This paper addresses the challenge of assessing uncertainty in energy flexibility predictions, which is a significant open question in the energy flexibility assessment field. To address this challenge, a methodology that quantifies the flexibility of multiple thermal and electrical systems is developed using appropriate indicators and considers the different types of uncertainty associated with building energy use. A Bayesian convolutional neural network is developed to capture aleatoric and epistemic uncertainty related to energy conversion device operation and temperature deviations resulting from exploiting building flexibility. The developed prediction models utilise residential occupancy patterns and a sliding window technique and are periodically updated. The energy systems evaluated include a heat pump, a photovoltaic system, and a stationary battery, and use synthetic datasets obtained from a calibrated physics-based model of an all-electric residential building for two occupancy profiles. Simulation results indicate that building flexibility potential predictability is influenced by weather conditions and/or occupant behaviour. Furthermore, the day-ahead and hour-ahead prediction models show excellent performance for both occupancy profiles, achieving coefficients of determination between 0.93 and 0.99. This methodology can enable electricity aggregators to evaluate building portfolios, considering uncertainty and multi-step predictions, to shift electricity demand to off-peak periods or periods of excess onsite renewable electricity generation in an end-user-customised manner.

Substation with various communication and control devices has become a major target of cyber-attacks. In general, two main types of cyber-attacks can be performed on substations through direct intelligent electronic device connections from remote accesses or via compromising local substation supervisory control center and data acquisition systems, thus respectively resulting in different damage to the power systems. In this paper, the combination of two types of cyber-attacks on the power systems are considered. The generalized stochastic Petri net is utilized to simulate dynamic intrusion processes of these two types of cyber-attacks. Furthermore, the successful attack probabilities of cyber-attacks are calculated based on simulation results. Then, a probabilistic bi-level optimization model is proposed to identify critical components with the maximum cyber risk and the types of cyber-attacks. The two types of cyber-attacks initiated from substation space to individual components are particularly modeled in the proposed model. Finally, a two-stage algorithm using the network flow is proposed to solve the proposed model more efficiently. Simulation results tested on the IEEE RTS 24-bus and the 118-bus systems validate the effectiveness of the proposed model. Results also show that the devised algorithm can improve the computation performance in dealing with larger-scale power systems.

With the increase of energy demand and the continuous development of renewable energy technology, active distribution networks have become increasingly important. However, the introduction of a large amount of renewable energy has made the structure of ADN increasingly complex and fragile, and emergency fault caused by emergencies may often occur. Voltage control in the emergency fault event is particularly important. In this context, this paper presents a meta-learning based voltage control strategy for renewable energy integrated active distribution network. A general regression neural network is first applied to extract features from the operation data. Then, the local cross-channel interaction network is adopted to capture targeted information that is most related to emergency fault from the features and induce knowledge transfer to update the voltage control strategy. This allows the proposed strategy to make optimal decisions quickly when only limited data are available under an emergency fault that has never occurred. Comparison results based on a 69-bus distribution network validate the effectiveness and robustness of the proposed strategy.

Transportation is vital to meeting China's carbon neutrality target by 2060. Nevertheless, the question of how to reach it remains unclear. Here, we employ a bottom-up energy system optimization model to investigate carbon dioxide emission trends using two sets of scenarios. The first relies solely on the efforts of the transportation sector, employing the avoid-shift-improve approach. In contrast, the second set of scenarios involves collaborative collaboration from the transportation, power and hydrogen sectors. The results reveal that achieving carbon neutrality solely through the efforts of the transportation sector is a challenging task. However, integrating negative emission technologies from the power and hydrogen sectors makes it feasible for the transportation sector to achieve carbon neutrality. Our findings suggest that in order to meet the carbon neutrality target, the energy structure of the transportation sector will undergo a fundamental transformation, with a significant increase in the use of electricity and hydrogen by 2060. Meanwhile, the power and hydrogen sectors will need to rely heavily on renewable energy sources and implement carbon capture and storage technologies to achieve substantial emissions reductions and offset the residual emissions from transportation. This study puts forward a comprehensive pathway that integrates the transportation sector with the power and hydrogen supply sectors, aiming to achieve carbon neutrality by 2060.

