The current high energy demand and the urge to fight climate change, in accordance with the United Nations Sustainable Development Goals, have promoted the utilisation of digital tools that contribute to more efficient and sustainable energy systems. Thus, an Internet of Things (IoT) application in the energy sector can be the implementation of Advanced Metering Infrastructure (AMI) which has a great potential to contribute to more reliable energy grids and the introduction of renewable energies. AMI is a three-component technology composed of smart meters, a complex communication network and a data management system which combined enable a two-way communication. This study discusses the multiple benefits that AMI deployment can offer, including real-time data readings reported to the customer and the utility operator – supporting better decision making as well as energy and costs savings –, better O&M of the grid, automated billing with better accuracy, and less greenhouse gases emissions. Likewise, the risks and challenges derived from this technology are explored, considering data privacy and security, investment required, social acceptance and inclusion, among others. Finally, the minimum requirements for AMI proper functioning and opportunities for future improvement are covered to achieve greater efficiency in a sustainable and trustable energy system that involves stakeholders.
Digital Twin technology, a transformative innovation in the Infrastructure industry, has the potential to drive a sustainable energy transition. By creating interactive virtual representations of physical systems, it boosts operational efficiency, enabling efficient integration of renewable power, interoperable grid components, and long-term decarbonisation planning. It also enables predictive maintenance, reducing energy use and operational costs, thereby democratising energy access. Nonetheless, these important benefits do not come without a price; as digitalisation penetrates the energy grid, it becomes vulnerable to cyberattacks and data interception, while the quality and interpretation of input data bring uncertainty. Critically assessing the holistic impact, underlining the importance of supporting the energy transition, this paper proposes also mitigation strategies to maximise this promising tool’s performance. Well-defined cybersecurity regulation, clarity on stakeholder responsibilities, and safe data handling should be prioritised. Furthermore, advanced protection digital tools and a standardised system for intersectoral Digital Twins would unlock additional capabilities.
In order to solve the problem of huge electricity demand in the tailings ecological restoration process, according to the natural conditions of the tailings area, combined with the rich characteristics of renewable natural resources, the integrated system of solar energy and storage is designed and analyzed with examples. Considering the complex terrain characteristics of the mining area, the multi-channel MPPT photovoltaic array is selected to adapt to the complex terrain environment for power generation, and the energy storage unit is added for peak shaving. While solving the problem of power shortage in local ecological restoration, the impact of the system on the fluctuation of the grid voltage is alleviated. Finally, a typical application analysis of a tailing mine in Chuxiong City, Yunnan Province was carried out to discuss the rationality of the designed system. The results show that the designed system can effectively alleviate the power consumption problem in the ecological restoration of mining areas, which provides an important reference value for the ecological restoration of similar tailings areas and has certain popularization.
The increasing deployment of renewable energy requires larger flexibility in the energy system, in which conventional combined heat and power (CHP) plants are also an important source of flexibility. However, the provision of flexibility depends on the operation region of a CHP plant, i.e., the electricity and heat production of a CHP plant are inter-related.
Therefore, this paper aimed to assess the flexibility of coal-based CHP plants in various heating modes. To this aim, various heating modes of CHP plants, including: main steam bypass heating, reheat steam bypass heating, intermediate-pressure turbine (IPT) outlet steam heating, high back-pressure (HBP) heating, and low-pressure turbine zero output (LZO) heating, were respectively investigated. An energy balance matrix method was employed to determine the operation regions of CHP plants in various heating modes.
Results show that, compared with other heating modes, CHP plants in the reheat steam bypass heating mode and in the HBP heating mode can provide larger power-adjustment flexibilities without affecting heating loads. This implies that CHPs in these heating modes can obtain larger profits on a day-ahead market. A CHP plant in the LZO heating mode can reduce its power load to the lowest level and thus it yields the largest flexibility for accommodating renewable energy.
Solar energy is a critical resource in the fight against climate change, yet a significant portion of solar radiation is dissipated as heat in photovoltaic (PV) systems, impairing their performance. Conventional solar cell cooling technologies are energy-intensive and demand regular maintenance. Here, we propose a scalable and economically viable radiative cooling cover employing randomly doped particle structures to combat these issues. The cover’s solar transmittance and “sky window” emissivity were investigated numerically, using a combination of Mie theory and Monte Carlo method. The optimal design yields a solar transmittance of 94.8% and a “sky window” emissivity of 95.3%, resulting in a power generation of 147.8 W/m² for the radiative cooling PV (RCPV) module. A comparison of this module’s power efficiency under various environmental conditions with bare crystalline silicon solar cells and covered glass covers indicated that the PV surface temperature was 10.3 K lower in our module, closely approximating the ideal scheme. This innovative approach offers a pathway for enhancing the efficiency and sustainability of PV systems, contributing to the broader adoption of solar energy in combating climate change.
