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Abstract This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC.

Abstract This paper presents an innovative approach to decentralized data management in the German energy market, focusing on the use of decentralized data management with the help of Data Spaces to facilitate the automated change of energy suppliers within 24 h. The central focus of this research is the MakoMaker Space, a demonstrator project that employs the Connector from the Eclipse Data Space Components. The MakoMaker project demonstrates the successful automation of energy supplier changes, emphasizing the preservation of customer data sovereignty. It shows an alternative approach to the process, putting the customer into the center. Customers retain control of their data, which is accessible to providers as needed. While the paper discusses the potential for further enhancements, such as the integration of an identity provider and the development of a sustainable business model for service coordination, the primary focus is on the demonstrator’s successful application in a pilot setting.

Abstract This study provides a detailed exploration of how innovation management and digital entrepreneurship models can help transform technical use cases in smart grid contexts into viable business cases, thereby bridging the gap between technical potential and market application in the field of energy informatics. It focuses on the I-GReta project Use Cases (UCs). The study employs methodologies like Use Case Analysis, Portfolio Mapping of Innovation Level, Innovation Readiness Level, and the Tech Solution Business Model Canvas (TSBMC) to analyse and transition from technical use cases to viable business cases. This approach aligns technological solutions with market demands and regulatory frameworks, leveraging digital entrepreneurship models to navigate market challenges and foster energy management, sustainability, and digitalization.

ENTIRETY 1 is a web application for governing and provisioning IoT devices. Due to the rapid evolution and digitization of energy systems, energy domain ontology is required to manage the data in a structured way. This paper presents the ontology-based web application ENTIRETY 2, which is the updated version of ENTIRETY 1. This updated version provides an interface for data collection and transmission. The data collected by the IoT device are provided to the ICT platform according to the ontology. ENTIRETY 2 overcomes the challenges of the mapping between the different network protocols and automates the generation of JSON-LD based on ontology files. Therefore, the ENTIRETY 2 can be easily extended to support other ontologies.

The increasing dominance of inverter-based resources (IBRs) in power generation leads to significantly faster frequency dynamics in modern power systems. Therefore, the secondary control within the classical hierarchical structure must operate significantly faster. However, high communication burdens are inherent to centralized implementations of secondary control. This paper proposes a faster, distributed model predictive control (MPC) scheme for secondary frequency control of inverter-based power systems. The controller predicts the IBRs’ local frequency dynamics by considering the impact of the primary control of nearby IBRs. To obtain an optimal model-predictive control problem consisting of distributable subproblems, we introduce a novel trimming procedure to the frequency divider concept, an analytical formulation that estimates the local frequency at system buses. These subproblems have a tunable degree of mutual coupling. The presented distributed MPC achieves fast, error-free frequency regulation in the presence of abrupt load changes while also being robust to communication losses of participating IBRs. By design, the distributed control relies on a sparse neighbor-to-neighbor communication structure and uses local MPC problems that do not scale in complexity with system size. Numerical simulations of the IEEE 39-bus system and an extended CIGRE medium voltage power system demonstrate the fast performance of the proposed distributed MPC scheme for secondary frequency control.

whiteThis paper presents a whitenovel Nested Identical Control (NIC) whitemethod for load frequency control of multi-area microgrid systems employing two novel variants of the Chameleon Swarm Algorithm (CSA). Load frequency control, a common strategy to counter power system instabilities from load fluctuations, poses a significant challenge. Maintaining system stability amidst substantial load shifts becomes particularly challenging in complex power networks spanning multiple areas with several generators. To address this, we first designed two novel variants of CSA called Quasi-oppositional CSA (QCSA) and Quasi-Levy-oppositional CSA (QLCSA). In QCSA, a quasi-oppositional-based learning operator is used to improve the diversification and intensification of CSA. The QLCSA incorporates the Levy flight operator into the QCSA to avoid stagnation and local minimal entrapment. We evaluated the efficiency of these variants against the original CSA and five other highly performing algorithms on the IEEE Congress on Evolutionary Computation benchmark functions. whiteSubsequently, we employed the new CSA variants to design a NIC for load frequency control of a two-area microgrid system, considering different cases of uncertainties. The NIC strategy is designed such that the controller parameters of each loop in one area are inherited by the controller of the corresponding loop in the second area, with a target to achieve $\mathrm {10 sec}$ of settling time. Simulated results on the benchmark functions prove the superiority of QLCSA and QCSA over CSA and the selected highly-performing algorithms. The dynamic responses of the NIC-controlled microgrid system whitein numerical time-domain simulations show the effectiveness of the proposed control approaches when compared with PID control tuned using MATLAB’s control system tuner.

Medium-voltage distribution systems (MVDSs) mainly consist of a feeder head, lines, distribution transformers, and the equivalent load or power supply interfaced with the distribution transformers. The information of such load or power supply can be measured via the three-wattmeter method (THM) and the two-wattmeter method (TWM). The measurements can be used to perform the control of the power supply and simulate the characteristics of the load, so the models of the load and the power supply need to consider the measurement characteristics. Existing research works on three-phase power flow (PF) just consider the measurement characteristics of THM. Hence, the PF equation of the bus measured via TWM is firstly built. Based on conventional measurements, an accurate and general model of the grounded and ungrounded slack bus is proposed. Furthermore, the influence arising from the connection type and angle shift of distribution transformers on the admittance matrix is considered, and thus a general three-phase transformer model is summarized, which is applicable for all the transformers mentioned herein. Finally, Newton's method is adopted to solve the PF calculation, and the performance of the proposed PF model is demonstrated through designed tests.