Combined cooling, heating and power system (CCHP) as a typical distributed energy utilization technology, is usually installed close to the end-user to satisfy the diverse energy demands, which also has multiple advantages of energy-efficient and environment-friendly. While the simultaneously changing user energy loads brings about a huge challenge for the system energy conversion and regulation process, and then significantly affects the system operation flexibility. Therefore, the new method of thermochemical recuperation (TCR) is employed to enhance the system dynamic energy utilization performances, and also efficiently converts the high-temperature exhaust gas heat into hydrogen-enrich syngas for further application. In this work, by considering the practical system operation, the off-design TCR operation adjustment capacity for the system diverse energy outputs is important, which is extensively investigated. Based on the developed mathematic model, different-size (small, middle and large) gas turbine scenarios are considered, the results indicate that the TCR process effectively optimize the system energy conversion process, and the corresponding power efficiencies are improved by 10.53%–13.87%, 8.56%–14.46% and 14.23%–14.42%, respectively. In addition, with the merits of adaptively coordinating the multi-energy production, the off-design energy outputs boundary of the CCHP system can be extended and then readily match the fluctuant user loads. Specifically, as for the small scale GT-based system application in a hotel building case, the system annual recuperated fuel ratio reaches to 0.54 with the annual coordinated recuperation operation time ratio of 0.70, and then the system operation flexibility is thus enhanced with the annual fuel saving ratio of 6.03%, which also contributes to the CO2 emission reduction. With the favorable potential to optimize the CCHP system operation regulation, the thermochemical recuperation technology provides a feasible pathway to enhance the distributed energy system application feasibility.

Combining solar light and heat in dry reforming of methane (DRM) is a promising technology for reducing CO2 to a valuable syngas and increasing the utilization of solar light in the solar-to-fuel process. The reaction temperature is still high and the mechanism remains unclear in DRM. Clarifying the synergistic mechanism of solar light and heat is pivotal for industrial applications. To this end, a nanoscale Ni/CeO2 catalyst is prepared, and the synergistic effect of solar light and heat in DRM is experimentally investigated. The experimental results show that photo-thermochemistry leads to a 39.74% increase in relative conversion rate of CH4 compared to thermochemistry, while the reaction temperature is reduced by 45 °C. The syngas production rate and the selectivity of DRM are improved by solar light. The in-situ infrared study indicates that solar light enhances the dissociation process of CH4 and the production of HCOO⁎ on the surface of the Ni/CeO2 catalyst. The light response in the catalyst and microscopic enhancement of intermediate products are responsible for the increased DRM performance under photo-thermochemical conditions. These findings contribute to the understanding of the synergistic effects of solar light and heat, and guide the conversion of CO2 into fuel with the aid of solar energy.

In the current deregulated electricity market, flexible consumers are more active in participating in market activities via the representation of electricity retailers. However, without an effective communication infrastructure, the connection between retailers and the consumers they serve is incomplete. Nowadays, smart meters are being rolled out worldwide to enhance the connection and data exchanges between retailers and consumers. Specifically, smart meters enable retailers to provide customers with detailed information about retail tariffs and their energy usage at different times of the day, which in turn enables customers to manage their energy use more proactively. This paper drops this assumption and makes use of data acquired from smart meters to design a personalized retail pricing scheme for different types of consumers. To formulate this problem, a bi-level optimization model is proposed, with the upper-level problem representing the pricing decision made by the retailer and two lower-level problems representing the demand response of consumers and the wholesale market clearing process, respectively. Afterward, we convert this bi-level optimization model into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker optimality conditions and complementary conditions. The scope of the examined case studies is fourfold. First, consumers are classified based on their daily load profiles using the advanced clustering method. Second, the physical benefit of fully exploring the consumer’s demand flexibility as well as the economic benefits of increasing retailers’ profitability and reducing consumers’ energy bills are evaluated with respect to the traditional uniform retail pricing scheme. Third, the impacts of consumers’ demand flexibility on electricity market outcomes and business cases are investigated. Finally, the proposed personalized retail pricing scheme is verified to relieve the strategic retailer’s market power reduction caused by the flexibility of demand, which is beneficial to the retailer’s profitability.

