As the Netherlands moves toward climate neutrality by 2050, the national power system will rely heavily on variable renewable energy sources (VRES) such as offshore wind and solar photovoltaics. While previous studies have examined steady-state implications of overplanting and grid reinforcement, less attention has been given to assessing the effectiveness of flexibility resources during longer time periods. This paper presents an operational assessment of the 2050 Dutch transmission system using full-day optimal power flow simulations for typical summer and winter conditions.The synthetic model of the transmission system is developed in DIgSILENT PowerFactory and includes distributed and centralized supply, batteries, electrolyzers, and demand response mechanisms. Using the Mean-Variance Mapping Optimization (MVMO) algorithm with 15-minute resolution, system operation is optimized to minimize active power losses while respecting voltage and thermal limits. The results show that flexibility resources are essential to ensure demand coverage and reduce transmission congestion, especially during periods of high VRES generation. In winter, the centralized nature of offshore wind leads to regional overloads and higher losses, while summer benefits from decentralized PV generation and more balanced load matching. Batteries and hydrogen units show distinct operational patterns, emphasizing the importance of their strategic placement. These findings support the design of control strategies and infrastructure planning for high-VRES transmission systems.

As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL) algorithms in the form of a Gym environment, and those that do usually lack depth in modeling Vehicle-to-Grid (V2G) scenarios. To address the aforementioned issues, this paper introduces EV2Gym, a realistic simulator platform for the development and assessment of small and large-scale smart charging algorithms within a standardized platform. The proposed simulator is populated with comprehensive EV, charging station, power transformer, and EV behavior models validated using real data. EV2Gym has a highly customizable interface empowering users to choose from pre-designed case studies or craft their own customized scenarios to suit their specific requirements. Moreover, it incorporates a diverse array of RL, mathematical programming, and heuristic algorithms to speed up the development and benchmarking of new solutions. By offering a unified and standardized platform, EV2Gym aims to provide researchers and practitioners with a robust environment for advancing and assessing smart charging algorithms.

As electrical systems become increasingly complex with the integration of new electronic loads and variable renewable energy sources (VRES), modern tools are essential for their effective management and operation. This paper discusses an initial step toward the complete implementation of a digital twin for the Dutch electrical power grid: the development of a real-time digital model. This model represents the Randstad region’s electrical grid, which has recently been enhanced by substantial offshore wind power installations, including Hollandse Kust Zuid and Hollandse Kust Noord.The Real-Time Electromagnetic Transient (EMT) model described in this study enables the assessment of the impacts of offshore wind integration on network stability and power quality. Network elements have been modeled using RSCAD and implemented within the Real-Time Digital Simulator (RTDS). Detailed simulations are conducted to evaluate the grid’s capacity to handle the active and reactive power influx from the offshore wind farms. This study highlights the critical role of precise modeling in ensuring the reliability and efficiency of wind power integration into the national grid.

This study investigates the dynamic performance of hybrid power systems, with a focus on Multi-Terminal Direct Current (MTDC) interconnected offshore-onshore systems, under various disturbances. Conventional performance metrics such as Rate of Change of Frequency (RoCoF), commonly used for AC systems, are utilized to assess frequency response. Additionally, a modified Rate of Change of Voltage (RoCoV) metric is proposed to capture DC voltage behavior. The effectiveness of these metrics is evaluated through simulations involving various disturbances, including generator outages, line outages, converter outages, and faults. The results demonstrate the ability of the proposed metrics to effectively capture the impact of disturbances on system response, while also identifying limitations in capturing oscillating responses. Furthermore, the parametric sensitivity of control parameters in the converter’s outer control loop is analyzed to assess their influence on system behavior.

High-Voltage Direct Current (HVDC) transmission with Modular Multi-level Converter (MMC) - Bipolar Point-to-Point (BPP) configuration is gaining traction as a solution for integrating renewable energy sources into future power grids. However, one critical challenge associated with MMC-BPP systems is the occurrence and mitigation of oscillations on the DC side. These oscillations can arise due to various factors, including interactions between the AC and DC systems, converter de-blocking after fault events, and the dynamic behavior of connected power sources. The research work presented in this paper addresses a gap by investigating and mitigating oscillations specifically occurring during post-fault converter de-blocking. An enhanced active damping method is proposed that achieves a substantial reduction of these oscillations, ensuring improved system stability during this critical phase. Furthermore, a meticulous parametric sensitivity analysis is conducted on a four-terminal MMC-BPP test system using a real-time simulator to extract valuable insights into the damping method’s effectiveness under various operating conditions.