In modern power systems, testing protection systems and equipment before the field implementation is vital to ensure the correct operation and guarantee reliability, quality and security of electricity supply. This paper focuses on the performance testing of the distance protection, whose state-of-the-art test methodologies (including the recommendations of the IEC 60255-121:2014 standard) can quickly lead to time/money resource limitations. As budget-wise considerations should not justify an “arbitrary” or “convenient” selection of which and how many tests to perform, this paper shows how the current testing methodologies benefits from the statistical design of experiments (stat-DOE). It is proven how the stat-DOE supports the performance testing in the efficient selection of the optimal tests to conduct, in the systematic investigation of the effect of different factors, and in the robust definition of pass/fail criteria for specifying acceptance tests. A step-to-step practical guideline for adopting the stat-DOE is offered to conduct a realistic performance testing, accounting for operator-specific requirements (e.g., maximum affordable number of tests) and physical constraints among factors. The results allow to propose lines of refinement and recommendations for the stakeholders. Finally, not only this paper serves as a guide to replicate the performance testing of other protection functions or in alternative scenarios, but it also ultimately paves the way for the routine adoption of the stat-DOE in the definition and refinement of test methodologies for power system protection testing at large towards the achievement of a standardized basis for it.

Abstract Data integration in the energy sector, which refers to the process of combining and harmonizing data from multiple heterogeneous sources, is becoming increasingly difficult due to the growing volume of heterogeneous data. Schema matching plays a crucial role in this process by giving each representation a unique identity by matching raw energy data to a generic data model. This study uses an energy domain language model to automate schema matching, reducing manual effort in integrating heterogeneous data. We developed two energy domain language models, Energy BERT and Energy Sentence Bert, and trained them using an open-source scientific corpus. The comparison of the developed models with the baseline model using real-life energy domain data shows that Energy BERT and Energy Sentence Bert models significantly improve the accuracy of schema matching.

Interoperable multi-vendor High-Voltage Direct-Current (HVDC) grids are a key enabler for the integration of renewable energy (in particular offshore wind) and its transmission over longer distances to consumers. However, most HVDC systems today are single-vendor and point-to-point. Various technical and non-technical aspects need to be considered, for example, (real-time) testing, legal aspects (intellectual property and regulation), and the multi-vendor interoperability process. This paper presents findings from the READY4DC project, which is a larger and open European effort involving diverse stakeholders, including HVDC manufacturers, transmission system operators, wind developers, academia, and research institutes. It summarizes key technical recommendations, emphasizing comprehensive interaction studies and the development of a structured legal framework to facilitate the development and operation of a multi-vendor, multi-terminal HVDC grid. The READY4DC project highlights the need for increased harmonization, transparent communication among stakeholders, and future-oriented research to ensure the robustness and interoperability of interconnected grids. Collaborative efforts are key for addressing technical complexities and advancing the deployment of multi-vendor multi-terminal HVDC technology.

In recent years, nacelle test benches for wind turbines have been developed internationally. New standards are currently being developed that explicitly refer to the measurement of the electrical properties of wind turbines on these test benches. Thus, they are suitable for measuring the electrical properties required for certification. Another part of the certification is the creation and validation of suitable models of the wind turbine, which are used for stability analyses of the utility grid. Validation requires a suitable model of grid replication on the test benches, which is not yet covered by any applicable standard. Such models should be as simplified a representation of the artificial grid replication as possible to ensure that they are accessible to certification bodies. A model of the grid emulator installed at the CWD of RWTH Aachen University, which was validated with real measurement data, serves as a reference. A step-by-step reduction of the model’s depth up to the system’s technical representation is followed by a model evaluation with respect to the level of detail and an analysis of time and frequency. The evaluation shows that even a highly simplified model consisting of a reference voltage and an impedance replica meets the requirements for the validation of wind turbine models according to IEC 61400-27-2.

The electric power system is transforming from a strict hierarchy into a distributed scheme that consists of prosumers and other service providers. Some services require interactions more complex than request-response. To reach functional interoperability, such services necessitate an agreement about what actions happen, in which order, and in which conditions. Such agreements can be modeled as service choreographies, a concept familiar from microservice architectures (MSA). However, the management of choreographies is demanding, especially in inter-organizational (or actor-to-actor) schemes, if changes occur frequently or the number of participants and choreography instances is high. This article proposes Discografia, a framework to support the distributed execution of service choreographies modeled with the standard Business Process Model and Notation 2.0 (BPMN2). Beyond distributed execution, certain operations must be centralized, specifically for security as well as lifecycle and execution support. The framework is proven with a software prototype to demonstrate applicability. The prototype coordinates the charging of electric vehicles (EV) among competing aggregators operating in urban charging hubs (UCH). Additionally, the concept is evaluated with theoretical calculations that suggest reduced manual effort in communicating choreography specifications. Thus, this paper argues that Discografia concept for distributed service choreographing facilitates inter-organizational interoperability, an essential factor in future power system.

The Kopernikus ENSURE Co-Demonstration Platform enables the investigation of components and processes for the changing power system with grid models adapted to the respective problem. The platform is developed from the power system's perspective. A central aspect of the platform is to analyze several technical solutions of different stakeholders in shared power system models. Furthermore the platform enables the investigation of interactions between different technologies, the system stability or protection scenarios that cannot be simulated in the real power system or only to a very limited extent. The comparison of solution approaches on a common model basis enables reliable overall systemic statements. To realize this, a multi-domain and multi-vendor approach is pursued, covering electromagnetic transients, electromechanical transients, continuous power flow simulations, and real-time simulators from different manufacturers. The core components of the platform are distributed real-time simulations, including the coupling of different laboratories in Germany and the developed power system models. Investigations with the real-time capable platform can be carried out purely simulatively or, depending on the connected laboratory, as Control/Power-Hardware-in-the-Loop simulations.

