• Energy Research
• 2021-2025close
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This paper presents a new framework for optimal planning of electrical, heating, and cooling distributed energy resources and networks considering smart buildings' contribution scenarios in normal and external shock conditions. The main contribution of this paper is that the impacts of smart buildings' commitment scenarios on the planning of electrical, heating, and cooling systems are explored. The proposed iterative four-stage optimization framework is another contribution of this paper, which utilizes a self-healing performance index to assess the level of resiliency of the multi-carrier energy system. In the first stage, the optimal decision variables of planning are determined. Then, in the second stage, the smart buildings and parking lots contribution scenarios are explored. In the third stage, the optimal hourly scheduling of the energy system for the normal condition is performed considering the self-healing performance index. Finally, in the fourth stage, the optimization process determines the optimal scheduling of system resources and the switching status of electrical switches, heating, and cooling pipelines’ control valves. The proposed method was successfully assessed for the 123-bus IEEE test system. The proposed framework reduced the expected values of aggregated system costs and energy not supplied costs by about 49.92% and 93.64%, respectively, concerning the custom planning exercise. ; © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; fi=vertaisarvioitu|en=peerReviewed|

Abstract A novel integrated energy system based on a geothermal heat source and a liquefied natural gas heat sink is proposed in this study for providing heating, cooling, electricity power, and drinking water simultaneously. The arrangement is a cascade incorporating a flash-binary geothermal system, a regenerative organic Rankine cycle, a simple organic Rankine cycle, a vapor compression refrigeration cycle, a regasification unit, and a reverse osmosis desalination system. Energy, exergy, and exergoeconomic methods are employed to analyze the suggested system. A parametric study based on decision variables is carried out to better assess the system performance. Four different multi-objective optimization problems are also carried out. At the most excellent trade-off solution specified by the TOPSIS method, the system attains 29.15% exergy efficiency and 1.512 $/GJ total product cost per exergy unit. The main output products are consequently calculated to be 101.07 kg/s cooling water, 570.44 kW net output power, and 81.57 kg/s potable water.

Abstract In the paper a novel approach to thermochemical utilization of low rank coal, flotation concentrates and municipal refuse derived fuels was presented. The economic attractiveness of low rank coals and flotation concentrates is limited and that is why they are commonly stored at excavation heaps causing additional costs and the risk of endogenous fires occurrence. One of the crucial parameters determining the attractiveness and usability of a fuel in the gasification process is its reactivity. In the study several low rank coals, flotation concentrates and municipal refuse derived fuels were tested in terms of their reactivity in the process of steam gasification. The reactivity of low rank coal and flotation concentrates at 50% of carbon conversion, R50, varied between 1.46·10−4 and 2.39·10−4 s−1, whereas the maximum reactivity, Rmax, from 3.28·10−4 to 4.62·10−4 s−1. Advanced mathematical models were developed to investigate the similarities and dissimilarities between the studied fuels as well as the relationships between the physical and chemical parameters and the reactivities of fuel chars in steam gasification. On this basis, a low rank coal was selected and blended with 20%w/w of municipal refuse derived fuel in co-gasification experiments. The aim of the research was to utilize the low rank coal characterized by the lowest reactivities (R50 and Rmax of 1.46·10−4 and 3.28·10−4 s−1, respectively) in steam co-gasification to hydrogen-rich gas with an alternative fuel in a fixed bed reactor at the temperature of 800 °C. The selected low rank coal was blended with 20%w/w of municipal refuse derived and the resulting fuel yielded the average concentration of hydrogen in the produced gas of 58.99%vol.

Abstract Accurate installed capacity forecasting can provide effective decision-making support for planning development strategies and establishing national electricity policies. First, considering the data limitation in quantity and accuracy, this paper proposes a multi-factor installed capacity forecasting framework combining the fuzzy time series method and support vector regression. Compared with four benchmark models, the proposed model shows advantages in installed capacity prediction. Second, the predictability dynamics of national installed capacity are explored from the perspective of country clusters. It is revealed that highly predictable countries usually obtain high forecasting accuracy with all forecasting models and are less sensitive to forecasting models. Using the k-means clustering method, this paper divides 136 sample countries into four categories according to the predictability. Third, based on the mean impact value analysis, this paper differentiates and ranks the importance of input variables on installed capacity development. The two most important factors influencing installed capacity are installed capacity development in the previous period and population. Overall, these results are of practical value to the operating decisions of electric power enterprises and the electricity plans of governments.

