This paper presents an applied IoT architecture for smart microgrids. Smart microgrids use -enabling technologies conjointly with power system equipment to deliver additional services on top of the basic supply of electricity to local networks that operate in parallel with the regional grid or autonomously. Such ancillary services offered by the microgrid e.g., local balancing, internal congestion management, and aggregation to support market or grid operator activities can create value for its end-users and other stakeholders. A Systems Engineering design approach is used to apply two reference architectures from different domains (power systems and IoT) to create a novel, high-level framework for the design of ICT systems for smart microgrids. The framework covers the device layer, connectivity system, data processing and storage layers, and business applications. The IoT architecture for smart microgrids is applied to a case study of a pilot project in an industrial and commercial area in the Netherlands.

This paper presents a demand response (DR) framework that intertwines thermodynamic building models with a genetic algorithm (GA)-based optimization method. The framework optimizes heating/cooling schedules of end-users inside a business park microgrid with local distributed generation from renewable energy sources (DG-RES) based on two separate objectives: net load minimization and electricity cost minimization. DG-RES is treated as a curtailable resource in anticipation of future scenarios where the infeed of DG-RES to the regional distribution network could be limited. We test the DR framework with a case study of a refrigerated warehouse and an office building located in a business park with local PV generation. Results show the technical potential of the DR framework in harnessing the flexibility of the thermal masses from end-user sites in order to: (1) reduce the energy exchange at the point of connection; (2) reduce the cost of electricity for the microgrid end-users; and (3) increase the local utilization of DG-RES in cases where DG-RES exports to the grid are restricted. The results of this work can aid end-users and distribution network operators to reduce energy costs and energy consumption.