With the ongoing digital transformation and multi-domain interaction occurring in the buildings, a huge amount of heterogeneous data is generated and stored on a daily basis. To take advantage of the gathered data and help better decision makings, suitable methods are needed to meet the demand for building operations and reinvestment planning. Ontology, which provides not only the vocabulary of a certain domain but also the relationship between each other has been used in multiple engineering fields to manage heterogeneous data. A plethora of ontology development methodologies have been developed in the last decade, whereas those methods are still really time-consuming and in a low degree of automation. In this paper, we approach the problem by first presenting a semi-automatic ontology development framework that integrates existing automatic ontology tools and reuses existing ontology and data model. Based on this framework, we create a building energy management ontology and evaluate the data coverage of several real-life data sets.

Local Energy Communities (LECs) provide a framework for community-oriented use of prosumer-owned renewable generation and storage units. For LECs to gain acceptance and eventually become widespread actors in future energy systems, their impact s on the grid and its participants must be investigated. However, the scientific landscape lacks sufficient quantitative analyses that specifically investigate the effects of LECs on the underlying grid infrastructure. This work conducts a case study to understand the system effects that may arise from the local cooperation of prosumers in future low voltage systems in Germany. To analyze these effects, performance metrics are defined, which help to compare the energy neutrality and grid reliability of low voltage (LV) grids with and without local energy exchanges. Furthermore, representative test grids are synthesized by considering the existing (2020) and predicted future (2030) distribution of energy assets, such as photovoltaics, battery storage, electrical heat pumps, and electric vehicles in Germany. For these grids, simulations considering different scenarios, including both the reference and community behavior of their participants, are conducted, and the defined performance metrics are applied. The simulations show that the LEC provides significant benefits in terms of energy neutrality and grid reliability. In this way, this study complements ongoing efforts in the design of LECs and incentives for customers, for example, in the form of favorable tariffs for locally generated energy.

The existing Overcurrent Protection Function (OCPF) of distribution systems can be coordinated for all fault current values involving single feeders. However, it is vulnerable to Multiple Feeder Faults (MFF), which involve at least two feeders simultaneously, leading to the incoming feeder relay trip faster than the faulted feeder relays. In this case, all feeders connected to the power transformer are de-energized, even the healthy ones, directly impacting the network reliability, service quality and availability of the electricity utilities and ultimately leading to outage costs for the customers. In light of this, an IEC 61850-based OCPF scheme with the directional element is proposed to support existing OCPF to deal with MFF and encompass the integration of distributed generation, and can be directly implemented in the digital substations without requiring further equipment installation. Moreover, a hardware-in-the-loop platform is developed for testing the interoperability of the proposed scheme with multi-vendor protection relays, which can also assist electricity utilities in the validation of their protection and control schemes based on their requirements before the field implementation. Test cases are specifically elaborated to study the interoperability of the proposed scheme with three real devices—whose configuration details are also provided to aid protection engineers—in a real distribution system under different conditions such as switching operations, MFF types, resistance, location, and integration of distributed generation. The results show that the proposed scheme manages to enhance the selectivity and reliability of the digital substation protection system, hence representing a valuable complement to existing OCPF schemes.

This paper presents formulation and analysis of a distributed consensus-based controller for power electronics based microgrids (MG) with grid forming (GFM) and grid feeding (GFD) inverters. The control is supported by measurements from high reporting rate meters that improve the dynamics of consensus control and enables participation of buses with no dispatchable units. The study is performed on a developed cyber-physical (CP) model in DQ domain that allows for the analysis of arbitrary MGs. The control is established on fundamental principles of consensus and provides power sharing and voltage regulation, maintains power reserve in MG, and assumes distinct roles for different types of inverters. The analysis of the control scheme includes aspects of coupling between cyber and physical layers and stability under multi-layer communication structure. The study addresses disadvantages of classical hierarchical control and enhances analysis of distributed controllers through the proposed CP model.

Modern distribution grids are transforming due to the increasing amount of load and generation based on DC technologies, whose interconnection is facilitated by the upcoming AC-DC distribution networks. Their fault management is challenged by new issues related to the power transferred among AC and DC sub-networks. In light of this, the present paper proposes a novel Service Restoration (SR) algorithm, specifically tailored for AC-DC distribution grids, that optimizes the re-energization by establishing priorities of disconnected bus groups and computing, as candidate solutions, hierarchical combinations of normally open and normally closed switches. To overcome the limitations of existing approaches, a Multiple Criteria Decision Analysis (MCDA) is proposed to combine and objectively prioritize different SR goals (i.e., the use of telecontrolled switches, the minimization of power losses, and the applicability of the proposed solutions in a defined time horizon), and ultimately make the grid operator benefit from the possibility to flexibly tune different operational objectives. The proposed algorithm allows to effectively discriminating competing solutions, i.e., whence and how to re-energize disconnected buses, and complies with the time requirements for field implementation. The results prove the crucial role of DC sub-networks, associated to the control of power injection from AC-DC converters, in enhancing the SR by improving its targets and increasing the number of re-energized loads. Ultimately, the effect of MCDA comparison parameters on the SR outcomes is quantitatively investigated via global sensitivity analysis, whose adoption is recommended for supporting the grid operator in the algorithm implementation.

