Abstract

This paper addresses the imperative goal of achieving carbon neutrality in Tokyo’s historic Nihonbashi district by 2050. The proposal of a systems design model based on the reconstruction and renovation of the existing built environment through design changes enables meeting the goals of net-zero emissions targets. Our study proposes the integration of 3D modeling techniques and scenarios generation for complex urban neighborhood systems and explores design decisions aimed at further reducing carbon emissions. By leveraging advanced technologies, such as digital urban modeling tools (building energy modeling and design generation based on Rhino 3D, Grasshopper, Ladybug, Climate Studio, and ArcGIS) as well as systems architecting tools (including morphological matrix, alternative concepts analysis, and model-based systems engineering, MBSE), we seek to provide a comprehensive framework for decision-makers and urban planners to assess the environmental impact of design choices. Our approach involves developing a digital representation of the Nihonbashi district, incorporating various architectural and environmental parameters. Through simulation, we analyze the potential carbon reduction benefits of alternative design interventions, including the following variables: materiality (structure), building density, building type/use, facade, and renewable energy integration. The findings of our research highlight the potential for significant carbon emission reductions through innovative design decisions. By quantifying the environmental impact of various design scenarios, decision-makers can make informed choices that align with the district’s carbon neutrality objectives. Additionally, our study investigates the economic viability and feasibility, as well as the equitable impacts of implementing these design changes, considering factors such as cost, construction logistics, and stakeholder involvement. This paper contributes to the existing body of knowledge related to planning decision support systems by exploring the role of digital modeling as a transformative tool of urban planning for carbon neutrality. By emphasizing the importance of incorporating design changes to address carbon emissions, we aim to provide actionable insights for urban development in Nihonbashi and inspire similar initiatives worldwide.

Abstract

Climate scenarios and extreme weather surprises strongly suggest that faster energy transitions are necessary IF we are to stay below a 1.8o Celsius rise by 2050 and bend our carbon curve. A review of efforts to decarbonize heavy industry with cleaner hydrogen involves significant capital investment, continued R & D, and a refiguration of our energy supply chains. Our current investment typology and funding structure – in spite of COP28 pledges — does not have the capacity to provide up to $13 trillion for hydrogen over the next five years, or more for our electrical grids. We need to configure a flexible – heterogenous — investment framework to accelerate our energy transitions. Much like the Marshall plan, a focused energy bank using clean hydrogen as an illustrative case study shows the investment funds required to build a more decarbonized world by 2035.

Abstract

The development of smart grids in power system necessitates the need for forecasting the electricity load for the safe and economic functioning of electricity markets. A case study has been carried out considering a city’s electricity load data using Multivariate Regression model. An input database of the model is generated taking into account of peak and off-peak hours based on maximum and minimum load data obtained from the utility operator. The characteristics of the electricity load over the whole year have been primarily analyzed to obtain a better intuition on the load behavior. In this context, the information in the form of temperature, days, different time duration i.e., peak and off-peak hours and past load data have been given as input to the regression model. The accuracy of the method has been evaluated using Root Mean Square Error (RMSE). The results of the adapted model have been compared with Neural Network, Ensemble and Kernel methods.

Abstract

The energy management system and thermal control of fuel cell in fuel cell vehicles plays a crucial role in ensuring their stable and efficient operation. This study presents a novel fuel cell powertrain energy management system control strategy considered the temperature fluctuation based on deep reinforcement learning. A comprehensive SIMULINK model, encompassing fuel cell cooling system and stack models, was constructed for the fuel cell, followed by simulation testing under various temperature scenarios. To validate the robustness and stability of the control system, the standard operating conditions – US06 were employed for experimental verification. The experimental results highlight the effectiveness of the designed fuel cell energy management system in achieving transient temperature stabilization. Additionally, the results revealed that stable operation temperatures correlate with reduced hydrogen consumption. Furthermore, it’s noted that fuel cell hydrogen consumption displays substantial variation under uniform operating conditions at varying temperatures. This highlights the key role of temperature in fuel cell performance. These findings serve as valuable reference points for the refinement of energy management system designs with thermal control of fuel cell, contributing to the advancement of fuel cell vehicle technology

Abstract

How does paying energy bills impact occupants’ comfort regarding the thermal environment? Are they more comfortable, when all energy bills are paid for them, as compared to when they are responsible for their energy bills? 40% of the energy use is spent for space heating and cooling. The recent energy crisis and the increase of the energy bills significantly impacted the affordability of space heating. Also, post COVID-19, working from home became part of the working arrangements for many people, which also signifies the impact of heating affordability. This work investigates the impact of paying energy bills on the behaviour and thermal comfort of occupants. Three work settings were explored, including office settings and two home environments, one with all bills included in the rent and one, in which the occupants paid their own energy bills. Only in the latter, participants paid the energy bills. Field test studies of thermal comfort were applied in the UK in the winter of 2021. 57 participants responded to thermal comfort surveys three times a day during five days, while the environmental measurements were recorded. Additionally, ethnographic behavioural video recordings were applied using a thermal camera to capture environmental and personal adjustments, as well as surface temperatures of the surroundings, while occupants were working. Overall, 601 datasets were included in this work. The results did not suggest any significant differences in the comfort of the occupants in the three environments. However, significant differences were found between the energy uses of the three environments. The home, in which all bills were included in the rent used 9.2 times more energy, as compared to the home environment, where the occupants were responsible for paying their own energy bills, and 2.4 times more energy use, as compared to the office settings.

