European (energy) data exchange reference architecture 3.0

This is the third version of Data Exchange Reference Architecture – DERA 3.0. BRIDGE report on energy data exchange reference architecture aims at contributing to the discussion and practical steps towards truly interoperable and business process agnostic data exchange arrangements on European scale both inside energy domain and across different domains.DERA 3.0Recommendations related to the implementation of DERA:A. Leverage Smart Grid Architecture Model (SGAM) usage by completing it with data governance requirements, specifically from end-customer perspective, and map it to the reference architectures of other sectors (similar to the RAMI4.0 for industry – Reference Architecture Model Industrie 4.0; and CREATE-IoT 3D RAM for health – Reference Architecture Model of CREATE-IoT project), incl. for basic interoperability vocabulary with non-energy sectors.B. Facilitate European strategy, regulation (harmonisation of national regulations) and practical tools for cross-sector exchange of any type of both private data and public data, e.g. through reference models for data space, common data governance and data interoperability implementing acts.C. Ensure cooperation between appropriate associations, countries and sector representatives to work on cross-sector and cross-border data management by establishing European data cooperation agency. This involves ongoing empowering/restructuring of the Data Management WG of the BRIDGE Initiative to engage other sectors and extend cooperation with projects that are not EU-funded and with European Standardisation Organisations (CEN-CENELEC-ETSI).D. Harmonise the development, content and accessibility of data exchange business use cases for cross-sector domain through BRIDGE use case repository. Track tools that identify common features on use cases, e.g. interfaces between sectors, and enable the alignment with any potential peer repositories for other domains. Also, the use case repository must rely on the HEMRM with additional roles created by some projects or roles coming from other associations (related to another sector than the electricity/energy sector).E. Use BRIDGE use case repository for aligning the role selection. Harmonise data roles across electricity and other energy domains by developing HERM – Harmonised Energy Role Model and ensure access to model files. Look for consistency with other domains outside energy based on this HERM – cross-sectoral roles. Harmonised EnergyData EndpointsData SpaceConnectorData ProcessingStandard CommunicationProtocols& FormatsData HarmonizationData PersistanceVocabularyProviderCredentialManagerIdentityManagerMonitoring& OrchestrationData DiscoveryData IndexerLocal AI/ML ServicesDigital TwinsMarketplace BackendStandard CommunicationProtocols& FormatsMarketplace FrontendFederatedUse Cases and Business needsLocal Use Cases and Business needsEnergy RegulationEU Re-gulationActorsBusinessFunctionInformationComp.CommsNon-personal dataSecurity/ResilienceUserAcceptanceSovereigntyOpen SourceInteroperabilityLocalFederatedInteroperabilityTrustData valueGovernance9DATA MANAGEMENT WORKING GROUPEuropean (energy) data exchange reference architecture 3.0Role Model shall have clear implications and connections with data (space) roles such as data provider/consumer, service provider etc.F. Define and harmonise functional data processes for cross-sector domain, using common vocabulary, template and repository for respective use cases’ descriptions. Harmonisation of functional data processes for cross-sector data ecosystems including Vocabulary provider, Federated catalogue, Data quality, Data accounting processes, Clearing process (audit, logging, etc.) and Data tracking and provenance.G. Define and maintain a common reference semantic data model, and ensure access to its model files facilitating cross-sector data exchange, by leveraging existing data models like Common Information Model (CIM) of International Electrotechnical Commission (IEC) and ontologies like Smart Appliances Reference Ontology (SAREF).H. Develop cross-sector data models and profiles, with specific focus on private data exchange. Enable open access to model files whenever possible.I. Ensure protocol agnostic approach to cross-sector data exchange by selecting standardised and open ones.J. Ensure data format agnostic approach to cross-sector data exchange. The work done by projects like TDX-ASSIST and EU-SysFlex (using IEC CIM), and PLATOON (using SAREF) must be shared and made known to consolidate the approach in order to reach semantic interoperability. Metadata must also be taken into account.K. Promote business process agnostic DEPs (Data Exchange Platforms) and make these interoperable by developing APIs (Application Programming Interfaces) which enable for data providers and data users easy connection to any European DEP but also create the possibility whereby connecting to one DEP ensures data exchange with any other stakeholder in Europe. DEPs shall explore the integration of data space connectors towards their connectivity with other DEPs including cross-sector ones.L. Develop universal data applications which can serve any domain. Develop open data driven services that promote also cross-sector integration collectively available in application repositories.Possible next steps (“sub-actions”) for 2023/2024:➢ Release BRIDGE Federated Service Catalogue tool and associated process.➢ Release DERA interactive visualisation tool.➢ Follow up the implementation of DERA 3.0 in BRIDGE projects (mapping to DERA)➢ Update recommendations to comply with DERA 3.0.➢ Develop / enhance the “data role model”

Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm

The purpose of this study is to develop an effective control method for a hybrid energy storage system composed by a flow battery for daily energy balancing and a lithium-ion battery to provide peak power. It is assumed that the system operates behind the meter, the goal is to minimize the energy cost in the presence of a PV installation (as an example of a local renewable source) and energy prices are determined by 3-zone tariffs. The article presents the application of an optimization method to schedule the operation of each battery in the system. The authors have defined an optimization method aimed at minimizing the total cost of the system, taking into account energy costs and batteries depreciation. The techno-economical model of the system, including battery degradation, is constructed and the cost optimization methods are implemented in Python. The results are validated with real energy and price profiles and compared with conventional control strategies. The advantages of optimization in terms of energy cost are discussed. The experiment shows that not only is a hybrid energy system successful in lowering the total operation cost and in increasing self-consumption but also that the implemented methods have slightly different properties, benefits and issues

Model funkcjonowania energetyki rozproszonej w oparciu o blockchain i systemy zarządzania energią

Rozwój energetyki rozproszonej pozwala na tworzenie lokalnych mikrosieci energetycznych z dużym udziałem OZE oraz lokalnych rynków energii. Ich funkcjonowanie zdeterminowane jest z jednej strony przez systemy dedykowane do sterowania rozproszonymi zasobami energetycznymi i magazynami energii, z drugiej zaś strony przez mechanizmy umożliwiające obrót energią w sposób zdecentralizowany

Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm

The purpose of this study is to develop an effective control method for a hybrid energy storage system composed by a flow battery for daily energy balancing and a lithium-ion battery to provide peak power. It is assumed that the system operates behind the meter, the goal is to minimize the energy cost in the presence of a PV installation (as an example of a local renewable source) and energy prices are determined by 3-zone tariffs. The article presents the application of an optimization method to schedule the operation of each battery in the system. The authors have defined an optimization method aimed at minimizing the total cost of the system, taking into account energy costs and batteries depreciation. The techno-economical model of the system, including battery degradation, is constructed and the cost optimization methods are implemented in Python. The results are validated with real energy and price profiles and compared with conventional control strategies. The advantages of optimization in terms of energy cost are discussed. The experiment shows that not only is a hybrid energy system successful in lowering the total operation cost and in increasing self-consumption but also that the implemented methods have slightly different properties, benefits and issues

Testing of a price-based system for power balancing on real-life HVAC installation in real life

HVAC systems use a substantial part of the whole energy usage of buildings. The optimizing of their operation can greatly affect the power use of a building, making them an interesting subject when trying to save energy. However, this should not affect the comfort of the people inside. Many approaches aim to optimize the operation of the heating and cooling system; in this paper, we present an approach to steer the heat pumps to reduce energy usage while aiming to maintain a certain level of comfort. For this purpose, we employ a market-based distributed method for power-balancing. To maintain the comfort level, the market-based distributed system assigns each device a cost-curve, parametrized with the current temperature of the room. This allows the cost to reflect the urgency of the HVAC operation. This approach was tested in a real-world environment: we use 10 heat pumps responsible for temperature control in 10 comparable-sized rooms. The test was performed for 3 months in summer. We limited the total peak power, and the algorithm balanced the consumption of the heat pumps with the available supply. The experiments showed that the system successfully managed to operate within the limit (lowering peak usage), and - to a certain point - reduce the cost without significantly deteriorating the working conditions of the occupants of the rooms. This test allowed us to estimate the minimal peak power requirement for the tested set-up that will still keep the room temperatures in or close to comfortable levels. The experiments show that a fully distributed market-based approach with parametrized cost functions can be used to limit peak usage while maintaining temperatures