This paper presents the development of an energy management system (EMS) for a renewable energy community (REC) with the load-generation balancing objective. In this regard, rule-based and optimization mechanisms are proposed for the REC management in line with the scope of a field trial and considering the scarcity of the measurement and historical data. This typical data scarcity along with the intermittent behaviour of renewable energy resources introduce an unavoidable level of uncertainty— not being adequately addressed in the EMS literature— that might ultimately affect the proper REC management. Hence, a comprehensive performance analysis of the proposed EMS has been conducted via global sensitivity analysis (GSA). Particularly, variance-based sensitivity analysis has been employed to investigate how the variability of a set of selected indicators of the REC performance is apportioned to the different sources of uncertainty specifically related to the forecast and flexibility availability. Results show that the EMS performance is consistent with the EMS objective. The application of GSA reveals though interesting findings that contradict antecedent misconceptions about how different uncertainty sources affect the EMS performance. Although being related to the specific REC under study, the present work specializes GSA method in novel ways that pave the path for its reusability in the context of other EMS applications with different boundary conditions. By highlighting the necessity of GSA and showcasing its suitability to study the EMS performance under an uncertainty framework, the present work offers a precious tool to support system operators in their decision making process.

Today, the energy supply does not follow the demand in a controlled manner anymore. Thus, forecasting the electricity consumption became essential for the operation of power systems. Already numerous open source software tools exist that provide forecasting models, which are configurable for different forecasting tasks. In the case of electrical energy demand, a change in the geographical or temporal settings, requires specific domain knowledge on relevant data and influencing factors that are to be considered when developing data-driven forecasting models. With ProLoaF, we propose a holistic machine-learning based forecasting project, which offers the developer a continuous deployment of reliable forecasts for the power system domain. ProLoaF serves for probabilistic forecasts of the electric energy consumption and non-controllable generation in future power system operation. By overlapping Machine Learning (ML), DevOps and power systems engineering disciplines, we aim to accelerate future forecasting model development by reducing consultation work between domain experts.

Forecast developers predominantly assess residuals and error statistics when tuning the targeted model’s quality. With that, eventual cost or rewards of the underlying business application are typically not considered in the model development phase. The analysis of the power system wholesale market allows us to translate a time series forecast method’s quality to its respective business value. For instance, near real-time capacity procurement takes place in the wholesale market, which is subject to complex interrelations of system operators’ grid activities and balancing parties’ scheduling behavior. Such forecasting tasks can hardly be solved with model-driven approaches because of the large solution space and non-convexity of the optimization problem. Thus, we generate load forecasts by means of a data-driven based forecasting tool ProLoaF, which we benchmark with state-of-the-art baseline models and the auto-machine learning models auto.arima and Facebook Prophet.

Abstract This study proposes a load management strategy for parking and charging facilities with the capacity to serve several hundreds of electric vehicles. The strategy is built upon two assumptions on power distribution systems of large charging stations: i) they are configured as clusters, each comprising a number of charging units for reduced cabling complexity, ii) the power delivery components (such as feeders and circuit breakers) of individual clusters are sized for load factors smaller than 100% to reduce the capital costs. Unless controlled, the load demand can concentrate into particular cluster(s) whereas other clusters can still tolerate additional demand. This may lead to avoidable load interruptions and, thus, reduced energy provision. To address this issue, a load management strategy that optimises the distribution of vehicles across the clusters and their charging profiles is proposed. The strategy is compared in simulation with a benchmark strategy in different commercial parking lot scenarios. The results demonstrate that the optimal management achieves identical demand fulfilment rates despite more pronounced load factor limitations as compared to the benchmark strategy. This can enable further reduction in system component sizing. In the tested scenarios, the proposed strategy leads to increased long term profits ranging between 12% and 43%.

Fast digitalization of the power grids and the adoption of innovative software solutions is key to a successful energy transition. In other sectors, such as telecommunication or cloud computing, open-source software has already proven capable of transforming entire industries, by speeding up development and lowering development costs while achieving high levels of stability, interoperability, and security. However, the energy sector has not yet embraced open-source software to the same level. We discuss how existing open-source software principles can be applied to the unique challenges of the energy sector during the transition towards higher penetration of renewable energy resources. To provide an overview of the current state of the open-source software landscape, we collected and analyzed 388 open-source projects, in terms of project activities, community composition, relevant licenses, and commonly used programming languages. One finding was that the majority of projects are currently driven by academic contributors, but that commercial players do also play a role, and we identify positive examples of collaboration between the two, mostly related to standardization.

The real-time monitoring of electric distribution grids via state estimation is a fundamental requirement to deploy smart automation and control in the distribution system. Due to the large size of distribution networks and the poor coverage of measurement instrumentation on the field, designing fast state estimation algorithms and achieving accurate results are two major challenges associated to distribution system state estimation. In this paper, an efficient and accurate solution for performing state estimation in multi-feeder radial distribution grids is presented. The proposed algorithm is based on a two-step approach. In the first step, state estimation is performed in parallel on the different feeders suitably processing the available measurements and pseudo-measurements and taking into account their uncertainty characteristics. In the second step, the results on each feeder are post-processed to refine the estimations and to improve the accuracy performance. To this purpose, the second step considers how measurement uncertainties propagate towards the final estimates and how measurements shared among the feeders could adversely affect the final estimation. Performed tests show that the conceived design leads to accuracy performance very close to those achievable by running state estimation on the full grid. At the same time, the parallelization of the estimation process on the different feeders allows decentralizing the state estimation problem, with the associated benefits in terms of computation time and distribution of the communication and storage requirements.

Control systems of modular multilevel converters can be partitioned into several levels. Such partitioning is becoming relevant in multi-vendor high-voltage direct-current systems where one party may deliver the converter hardware and associated control, whereas another party may deliver the system-related control. However, the exact control system partitioning has not been subject to research. This paper presents a method for partitioning control systems into two levels with minimum dependencies between the two levels. Furthermore, the impact of specific control designs on the partitioning and integration effort is discussed using an example. The presented method is useful for the design of multi-vendor control systems for modular multilevel converters whose partitioning otherwise would be based on engineering intuition.

