This paper outlines a modular system architecture that facilitates the intelligent integration of Electric Vehicle (EV) charging with Energy Management Systems (EMS) in workplace settings. The strategy focuses on establishing interoperable communication protocols, decision-making logic, and simulation tools to enhance coordination between energy demands and EV charging needs. The architecture comprises three main modules: EMS, Charging Station Management (CSM), and a Simulator. By utilizing synthetically generated data and MQTT-based communication, the system enables real-time energy distribution, simulations of various charging scenarios, and equitable power allocation through multifactor weighting strategies. A prototype developed in a lab setting showcases the architecture’s functionality and adaptability, illustrating its potential to address grid limitations, user preferences, and renewable energy availability in practical applications. This research opens the door for further exploration of automated negotiations among distributed systems for energy prioritization.

Fast DC charging of BEVs is required to enable extended travels and smart charging is necessary to limit power peaks. Fast charging comes with a state-of-charge related charging power decline, whereas smart charging introduces a charging power adjustment that in its simplest form prevents overloading the electric grid access. The presented work addresses primarily the impact of these mechanisms on the BEV charging performance. First, we introduce the approach to analyse charging site designs as finite multi-server queueing systems using event based simulation. Second, two exemplary scenarios prove the utility of fast and smart charging. It becomes evident that stressing the electricity grid with power peaks that equal the sum of the peak powers of all charging points is not required to satisfy the customer needs. In conclusion, it is assumed that dynamic and energy system sensible smart charging could utilise and mitigate the volatility of local renewable energy sources, offering a win-win situation for the charging site operator and the environment.

Production planning is not normally concerned with energy costs, but with the increasing share of volatile renewable energy, energy prices are becoming volatile, too. Production planning will ultimately have to take this into account. The problem here is in the uncertainly of price predictions and the associated risk. The present work investigates how to integrate dynamic energy price prediction uncertainty into the optimization of energy consumption in an industrial production setting. To that end, we incorporate the cost of prediction errors into the predicted energy prices up to the planning horizon. Such risk-adjusted prices are then used as the basis for energy-aware production planning. First results show that this concept can handle different error distributions and can be assumed feasible.

In the course of the energy transition novel solutions are needed to solve challenges that arise. Many customer owned energy resources become connected and shall contribute to the demand-supply balancing process, eventually. Small size suggest to solve related volatility issues also locally. Flexibilities of customer appliances provide the means to mitigate/manage the impact of volatile energy production close to the source. This paper presents an approach to rate appliance specific flexibility offers. Provider and user needs are addressed independently via the metrics benefit and quality. Introducing the problem and the proposed 2D-rating, we sketch exemplarily how PV and electric room heating flexibility can be predicted, offered, and used.

The ambitious climate goals of the European Union to complete the transition toward an entirely renewable energy supply request new balancing concepts to better cope with the required increase of distributed and volatile energy sources. Energy Communities could be an enabler thereto, the idea being that the community management coordinates heterogeneous energy assets, owned and operated by community members, to jointly utilize their resources internally and thereby reduce external grid load. In this paper we present an analysis of functional requirements to initiate a new technical framework for Energy Communities. This first functional description of Energy Community management aiming on community internal energy balancing is provided as a starting point for integration profiles development according to the Integrating the Energy System (IES) approach.

The increasing use of renewable energy sources, together with an ever-increasing energy demand, puts the electrical grid under heavy pressure. Grid extensions mitigate these factors, but are very costly. Other methods enabling an economically viable energy transition towards more renewable energy sources should be explored. We present a preliminary assessment of the impact of different mixes of prosumers on the energy balance and self-consumption of a rural community. We quantify unused potential regarding demand-side management, the extension of photovoltaic peak power, as well as battery storage. We implement models for electric production and consumption profiles and simulate scenarios with varied prosumer compositions. The results show that demand-side management does not change the self-consumption of photovoltaic energy significantly, whereas installation of more battery storage together with increased photovoltaic production promises better results for the rural community studied.

In optical burst-switched (OBS) networks, packets are aggregated into bursts that are sent an offset time after the corresponding control packet. The process of aggregating high and low-priority traffic in the same burst is known as composite burst assembly, where high-priority and low-priority packets are placed at the head and the tail end of the burst, respectively. Such composite bursts are beneficial in order to resolve the contention by dropping low-priority packets when preemptive minimum overlap channel (P-MOC) scheduling algorithms are used. For non-preemptive minimum overlap channel with void-filling (NP-MOC-VF) scheduling algorithm, we propose that high-priority packets should be placed in the middle of a burst and low-priority packets at the head and tail ends of a burst. That allows dropping of low-priority packets from the head and the tail of a burst when it contends with two other bursts where the head of the burst contends with one burst and the tail contends with another. The simulation results show that high-priority traffic faces reduced loss at the cost of increased loss of low-priority traffic. In this paper, the effect of varying the proportion of high-priority packets in the middle of the burst has been studied.

We propose a new approach to constraint-based path selection for dynamic routing and wavelength allocation in optical networks based on WDM. Our approach considers service-specific path quality attributes, such as physical layer impairments, reliability, policy, and traffic conditions, and uses a flooding-based transfer of path information messages from source to destination to find multiple feasible paths. It is fully decentralized, as it uses local network state information. To better understand how multiple constraints impact the efficiency of wavelength routing, and consequently provision the service guarantees, we specifically focus on electronic regenerators that, while being widely considered as the basic building blocks for optical switching nodes, are likely to impose conflicting constraints on routing. For example, electronic regenerators extend the optical reach and could perform wavelength shifting, but also induce impairments, such as delays and operational costs. The question for constraint-based routing is how to account for these conflicting effects. To validate the network modeling, a wide range of networking scenarios are simulated, such as ring, mesh and interconnections of all-optical networks with electronic gateways. For all these scenarios, our approach is shown to efficiently accommodate multiple, conflicting routing metrics related to different services and network architectures.

We propose a framework for decentralized path selection and on-demand wavelength channel provisioning in WDM networks with routing constraints. Within this framework, the path information of choice, such as transmission quality, reliability, policy and traffic conditions, is updated following a connection request, based on a local, autonomous and service-differentiated characterization of optical network elements. It is this local and autonomous network state information that makes our approach particularly suitable for distributed implementation, and applicable to optical network architectures and control protocols that support service level guarantees. To illustrate this, we study mesh networks consisting of transparent, short-reach networking segments interconnected by long-reach WDM links with electronically regenerative gateways, where different link types show different SNR degradation, reliability and delay. On the example of electronically regenerative gateways, we address a new class of constraint-based path selection problems, where a certain a type of network element can at the same time deteriorate and improve network performance; e.g. electronic regenerators improve optical path quality and can adapt to wavelengths, but induce delays or operational costs. The performance study has shown the capability of our methods to accommodate arbitrary number and type of optical path properties, related to different network architectures and services.