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Abstract To guarantee the space heating in the heating season, conventional combined heat and power (CHP) plants operate in a heat-controlled operation mode, resulting in restricted peak-shaving ability (PSA). To improve the CHP plant’s PSA, a novel solar aided CHP (SA-CHP) system is proposed and simulated in this paper. In the new system, solar heat could be flexibly used to generate power or to supply heat according to the heating and power demands, thereby realizing the heat-power decoupling. A set of models for the SA-CHP system is developed and validated. The PSA, the standard coal consumption (SCC) and the techno-economic performances of a 330 MWe SA-CHP system are comprehensively analyzed in this paper. The results show that the SA-CHP system can significantly improve (up to double) the PSA compared with the CHP plant under the same rated heating power. The feasible operation region area of the SA-CHP system is 74.7% larger than that of the CHP plant. The annual SCC of the SA-CHP system are 17378.23 t less than that of the CHP plant. The net annual revenue of the SA-CHP system is $2.24 M. Besides, techno-economic performances of SA-CHP systems with two different heat storage systems are compared.

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 To understand the relationship between green building energy performance and regional commercial estates, this study analysed Australia’s Commercial Building Disclosure (CBD) program database. This database discloses the annual energy use intensity (EUI) and the corresponding energy rating (1–6 stars) of 2460 National Australian Built Environment Rating System (NABERS) certified office buildings. The study selected for analysis Australia’s six largest cities and then used panel data regression, where commercial estate factors (total stock of office buildings, vacancy rate, average gross face rent, and government incentives such as financial support) served as independent variables and the EUI was the dependent variable. The p-values of all the models are below 0.05, indicating that the results are statistically significant. Results showed the commercial real estate factors were significantly related to the EUI for buildings with a rating of 1 star and above. The correlation between EUI and commercial real estate factors became less strong with the rating level increasing. The effect of ‘green building’ branding makes the office buildings more attractive with regard to tenancy and their energy performance more reflective of the variation in the commercial real estate market. This study is a frontrunner in contextualising green building energy performance and ratings in the context of regional commercial estate, and the regression models employed in the study could be used to define regional baselines for energy ratings in future studies.

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 energy demand forecasting, the objective function is often symmetric, implying that over-prediction errors and under-prediction errors have the same consequences. In practice, these two types of errors generally incur very different costs. To accommodate this, we propose a machine learning algorithm with a cost-oriented asymmetric loss function in the training procedure. Specifically, we develop a new support vector regression incorporating a linear-linear cost function and the insensitivity parameter for sufficient fitting. The electric load data from the state of New South Wales in Australia is used to show the superiority of our proposed framework. Compared with the basic support vector regression, our new asymmetric support vector regression framework for multi-step load forecasting results in a daily economic cost reduction ranging from 42.19 % to 57.39 % , depending on the actual cost ratio of the two types of errors.

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 Using a panel data analysis of a newly developed sample of monthly data by state for January 2005 to December 2019, we estimate a series of error correction models for US residential electricity demand postulated to move with electricity price, natural gas price, income, and weather. Our key findings are as follows. First, the short-run own-price elasticity estimate is not statistically different from zero (p-value > 0.8). Second, the long-run own- and cross-price elasticity estimates are −0.054 (p-value = 0.000) and 0.019 (p-value = 0.000) under the double-log specification, smaller in size than the long-run own- and cross-price elasticity estimates of −0.120 (p-value = 0.000) and 0.069 (p-value = 0.000) under the linear demand specification. Third, price elasticity estimates have been shrinking in size over time. Fourth, erroneously ignoring the panel data's cross-sectional dependence tends to more than double the long-run price elasticity estimates. Fifth, mismatching the timing of price information's availability and consumption decision leads to anomalous price elasticity estimates. Finally, our new empirics' key takeaway of low price-responsiveness supports continuation of energy efficiency standards and demand-side management programs.

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.