This paper proposes a strategy to manage an electric vehicle charging station (EVCSs) with a grid-side interface based on a Modular Multilevel Converter (MMC). The MMC topology is studied due to its potential for reducing the footprint and the use of active material in the internal distribution system by allowing for transformer-less connection to the medium voltage distribution grid. However, heterogeneous charging demands and arrival-departure profiles of the electric vehicles (EVs) could lead to significant loading unbalances among the MMC arms and among the modules of a single arm. Nevertheless, the current in the grid interface must be kept balanced and sinusoidal. Furthermore, the voltages of the modules of an arm must be balanced. This work combines a load management (LM) algorithm with a power flow management (PFM) algorithm to achieve the required characteristics of grid current and module voltages under the heterogeneity of the charging demand in MMC-based EVCSs. The PFM algorithm controls the circulating currents to compensate the phase-to-phase, arm-to-arm and intra-arm unbalances of the given loading. To minimize the additional losses resulting from active balancing by the PFM, the LM optimizes the charging schedules and allocations of incoming EVs into charging units in order to minimize phase-to-phase and arm-to-arm unbalances in the system. The performance of the proposed optimization-based LM is compared with a rule-based benchmark LM by simulating the daily operation of an example shopping mall parking with MMC-based grid interface. In scenarios with pronounced unbalance limitations, the optimization-based LM increases the supplied energy significantly. Real-time (RT) simulations demonstrate a balanced and sinusoidal grid current profile and balanced module voltages in MMC arms over the daily scenarios. These results indicate that the proposed strategy combining LM and PFM is applicable for real-world deployments.

Buildings with generation and storage assets have the opportunity to trade in electricity markets. Residential buildings, however, have load patterns that are more difficult to predict and less flexible – introducing uncertainties to their operation. In addition, local intermittent renewable energy sources further increase these uncertainties. In this regard, an uncertainty-aware model predictive control (UA-MPC) is proposed in this paper. The proposed UA-MPC allows residential building energy management systems to trade in intraday markets without violating the operational constraints of their batteries despite the uncertainties in load consumption and solar irradiation. Moreover, the proposed UA-MPC only needs prediction intervals instead of detailed probability distribution functions to describe the uncertainties. Furthermore, in the proposed UA-MPC, the optimization is formulated as a shortest path problem. Thus, the optimization can be solved in polynomial time, which is desirable in intraday settings. Numerical simulations for two active residential buildings in grid-connected and isolated-neighborhood scenarios have been used to evaluate the proposed UA-MPC. Furthermore, the performance of the proposed UA-MPC was compared with the performance of a deterministic model predictive control (MPC) and a robust MPC. The results show that the proposed UA-MPC eliminates constraint violations that would otherwise occur in using deterministic MPC while providing lower costs than those using robust MPC. The results also show that the proposed UA-MPC can generate bidding curves in a few seconds, demonstrating its applicability in intraday market settings.

Investigations of the dynamic behaviour of power electronic components integrated into electric networks require suitable and established simulation methodologies. Real-time simulation represents a frequently applied methodology for analyzing the steady-state and transient behavior of electric power systems. This work introduces a guideline on how to model power electronics converters in digital real time simulators, taking into account the trade-off between model accuracy and the required computation time. Based on this concept, possible execution approaches with respect to the usage of central processing unit and field-programmable gate array components are highlighted. Simulation test scenario, such as primary frequency regulation and low voltage ride through, have been performed and accuracy indices are discussed for each implemented real-time model and each test scenario, respectively. Finally, a run-time analysis of presented real-time setups is given and real-time simulation results are compared. This manuscript demonstrates important differences in real-time simulation modelling, providing useful guidelines for the decision making of power engineers.

High penetration of variable renewable energy resources to distribution networks brings new challenges to frequency control in transmission systems; notwithstanding, coordinated exploitation of some widely distributed resources in distribution networks could support overall system frequency control. We developed a centralized control scheme to utilize existing solid oxide fuel cell systems in distribution networks to mitigate frequency deviation in the transmission system. This novel control scheme minimizes curtailing renewable resources. It appropriately prioritizes adjusting active or reactive power of medium voltage connected fuel cell systems to efficiently provides frequency support, while keeping the renewable sources connected. A co-simulation platform was also developed to allow for analyzing the interactions between transmission and distribution networks. It enhances simulation capability through parallel distributed real-time simulations. This platform enables ex-ante analysis of the impact of control strategies of distribution system on transmission system. A set of experimental results on European transmission and distribution network benchmarks demonstrated the performance of the developed primary frequency control scheme using solid oxide fuel cell systems.

Advanced control techniques for modern distribution grids are becoming fundamental for the reduction of grid reinforcements while maintaining network performances. In particular, as shown in literature, distributed algorithms for voltage control have gained much interests in comparison with the typical centralized formulation for its feasibility. Distributed model predictive control (MPC) is shown to optimally manage the Distributed Generators (DGs) over time and it can be implemented locally at the controllable sources. For this purpose, this paper adopts a distributed algorithm recently presented in the literature for solving a constraint-coupled optimization problem for model predictive voltage control. A detailed reformulation of the original MPC problem for the specific application is presented. Besides, this paper provides a calculation of the convergence limit for the value of the iteration step size of the algorithm, supported by numerical results. The proposed distributed solution of model predictive voltage control is compared with a centralized formulation via numerical simulation in terms of the percentage of error with the centralized solution and number of iteration for the convergence.