The solar industry in residential areas has been witnessing an astonishing growth worldwide. At the heart of this transformation, affecting the edge of the electricity grid, reside smart inverters (SIs). These IoT-enabled devices aim to introduce a certain degree of intelligence to conventional inverters by integrating various grid support capabilities (e.g., voltage and frequency control). However, with the remarkable automation of these devices come enormous security risks. Thus, rising rates of vulnerabilities have increased the necessity for designing resilient, auditable, and secure SIs’ firmware over the air (FOTA) amendment schemes suitable for this heterogeneous SIs-based ecosystem. In this regard, we propose leveraging blockchain as an innovative technology to guarantee these cybersecurity requirements. In this article, we present the design of a distributed FOTA scheme, namely, RASSIFAB, governing the process of amending SIs’ firmware within residential areas in an immutable and scalable manner. The scheme was implemented on a blockchain test network to assess its functionalities and performance. We also carried out a security evaluation to determine whether RASSIFAB is resistant to various identified threats. The obtained results confirm that the scheme is efficient and sound. They also indicate that RASSIFAB ensures reliable and authentic firmware amendments even with malicious insiders, differentiating our framework from the existing ones.

Synthetic networks aim to generate realistic projections of real-world networks while concealing the actual system information. Researchers have mainly explored methods to create synthetic power systems. However, with the rapid power grid digitalization, new methods are needed for synthetic communication networks of cyber–physical power systems (CPPS). In this article, we propose a two-stage generative model for generating synthetic communication topologies of large-scale CPPS based on the existing power grids. It reproduces the existing communication network design process and is capable of generating statistically realistic networks. The proposed method is implemented to create a realistic, large-scale synthetic CPPS for the interconnected power grids in continental Europe. The method is validated by comparing the generated communication network with 18 realistic communication network topologies with different system sizes. The experimental results validate the scalability and effectiveness of the generative model.

Cascading failures in power systems are extremely rare occurrences caused by a combination of multiple, low probability events. The looming threat of cyberattacks on power grids, however, may result in unprecedented large-scale cascading failures, leading to a blackout. Therefore, new analysis methods are needed to study such cyber induced phenomena. In this article, we propose a data-driven method for dynamical analysis of power system cascading failures caused by cyberattacks. We provide experimental proof on how attacks may accelerate the cascading failure mechanism, in comparison to historically observed blackouts. Using a dynamic power grid model, consisting of multiple, coordinated protection schemes, we define and analyze the point of no return in a cascading failure sequence by applying the Hilbert–Huang transform for time-frequency analysis. Numerical results indicate, cyberattacks may accelerate cascading failures at least by a factor of 3x. This is due to the excitation and non-damping of multiple frequency modes greater than 1 Hz in a short time span. The proposed method is tested using time domain simulations conducted through a modified IEEE 39-bus test system, which can simulate cascading outages using coordinated protection schemes.

Anomaly detection is of considerable significance in engineering applications, such as the monitoring and control of large-scale energy systems. This article investigates the ability to accurately detect and localize the source of anomalies, using an autoencoder neural network-based detector. Correlations between residuals are identified as a source of misclassifications, and whitening transformations that decorrelate input features and/or residuals are analyzed as a potential solution. For two use cases, regarding spatially distributed wind power generation and temporal profiles of electricity consumption, the performance of various data processing combinations was quantified. Whitening of the input data was found to be most beneficial for accurate detection, with a slight benefit for the combined whitening of inputs and residuals. For localization of anomalies, whitening of residuals was preferred, and the best performance was obtained using standardization of the input data and whitening of the residuals using the zero-phase component analysis (ZCA) or zero-phase component analysis-correlation (ZCA-cor) whitening matrix with a small additional offset.

This paper concerns with the determination of a suitable level of overplanting for photovoltaic systems. For this purpose, six futuristic operational scenarios for the Dutch electrical power system are generated for year 2050. A synthetic model is developed by using DIgSILENT Power Factory 2022 SP3 to investigate the steady-state systemic performance in each operational scenario, taking into account three cases with different levels of overplanting. Power flow calculations are conducted to reflect on the resulting voltage profiles and active power losses as well as on the implications on the required network upgrades (e.g. addition of lines, transformers, and reactive power compensation devices).

