394 research outputs found
A Case Study of the Use of Smart EV Charging for Peak Shaving in Local Area Grids
Electricity storage systems, whether electric vehicles or stationary battery storage systems,
stabilize the electricity supply grid with their flexibility and thus drive the energy transition forward.
Grid peak power demand has a high impact on the energy bill for commercial electricity consumers.
Using battery storage capacities (EVs or stationary battery systems) can help to reduce these peaks,
applying peak shaving. This study aims to address the potential of peak shaving using a PV plant
and smart unidirectional and bidirectional charging technology for two fleets of electric vehicles and
two comparable configurations of stationary battery storage systems on the university campus of
Saarland University in SaarbrĂĽcken as a case study. Based on an annual measurement of the grid
demand power of all consumers on the campus, a simulation study was carried out to compare the
peak shaving potential of seven scenarios. For the sake of simplicity, it was assumed that the vehicles
are connected to the charging station during working hours and can be charged and discharged
within a user-defined range of state of charge. Furthermore, only the electricity costs were included
in the profitability analysis; investment and operating costs were not taken into account. Compared
to a reference system without battery storage capacities and a PV plant, the overall result is that
the peak-shaving potential and the associated reduction in total electricity costs increases with the
exclusive use of a PV system (3.2%) via the inclusion of the EV fleet (up to 3.0% for unidirectional
smart charging and 8.1% for bidirectional charging) up to a stationary battery storage system (13.3%)
Numerical Investigation of the Adsorption Process of Zeolite/Water in a Thermochemical Reactor for Seasonal Heat Storage
Zeolite 13X molecular sieve with high sorption capacity and significant sorption rate has
been considered a promising candidate for seasonal heat storage. In this study, a code is developed
to simulate the adsorption process between zeolite and water in all ranges of partial pressures,
temperatures, and sorbate loadings. The results from the proposed code were compared with
experiments and good agreement was observed. After validation, the developed model was used
to study the effective parameters involved in the adsorption process of binder-free Zeolite 13X. A
parametric study considering various temperatures and water content in the inflow air was conducted
and the influence of different factors on the outlet temperature and adsorption enthalpy has been
studied. This parametric study gives a good insight into the measures which can be taken for
achieving the desired released energy or having the outlet temperature in the preferred range. The
simulations have been conducted in a variety of temperature ranges provided during the desorption
process, the humidity amount, and the mass flow rate of the incoming air. The relative influence
of each parameter in the specified ranges is presented. The results have demonstrated the direct
relationship of the partial pressure of water vapor and the desorption temperature with the adsorbed
water amount and adsorption enthalpy while changing the mass flow rate mostly influences the
discharging time
Multi-Objective Techno-Economic Optimization of Design Parameters for Residential Buildings in Different Climate Zones
The comprehensive approach for a building envelope design involves building performance
simulations, which are time-consuming and require knowledge of complicated processes. In addition,
climate variation makes the selection of these parameters more complex. The paper aims to establish
guidelines for determining a single-family household’s unique optimal passive design in various
climate zones worldwide. For this purpose, a bi-objective optimization is performed for twenty-four
locations in twenty climates by coupling TRNSYS and a non-dominated sorting genetic algorithm
(NSGA-III) using the Python program. The optimization process generates Pareto fronts of thermal
load and investment cost to identify the optimum design options for the insulation level of the
envelope, window aperture for passive cooling, window-to-wall ratio (WWR), shading fraction,
radiation-based shading control, and building orientation. The goal is to find a feasible trade off between thermal energy demand and the cost of thermal insulation. This is achieved using
multi-criteria decision making (MCDM) through criteria importance using intercriteria correlation
(CRITIC) and the technique for order preference by similarity to ideal solution (TOPSIS). The results
demonstrate that an optimal envelope design remarkably improves the thermal load compared
to the base case of previous envelope design practices. However, the weather conditions strongly
influence the design parameters. The research findings set a benchmark for energy-efficient household
envelopes in the investigated climates. The optimal solution sets also provide a criterion for selecting
the ranges of envelope design parameters according to the space heating and cooling demands of the
climate zone
Optimized Design of Thermoelectric Energy Harvesting Systems for Waste Heat Recovery from Exhaust Pipes
With the increasing interest in energy efficiency and resource protection, waste heat recovery processes have gained importance. Thereby, one possibility is the conversion of the heat energy into electrical energy by thermoelectric generators. Here, a thermoelectric energy harvesting system is developed to convert the waste heat from exhaust pipes, which are very often used to transport the heat, e.g., in automobiles, in industrial facilities or in heating systems. That is why a mockup of a heating is built-up, and the developed energy harvesting system is attached. To build-up this system, a model-based development process is used. The setup of the developed energy harvesting system is very flexible to test different variants and an optimized system can be found in order to increase the energy yield for concrete application examples. A corresponding simulation model is also presented, based on previously developed libraries in Modelica®/Dymola®. In the end, it can be shown—with measurement and simulation results—that a thermoelectric energy harvesting system on the exhaust pipe of a heating system delivers extra energy and thus delivers a contribution for a more efficient usage of the inserted primary energy carrier
Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)
Internet of energy based smart power grids demonstrate high in-feed from renewable energy resources (RESs) and lofty out-feed to energy consumers. Uncertainties evolved by incorporating RESs and time-varying energy consumption present immense challenges to the optimal control of smart power networks. To deal with these challenges, it is important to make the system deterministic by making time-ahead prediction and scheduling of power supply and demand. The present work confers a model of a co-scheduling framework, organizing cost-efficient activation of energy supply entities (ESEs) and load demands in a home area power network (HAPN). It integrates roof-top photovoltaic (PV) panels, diesel energy generator (DE), energy storage devices (ESDs), and smart load demands (SLDs) along with grid-supplied power. The scheduling model is based on mixed-integer linear programming (MILP) framework, incorporates a “min-max” optimization algorithm that reduces the daily energy bills, maintains high comfort level for the energy consumers, and increases the self-sufficiency of the home. The proposed strategy exploits the flexibility in dynamic energy price signals and SLDs of various classes, providing day-ahead cost-optimal scheduling decisions for incorporated energy entities. A linearized component-based model is developed, considering inefficiencies, taking various power phase modes of the SLDs along with the cost of operation, maintenance, and degradation of the equipment. A case study based on numerical analysis determines the particular features of the proposed HAPN model. Simulation results demonstrate the real prospect of our implemented strategy, utilizing a cost-effective optimal blend of distinct energy entities in a smart home
Introducing Explicit Causality in Object-oriented Hybrid System Modeling
International audienceAlong with the rapid development of embedded devices and network technology, the area of CyberPhysical Systems (CPS), has arisen. In terms of modeling and simulation, CPS—like many technical systems—have ahybrid nature, i.e., discrete-event behavior and continuous-time dynamics have to be integrated with each other.Basically, this integration is supported by modern object-oriented modeling paradigms such as Modelica®. Theequation-based concept resolves the causality between interconnected components, which qualifies this modelingscheme for complex multi-domain systems. However, in hybrid systems, explicit causality is required to correctlymanage iterative events. This paper highlights these issues, including algorithmic loops and instantaneous multipleupdates, which essentially arise from incompatibilities between the object-oriented concept and specific discrete-eventphenomena. We discuss several possible solutions and introduce the concept of re-allocating the objects’ behavioralintelligence
Modeling and Simulation of a Wastewater Pumping Plant.
Modeling wastewater pumping plants is rarely addressed in the literature. Standard component models as found in fluid simulation tool libraries are too complex, due to their projected generality, to be used for these applications. Lack of models results in a burden on engineers who have to test their control scenarios on real implemented systems. This may lead to unexpected delays and painful costs. In this work, easily manageable component-oriented models are derived and applied to the modeling and simulation of a real wastewater pumping system. The model derived in this paper is implemented in Modelica, and it helps better understanding the system dynamics. Thereby, a tool is provided for evaluating the performance of possible control schemes
Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets
To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV)
systems are gaining popularity. However, intermittent PV power supply, changing consumer load
needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy
management system (MP-iEMS) integrated home area power network (HAPN) is being proposed
to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for
various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This
paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution
in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a
case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery
(dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels
and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures
users’ comfort and low energy costs (i.e., relative 13% cost reduction). However, a battery life-cycle
degradation model calculates an annual decline in the storage capacity loss of up to 0.013%
A framework for modeling and control of wastewater pumping stations
In waste water pumping stations, centrifugal pumps driven by induction motors are used to transport the effluent collected from residential and commercial buildings to the treatment plants. Due to the varying nature of collected effluent rate, means of pump flow control should be applied. Recently, there is an engineering debate on either recommending frequency converters control or on-and-off control using soft starter technology. While there are obvious reward and cost of utilizing either approach, the lack of a simulation model makes the selection decision a matter of poor agreement. This is likely to happen in developing areas where abnormal running conditions such as power failure, excess flows, and lack of spare parts are frequently encountered. In this paper, a method for modeling wastewater pumping stations using the component oriented modeling language Modelica is presented. The model provides a valuable simulation tool to validate and judge on the different control schemes of these stations. This approach is applied successfully on a real pumping station located at the northern part of Gaza. The derived model facilitates tuning the control parameters and allows better understanding of the system dynamics
Warfarin pharmacodynamics and pharmacokinetics are not affected by the soluble guanylate cyclase stimulator riociguat (BAY 63-2521)
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