• Energy Research
• 2021-2025close
• US close
• DE 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 Global demand for methanol as both a chemical precursor and a fuel additive is rising. At the same time, numerous renewable methanol production pathways are under development, which, if commercialized, could provide significant environmental benefits over traditional methanol synthesis pathways. However, it is difficult to compare technologies at different maturity levels, with differing feedstocks, and with significant differences in overall process design. Thus, there is a need to harmonize the analyses of renewable pathways using a consistent techno-economic approach to evaluate the potential for commercialization of various pathways. This analysis uses a novel cross-comparison method to assess near-term and long-term viability of both low- and high-maturity level technologies. The techno-economic assessment considers cost factors critical to market acceptance combined with carbon- and energy-efficiency assessments of three renewable pathways compared with a commercial baseline. We find that biomass gasification to methanol represents a near-term viable pathway with a high technology readiness level and commercially competitive market price. If cost-reducing technological improvements can be realized and scaled up in the CO2 electrolysis pathways, the potential for higher carbon efficiencies may help drive market adoption of these more modular, direct conversion pathways in future markets as they present an opportunity to better support global decarbonization efforts through efficient waste carbon utilization.

Abstract Uninterrupted energy harvest is critical for self-sustained wastewater monitoring in order to achieve efficient and resilient operation of decentralized onsite wastewater treatment facilities. To address this long-standing challenge, an integrated power entity consisting of a miniature microbial fuel cell (volume: 1.5 mL) and a triggered power management system was developed in this study to power the potentiometric millimeter-sized solid-state water sensors for real-time in situ monitoring and uninterrupted transmission of sensor readings (indicating ammonium concentration) under both ammonium shock and toxic shock in wastewater. Specifically, a data trigger including two capacitors, an operation amplifier and a low-power comparator is equipped in the power management system as a switch for turning on power discharge for data transmission once the ammonium shock is captured by the potentiometric sensors, enabling a sufficient recharge duration to store the power needed for high frequency data transmission (16.23 times/min) required under shocks. Furthermore, this power-sensor entity possesses a unique dual-screening capability of capturing the ammonium and toxic shocks, providing an early warning for swift decision making, reducing ~17% of ammonium discharge and saving ~42% of energy consumption in decentralized onsite wastewater treatment facilities.

Abstract Carbon capture and storage (CCS) technology is an important option in the portfolio of emission mitigation solutions in scenarios that lead to deep reductions in greenhouse gas (GHG) emissions. We focus on CCS application in hard-to-abate sectors (cement industry, iron and steel, chemicals) and introduce industrial CCS options into the MIT Economic Projection and Policy Analysis (EPPA) model, a global multi-region multi-sector energy-economic model that provides a basis for the analysis of long-term energy deployment. We use the EPPA model to explore the potential for industrial CCS in different parts of the world, under the assumptions that CCS is the only mitigation option for deep GHG emission reductions in industry and that negative emission options are not available for other sectors of the economy. We evaluate CCS deployment in a scenario that limits the increase in average global surface temperature to 2 °C above preindustrial levels. When industrial CCS is not available, global costs of reaching the target are higher by 12% in 2075 and 71% in 2100 relative to the cost of achieving the policy with CCS. Overall, industrial CCS enables continued growth in the use of energy-intensive goods along with large reductions in global and sectoral emissions. We find that in scenarios with stringent climate policy, CCS in the industry sector is a key mitigation option, and our approach provides a path to projecting the deployment of industrial CCS across industries and regions.

Access to decentralized energy solutions is critical for the development of rural communities in Sub-Saharan Africa (SSA). One form of decentralized energy technology, agrivoltaics (AV), has gained attention in the last decade as a means to tackle energy poverty in off-grid communities. The success of renewable energy (RE) projects of any kind, however, depends on existing challenges and risks that may be faced at different stages. Therefore, the aim of this paper is to identify and analyze the main risk factors when implementing agrivoltaics on a community level in SSA. Based on the PESTLE framework, the paper develops a first risk classification system for international agrivoltaic projects in rural farming communities in SSA. By making use of a mixed-methods approach, the research enables the prioritization of potential risks in terms of their occurrence and significance; furthermore, it discusses mitigation strategies. Results indicate that there are various multifaceted risks associated with agrivoltaics, primarily including financial challenges and regulatory barriers. In addition, the study emphasizes the importance of stakeholder involvement as well as a need for long-term community engagement and capacity building to ensure a successful implementation and sustainability of agrivoltaic projects.