Abstract

With the penetration of renewable generation and terminal electrification, there is a huge demand of peak shaving for power system. Heating equipment are recognized as flexible resources for peak shaving, since there are numerous controllable variables and great power in heating systems, and buildings have significant thermal inertia.
This paper proposed a novel control strategy based on operating data to coordinate distributed space heating equipment across numerous residential households. Heating characteristics of each households, thermal inertial of indoor air, and power characteristics of heating equipment were obtained based on operating data analysis. Thereafter, load shifting potential under different scenarios were determined through historical operating data and current operational status. Based on the principle of minimal impact on indoor air and fairness, target heating equipment was adjusted by remote control instructions. Additionally, local adjustment by users has priority over remote control to assure thermal comfort requirements, and participation in regulation brings subsidies to users. A case study shows the strategy can produce 8% peak shaving and 27% reduction of peak-valley ratio under the same heating cost.
Moreover, the purpose of regulation could be modified according to specific needs, including the cost saving of induvial heating, and the reduction of peak power of the grid.

Abstract

Tight gas is an unconventional gas resource. Hydraulic fracturing is necessary to the development of tight sandstone gas reservoirs[1-5].
In this paper, a Discrete fracture and matrix model is based on the open-source reservoir simulation software, MRST. It is also called Non-uniform DFM-WPA. It includes gas transport, non-linear gas properties and non-uniform conductivity hydraulic fracture.
We demonstrate the application of the new formulation to model the performance of the vertical well with a non-uniform conductivity hydraulic fracture in tight sandstone gas reservoirs by use of the Non-uniform DFM model.
Numerical examples are presented to prove the capabilities of the proposed approach. The application of this field case is also to prove the availability of this approach. It can overcome the limitation of conventional discrete-fracture matrix model about the characterization of non-uniform conductivity hydraulic fracture

Abstract

Ensuring high performances and lifetime of battery packs has critical importance, because of the transition toward electric mobility. Therefore, correct estimation of the battery state with ad-hoc designed Battery Management Systems (BMS) is pivotal to address this challenge. In this context, application of Machine Learning (ML) is gaining increasing research interest as it includes data-driven algorithms that enable accurate and fast predictions of the battery state. For this reason, this paper aims to contribute with: (i) a survey of the newest contributions to the prediction of the State of Charge/Health (SoC/SoH), and (ii) by schematizing a methodology that uses simulated data to train state-of-the-art types of neural networks (NNs) for SoC and SoH estimation of a LiNMC battery cell. Research papers considered in this review included applications of deep NN, and other ML algorithms. The impact of the training dataset on the performances of the ML models and their capability to generalize is remarked throughout the paper. For this reason, a validated electro-thermal model is used to generate data that accounts for different temperatures and current loads to simulate scenarios with different environmental conditions and driving cycles.

Abstract

This paper addresses the environmental and sustainability challenges stemming from the improper disposal of used cooking oils in popular Middle Eastern cuisines, particularly falafel. With an estimated annual waste oil production of 54 million liters from approximately 20,000 restaurants in Jordan, a multifaceted methodology is employed to convert burned falafel oil into biodiesel. The approach integrates survey data, laboratory analysis, the design of a biodiesel converter, and a feasibility study to assess the viability of this waste-to-energy initiative. Laboratory results confirm the successful transesterification process, yielding biodiesel with superior combustion properties compared to regular cooking oil, meeting standard biodiesel density criteria. The feasibility study unveils an estimated annual revenue of $54.98 million USD from selling biodiesel, with catalyst costs (methanol and KOH) amounting to $8,013,600 USD, suggesting a positive economic outlook. Beyond economic viability, this initiative aligns with global sustainability goals, emphasizing the potential for biodiesel production from waste falafel oil to be a pioneering solution in waste management and renewable energy in Jordan. Future research directions could focus on scaling up production, conducting environmental impact assessments, and exploring broader applications for biodiesel derived from waste falafel oil. This study contributes to the discourse on sustainable practices, offering a unique and transformative approach to address both environmental and economic challenges associated with waste cooking oils in the culinary industry in Jordan.

Abstract

To achieve the goals of carbon peak and neutrality, energy efficiency and carbon reduction in the building sector are crucial. Zero energy buildings (ZEBs) have emerged as a solution to address these challenges. This paper takes the example of R-CELLS, the champion competition entry from team Tianjin U+ in the Solar Decathlon China 2022 (SDC 2022), to introduce the energy performance of a residential ZEB combined with PEDF (photovoltaics, energy storage, direct current, flexibility). To achieve the building net-zero goal, the R-CELLS integrates various renewable energy sources (building integrated photovoltaics (BIPV), photovoltaic-thermal (PV-T), and building integrated wind turbines (BIWT)), energy storage systems (battery energy storage system (BESS), hot water tank (HWT), and phase change materials (PCM) thermal storage), an alternating current (AC) and direct current (DC) hybrid power distribution system, and a weekly-daily-hourly tri-period energy management strategy. Based on the actual operation during SDC 2022, the performance and influencing factors of R-CELLS in both grid-connected and off-grid modes are analyzed. The results demonstrate that R-CELLS can meet the standards of the residential ZEB and can achieve daily zero-energy consumption.