Power electronic interfaced generation (PEIG) has become significantly dominant in the electrical power grid. This development is leading to a decrease in systemic inertia and damping against electrical oscillations. This causes the introduction of new and faster dynamic phenomena. One of these phenomena is sub-synchronous control interaction (SSCI), occurring as sub-synchronous oscillations (SSOs) in the system. Several real-world events reported so far have been related with large wind power plants (WPPs) and the improper tuning of the grid side converter (GSC) of (type-4) fully rated converter (FR C) wind turbines. As other PEl G have similar topologies and control systems, it is a very relevant topic. Weak grid conditions often contribute to the risks of SSO events. This paper proposes supplementary wide-area damping (WAD) to the control system of the GSC, focused on damping excursions of the phase locked loop (PLL). Signals measured by a remote phasor measurement unit (PMU) are communicated to the control system, which uses it for dynamic damping control. The effects of the WAD are tested by comparing the results of linearization-based eigenvalue analysis with and without the addition of WAD. Supplementary analysis conducted by using time-domain simulations and Prony analysis confirm the positive effect of WAD. Numerical tests are performed in DlgSILENT PowerFactory 2023 SP2 on a modified IEEE-39 bus test system.

In this paper we propose a Quantile Regression Deep Neural Network capable of forecasting multiple quantiles in one model using a combined quantile loss function, and apply it to probabilistically forecast the prices of 8 European Day Ahead Markets. We show that the proposed loss function significantly reduces the quantile crossing problem to (near) 0% in all markets considered, while in some cases simultaneously increasing forecasting performance based on classical point forecast metrics applied to the expected value of the probabilistic forecast. The models are optimized using an automated approach with an elaborate feature- and hyperparameter search space, leading to good model performance in all considered markets.

The frequency stability of the power system is challenged by the high penetration of power electronic interfaced renewable energy sources (RES). Energy storage systems (ESS) are used to supply extra power injection to enhance the frequency stability during a disturbance. This paper presents a novel approach for improving the frequency dynamics by incorporating a designed ultracapacitor (UC) with a fully decoupled wind power generation (FDWG) unit. To this aim, a suitable model implementation of UC for real-time simulations is presented. The model constitutes a parallel RC branch, which is appropriate for illustrating the relevant fast UC dynamics that occur within the first milliseconds of the time period of action for fast active-power frequency control services. The frequency performance achieved by the support of the FDWG equipped with UC is compared against the performance achieved by using electrical batteries. The comparison includes the application of droop-derivative frequency control.

Cossembler (co-simulation assembler) is a rapid prototyping tool for co-simulation. The tool is created to expedite the process of co-simulation development for power and energy system studies targeting user groups of power engineers, energy consultants and grid operators. Instead of focusing on message encoding, transportation and synchronization, as many other co-simulation tools do, Cossembler emphasizes application-level functionalities which are of interest to the intended users (such as power flow studies, stability simulations, market simulations, etc.). Cossembler is a block modeling tool whose blocks reflect these main functionalities in power and energy sector. In this paper, we show the main characteristics of Cossembler architecture, discuss some of its advantages and disadvantages, and finally, show examples of its use.

As future power systems become increasingly complex and interconnected to other energy carriers, a single research infrastructure can rarely provide the required test-beds to study a complete energy system, especially if different types of real power hardware are expected to be in-the-loop. Therefore, virtual interconnection of laboratories for large-scale systems plays an important role for geographically distributed realtime simulation. This paper presents the improvements made in simulation fidelity as well as usability for establishing future simulator and laboratory connections. A general procedure is proposed and analyzed for geographically distributed real-time simulation, which allows users easily to adapt this procedure to specific test cases. A systematic and comprehensive analysis of a dynamic phasor based co-simulation interface algorithm and its improvements are provided to demonstrate the advantages as well as limitations of this approach.