Integrating CO2 capture with biomass/waste fired combined heat and power plants (CHPs) is a promising method to achieve negative emission. However, the use of versatile biomass/waste and dynamic operation of CHPs result in big fluctuations in the flue gas (FG) and heat input to CO2 capture. Dynamic modelling is essential to investigate the interactions between key process parameters in producing the dynamic response of the CO2 capture process. In order to facilitate developing robust control strategies for flexible operation in CO2 capture plants and optimizing the operation of CO2 capture plants, artificial intelligence (AI) models are superior to mechanical models due to the easy implementation into the control and optimization. This paper aims to develop an AI model, Informer, to predict the dynamic responses of MEA based CO2 capture performance from waste-fired CHP plants. Dynamic modelling was first developed in Aspen HYSYS software and validated against the reference. The operation data from the simulated CO2 capture process was then used to develop and verify Informer. The following variables were employed as inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, lean solvent flow rate, heat input to CO2 capture. It was found that Informer could predict CO2 capture rate, reboiler temperature and energy consumption with the mean absolute percentage error of 6.2%, 0.08% and 2.7% respectively.
In the past decade, the number of students at high schools in China has risen to over 80 million. School campuses are high-density and administratively independent communities where a vast amount of food waste is produced daily. Although there are studies on the reduction and management of food waste on university campuses, little attention has been paid to high school campuses in this respect. Given the large number of high school students, it is important to study how the recycling attitude and behavior of this group of students towards food waste could help achieve zero waste on school campuses. This paper subjectively examines high school students’ environmental awareness and pro-environmental behavior through a questionnaire survey administered in several local high schools in Suzhou and Nanjing, China. The data from the survey was analyzed to identify the factors influencing students’ attitudes towards food use and food waste, thus establishing a possible correlation between the factors and providing evidence for a future food waste reduction plan. The study divided students’ attitudes to food waste on high school campuses into the following categories: socio-psychological factors, individual characteristics, and dining factors. The research findings will help to improve waste management and recycling on school campuses and ultimately achieve the goal of a green and sustainable environment for high school students.
With the features of large-scale and long-term energy storage, compressed CO2 energy storage (CCES) represents an efficient way to achieve load shifting and reduce fluctuations of electricity load. Therefore, the economic performance of a CCES system for load shifting was assessed in this paper. The two optimization modes, i.e., single-objective and multi-objective optimization, were proposed to determine the CCES operation. The reduction of load variance is the sole objective of single-objective optimization. In the case of multi-objective optimization, the objective is to maximize CCES system income while minimizing load variation. According to the simulation findings, the load variance can be decreased from its original value of 8725.4 to 678.5 in the single-optimization outcome, and the CCES system can generate an income of 49.6 kUSD. The Pareto optimality in the multi-objective optimization demonstrates a negative correlation between the variation of the load and the income of the CCES system.
Acquisition of Lithium-ion battery’s (LIB) internal temperature is crucial to ensure the safety of the battery system for electric vehicle (EV), yet the immeasurable nature of it renders this goal as challenging. Approaches based on contact sensing and thermal models are widely investigated, but their realizability and effectiveness still beg for further validation. Methods based on electrochemical sensing are receiving more attention due to their attractive features. However, the largest gap lies in the access to the impedance information for on-vehicle application. Using results derived from the passive electrochemical impedance spectroscopy (EIS) with real driving conditions, this paper presents a thorough solution to obtain the internal temperature of LIB. With the accurate high-frequency part of passive EIS at hand, the internal temperature is estimated by employing the regression relationship between temperature and its corresponding EIS landscape. Independent of models and sensors, the proposed scheme uses the mere electrical measurements of LIB and achieves the internal temperature estimation with refreshing rate of 1Hz. This sensorless scheme can meet the real-time requirements of battery management so that precious time for safety countermeasures to act is saved. The proposal in this paper is expected to supplement the battery management techniques with critical inputs to secure the safer use of EV.