The hardware deployed in SEMIAH:
Software, platforms and applications
The “DSO Grid Constraints” is a service for configuring grid constraints for a collection of households belonging to a SEMIAH aggregator. The interface is used by DSOs to define boundaries for the operation of the scheduling of power consumption for the Collection. This interface complies with the operating regimes as defined by Universal Smart Energy Framework (USEF).
Currently, the service supports the soft and hard ECTs. As the electricity demand increases, the DSO moves gradually from the normal operation schemes to the capacity management regime. This demarcation point is marked by the soft ECT and triggers the SEMIAH scheduler to start shifting electricity loads to the future. At the same times the regulating electricity markets start planning a significant role providing peak load reduction and power balancing between electricity supply and demand through market mechanisms. Should the DSO not be able to successfully manage capacity, there is a risk of hitting the second demarcation point in which the regime of graceful degradation kicks in. This is marked by the hard ECT. When crossing this boundary, the DSO may take action to curtail loads regarding any service-level agreement promises just to secure the grid supply and to regain control of the network.
GridSim is an open source simulator. It aims at providing a tool to simulate the load flow on a grid, and offers also the possibility to simulate physical model such as houses or heat-pumps, to evaluate the flexibility and model demand-side management. GridSim can model the complex interactions between energy carriers. It is not limited to a single energy carrier like electricity or heat. GridSim should be seen as an open toolbox to quickly design the simulator adapted to specific needs.
The main features of the GridSim simulator are listed below:
- The simulator allows to define the topology of an energy system (electrical grid, generation, consumption and storage elements, elements transforming energy from a form to another form) as well as dynamic environment parameters (for example, temperature or solar radiation over time at a given point).
- An energy system is simulated over a given time period (simulation period, typically one year) with a given unit time step (typically one second).
- Any energy system parameter can be recorded over the simulation period.
SmartAMM is a middleware written in Java by DEVELCO Products. It is capable of handling communication with several HEMGs (the number depends on the selection of network type), as well as a number of “back-end applications”. The server can be used for configuration/consolidation of the SmartAMM gateway deployments as well as delivery and reception of all types of data to and from the gateways.
OGEMA (Open Gateway Energy Management) is an open software platform, developed by FRAUNHOFER that supports standardized building automation and energy management. The OGEMA platform can be applied in households, commercial environment and industries. Below is an overview of the OGEMA platform.
OGEMA features an object oriented data model for internal representation of physical devices, controllable loads, generators, sensors, actors, business related data (e.g. prices), etc. Data are persistently stored in an internal database. Applications (Apps) implement functionality based on data modification events or timer events. Drivers are special Apps that provide a connection between internal and external data resources. OGEMA features a comprehensive Application Programming Interface (API, current version 2.0.4, cp. www.ogema-source.net) that encapsulates basic services, e.g. data administration, timing, web server, and a RESTful server for machine to machine communication.
Cloud.iO is a scalable open source Internet of Things (IoT) solution provided by HES-SO. It offers an infrastructure to monitor and control a huge number of I/O devices on a central cloud platform. Cloud.iO can provide both, real-time monitoring and control data and historic data storage in a single place. Cloud.iO lets the application domain define its data model freely. It is a lightweight framework based on field-proven protocols and open source components developed for the IoT. It is built up of a set of independent micro-services leveraging the power of raw components for data acquisition and distributed control. The scalability of Cloud.iO is guaranteed by the scalability of its underlying components, which are at the heart of world scale internet services.
For the identification of the devices’ shifting potential into times where the energy consumption of these devices is more profitable, a Flexibility Forecast is needed. The flexibility forecast module is implemented in Python and runs on a virtual machine hosted at FRAUNHOFER. The core components and their functionalities can be summarised as follows:
- Database component: As a database, SEMIAH uses a MongoDB NoSQL database, where all raw data and pre-processed data are stored.
- Grib-Reader component: This component decodes the incoming WMO FM-92 GRIB messages received from the European Centre for Medium-Range Weather Forecasts (ECMWF). After decoding it stores all weather-forecast into the database.
- REST framework component: As a REST framework, SEMIAH uses the Django REST framework. This component provides interfaces for the communication with OGEMA and GVPP.
- FORECAST component: This component is based on the open source machine learning library scikit-learn. This component generates the flexibility forecast model. Based on this model a flexibility forecast is then created every 15 minutes. Each forecast result is stored into the database.
- Task-Scheduler component: This component handles the temporal execution of the GRIB-Reader and FORECAST component.
Virtual Power Plant IWES.VPP
The back-end system of SEMIAH is based on the IWES.vpp. The IWES.vpp is a software for the intelligent integration of producer, consumer and storages into the market. It offers a continuous monitoring, administration and controlling for the power plant portfolio on the base of client-server-application. It can be integrated into an existing infrastructure. Customer specific modules can easily be added to meet new requirements.
Below are some of the features:
- Real-time capability – seconds accurate detection of measurements from the decentral energy resources
- Forecast – Integration of energy and price forecast from different forecast providers
- Resource planning – Automated calculation of schedules for all connected power plants. Additional provisioning of manual controlling capabilities
- Integration – Integration with existing IT infrastructure or stand-alone
- Power plant connection – Simple and dynamic integration of power plant to the virtual power plant at the base of different international protocols (IEC61400-25, IEC61850 and OPC XML-DA.)
The EnergyOn platform constitutes a virtual power plant. The participating installations, power plants,storage facilities or loads provide flexibility. The platform aggregates these elements and exploits them on the balancing energy market, the day-ahead market and the intraday market. The optimization
approach selects the best option bearing in mind various data and forecasts. In addition, the costs for balancing energy, operation and network are also included in the evaluation, so that these are kept as low as possible with maximum yield. The EnergyOn platform is constantly in data communication with
the power plants via an online interface. This allows it to adjust schedules to suit circumstances that are continually changing.