The eleven papers in this special section focus on new trends in residential and home energy management. As an important branch of power demand side management, residential energy management plays an important role in reducing the emission and enhancing the energy efficiency in the energy delivery side. Recent technical advances bring significant transformations to energy end-users. First, increasing penetrations of residential renewable energy source, electric vehicle, and residential energy storage system have been transforming residential energy consumers to be “Energy Prosumers (Producer and Consumer. Second, the two-way communication infrastructure enables residential energy entities interact and exchange information flows with the external environment. Third, recent advances in ubiquitous sensing and metering technologies, such as Internet of Things, nonintrusive load monitoring, and advanced metering infrastructure, enable the deep understanding on behaviors of energy end-users and related environments. These technical advances consequently drive residential energy entities to become complex cyber-physical-social systems, which require newsolutions for coordinating, managing, and optimizing residential energy resources with the active participations of end users.

High Impedance Fault (HIF) is a low fault current event which cannot always be efficiently detected or cleared by conventional protection systems. Voltage or current signal distortions are often a possible indicator of HIF signatures which need to be carefully analyzed. This paper proposes a new technique based on second-difference approach to detect signal distortions. The technique does not require large database and is computationally very lightweight. The performance of the proposed technique is compared against commercial protection relays connected in Hardware-in-Loop (HIL) mode to a Real Time Digital Simulator (RTDS) simulating HIF in an IEEE 9-bus system. Test result evaluation show that the proposed technique is accurate and dependable.

Predictive analysis of post fault system dynamic behavior can be a vital resource for better control and reliability improvement of the overall system. This article presents methods for predictive analysis of Fault Induced Dynamic Voltage Recovery (FIDVR) event using a faster than real-time digital replica of a power system. The methods proposed include use of quick algorithms for detection of FIDVR events and metrics for predicting dynamic behavior of the power system impacted by the detected FIDVR event. We show that, using a digital faster than real-time replica, the FIDVR event can be detected in required time and that the transient voltage deviation index (TVDI) can be quickly calculated.

This paper deals with the process of identifying the parameters of the dynamic equivalent (DE) load model of an active distribution system (ADN) simulated in RTDS using mean-variance mapping optimization (MVMO) algorithm.MVMO is an emerging variant of population-based, evolutionary optimization algorithm whose features include evolution of its solutions through a unique search mechanism within a normalized range of the sample space.Due to the prominent largescale integration of DG in low and medium voltage networks, it is important to develop equivalent models that are suitable for representing the resulting active distribution network in dynamic studies of large power systems.This would significantly reduce the computational demands and simulation time.Moreover, only a defined portion of a system is usually studied, which means that the external system can be substituted with DE thereby allowing the detailed modelling of the focus area.The IEEE 34-Bus distribution system was modified and used as the reference network where measurement data were gathered for identification of the parameters of its developed DE.An optimization-enabled simulation involving MATLAB, which host the MVMO algorithm and RTDS, which simulates the models was established.The reactions of the detailed network and the DE were compared upon subjecting them to different disturbances in the retained system.The effectiveness of the MVMO algorithm in identifying DE parameters based on its unique mapping function is reflected through the results of the response comparison.

It has shown that with perfect knowledge of the system model and the capability to manipulate a certain number of measurements, the false data injection (FDI) attacks, as a class of data integrity attacks, can coordinate measurements corruption to keep stealth against the bad data detection schemes. However, a more realistic attack is essentially an attack with limited adversarial knowledge of the system model and limited attack resources due to various reasons. In this paper, we generalize the data attacks that they can be pure FDI attacks or combined with availability attacks (e.g., DoS attacks) and analyze the attacks with limited adversarial knowledge or limited attack resources. The attack impact is evaluated by the proposed metrics and the detection probability of attacks is calculated using the distribution property of data with or without attacks. The analysis is supported with results from a power system use case. The results show how important the knowledge is to the attacker and which measurements are more vulnerable to attacks with limited resources.

Prevailing low energy buildings attracts lots of attention in the world. Many studies have contributed in introducing higher thermal efficiency towards rooms with low energy heating, ventilation, and air-conditioning (HVAC) systems. However, current HVAC systems do not consider CO2concentration change and thermal contribution towards human bodies in a room. This paper presents a novel method to predict thermal dynamics, including person count. Occupancy data are dynamically estimated by CO2concentration and thermal contribution from the human bodies. The model is formulated as a resistor-capacitor circuit (RC circuit) in the Modelica modeling language. All parameters in a simulation are identified using actual building data during the winter season in Japan. Results are validated using measured information of actual building environment, and the test results concluded an improvement of absolute percentage error by 0.16 % over the conventional model. From the test results, it was concluded that the moving average filter of 20 minutes was an appropriate mean time to represent the time delay value.

