Results 1 - 10 of 309
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[en] Highlights: • Analysis of the impact of reduced system inertia on primary frequency control. • Quantification of the primary frequency response requirements in the future GB low-inertia systems. • Assessment of the cost and emission driven by primary frequency control. • Evaluation of the benefits of EVs in supporting primary frequency control. • Identification of the synergy between primary frequency control support and “smart charging” strategy. - Abstract: System inertia reduction, driven by the integration of renewables, imposes significant challenges on the primary frequency control. Electrification of road transport not only reduces carbon emission by shifting from fossil fuel consumption to cleaner electricity consumption, but also potentially provide flexibility to facilitate the integration of renewables, such as supporting primary frequency control. In this context, this paper develops a techno-economic evaluation framework to quantify the challenges on primary frequency control and assess the benefits of EVs in providing primary frequency response. A simplified GB power system dynamic model is used to analyze the impact of declining system inertia on the primary frequency control and the technical potential of primary frequency response provision from EVs. Furthermore, an advanced stochastic system scheduling tool with explicitly modeling of inertia reduction effect is applied to assess the cost and emission driven by primary frequency control as well as the benefits of EVs in providing primary frequency response under two representative GB 2030 system scenarios. This paper also identifies the synergy between PFR provision from EVs and “smart charging” strategy as well as the impact of synthetic inertia from wind turbines.
[en] Highlights: • A multi-objective MPC strategy for residential heating with heat pumps is presented. • The simulations employ detailed models for heat pump and thermal energy storage. • The feedback of individual controllers on the electricity generation is included. • Results show a significant reduction in required generation capacity is possible. • Costs carried by the consumer rise when demand response is applied. - Abstract: Shifting residential space heating from the use of gas boilers towards the use of heat pumps is recognized as a method to reduce green house gas emissions and increase energy efficiency and the share of renewable energy sources. Demand response of these systems could aid in reducing peak loads on the electricity grid. Extra flexibility can be added in the form of a thermal energy storage tank. This paper proposes a multi-objective model predictive control strategy for such a system, which takes into account the users energy cost, the environmental impact of energy use and the impact of expanding the electricity generation capacity. This control strategy is used in a case study inspired by the Belgian electricity generation park with 500,000 heat pumps to investigate the effect of the size of a space heating storage tank on consumer cost, energy use and required electricity generation capacity. Results indicate that the proposed demand response strategy reduces the required peak load capacity substantially with only a small increase in costs for the consumer. When adding a large hot water storage tank, the required additional capacity is nearly eliminated. Independently of the required capacity, the controller shifts energy use from peak to base generating plants. Increasing the storage tank size increases the amount of energy that is shifted. However, when demand response is applied by using a space heating storage tank, the costs for the consumer always increase relative to the case without demand response or storage tank. If demand response is desired by the grid operator, heat pump owners should be encouraged to participate by remunerating them for their additional expenses.
[en] Highlights: • A dynamic model of Steam Turbine control valve and actuation systems is proposed. • An innovative study of the equations that rule the assembly movement is provided. • Control valve response and accuracy is analyzed in detail with test and simulation. • System upgrade is achieved with Electro-Hydrostatic Actuation technology. - Abstract: The paper describes a study conducted on the control valve and the actuation systems of a Steam Turbine. These devices are of utmost importance, as they rule the machine final power production and rotational speed, thus their accurate modelling is fundamental for a valuable dynamic analysis of the whole system. In particular, a dynamic model developed in the Matlab/Simulink environment is proposed, which supports the analysis of the operational stability of the hydro-mechanical system as well as the failure modes that it may face during operation. The model has been successfully validated through specific field tests conducted on the actuation system at a cogeneration plant located in the General Electric Oil & Gas - Nuovo Pignone facility of Florence. The proposed work also highlights the requirements that new actuation technologies should fulfill in order to meet control valve system performance criteria and is thus useful as both a methodological approach and a “virtual benchmark” allowing to validate in advance any new actuation system.
