latency and bandwidth requirements. Hence, it offers innovative contemporary solutions for effectively monitoring remote areas. In contrast to traditional centralized servers, edge servers are located close to the network edge, to data sources, and to the end-users [58]. They can be found in smart homes, IoT-powered factories, and self-driving cars. Edge servers have powerful computing capabilities and can offer many advantages. Edge computing has the potential to harness the full computing capacity of both edge devices and edge servers. To obtain lower inactivity and preserve security and privacy, edge computing shifts data processing from centralized cloud servers to edge nodes closer to the source, facilitating management of the growing complexity and real-time requirements of IoT applications. Moreover, edge computing supports PEDs through real-time data processing, energy management optimization, and integration of renewable energy resources at urban local level. By processing data near the points of energy production and consumption, edge computing enhances the efficiency, resilience, and sustainability of PEDs [59]. Dynamic integration of diverse renewable energy systems and multiple stakeholders is an explicit characteristic of PEDs that is supported by edge computing. This helps PEDs to become more resilient to energy disruptions and more adaptable to changing urban and climatic conditions [59]. For real-time systems, time-sensitive standards are fundamental in enabling low-latency and high-reliability communication [60]. Despite its potential, the energy efficiency of edge computing is still largely unexplored, due to the complicated structures and interactions between edge devices, edge servers, and data centers [58]. Because of the increased use of the IoT, challenges regarding management of huge volumes of data, which are usually of different types, have appeared. Additionally, complexity is amplified by the increasing number of cameras needed. The standard that exists today of using the IoT together with cloud computing has proven not to be sustainable [61]. Instead, by utilizing edge computing, IoT data is processed close to the location where the data is generated and Internet latency of data transmission can be efficiently reduced for time-constrained IoT applications [59]. Computation is performed locally, so the volume of data for transmission to remote data centers and cloud storage is minimized, the response time is reduced, and processing availability is improved. However, due to the complexity of edge-based IoT deployment, effective, efficient, and automatic management is needed [61]. Practical application cases for edge computing in PEDs include the following:  
1. _Real-Time Energy Monitoring and Optimization_: Edge devices monitor energy consumption and production (e.g., from heat pumps, solar panels) in real time through the collection of building-level data by a local edge server to fine-tune HVAC settings or battery storage use depending on demand and weather forecasts. This enables optimization of energy balance locally without the need for cloud computing. 2. _Demand Response Management_: Edge systems detect peak demand patterns (e.g., dishwashers, EV charging) and autonomously trigger load shifting or load shedding. As a result, grid stress is avoided and reliance on external control centers is reduced. 3. _Local Renewable Energy Integration:_ Edge computing coordinates local energy generation (e.g., PV systems) and storage units (e.g., home batteries), and decides when to use solar power, store it, or feed it into the grid based on real-time conditions. This enables better self-consumption and local balancing of supply/demand. 4. _Microgrid Control and Islanding_: Edge computing enables a PED to switch into island mode and operate autonomously in case of grid failures for increased resilience and continuity of energy supply. 5.