In recent decades, the global cooling demand has significantly increased and is expected to grow even further in the future. However, knowledge regarding the spatial distribution of cooling demand is sparse. Most existing studies are based on statistical modelling, which lack in small-scale details and cannot accurately identify individual large cooling producers. In this study, we implement and apply a novel method to identify, map and estimate nominal cooling capacities of chillers using deep learning. Chillers typically use air-cooled condensers and cooling towers to release excess heat, and produce most of the cooling needs in the commercial and industrial sectors. In this study, these units are identified from aerial images using specifically trained object detection models. The corresponding nominal cooling capacity is then estimated based on the number of fans of air-cooled condensers and the fan diameters of the cooling towers, respectively. Both detection and capacity estimations are first evaluated on test data sets and subsequently applied to an industrial area (Brühl) and the city center in Freiburg, Germany. In Brühl, aerial images show chillers with an estimated nominal cooling capacity of 205 MW, of which the model detected 88%, while 88% of all detections are correct. In the city center, a nominal capacity of 18.6 MW is estimated, of which the model detected 87% with 77% of all detections being correct. Hence, the developed approach facilitates a reliable analysis of the installed nominal cooling capacity of individual buildings at large scales, such as districts and cities. This information could be further used to locate areas for investments and support planning of eco-friendly, centralized supply of cooling energy, for example district heating and cooling systems or shallow geothermal energy systems such as aquifer thermal energy storage (ATES).

The gas production from marine gas hydrate has always been plagued by low productivity; the complex geological environment poses a significant obstacle to its commercialization. Horizontal wells are thus getting increasing attention due to their advantages in facilitating pressure propagation. Here a pre-embedded dual horizontal well was used to probe its effects in the local temperature distribution and gas production behavior. By doubling the number of boreholes, the flow channels of gas were increased enlarging the decomposition zone. Specifically, this was found to raise the reservoir temperature from −1 °C in a single well to approximately 0 °C. The temperature decline was more moderate due to a weakened Joule-Thomson effect. Consequently, almost 90% of the cumulative gas yield was produced before the lowest temperature occurred, compared to ∼30% in the single vertical well case. This indicates that the gas production from a dual well case was proceeding at a relatively higher temperature, potentially benefitting an enhanced gas production efficiency. An enlarged depressurization region was thus suggested in the field test for a controlled temperature decline to make more use of the sensible heat of the reservoir.

Ultra-low temperature (ULT) freezers store perishable bio-contents and have high energy consumption, which highlight a demand for reliable methods for intelligent surveillance and smart energy management. This study introduces a novel grey-box modelling approach based on stochastic differential equations to describe the heat dynamics of the ULT freezing chambers. The proposed modelling approach only requires temperature data measured by the embedded sensors and uses data from the regular operation periods for model identification. The model encompasses three states: chamber temperature, envelope temperature, and local evaporator temperature. Special attention is given to the local evaporator temperature state, which is modelled as a time-variant system, to characterise the time delay and dynamic variations in cooling intensity.The model has three states, of which a time-variant model with nonlinear input for the local evaporator temperature state is specifically established to adapt to the variation of the cooling intensity at the position of the embedded chamber control probe. Two ULT freezers with different operational patterns are modelled. The unknown model parameters are estimated using the maximum likelihood method. The results demonstrate that the models can accurately predict the chamber temperature measured by the control probe (RMSE <0.19°C) and are promising to be applied for forecasting future states. In addition, the model for local evaporator temperature can effectively adapt to different operational patterns and provide insight into the local evaporation cooling supply status. The proposed approach greatly promotes the practical feasibility of grey-box modelling of the heat dynamics for ULT freezers and is a step forward in future digital operationscan serve several potential digital applications. A major limitation of the modelling approach is the low identifiability, which can potentially be addressed by inferring model parameters based on relative parameter changes.