[en] Highlights: • Optimization model for BtL production considering competing utilization paths. • Supply chain with decentralized pre-treatment via torrefaction and fast pyrolysis. • Local supply curves are used to model diseconomies of scale in biomass supply. • Synthetic gasoline can be produced at a cost of 0.8–0.9 € per liter. • BtL feedstock costs are 20–50% higher compared with established consumers. - Abstract: Second generation biofuels offer the opportunity to mitigate emissions from the growing transportation sector while respecting the scarcity of arable land in agriculture. Biomass-to-liquid (BtL) concepts based on large-scale gasification are capable of using low-quality residual feedstock, such as wheat straw or forest residues, for the production of transportation fuels. However, large amounts of biomass feedstock are required to achieve the economic capacity of a synthesis plant. Depending on the steepness of the terrain and the role of feedstock owners, biomass potentials can only be utilized to a large extent at increasing costs per ton. Such diseconomies of scale are particularly problematic in the presence of already established value chains consuming the easily accessible and low-cost feedstock. As a result, second-generation biofuel supply chains face steep supply curves with sharply increasing unit costs. This article investigates the impact of established utilization paths on a large-scale biofuel production value chain. To do so, a mixed-integer linear model is presented which first determines the allocation of biomass resources to CHP plants and domestic consumers. Based on the resulting costs and supply curves, the model then determines the optimum configuration of the synfuel supply chain including locations and capacities of conversion plants, feedstock procurement and transportation. The model is applied to a case study covering six regions in south-central Chile. The total supply chain cost for the production of synthetic gasoline is estimated to amount to 0.8–0.9 € per liter. Feedstock costs of the synfuel supply chain are 20–50% higher in comparison to the price paid by CHP plants and households. The results indicate that both torrefaction and fast pyrolysis can be applied beneficially to utilize remote biomass resources which are less in demand by established consumers.
[en] Highlights: • Novel solution for passive thermal management of BICPV with microfins, PCM, n-PCM. • Average temperature reduction with PCM, 9.6 °C (13.9%); 11.2 °C (16.2%) with n-PCM. • With microfins, PCM reduced temperature by 10.7 °C (15.9%); n-PCM by 12.5 °C (18.5%). • Individual effectiveness of n-PCM was the highest followed by PCM and microfins. • PV electricity output predicted to theoretically increase by 15.15 GW worldwide. - Abstract: Building-Integrated Concentrated Photovoltaics (BICPV) is based on Photovoltaic (PV) technology which experience a loss in their electrical efficiency with an increase in temperature that may also lead to their permanent degradation over time. With a global PV installed capacity of 303 GW, a nominal 10 °C decrease in their average temperature could theoretically lead to 15 GW increase in electricity production worldwide. Currently, there is a gap in the research knowledge concerning the effectiveness of the available passive thermal regulation techniques for BICPV, both individually and working in tandem. This paper presents a novel combined passive cooling solution for BICPV incorporating micro-fins, Phase Change Material (PCM) and Nanomaterial Enhanced PCM (n-PCM). This work was undertaken with the aim to assess the unreported to date benefits of introducing these solutions into BICPV systems and to quantify their individual as well as combined effectiveness. The thermal performance of an un-finned metallic plate was first compared to a micro-finned plate under naturally convective conditions and then compared with applied PCM and n-PCM. A designed and fabricated, scaled-down thermal system was attached to the electrical heaters to mimic the temperature profile of the BICPV. The results showed that the average temperature in the centre of the system was reduced by 10.7 °C using micro-fins with PCM and 12.5 °C using micro-fins with n-PCM as compared to using the micro-fins only. Similarly, the effect of using PCM and n-PCM with the un-finned surface demonstrated a temperature reduction of 9.6 °C and 11.2 °C respectively as compared to the case of natural convection. Further, the innovative 3-D printed PCM containment, with no joined or screwed parts, showed significant improvements in leakage control. The important thermophysical properties of the PCM and the n-PCM were analysed and compared using a Differential Scanning Calorimeter. This research can contribute to bridging the existing gaps in research and development of thermal regulation of BICPV and it is envisaged that the realised incremental improvement can be a potential solution to (a) their performance improvement and (b) longer life, thereby contributing to the environmental benefits.