Advanced metering infrastructure (AMI) systems have been developed to perform automated meter reading, reduce peak loads, and use energy efficiently. Two issues exist regarding this system. The first issue is the communication and handling of consumer data concerning electricity collected by power utilities. The second issue is the management of communication network resources and scheduling of metering to avoid congestions and communication errors. The major device for addressing these two issues is a concentrator that acts as a data relay point in an AMI system. The concentrator collects data from the meter and sends them through communication networks. This study discusses the aggregation methods of the concentrator with respect to the aforementioned two issues and proposes a method to reduce network utilization and message size on a server. The method concatenates small smart metering messages sent from relevant meters. The traditional method aggregates and concatenates messages without numerical processing. The proposed method processes messages at the concentrator to reduce total message size and calculation cost on the server. Moreover, the method that combines the traditional and proposed methods was evaluated by considering a real-world case. These methods were simulated by using an ns-3 network simulator to evaluate their efficiency in sending messages concerning the volume of power consumption to the server. The results of the simulations show that the proposed methods reduce message size by as much as 98.5% in some cases and, by means of the concentrator, shorten the communication time between meters and the server. The proposed method can help to reduce loads on networks and servers.

Using an existing decision support system in different data availability situations is a challenge. This is due to the significant variance in terms of what data can be acquired as input for such systems. In urban energy planning, the problem of data availability is even more crucial because of the large amount of data that is required. This problem affects the portability and the viability of such systems. i.e. they can only be used in one single specific situation at a certain point of time, as long as data are available in the same way. This paper presents an ontology-based approach for developing and keeping such systems more robust against data availability problems. The methodology allows integrating the initial requirements of the system, the domain semantics, and a multiple level-of-detail answering mechanism. The results presented in this paper are validated against an application in modeling a modular ontology-based urban energy planning support system. The proposed solution, in this paper, allows the flexibility of these systems in terms of input data. Furthermore, it allows better traceability of how the system fulfills the initial requirements.

Building automation is the key to sustainable, safe and comfortable buildings as well as to the integration of buildings into smart grids and with other external applications such as cloud computing. In a typical smart community scenario, various household appliances of multiple residential users are connected via a Home Area Network. HANs are further connected to the local power distribution network via smart meters, forming a LAN, where also renewables communicate. Methods are needed to design and integrate networks with hundred thousands of nodes in a cost-efficient way. The key to providing improved services in building automation is to process complex scenarios in an adequate way. Furthermore, building automation systems must be seen as dependable systems covering both safety and security aspects. The main objective of this Special Section is to bring the ideas of the worldwide research community into a common platform, to present the latest advances and developments.

A main goal of hot water distribution research is to improve the system's efficiency, i.e., to fulfill hot water requirements while minimizing energy and water losses. Central domestic hot water (CDHW) systems represent an important part of current installations worldwide, e.g., hotels, hospitals, sports centers, social facilities, and multifamily residential or apartment buildings. The optimization of such systems claims for forecasting capabilities and context-aware enhancements are based on patterns of use. Thus, the level of uncertainty is reduced, and systems are not forced to operate using blind/oversized/generic assumptions. This paper presents a novel control strategy based on habit profiles for the management of a CDHW system. A simulated environment is utilized to compare the introduced strategy with habitual performances. Simulations are supported by real databases concerning users' behavioral patterns. Results are promising and point to place profile-based strategies as a suitable approach for an optimized water and energy management in future buildings.

Cyber physical systems (CPSs) are the systematic combination of physical processes and information and communication technology (ICT). They constitute the next generation of networked, embedded systems that explicitly consider the physical parts during their design and operations. CPS applied to the energy system leads to the possibility of more sophisticated controls, the interworking of different energy types, cooperative loads, smart factories, the interaction of markets and infrastructure, smart integration of renewable energy sources, automated and grid-friendly buildings, more knowledge about the system due to sensor networks and analytics, multiagent systems, usage of smart storage, information technology (IT) security challenges, and many other aspects of what is sometimes called smart grids.