Modern distribution grids are complex systems that need advanced management for their secure and reliable operation. The Information and Communication Technology domain today offers unprecedented opportunities for the smart design of tools in support of grid operators. This paper presents a new philosophy for the digitalization and automation of distribution grids, based on a modular architecture of microservices implemented via container technology. This architecture enables a service-oriented deployment of the intelligence needed in the Distribution Management Systems, moving beyond the traditional view of monolithic software installations in the control rooms. The proposed architecture unlocks a broad set of possibilities, including cloud-based implementations, extension of legacy systems and fast integration of machine learning-based analytic tools. Moreover, it potentially opens a completely new market of turnkey services for distribution grid management, thus avoiding large upfront investments for grid operators. This paper presents the main concepts and benefits of the proposed philosophy, together with an example of field implementation based on open source components carried out in the context of the European project SOGNO.

Modelling the fast dynamics of power converters is of growing concern in power grids and Microgrids. Dynamic phasor (DP) concept has been widely applied to switched power converter for modelling fast transients efficiently due to the inherent frequency shift property of DPs. The dynamics introduced by the DC/AC power converters depend on the controllers, which are implemented either in the stationary frame or on the synchronous reference frame (SRF). Hybrid closed-loop modelling methods that consider DP-based power converter plant model are not established and the applicability and accuracy of such hybrid approaches are not fully understood. This paper attempts to address this gap by proposing two hybrid closed-loop modelling approaches: Hybrid DP-DQ and Hybrid DP-EMT and discusses the applicability and accuracy for various controller types. Furthermore, this paper presents a DP switched model of single and three phase two level power converter and discusses the selection of harmonics to reduce model complexity. The proposed hybrid approaches were validated against detailed switched power converter models and for a wide range of scenarios, the Hybrid DP-EMT method is found to be superior compared other methods. Finally, application dependent recommendations are made for the selection of suitable hybrid closed-loop model for the accurate simulation of single phase and three phase power converters.

In this article, we present a detailed derivation of a fourth order co-simulation method. This method is implicit and based on Hermite extrapolation with two support points and their derivatives. The convergence order of the local and global errors is analyzed and tested on two linear systems from the mechanical and the electrical fields. Both systems are decomposed into two subsystems to perform co-simulation. We compare the fourth-order method with lower-order methods, confirming the expected convergence of the local error and showing that the global error of the derived method corresponds to O(H4).

This paper provides an overview of modern feedback control methods for the voltage regulation in DC/DC converters of DC microgrids. Control objectives and practical restrictions are defined and used as indicators for the analysis and performance assessment of the control methods. After presenting the concept of each control method, the advantages and limitations in the converter applications are discussed. The main conclusions of this overview can be used as recommendations for the selection of the suitable control method according to the control requirements in the DC microgrid. The low robustness against disturbances is a major issue in all control methods. For the enhancement of the robustness of the feedback control methods, three approaches are reviewed. Applications of these approaches in DC/DC converters are compared with regard to the achieved disturbance rejection and the related cost of nominal performance degradation. The disturbance/uncertainty estimation and attenuation (DUEA) framework appears to be the most promising approach to compromising these opposing control objectives. This overview is presented for a general DC/DC converter, without any additional control design requirement imposed by a specific converter plant. This allows the generalisation of the conclusions of the performance assessment, which can facilitate the application of the control methods in similar systems, such as in AC/DC converters or motor drives.

Abstract In a modern smart energy system with increased penetration of renewable energy resources, the amount of data from various sources is growing significantly. Such systems require complex algorithms and controlling on-demand. These requirements can be addressed with on-demand scalability and a stable system. Nowadays, on-demand scalability is achieved by considering cloud computing and Internet of Things (IoT) technologies. This paper presents a cloud-based platform based on service-oriented architecture to perform analyzes on smart energy system services. It is the result of the European FISMEP (FIWARE for Smart Energy Platform) project to demonstrate an information and communication technology (ICT) architecture for the smart energy sector. The presented architecture is powered by FIWARE, open-source and customizable building blocks for future internet applications and services. Furthermore, the feasibility of the architecture is evaluated using various test cases.

ENTIRETY is an open-source web application that provides consistency for the devices in IoT platforms with a simple and unified way of governing and provisioning. This service primarily targets smart energy domain applications in a large-scale scenario but can be easily extended to support other domain-specific devices. To overcome the IoT diversity challenges, the core of the ENTIRETY is a SARGON ontology which is the semantic data model designed for cross-cutting the domain-specific information in the smart energy system which is modular, extendable and reusable. Consequently, ENTIRETY is intended on underlying principles such as reusability and extensibility, it applies a template-based approach and can be run as a Docker image.

Abstract This study proposes a hierarchical control structure for load power‐sharing among distributed generations (DG) in a multi‐bus microgrid (MG). The proposed control structure uses an average consensus‐based distributed protocol (CDP) for the secondary control layer and virtual oscillator control (VOC) as a primary controller. The proposed method can nullify the power‐sharing inaccuracy caused by the mismatches of the equivalent impedances seen from the outputs of DGs. The eigenvalue‐based small‐signal analysis is used to analyse the secondary controller and communication time‐delay on system stability. The results of the stability analysis are used to design secondary controller and time‐delay compensator. The validity of the proposed method is verified by both offline simulations in Matlab/Simulink and control hardware‐in‐the‐loop (CHiL) using a real‐time Opal‐RT platform.