[en] Highlights: • Three different technology learning approaches are introduced in the Global TIMES model. • Technology diffusion can effectively reduce global mitigation costs and welfare losses. • Developed countries should take the lead in low-carbon technologies’ deployment. • Technology cooperation will improve mitigation capability of developing countries. - Abstract: Low-carbon power generation technologies such as wind, solar and carbon capture and storage are expected to play major roles in a decarbonized world. However, currently high cost may weaken the competitiveness of these technologies. One important cost reduction mechanism is the “learning by doing”, through which cumulative deployment results in technology costs decline. In this paper, a 14-region global energy system model (Global TIMES model) is applied to assess the impacts of technology diffusion on power generation portfolio and CO2 emission paths out to the year 2050. This analysis introduces three different technology learning approaches, namely standard endogenous learning, multiregional learning and multi-cluster learning. Four types of low-carbon power generation technologies (wind, solar, coal-fired and gas-fired CCS) undergo endogenous technology learning. The modelling results show that: (1) technology diffusion can effectively reduce the long-term abatement costs and the welfare losses caused by carbon emission mitigation; (2) from the perspective of global optimization, developed countries should take the lead in low-carbon technologies’ deployment; and (3) the establishment of an effective mechanism for technology diffusion across boundaries can enhance the capability and willingness of developing countries to cut down their CO2 emission.
[en] Highlights: • The overheating rate reduction using PCM solutions. • Thermal dynamic simulation of a department building: comfort and energy analyses. • Building features optimization, aiming the energy reduction during the building use. • Economic analysis to estimate the payback period for the PCM constructive solutions. - Abstract: Sustainable energy and thermal retrofit design of buildings or districts has a strong global impact in the viewpoint of economies and energy-efficiency perspectives. Several aspects such as architectonic design, building materials, construction technology, mechanical systems and outdoor climate determines the thermal behaviour of buildings and their ability to provide indoor thermal comfort to occupants. The use of geothermal energy and phase change materials (PCMs) in the construction systems are an opportunity that may attenuate indoor air temperature fluctuation as well as overheating risk. This paper presents the results of a study on indoor thermal comfort and energy efficiency regarding the PCM’s positive role when applied to new constructive solutions, inside a building with a geothermal system linked to the air conditioning system. The PCM study was based on real and simulated investigations in two rooms of a new university department at the Aveiro campus. Higrothermal monitoring (indoor air temperature) of two rooms in which one of them has PCM panels incorporated into gypsum board partition wall and into a suspended ceiling. The scope was driven to investigate the potential of these solutions for overheating mitigation. The numerical study was conducted by using an evolutionary algorithm coupled with the software EnergyPlus® used in simulations. In the scope of this optimization process, constructive solutions with the incorporation of different types of PCM with different melting temperatures and enthalpy, and different flow rates of natural ventilation were combined to investigate the potential and the payback time of these novel solutions. The results for the room measurements show that the indoor thermal comfort of the rooms, present long periods of discomfort namely in overheating. However, it was proved that the PCM application in one of the rooms lead to an overheating reduction of 7.23% representing a PCM efficiency of 35.49%. After the optimization process an overheating reduction of about 34% was attained by the use of PCM in one of the rooms. Regarding the economic analysis of the use of the PCM for cooling demand reduction, a payback time of 18 years was attained.
