• Energy Research
• 2021-2025close
• 7. Clean energy close
• 13. Climate action close
• Applied Energy close

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Abstract Economically harvesting energy from a microbial fuel cell (MFC), increasing its electrical power production, and developing its role as a practical energy supply, needs a low-cost and high-performance design of the MFC compartments. According to this strategy, a novel monolithic membrane electrode assembly (MEA) was fabricated and evaluated as an air–cathode in a single-chamber MFC (SCMFC). The MEA was made of bacterial cellulose (BC), conductive multi-walled carbon nanotubes (CNT), and nano-zycosil (NZ). BC, as a nano-celluloses with oxygen barrier property, can maintain anaerobic conditions for the anode compartment. Binder-less CNT coating on BC avoids costly binders such as poly-tetra fluoro ethylene (PTFE) and Nafion and decreases the MEA charge transfer resistance. NZ, as a very cheap modifier, not only prevents the anolyte leakage but also provides more MEA’s active sites for the oxygen reduction reaction (ORR). The electrochemical performance of the MEA was compared to a PTFE- based gas diffusion electrode (GDE) in the SCMFC. The MEA cell provided a pulse power density of 1790 mW/m2, roughly twice as high as the pulse power density of GDE (920 mW/m2). SCMFC’s internal resistance decreased from 1.84 KΩ (with GDE) to 0.8 KΩ (with MEA). Also, the cell’s columbic efficiency increased from 4.2% (with GDE) to11.7% (with MEA). Additionally, the capacitance of the MEA (65 mF) was much higher than the value for GDE (0.73 mF). Thus, the MEA compared to the GDE showed higher performance in the SCMFC for electricity generation and wastewater treatment at a lower cost.

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training. Manuscript accepted for publication in Applied Energy

Abstract Computable general equilibrium models are becoming popular for simulating emissions trading systems. However, these models make various assumptions about the production structure and the possibilities of substituting energy for productive factors or inputs. Therefore, this study aims to analyze how different options to incorporate energy into the nested production structure of a computable general equilibrium model affect the obtained impacts when an emissions trading system is implemented. A flexible computable general equilibrium model called ECOMODEL is developed, which is the first model that allows choosing any of the three nested production structures most used in the literature (KEL-M, KL-EM, or KLE-M). The Chilean economy is used as a case study since there is a current database with high disaggregation of the energy sector for calibrating the computable general equilibrium model. The results show that the simulated impacts with KEL-M and KL-EM structures are the best when elasticities of substitution equal to the values ​​most frequently used in previous studies are chosen. However, the KEL-M structure that considers energy as a substitute for capital provides overly optimistic environmental results when high elasticities of substitution are used, obtaining the lowest reduction in Gross Domestic Product and the lowest price of the emissions trading system. Furthermore, the KLE-M structure gives unrealistic results regardless of the elasticities of substitution used. In consequence, a KL-EM nested production structure should be prioritized to simulate an emissions trading system since it provides realistic results and is less sensitive to the values of the elasticities of substitution.

Abstract The urge to increase renewable energy penetration into the power supply mix has been frequently highlighted in response to climate change. South Korea was analyzed as a case study for which the government has shown motivation to increase renewable energy penetration. Herein, a hybrid renewable energy system (HRES) including solar and wind energies were selected due to their relatively stable and mature technology. In addition, Power-to-X has been incorporated to cover other renewable energy options such as hydrogen and synthetic natural gas (SNG). Therefore, an approach of forecasting the weather characteristics and demand loading over a relatively long timeframe was implemented via deep learning techniques (LSTM and GRU) and statistical approaches (Fbprophet and SARIMA), respectively. A deployment strategy incorporating HRES and Power-to-X is then proposed in correspondence to the forecasted results of the 15 regions considered in this study. An extension of this, the reliability of the designed system is further assessed based on the probability of the demand losses with the aid of Monte-Carlo simulation. With the proposed deployment strategy, a total annual cost of 9.88 × 1011 $/year and a greenhouse gas reduction of 1.24 × 106 tons/year are expected for a 35% renewable energy penetration. However, only SNG shows relatively competitive cost (at 23.20 $/m3 SNG), whereas the average costs of electricity (0.133 $/kWh) and hydrogen (7.784 $/kg H2) across the regions are yet to be competitive compared to the current market prices. Nonetheless, the priority of deployment across regions has been identified via TOPSIS.

Abstract To transform the original centralized scheduling framework of integrated community energy systems (ICES), a decentralized scheduling method is proposed for the optimal operation of ICES. Firstly, linearized mathematical models of the electric distribution system and natural gas distribution system are established to eliminate the nonconvexity and nonlinearity of the multi-energy systems. Meanwhile, an improved energy hub model is proposed by introducing the state variable, which is able to obtain the optimal solution with better accuracy and higher efficiency. Secondly, a decentralized scheduling framework is developed via an improved consensus-based alternating direction method of multipliers (ADMM). By introducing several coupling links as consensus variables, the optimal operation of the ICES is decoupled according to the physical interactions among each sub-system, in which the original model can be solved parallelly in a decentralized manner. Only the consensus variables are exchanged among sub-systems so that the privacy and confidential items of each sub-system operated by different entities can be ensured. Finally, two different ICES test cases with different system scales are utilized to demonstrate the effectiveness of the proposed method, which is able to provide scheduling scheme same as the centralized method. Dynamic step size modification is further considered in the consensus-based ADMM. By updating the step size during each iteration, the computation expense of optimization process is reduced with less consuming time, iteration and apparent oscillation, by which the convergence performance of ADMM is improved with higher computation efficiency.

Abstract The rapid development of power electronic devices has made them have higher power density, which puts forward higher requirements for cooling technology. The contribution of this paper is the integrated design of the liquid cooled heat sink for a 30 kW motor inverter considering the distribution of power devices. In order to find an optimal heat sink configuration, the cooling performance of three different configurations of heat sinks was investigated. And the distribution characteristics of the temperature and fluid velocity field were compared and analyzed. The heat sink with serpentine channel configuration was selected due to its best temperature uniformity and cooling characteristic. In addition, the effects of geometric parameters (fin thickness) and flow parameters (flow rate) on cooling performance were further studied. On this basis, the geometrical configuration of the heat sink was optimized. The experimental results of the optimized heat sink are in good agreement with the numerical simulation results. The motor inverter achieves high power density of 9.677 kW/kg.

Abstract Integrating low-carbon technologies (e.g. heat pumps, photovoltaic systems) in buildings influences the stability of the low-voltage grid, which therefore often requires to be reinforced. This article proposes a techno-economic methodology to identify the reinforcements needed to maintain grid stability at the lowest life-cycle cost. Novel contributions include the consideration of three-phase connection of low-carbon technologies as a reinforcement option and the fact that we study to what extent grid reinforcements can mitigate voltage unbalance issues. Additionally, to reduce computing time, a dummy island approach is used, whereby one feeder is modelled in detail and the remainder of the distribution island is represented by an aggregated load. Finally, random repetitions are proposed, to consider uncertainties related to building properties, occupants and the location of low-carbon technologies in the feeders. The methodology is applied to investigate the integration of heat pumps and photovoltaic systems in typical Belgian rural and urban grids. For the rural grid, heat pumps may lead to significant reinforcement costs (up to 1230 €/dwelling), mainly due to voltage stability problems. For the urban grid, heat pump and photovoltaic integration causes low reinforcement cost (