The energy system of the future is expected to be composed of a large variety of technologies and applications. However, the diverse nature of these components, their interlinked topology, and the sheer size of the system lead to an unprecedented level of complexity. Industry is confronted with severe problems in designing interoperable grid components, analyzing system stability, and improving efficiency. This paper describes the main challenges of continuous time-based and discrete event-based models of such cyber-physical energy systems. Using a characteristic test model, the scalability of the two approaches is analyzed. The results show the strengths and weaknesses of these two fundamentally different modeling principles that need to be considered when working with large scale cyber-physical energy systems.

High Level Architecture (HLA) and Functional Mock-up Interface (FMI) are two simulation interoperability standards. The HLA is older, well established and popular in industry. The FMI is a new standard, with plenty of support from the open source community and scientists. In this paper, it is presented how the strengths of both, the HLA and the FMI, can be utilized to realize a distributed hybrid (or heterogeneous) simulation platform. Two different algorithms are proposed for such a platform. To demonstrate the correctness of algorithms, and their performance comparison, a simulation example is chosen from the domain of complex energy systems.

This paper presents the dynamics of wind-turbine driven Self Excited Induction Generator (SEIG) with the consideration of dynamic core losses and dynamic mutual inductance. The core losses when considered are often taken as function of air-gap voltage and very few researchers included the variation as function of stator synchronous frequency. Similarly in most of the cases mutual inductance is taken as constant which is a simplified version of true dynamics. In this paper simulation studies are carried out to assess the dynamic performance of SEIG considering both the core losses and mutual inductance as dynamic variables. The performance is assessed in presence of variations in rotor speed, simulated as wind's effect, by a simplified wind turbine model. It is observed that dynamic mutual inductance and dynamic rotor losses are important parameters for accurate voltage and current measurements.

The deployment of Simulation-aided Building Controls is a complex process due to the uniqueness of each building along with an increasing complexity of building systems. Typically the deployment tasks are performed manually by highly specialized personnel which results in a poorly documented and extremely scattered deployment process. This paper introduces a workflow for the deployment of a Simulation-aided Building Control service suitable for supporting the operation phase of a building. Some tasks in the deployment process may benefit from machine support, especially data intensive, repetitive and error prone tasks. These may be fully or semi-automated. The proposed approach reduces the complexity of the setup procedure, decreases problems related to the uniqueness of the infrastructure and supports the documentation of the deployment process. Specially large facility management service providers may profit from this deployment process.

Two-thirds of the overall primary energy in the world is consumed in cities, resulting in 71 percent of all energy related greenhouse gas emissions. There are many ongoing initiatives to develop strategies to lower the high amount of emissions at the level of cities. However, energy planners face problems that are related to the complexity of the urban energy systems, encapsulating the components of the city together with their properties and interactions that are relevant to the planning process. The complexity of the problem increases given the unavailability of data and their multiple levels of detail (LOD). Therefore, there is a need for adequate tools to support the development of integrated energy strategies, defining specific quantifiable CO2reduction measures to be implemented at the city level. In this research, we perform an analysis of urban energy planning processes. Then, we extract the general requirements of decision support in this discipline. Then, we perform a general data availability review in cities. This process results in formalizing a modular methodology for the development of urban energy planning support software. This methodology is specific to urban energy planning, it ensures the modularity of the development process, it leads to flexible software that operates under different available LODs of data, and it addresses the problem of data availability at the development stage.

This article discusses recent trends in Smart Grid technology. Three selected aspects are (1) distributed information technology (IT), used for smart metering and multi-agent based controls, (2) big-data, resulting out of metering, sensors and other IT-enabled sources of information, and (3) an intelligent demand side, where demand response serves as contribution to grid services. New elements in the grid, most notably large numbers of fluctuating renewable energy sources, and new functionality like markets make it necessary to introduce methods and technologies from other domains that already faced such changes.

The future energy system has to satisfy a continuously growing demand for electricity and to reduce greenhouse gas emissions. Fulfilling such diverse needs requires the integration of renewable energy resources on a large scale. However, the existing information and communication infrastructure controlling the corresponding power grids and components is not directly designed to master the ever increasing complexity. An upcoming requirement is the need for the functional adaption of the control systems during operation. The main aim of this article is to discuss and analyze requirements as well as to introduce a standard-compliant concept for a reconfigurable software architecture used in intelligent electronic devices for distributed and renewable energy resources. A simulation case study shows the applicability of this approach. A secure adaptation of the functional structure and the corresponding algorithms in device controllers can substantially contribute to a more efficient energy system, while at the same time responding to future needs.