Power flow (PF) is a fundamental tool for operation, automation and optimization of the power systems. Due to the nonlinearity of the PF system equations, the classical PF solutions are computationally very demanding. As a common approach in solving the nonlinear equations, linearization is a potential technique which can simplify and accelerate the PF calculations. In this context, this paper proposes a linear fast iterative method based on the fixed-point iteration technique in which a linearized model of generator along with a ZI load model are integrated in a simplified system of linear equations (SLE) of Yv=i . The relaxation method is used during the deriving process of generator equivalent current in this approach. However, the already developed ZI load model based on the curve-fitting technique has been exploited in this work. The accuracy of the proposed PF method has been compared with calculated results from DIgSILENT PowerFactory on the benchmark IEEE 33-bus test system and on a large medium voltage network in Germany.

This study proposes a hierarchical control structure for a smooth transition of multi-bus microgrids from islanded mode to grid-connected mode. The proposed control structure uses an average consensus protocol for the secondary control layer and Andronov-Hopf virtual oscillator control as a primary controller. The proposed method enhances the power-sharing accuracy among DGs in normal operation. A synchronization strategy is embedded into the secondary control layer in synchronization mode to force the MG-side voltage at the connection point to synchronize with the grid-side voltage. The influence of the secondary controller and communication time-delay on system stability and performance is analyzed with small-signal analysis. The proposed method is validated via control-hardware-in-the-loop (CHiL) using a real-time Opal-RT platform.

This paper presents a distributed optimal power flow (OPF) algorithm for the system-level control of multi-terminal DC (MTDC) distribution grids with distributed energy resources (DER). At each control period, the algorithm updates the nominal voltage and power set-points of the DER-interfacing converters, which operate according to active network management (ANM) concepts. To achieve this, the OPF problem, in its nodal formulation, includes power dispatch strategies for diverse DER according to their technical characteristics, which change during the system operation. This multi-objective OPF-for-ANM problem is solved by distributed control units (DCUs) according to the distributed algorithm for the alternating direction method of multipliers (ADMM). All DCUs have identical roles in the distributed control structure and thus the distributed OPF-for-ANM algorithm is highly modular. Simulation results in different IEEE standard systems and various scenarios demonstrate that the algorithm is fast and scalable, irrespective of the number and location of integrated DER, as well as the operating condition of the system. The convergence speed of the algorithm is analysed considering the computation and communication time needed for its execution. The online application in a computers cluster demonstrates the fast execution of the developed algorithm in a physically-distributed implementation. Through the proposed OPF-for-ANM algorithm, the system-level control can dispatch fast diverse DER in different coordination approaches in a distributed manner.

We introduce the open-source Python software framework pycity_scheduling for the effective development, testing, and assessment of optimisation-based power scheduling algorithms for local multi-energy systems in city districts. The framework primarily targets the elaboration of coordination concepts that can efficiently solve the power dispatch problem on the city district level. Its target users are researchers in the field of smart grid applications and the deployment of operational flexibility for local energy systems. Illustrative code examples demonstrate the capabilities of the pycity_scheduling framework and its use cases. The design principles established in pycity_scheduling allows users to access, extend, and modify the Python package without any need for commercial software or licensing concerns.

Due to its potential benefits in data transparency, maintenance, and optimization of operation, the Internet of Things (IoT) has recently emerged in the building automation system (BAS) domain. However, while various IoT devices have been developed, the integration into BAS remains a challenging task due to the variety of conventional interfaces used in existing BAS. From an objective point of view, integrating IoT connectivity on existing devices’ printed circuit boards (PCBs) would be the most efficient option in terms of cost and resources, but requires adaptation of product lines, and vendors would often couple this with their own services and without an option for customization. By contrast, the majority of research activities focus on developing alternative or additional measurement systems, rather than connecting with legacy system components. Furthermore, most research applications cover very simple and individual use-cases with a do-it-yourself character and limited applicability in industrial applications. In this study, we present a scalable, industrial-like embedded solution to connect to common interfaces in BAS applications and share all the hardware and software design as an open-source platform for public use, customization, and further enhancement. Moreover, a thorough measurement performance analysis was conducted, suggesting an acceptable trade-off among accuracy, flexibility, and costs, e.g., achieving a performance increase by over 75% and a cost reduction by roughly 34% compared to a previous design.

The increase in heterogeneous data in the building energy domain creates a difficult challenge for data integration. Schema matching, which maps the raw data from the building energy domain to a generic data model, is the necessary step in data integration and provides a unique representation. Only a small amount of labeled data for schema matching exists and it is time-consuming and labor-intensive to manually label data. This paper applies semantic-similarity methods to the automatic schema-mapping process by combining knowledge from natural language processing, which reduces the manual effort in heterogeneous data integration. The active-learning method is applied to solve the lack-of-labeled-data problem in schema matching. The results of the schema matching with building-energy-domain data show the pre-trained language model provides a massive improvement in the accuracy of schema matching and the active-learning method greatly reduces the amount of labeled data required.

The ramping trend of cheap and performant single board computers (SBC) is growingly offering unprecedented opportunities in various domains, taking advantage of the widespread support and flexibility offered by an operating system (OS) environment. Unfortunately, data acquisition systems implemented in an OS environment are traditionally considered not to be suitable for reliable industrial applications. Such a position is supported by the lack of hardware interrupt handling and deterministic control of timed operations. In this study, the authors fill this gap by proposing an innovative and versatile SBC-based open-source platform for CPU-independent data acquisition. The synchronized measurement unit (SMU) is a high-accuracy device able to perform multichannel simultaneous sampling up to 200 kS/s with sub-microsecond synchronization precision to a GPS time reference. It exhibits very low offset and gain errors, with a minimum bandwidth beyond 20 kHz, SNR levels above 90 dB and THD as low as −110 dB. These features make the SMU particularly attractive for the power system domain, where synchronized measurements are increasingly required for the geographically distributed monitoring of grid operating conditions and power quality phenomena. We present the characterization of the SMU in terms of measurement and time synchronization accuracy, proving that this device, while low-cost, guarantees performance compliant with the requirements for synchrophasor-based applications in power systems.