Renewable resources and energy storage systems integrated into microgrids are crucial in attaining sustainable energy consumption and energy cost savings. This study conducts an in-depth analysis of diverse storage systems within multi-energy microgrids, including natural gas and electricity subsystems, with a comprehensive focus on techno-economic considerations. To achieve this objective, a methodology is developed, comprising an optimization model that facilitates the determination of optimal storage system locations within microgrids. The model considers various factors, such as operating and emission costs of both gas and electricity subsystems, and incorporates a sensitivity analysis to calculate the investment and maintenance costs associated with the storage systems. Due to the incorporation of voltage and current relations in the electricity subsystem as well as gas pressure and flow considerations in the natural gas subsystem, the developed model is classified as a mixed-integer nonlinear programming model. To address the inherent complexity in solving, a decomposition approach based on Outer Approximation/Equality Relaxation/Augmented Penalty is developed. This study offers scientific insights into the costs of energy storage systems, potential operational cost savings, and technical considerations of microgrid operation. The results of the developed decomposition approach demonstrate significant advantages, including reduced solving time and a decreased number of iterations.

Abstract The commercial development of biofuels and bioproducts depends on whether renewable biomass feedstock is available while not directly competing with the production of food. Farmers are one of the most important stakeholders in the biofuel supply chain and confront a range of uncertainties while entering the bioenergy market. Their decision-making process is extremely complex and rarely purely rational. Modeling farmer behavior requires considering a wide range of individual-level factors, socio-temporal dynamics, institutional settings, and their interactions. These characteristics make agent-based modeling a suitable framework for evaluating such systems. We developed a model to simulate farmer bioenergy crop adoption behavior across a 50-county study region in Nebraska, Kansas, and Colorado. The analysis considers adoption decisions for two bioenergy feedstocks, crop residues and energy crops. We examine the influence of individual and farm characteristics, market structure, social networks, and media influence on farmer adoption decisions. Our results indicate that different factors can have varied impacts on the speed of adoption for the crop residues and energy crops. Identifying levers that have the most impact on grower adoption can inform the design of interventions both from policy and private sector standpoints with important implications for the future the bioenergy industry.

An operational cost minimisation model is established for a smart energy hub (S.E. Hub) consisting of a combined heat and power (CHP) unit, a heating, ventilation and air-conditioning (HVAC) system, and thermal and electricity storage units. The optimal operation of CHP is combined with the load management of HVAC under a time-of-use (TOU) tariff. The heat and power split ratio of CHP is dynamically determined during the operation. The scheduling of HVAC load and the charging/discharging of energy storage systems are also determined through the optimisation model. The energy management system can therefore shift the load demand and manage energy supply simultaneously. System operation requirements and environment factors including the outdoor air-temperature variation, seasonal variation, and battery degradation are considered. Comprehensive case studies are carried out to examine the effectiveness of the proposed strategy, from which insights are obtained for different energy management strategies and possible upgrade of S.E. Hub. Simulation results reveal that dynamic control of the CHP heat and power split ratio is an effective way to save the total operational cost, and a clear cost saving is shown through the proposed optimal operation strategy.

Abstract The univariate sweeping tests for fuel injection pressure, injection timing and exhaust gas recirculation (EGR) rate were conducted on a three-cylinder gasoline extended range Atkinson cycle engine (ACE). The effects of above parameters on combustion, emission and performance of ACE were studied and some useful conclusions were obtained. In general, injection pressure has little effect on combustion and engine performance except at the injection advance of 280 deg, while its effect on emission is obvious. With injection pressure rising, NOx first increases and then decreases, while CO follows a reverse trend. Compared with injection pressure, injection timing has more significant effects. As injection timing is advanced, the max combustion pressure first increases and then decreases and the peaks appear around 300deg and 320deg. The lower EGR rate reduces BSFC and NOx with little influence on other emissions. At the EGR rate of 7%, NOx decreases significantly (up to 57.5%), CO, particle number (PN) and particle density are almost unchanged, and BSFC decreases by 4 g/(kW·h) at most. As EGR rate increases to 11%, only NOx further decreases, while other emissions become worse. Thus, a lower EGR rate (e.g., 7%) combining with reasonable injection advance (300deg) is suggested to improve ACE performance.