In smart buildings enabled by IoT technologies, wireless sensor networks (WSNs) are promising platforms to implement novel fully distributed optimal control approaches according to the edge computing paradigm. This requires knowledge from wireless communication and distributed computation fields where communication topologies are both critical. Communication topologies are designed considering network energy consumption and stability in wireless communication field, while considering optimization convergence speed in distributed computation field. But there is no inter-disciplinary design method considering these issues simultaneously. This study therefore proposes an optimal design method for communication topology of WSNs to implement fully distributed optimal control approaches. System control performance, network energy consumption and network stability are integrated into the objective function for the design. For a WSN consisting of n sensors, an integer programming problem with n(n − 1)/2 design variables, i.e., elements in Laplacian matrix representing the existence of communication links, is formulated and solved by the genetic algorithm (GA). The optimal topology of a WSN, on which a fully distributed optimal control approach is implemented for optimally controlling a multi-zone dedicated outdoor air system (DOAS), is designed by the proposed method. A co-simulation testbed is constructed to test and validate the proposed method by comparing the optimal topology with different topologies. The optimal topology provides satisfactory system control performance (CO2Ave = 784 ppm, CO2Max = 916 ppm, CO2 unmet hour = 1.82 h and EDOAS = 122.50 kWh), low network energy consumption (2564.12 J/Day) and high network stability (53.90 days). The proposed method facilitates the development and applications of IoT technologies in smart buildings.

Antibiotic wastewater has caused serious environmental pollution and is hard to utilize. Thus, it is urgent to develop a green and efficient technology for treating antibiotic wastewater and effective utilization. The reported fuel cell technologies can degrade antibiotics and generate electricity simultaneously, but their cell performance is very bad and difficult application. Herein, we reported a new environment-friendly and low-cost redox flow fuel cell (RFFC) based on the photothermal synergistic effect to convert antibiotic wastewater into electricity directly and efficiently at low temperature. The developed RFFC can output the maximum power density of 98.2 mW cm−2 which is 545 times of the reported microbial fuel cells and 270 times of the reported photocatalytic fuel cells. And the photothermal degradation method is better than the thermal degradation and photo degradation. Furthermore, it can discharge stably >2.5 h at high current density of 2 A cm−2 and successfully power a small electrical fan (1.5 V). The reaction mechanism is studied by the density functional theory (DFT) calculation, and the results show that FeCl3 molecules as photocatalyst and electron carriers of the RFFC can complex with antibiotics to greatly reduce the energy gap between HOMO and LUMO of antibiotics, which make the antibiotic molecules easy to be excited to unstable excited state by visible and UV light (λ < 733 nm) and greatly beneficial for their photothermal degradation. Besides, when using cefuroxime sodium wastewater as the model wastewater, HS-GC–MS results show that cefuroxime sodium can be completely degraded into non-toxic micro molecules after generating electricity. This work shows promising potential application for high-value utilization of antibiotic wastewater and generating clean electricity.

The increased integration of variable renewable generation into the power systems, along with the phase-out of fossil-based power stations, necessitate procuring more flexibility from the demand sectors. The electrification of the residential heat sector is an option to decarbonise the heat sector in the United Kingdom. The inherent flexibility that is available in the residential heat sector, in the form of the thermal inertia of buildings, is expected to play an important role in supporting the critical task of short-term balancing of electricity supply and demand. This paper proposes a method for characterising the locally aggregated flexibility envelope from the electrified residential heat sector, considering the most influential factors including outdoor and indoor temperature, thermal mass and heat loss of dwellings. Applying the method to England and Wales as a case study, demonstrated a significant potential for a temporary reduction of electricity demand for heating even during cold days. For a scenario envisaged a fully electrified residential heat sector in England and Wales, total electricity demand reductions of approximately 25 GW and 85 GW were shown to be achievable for the outdoor temperature of 10 °C and -5 °C, respectively. Improving the energy performance of the housing stock in England and Wales was shown to reduce the magnitude of available flexibility to approximately 18 GW and 60 GW for the outdoor temperature of 10 °C and -5 °C, respectively. This is due to the use of smaller size heat pumps in the more efficient housing stock. However, the impact of the buildings' retrofit on their thermal mass and consequently on the duration of the flexibility provision is uncertain.