The current Distribution Grid Automation (DGA) Systems are being heavily dependent on the Information and Communication Technologies (ICT) infrastructure for its proper operation. The DGA architectures are predominantly centralized and usually deployed on a dedicated hardware. This increases the risk of blackouts under a coordinated cyber-physical attack. The compromise of the dedicated hardware that hosts the central coordinator of the DGA automation results in a blackout. Though many countermeasures have already been proposed for tackling different types cyber and physical attacks on the ICT infrastructure, very few measures have been proposed to ensure the availability of the grid operation functions, even when it is compromised. This study proposes an automatic, distributed approach based on Blockchain and Smart Contract that ensures the availability of the core DGA functions even if the central coordinator that operates the grid is compromised. This is done by virtualizing and migrating/re-initialising these functions from the dedicated hardware that was compromised to another. Additionally, a Multi-Attribute Decision Making based method is incorporated into the Smart Contract that helps in selection of the optimal hardware that can host the function considering its limitations (hardware and software). Finally, a proof of concept implementation of the proposed solution is presented that utilizes the Calvin IoT (Internet of Things) platform, Flow programming tool and Hyperledger fabric and its performance is evaluated.

Middleware platforms are key technology in any Internet of Things (IoT) system, considering their role in managing the intermediary communications between devices and applications. In the energy sector, it has been shown that IoT devices enable the integration of all network assets to one large distributed system. This comes with significant benefits, such as improving energy efficiency, boosting the generation of renewable energy, reducing maintenance costs and increasing comfort. Various existing IoT middlware solutions encounter several problems that limit their performance, such as vendor locks. Hence, this paper presents a literature review and an expert survey on IoT middleware platforms in energy systems, in order to provide a set of tools and functionalities to be supported by any future efficient, flexible and interoperable IoT middleware considering the market needs. The analysis of the results shows that experts currently use the IoT middleware mainly to deploy services such as visualization, monitoring and benchmarking of energy consumption, and energy optimization is considered as a future application to target. Likewise, non-functional requirements, such as security and privacy, play vital roles in the IoT platforms’ performances.

The building sector is undergoing a deep transformation to contribute to meeting the climate neutrality goals set by policymakers worldwide. This process entails the transition towards smart energy-aware buildings that have lower consumptions and better efficiency performance. Digitalization is a key part of this process. A huge amount of data is currently generated by sensors, smart meters and a multitude of other devices and data sources, and this trend is expected to exponentially increase in the near future. Exploiting these data for different use cases spanning multiple application scenarios is of utmost importance to capture their full value and build smart and innovative building services. In this context, this paper presents a high-level architecture for big data management in the building domain which aims to foster data sharing, interoperability and the seamless integration of advanced services based on data-driven techniques. This work focuses on the functional description of the architecture, underlining the requirements and specifications to be addressed as well as the design principles to be followed. Moreover, a concrete example of the instantiation of such an architecture, based on open source software technologies, is presented and discussed.

Electric Vehicles (EVs) are gaining acceptance due to the advantages they offer in the reduction of nitrogen oxide and carbon dioxide emissions. The need for emission reduction and the potential of EVs for these reductions is reflected in the current sustainable mobility policies of the EU as well as the German government. Increasing the penetration of EVs in the grid requires an expansion of EV charging infrastructure, which in turn requires either grid reinforcement or solutions for more efficient use of existing infrastructure to avoid or postpone grid reinforcement. Distribution transformers face increased loading due to EV charging and need to be protected from overloading during peak load periods to ensure continuity of service. Therefore, transformers are one of the components that are upgraded or replaced as a part of grid reinforcement. In this paper, we propose the connection of a Solid-State Transformers (SST) between two buses operating at the same-voltage level as an alternative to replacement or upgrading of conventional transformer as well as to prevent their overloading. We analyse how the proposed topology can be useful to reduce the impact of EV integration on the overloading of distribution transformers and node voltage violations in the distribution grid.

In a modern smart energy system, the amount of available data from various sources is growing significantly. Other sectors such as medical or social sectors exhibit the same phenomenon. Due to the amount, complexity and heterogeneity of data, a complex algorithm is required for the integration and analysis of heterogeneous data sources. The Web of Things and semantic-based approaches address the fragmentation of standards, platforms, services and technologies in smart energy and non-energy sectors, and enable heterogeneous data integration and interoperability. This paper presents SiSEG, a semantic annotation service that is developed to automate the process of annotating data and address the problem of heterogeneous data integration in a reusable and extensible way by using the fuzzy method. Moreover, the accuracy of SiGEG has been evaluated.

Recently developed nacelle test benches for wind turbines, equipped with multi-physics Hardware-in-the-Loop (HiL) systems, enable advanced testing and even certification of next-generation wind turbines according to IEC61400-21. On the basis of three experiments carried out with a commercial 3.2 MW wind turbine, this paper shows to which extent test bench hardware and HiL systems influence certification results. For the crucial Fault-Ride-Through tests, all deviations were found to be below 1% compared to field and simulation results. For this test, the power HiL system and the accuracy of its impedance emulation are found to be of most relevance. The results for the test items Frequency Control and Synthetic Inertia were found to be more sensitive to shortcomings of the mechanical HiL with its control system. Based on these findings, the paper mentions general procedures to ensure the quality of test benches with HiL systems and, with that, ensure the quality of certification.