Previously mostly independently considered topics grow more and more together to form the building blocks of the future smart city. The building itself needs to be built as a zero average energy consuming building, posing requirements mainly on thermal and electrical parameters. Those parameters need to be known and controlled, requiring intelligent control techniques and cross-layer optimized sensor networks. Finally, very differently used buildings together participate in the infrastructure of the city, which gives them freedom to passively burden the grids, or actively help stabilizing them, while benefitting from different tariffs in the smart grids. This paper summarizes the state of the art of those topics with respect to industrial electronics.

The building sector is rising rapidly which is not only the consequence of the world's increasing population but also due to higher requirements with regards to comfort. Buildings along with HVAC systems to run these buildings account for a large amount of the world's global energy consumption. Hence operating buildings as well as HVAC systems in an energy-efficient way plays the key role in reducing energy consumption and therefore making the contribution towards mitigation of the climate change. Studies about advanced control techniques for buildings and systems to run those have shown promising results with regards to lower energy consumption and the optimal use of renewable energy sources. In contrast to the conventional approaches (e.g.: PID, two-position controller, etc.) for building and HVAC control, model based concepts take advantage of the direct knowledge of the system behaviour using an image of the system. Model based control concepts facilitate the systematic approach towards the energy-efficient operation of building and HVAC systems. However, the quality of the model based controller strictly depends upon the quality of the model employed to describe the dynamic behaviour of the system being subject to control. The model-plant mismatch can negatively impact the energy-efficient control operation. This paper presents the design of an unscented Kalman filter (UKF) approach for the purpose of state and parameter estimation for solar thermal HVAC system control. This UKF concept incorporates the possibility of model update using measurements and therefore reduces model-plant mismatch which in turn improves the control quality and can be regarded an asset with respect to the energy-efficient control operation.

Simulation-driven design has become an important design process in many technological domains. It allows a more rapid deployment of innovative technology in products that have to fulfil high quality standards. In view of the success of this approach it is also becoming an increasingly important tool for the development of the future energy system. Due to the size and complexity of such systems this is however a challenging task. Energy systems combine not only a multitude of physical domains, related directly to the processes of generation, storage, distribution and consumption, but will in the future also increasingly rely on communication technologies and software in- frastructure for information exchange and control purposes. This article evaluates two distinct software tools, Simulink/Simscape and Ptolemy II, that nevertheless have the potential to serve as a framework for modelling, simulating and analysing such cyber- physical energy systems.

Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.

Building automation and control systems (BACS) are an important part of modern automated buildings. More and more they are also responsible for functions affecting people's safety, security and health. Thus the respective technology is supposed to work reliably, securely, safely and efficiently. The two important features of such a BACS are functional safety and system security (short safety and security) of both the network nodes and the communication protocols. Up to now little effort has been made to specify a life cycle for a safe and secure BACS that defines requirements for the different stages of the product life of a BACS. Special focus is related to the commonalities between the development of safety and security systems to benefit from these commonalities in development.

In safety related systems online hardware self tests are integrated to reach a defined level of hardware integrity. These tests comprise testing of the static and volatile memory and the CPU internals. The higher the level of integrity should be, the more efficient tests must be used. Additionally, the effort to verify the correctness of the tests is rising. Hence, designing the test, and the validation and verification of the tests, is a critical part in the development process of safety related systems. The verification of the correct behavior requires sophisticated methods for stimulating errors that must be detected by the tests. The document describes an environment based on boundary scan technology for testing the online hardware self tests automatically.

Automation networks tend to become smarter and more consumer-oriented. New technologies, initially not intended for automation purposes, offer new possibilities and increased functionality for control and monitoring applications. This paper gives an overview on how intercommunication between to entirely different networks such as fieldbus systems, used for automation networks and wireless personal area networks (WPANs), embedded in modern consumer electronic devices can lead to new services. The major challenge is the integration of WPANs into existing and proven automation applications. This introductory paper emphasizes on intercommunication, services at the application layer level and on how to combine the individual advantages of these networks to extend the functionality and allow new services. The approach presented here is universal and can be applied to any automation system. In section V we present some results of the implementation of selected services.