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Protection blinding is a challenging issue in renewables-penetrated distribution grids and refers to a situation where a circuit breaker may not trip due to fault current contribution from distributed generation. This research addresses how the distributed generation location and capacity impact the operation of the circuit breaker in terms of the response time of the circuit breakers. The relative electrical distances of the faults and distributed generation to the circuit breakers are considered. The impact of distributed generation capacity considering the fault location is characterized using a new index called the heterogeneity index. The electrical distance between distributed generations and circuit breakers and the electrical distance between fault and circuit breaker is considered by a second new index called the electrical distance ratio. Data analysis on simulation results shows that these indices capture the phenomena of protection blinding caused by distributed generation.
Results show that a higher distributed generation penetration and faults that are electri
cally further away from a circuit breaker show severe cases of protection blinding captured by the indices. Furthermore, it is demonstrated how these indices can identify the worst impacted locations in the distribution grid. A key result is that protection blinding does not necessarily occur solely due to the presence of distributed generation between a circuit breaker and a fault, but is dependent on factors such as distributed generation location in the distribution grid, fault level, fault level distribution across the generation units and fault location.
The increasing share of volatile renewables in power systems requires more reserves to balance forecast errors in renewable generation and power fluctuations. In contrast, common reserves such as gas-fired power plants are phased out, impeding the procurement of sufficient reserves. Alternative reserves, particularly on the demand side, such as battery storage systems, also exhibit some degree of freedom to deviate from their scheduled operating point to supply or consume more or less power, thus providing a flexibility potential. However, demand-side flexibility potentials are generally subject to uncertainties, and so is the generation of volatile renewables. The challenge is incorporating the uncertainties on both sides to procure sufficient (uncertain) flexibility potential in advance. Considering uncertainty is important to avoid additional, drastic measures in real-time to balance generation and demand, such as curtailing renewable generation or load shedding. This work presents a stochastic flexibility calculus that provides an indicator for computing the risk of insufficient flexibility potentials or, conversely, guarantees for sufficient flexibility potentials. Thus, the stochastic flexibility calculus contributes to overcoming the challenge of procuring sufficient flexibility potentials in renewable-based systems. An evaluation based on real data is performed using an example of a renewable energy community consisting of households equipped with photovoltaic power plants and battery storage systems. The newly introduced stochastic flexibility calculus computes the number of households that must operate their battery storage systems flexibly to balance forecast errors locally. The results show that the forecast method significantly influences this number. Some numerical results appear unexpected, as too many flexibility-friendly households can negatively impact the aggregated household flexibility potential.
The autonomic composition of Virtual Networks (VNs) and Service Function Chains (SFCs)based on application requirements is significant for complex environments. In this paper, we use graph transformation in order to compose an Extended Virtual Network (EVN) that is based on
different requirements, such as locations, low latency, redundancy, and security functions. The EVN can represent physical environment devices and virtual application and network functions. We build
a generic Virtual Network Embedding (VNE) framework for transforming an Application Request (AR) to an EVN. Subsequently, we define a set of transformations that reflect preliminary topological, performance, reliability, and security policies. These transformations update the entities and demands of the VN and add SFCs that include the required Virtual Network Functions (VNFs). Additionally, we propose a greedy proactive heuristic for path-independent embedding of the composed SFCs. This heuristic is appropriate for real complex environments, such as industrial networks. Furthermore, we present an Industrail Internet of Things (IIoT) use case that was inspired by Industry 4.0 concepts,in which EVNs for remote asset management are deployed over three levels; manufacturing halls and edge and cloud computing. We also implement the developed methods in Alevin and show exemplary mapping results from our use case. Finally, we evaluate the chain embedding heuristic while using a random topology that is typical for such a use case, and show that it can improve the admission ratio and resource utilization with minimal overhead.
Recent advancements in energy systems, such as the emergence of prosumers and sector coupling approaches, introduce additional flexibilities over multiple energy sectors, such as heating, electricity, and mobility. Due to the complexity of such distributed systems, the optimization of energy allocation is a non-trivial task, especially considering constraints and limitations introduced by distributed devices or sub-systems. Additionally, the variety of devices forces approaches to be highly situational and not universally applicable. In this paper, a two-level optimization scheme is proposed, which aims at reducing the optimization complexity of sector-coupled systems. The multi-vector optimization embedded in the two-level optimization scheme is formulated as a mixed-integer linear problem, optimizing the energy flow between domains, which are modeled as an abstraction of a sector. Distributed devices are modeled as components that represent an abstraction of devices connected to an energy domain. The optimization process is evaluated based on the data from a residential complex in Ghent, Belgium. It shows that the approach is capable of minimizing costs, CO2 emissions, and dependency on external resources.
Smart Grids are electrical grids that require a decentralised way of controlling electric power conditioning and thereby control the production and distribution of energy. Yet, the integration of Distributed Renewable Energy Sources (DRESs) in the Smart Grid introduces new challenges with regards to electrical grid balancing and storing of electrical energy, as well as additional monetary costs. Furthermore, the future smart grid also has to take over the provision of Ancillary Services (ASs). In this paper, a distributed ICT infrastructure to solve such challenges, specifically related to ASs in future Smart Grids, is described. The proposed infrastructure is developed on the basis of the Smart Grid Architecture Model (SGAM) framework, which is defined by the European Commission in Smart Grid Mandate M/490. A testbed that provides a flexible, secure, and low-cost version of this architecture, illustrating the separation of systems and responsibilities, and supporting both emulated DRESs and real hardware has been developed. The resulting system supports the integration of a variety of DRESs with a secure two-way communication channel between the monitoring and controlling components. It assists in the analysis of various inter-operabilities and in the verification of eventual system designs. To validate the system design, the mapping of the proposed architecture to the testbed is presented. Further work will help improve the architecture in two directions; first, by investigating specific-purpose use cases, instantiated using this more generic framework; and second, by investigating the effects a realistic number and variety of connected devices within different grid configurations has on the testbed infrastructure.