[en] Highlights: • This study reports a new concept, ‘random walk’ occupants’ presence. • According to building types, the characteristics of occupants’ presences can differ. • Different occupant models can cause significant performance gaps in energy prediction. - Abstract: Occupant behavior is regarded as one of the major factors contributing to the discrepancy between simulation prediction and real energy use. Over the past several decades, occupants have been represented as fixed profiles of occupant presence in building energy simulation tools. Recently, stochastic models have been introduced to account for dynamic occupant presence. This stochastic approach is based on the premise that occupant presence can be described by empirical and probabilistic transition rules, e.g. Markov Chain. This paper presents evidence that occupant presence in some rooms and buildings follows a “random walk” pattern. In other words, occupant presence in certain types of buildings cannot be predicted stochastically. In this study, occupants’ presence in two laboratories and three reading rooms at two universities was monitored. The hypothesis of the random walk pattern was tested using the Normalized Cumulative Periodogram (NCP) method. Based on a series of six experiments, it is shown that each occupant’s presence in the five locations follows a random walk pattern. Three different occupant models (fixed ASHRAE model, Markov Chain model, and Random Walk model) were applied in EnergyPlus simulation runs. The adjusted R2 for three experiments between the fixed AHSRAE model and the random walk model, and between the Markov chain model and the random walk model are 0.54, 0.02, 0.01 and 0.86, 0.19, 0.41, respectively. This does not negate the need for the fixed ASHRAE model or the MC model. Rather, this signifies that, for a certain type of building, another occupant presence model should be introduced, e.g. the RW Model.
[en] Highlights: • A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five different predictors along with their wavelet transformed are combined. • Bayesian model averaging technique is used to aggregate the multiple predictors. • Forecast framework is analyzed for multiple forecast horizons and buildings. • Significant error reduction in different test case studies using the framework. - Abstract: Buildings are one of the major sources of electricity and greenhouse gas emission (GHG) in urban areas all around the world. Since a large integration of solar energy is observed in the form of rooftop photovoltaic (PV) units, electricity use of buildings is highly uncertain due to intermittent nature of solar output power. This leads to poor energy management for both network operators and building owners. In addition, uncertain metrological conditions, diversity and complexity of buildings are big hurdles to accurate prediction of the demand. To improve accuracy of load demand forecast of PV integrated smart building, a hybrid ensemble framework is proposed in this paper. This is based on a combination of five different predictors named as backpropagation neural network (BPNN), Elman neural network (EN), Autoregressive Integrated Moving Average (ARIMA), feed forward neural network (FNN), radial basis function (RBF) and their wavelet transform (WT) models. WT is applied to historical load data to remove the spikes and fluctuations. FNN and RBF network were trained with particle swarm optimization (PSO) for higher forecast accuracy. The output of each predictor in the ensemble network is combined using Bayesian model averaging (BMA). The proposed framework is tested using real data of two practical PV integrated smart buildings in a big university environment. The results indicate that the proposed framework show improvement in average forecast normalized root mean square error (nRMSE) around 17% and 20% in seasonal daily and seasonal weekly case studies, respectively. In addition, proposed framework also produces lowest of nRMSE about 3.88% in seasonal monthly forecast of smart buildings with rooftop PV as compared to benchmark model. The proposed forecast framework provides consistent forecast results for global change institute (GCI) and advance engineering building (AEB) during seasonal daily and weekly comparison.
[en] Highlights: • A novel approach for the efficient management of wastewater pumps is presented. • This approach couples fuzzy logic and data-mining to reduce the pump energy consumption. • This approach enables to monitor the pumps and provide case-specific suggestions. • Short-term and long-term phenomena were identified and separately monitored. • Flow-related issues and early-stage failures were detected. - Abstract: Studies and publications from the past ten years demonstrate that generally the energy efficiency of Waste Water Treatment Plants (WWTPs) is unsatisfactory. In this domain, efficient pump energy management can generate economic and environmental benefits. Although the availability of on-line sensors can provide high-frequency information about pump systems, at best, energy assessment is carried out a few times a year using aggregated data. Consequently, pump inefficiencies are normally detected late and the comprehension of pump system dynamics is often not satisfactory. In this paper, a data-driven methodology to support the daily energy decision-making is presented. This innovative approach, based on fuzzy logic, supports plant managers with detailed information about pump performance, and provides case-based suggestions to reduce the pump system energy consumption and extend pump life spans. A case study, performed on a WWTP in Germany, shows that it is possible to identify energy inefficiencies and case-based solutions to reduce the pump energy consumption by 18.5%.