In this study, micro thermocouples were applied to operando measure the through-plane interlayer temperatures of the proton exchange membrane (PEM) electrolyzer cell. The heat transfer, mass transfer, and electrochemical processes inside the cell were analyzed under various operating conditions. By comparing the temperature characteristics and electrolysis performance of the cell in two different heating modes, the effect of the flow rate on the cell performance was elucidated, and the interlayer temperature distribution in the through-plane direction was investigated. The dynamic response characteristics of temperature and voltage under abrupt changes in current density were also explored. The water starvation experiments of the electrolyzer cell revealed in detail the behaviors of the temperature runaway and the voltage runaway. The results indicated that the heating mode of water preheating and cell heating effectively reduced the difference in the internal temperatures and maintained the cell performance. Under dynamic operating conditions, temperature exhibited a longer stabilization time than voltage. The onset of temperature runaway and voltage runaway induced by water starvation was random and unpredictable, which would irreversibly damage the electrolysis performance.

Comprehensive knowledge about wind energy potential is critical for decision-making to achieve carbon neutrality and shape future energy pathways. Wind turbine technology advances (e.g., higher hub-heights, larger rotor diameters and rated power) can better support wind energy harvesting and alter wind energy potential. This study established an integrated model to evaluate the impact of wind turbine technology advances on onshore wind energy potentials under different wind resource conditions by using China as a case study. We found that technological advances of wind turbine can significantly impact wind energy potential. Compared with land-based 1.5-MW turbines, the onshore wind energy potential using newer generation turbines (land-based 2.5-MW turbines) would increase by 43% to 14.8 PWh (i.e., 2.0 times the electricity consumption of China in 2020). Importantly, advances in wind turbine technology can significantly increase capacity factor in poor wind resource regions, and thus enable economically viable generation in more areas. This can significantly increase wind energy potential in poor wind resource regions with large electrical load (e.g., Central, South and East China). Moreover, in contrast to the wind production of 0.46 PWh in 2020, China's wind energy potential is projected to hit 20.1 PWh by 2030 under the assumption of developed turbine technology in line with the trends of the past decade. The corresponding potential CO2 emission reduction would range from 12.6 Gt in 2020 to 21.7 Gt in 2030. These values are 1.3 and 2.2 times higher than China's CO2 emissions in 2020, respectively. To enhance wind energy contribution toward China's carbon neutrality goal, we recommend that the conversion of wind turbines to be accelerated, especially in poor wind resource regions with large electrical load.

Integrated energy system (IES) is characterized by high self-consumption ratio of on-site generated renewable energy, high efficiency of conventional energy utilization and possesses a significant flexibility in its operation. This overall, constitute to the foundation of low-carbon communities. Considering economic and environmental benefits, this paper proposes a two-layer co-optimization model with the upper layer optimizing the IES configuration and the lower layer optimizing IES operation. A community in Beijing is introduced as a case study to analyze the benefits of IES and compared with the conventional energy system. Multiple scenarios are researched, including (a) IES for low-carbon communities with PV roof area limitation, (b) IES for each of the four building types with PV roof area limitation, (c) IES for low-carbon communities without PV roof area limitation. The results indicate that the optimized IES for community can reduce the cost by 25% and CO2 emissions by 32% per year by considering the limitation of roof area for the PV system. Without this constraint, the costs and emissions could be reduced by 20% and 62%, respectively. In addition, IES is more appropriate for office and commercial buildings since their load characteristics allow for load shifting and community-level IES costs and emissions are 8% and 10% lower than building-level due to the complementary of community load. This study provides suggestions for the IES planning and application in low-carbon community.

Active insulation systems (AISs) in buildings are envelopes that integrate thermal insulation, thermal energy storage, and controls. Although different designs for AISs have been proposed in the literature, a comprehensive analysis of feasible AISs is lacking. This paper discusses the energy performance, peak demand reduction potential, and performance characteristics of an AIS that uses a concrete wall as thermal mass sandwiched between two solid-state thermal switches (STSs). These STSs change their thermal conductivity using an on/off metal switch to create or break a thermal bridge across the STS. This paper first describes the experimental setup, used to determine the ratio of thermal resistance during R-high (low thermal conductivity) and R-low (high thermal conductivity) states of the STSs. This ratio was then used in whole-building energy simulations to evaluate the performance of AIS walls across different climate zones with/without a freeze timer of 60 min. The timer was added to reduce the number of switches of STSs from one state to another, and hence the energy needed for these switches. Analysis of the switching frequency and interval of STSs, thermal conductivity of walls, impact of wall orientation, and heat transfer through the wall from the use of AIS at different climate zones/locations were performed. The simulation results show that the AIS can achieve energy savings ranging from ∼ 980 to 2,290 kWh in a single-family home with a floor area of ∼ 220 m2 compared with an IECC 2018 baseline. The energy savings was higher in dry climate zones which represent 17% of residential buildings in the United States, compared to humid or marine climate.

