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In a previous paper, we have introduced a simulation and optimization framework for the integration of renewable generators into an electrical distribution network. The framework searches for the optimal size and location of the distributed renewable generation units (DG). Uncertainties in renewable resources availability, components failure and repair events, loads and grid power supply are incorporated. A Monte Carlo simulation–optimal power flow (MCS–OPF) computational model is used to generate scenarios of the uncertain variables and evaluate the network electric performance with respect to the expected value of the global cost ( ECG ). The framework is quite general and complete, but at the expenses of large computational times for the analysis of real systems. In this respect, the work of the present paper addresses the issue and introduces a purposely tailored, original technique for reducing the computational efforts of the analysis. The originality of the proposed approach lies in the development of a new search engine for performing the minimization of the ECG , which embeds hierarchical clustering analysis (HCA) within a differential evolution (DE) search scheme to identify groups of similar individuals in the DE population and, then, ECG is calculated for selected representative individuals of the groups only, thus reducing the number of objective function evaluations. For exemplification, the framework is applied to a distribution network derived from the IEEE 13 nodes test feeder. The results show that the newly proposed hierarchical clustering differential evolution (HCDE) MCS–OPF framework is effective in finding optimal DG-integrated network configurations with reduced computational efforts. [ABSTRACT FROM AUTHOR]

Highlights • A novel daylight quality evaluation metric named " N-Daylit area" was developed. • The daylighting simulation model in Daysim was experimentally validated. • The daylight and energy performance of STPV facades was investigated. • An optimal design of STPV facades was offered in view of daylight and energy saving. Abstract Building-integrated semi-transparent photovoltaics (STPV) technology has attracted more and more attentions for its obvious advantage in renewable energy utilization. However, the STPV facades have potential conflicts of interest between daylighting and energy efficient. Thus, this paper reported an investigation of the daylighting and overall energy performance of STPV facades in cold climatic regions of China, with the aim of resolving the conflicts. Firstly, the reliability of daylighting model in Daysim was experimentally verified. In addition, a novel dynamic daylighting evaluation metric named " N-Daylit area" was developed, considering the glare phenomenon and setting reasonable indoor illumination thresholds. On basis of this, the effect of several key design parameters, including transmittance、orientation and window-wall ratio (WWR), on the performance of STPV facades installed in a generic reference office was studied, by using a comprehensive simulation method. According to the simulation result, the transmittance and WWR of the STPV façade were recommended to be in the ranges of 50–60% and 40–50%, respectively, for the purpose to improve the energy efficiency of STPV facades with the premise of ensuring a satisfied daylight quality of the tested room. Furthermore, the results also revealed that south facing was the preferred orientation of STPV facades, to obtain the best daylighting quality and the lowest annual net electricity use of the space. [ABSTRACT FROM AUTHOR]

Smart Grids consist of multiple actors and physical phenomena, which are often difficult to capture in one single simulation framework. Therefore, researchers increasingly couple distinct simulators to form novel “co-simulations”. In this paper we present a literature survey of 26 smart grid co-simulation frameworks. First of all, we present our understanding of a co-simulation. We then classify the 26 frameworks on multiple characteristics, such as simulation tools, synchronization methods and research topics. Finally, we present correlations between different key characteristics, analyze possible research gaps and discuss possible trends and future development areas in the field of smart grid co-simulations. [ABSTRACT FROM AUTHOR]

This work describes and evaluates a new simulation model for direct steam generation in parabolic-trough solar collectors. In direct steam generation, water is heated and evaporated through a solar field to feed a steam Rankine cycle or an industrial process. However, the behaviour of the involved multiphase fluid poses some challenges to simulation models. The model explained in this work is based on a steady-state approach but deals with transient conditions such as start-up, shutdown and clouds in a reasonable computing time. A new simulation tool is implemented in the TRNSYS software environment by means of new components that are suitable to be integrated into a whole solar plant model in order to carry out long-term energy production analyses with low computational resources. The main advantages of the new quasi-dynamic approach include fast computation with satisfactory accuracy; consideration of thermal inertia when addressing transient conditions; and flexibility to use different types of collector or solar field configurations. The performance of the model is validated with real experimental data obtained from the DISS solar test loop in Plataforma Solar de Almería, Spain. This paper describes the modelling approach and summarizes the comparison of simulation results with measurements taken at the DISS facility. [ABSTRACT FROM AUTHOR]

Efforts to meet climate change mitigation and energy security targets spur investments in variable renewable energy sources. Their implications for the operation of power plants are frequently investigated drawing on unit commitment and dispatch models. However, the temporal granularity and operational detail these models consider is commonly omitted in the broader family of long-term energy system models. To compensate this short-coming, these two types of tools have sometimes been ‘soft-linked’ and harmonised for limited simulation years. This paper assesses an alternative approach. We examine an extended version of an open source energy system model (OSeMOSYS), which is able to capture operating reserve and related investment requirements within a single tool. The implications of these model extensions are quantified through comparison with an Irish case study. That case study examined the effects of linking a long-term energy system model (TIMES) with a unit commitment and dispatch model (PLEXOS). It analysed the year 2020 in detail, applying a yearly temporal resolution that is over 700 times higher than in OSeMOSYS. Without increasing temporal resolution (and computational burden) we show that results of the enhanced OSeMOSYS model converge to results of TIMES and PLEXOS: Investment mismatches decrease from 21.4% to 5.0%. The OSeMOSYS analysis was then extended to 2050 to assess the implications of short-term variability on future capacity investment decisions. When variability was ignored, power system investments in 2050 were found to be 14.3% lower. This might imply that energy policies derived from such long-term models – of which there are many – may underestimate the costs of introducing variable renewables and thus meeting climate change or energy security targets. [ABSTRACT FROM AUTHOR]