This study presents a mathematical programming approach for the strategic planning of electrical energy production. The proposed approach seeks the optimal selection of conventional and clean technologies and their production capacity in a given period. In addition, it aims to assess the amount of emissions generated and water consumption, without neglecting economic aspects. The planning considered in this study extends from 2020 to 2040, where the approach considers the implementation of deep learning models to forecast the future electricity demand of each year through historical data; thus obtaining a more precise energy configuration. Fossil fuels and biofuels are used as primary energy in the operation of different technologies. Therefore, both economic and environmental objectives are considered. The economic objective function determines the minimum total discounted cost, which encompasses energy production, operation, maintenance, primary energy, and investment costs. While the environmental objective focuses on reducing water consumption and emissions. Through Pareto curves, using the ɛ-constraint method, the proposed model determines the trade-offs between the total discounted cost and the CO2eq emissions generated. The Mexican electrical system is presented as a specific case study with real production data and available resources. The results show that it is possible to reduce the total discounted cost by up to 11.02%, while in environmental aspects, up to 28.27% in emissions and up to 20.23% in water consumption, achieving that the power produced by renewable energies increases progressively until reaching 70% of the energy demand in 2040, where the system is committed to implementing clean technologies and replaces a percentage of fossil fuels with biofuels in conventional technologies. The results highlight that by increasing the investment cost with clean technologies, the system is balanced with a lower total future cost in the planning presented, thus reducing the environmental burden, and determining trade-offs between cost and environmental aspects.

Lack of access to reliable energy is a major concern for countries in sub-Saharan Africa. The national grids are unable to consistently satisfy demand. Therefore, users turn to distributed generation systems in the form of back-up generators. However, such systems are usually designed based on a rule of thumb. We employ a mixed-integer linear programming model that considers several options such as renewable energy, combined heat and power, and storage technologies, in addition to those on-site, to provide optimal design and dispatch decisions that minimize total cost. We apply this model to a case study for a hospital in South Africa, considering its need for reliable electricity in light of multiple outages that might occur over the course of a year, as well as its high heating and cooling loads. Our results show that optimal design and dispatch decisions for the distributed generation system address reliability challenges, regardless of the time at which they occur. And, these solutions yield millions of dollars in savings, suggesting that technologies such as the absorption chiller may be overlooked in typical designs; its integration can reduce demand charges even in the absence of combined heat and power. We show that total cost is most sensitive to changes in site electrical demand, followed by capital cost, fuel cost, photovoltaic production, and monthly demand charges; changes in fuel cost primarily affect system sizes of combined heat and power and the absorption chiller, while photovoltaic system size is more sensitive to the changes in capital and fuel costs, photovoltaic resource availability, and hourly electrical demand. Finally, an outage simulator demonstrates the ability of our optimized system to sustain with no interruptions in power five-hour outages with probability 1.0 and ten-hour outages with probability 0.65, significant improvements over 0.5 and 0.0, respectively, under a business-as-usual case.

This paper proposes the mathematical model, and calculation method of available supply capability (ASC) for distribution systems. Firstly, the mathematical model for ASC is established, considering all allowed load increments from the present operating point to the security boundary of a distribution system. Secondly, the calculation method for the ASC model is proposed. The results of the proposed method can describe the available supply capability of a distribution system completely, including not only the ASC values, but also some other important data, such as all load growth patterns and cross-boundary points when the system reaches its security boundary. Finally, an IEEE RBTS test system with DGs is used to demonstrate the proposed models and method. Similar to the ATC of the transmission systems, the proposed ASC is an important operational index that can help operators accurately evaluate the supply capability margin of a distribution system. The ASC relevant operational guides are also given in this paper. This work lays the foundation for establishing the ASC theory for distribution systems, which corresponds to the ATC theory for transmission systems.