This paper contains a techno-economic feasibility study of implementing an anaerobic digester to supply biogas to a cogeneration system, meeting the electricity and heating demand on a small-scale Norwegian livestock farm. Three configurations, using the same model with different compositions of biowaste, were identified to meet the energy demand on the farm while being financially feasible. Running simulations in ECLIPSE found overall system efficiencies of 86.25%, 90.12% and 87.83% for a combined heat and power (CHP) unit. Annual emissions can be reduced from self-produced energy, carbon sequestration and replacing mineral fertilisers with digestate by up to 2,605, 153,096, and 10,958 kg of CO2eq, respectively. Economic analysis proved that with external funding, the payback period of the project would be between 10 and 19 years for the different options, which is within the 20-year lifetime of the system. Additionally, yearly savings of up to Â£1,406 and Â£2,761 could come from avoiding paying a potential carbon tax and using digestate.
Photocatalyst for water splitting is a promising method for producing clean hydrogen energy. The Z-scheme heterostructure photocatalysts have attracted considerable attention in recent years owing to the unique energy band structures and interfacial interactions. This work rationally constructed the cobalt tetraphenylporphyrin/ZnIn2S4 heterostructure. The hydrogen evolution reaction (HER) activity of the 25% cobalt tetraphenylporphyrin/ZnIn2S4 heterojunction was boosted considerably, reaching a maximum of 6.9 mmol h-1 g-1, which is 1.8 times of the single ZnIn2S4. The increased separation efficiency of photogenerated carriers and visible light absorption are responsible for the high HER activity of the cobalt tetraphenylporphyrin/ZnIn2S4 heterostructure. With the half-reactions using hole-sacrificing agents, this study constructed a reasonable Z-scheme heterostructure for efficient hydrogen evolution.
Water electrolysis supported by sustainable electricity has been regarded as the promising method for large-scale hydrogen (H2) production, but the sluggish anodic oxygen evolution reaction with high-energy barriers to overcome much limited its broad development. Herein, by taking use of the EO/EG process waste effluent, we comprehensively demonstrated the feasibility of developing electrochemical reforming of glycols to simultaneously construct an energy-saving electrolysis H2 production system and degrade pollutants to purify the effluents. The cathodic H2 harvesting could retain nearly 100% Faradic efficiency, along with ~ 17.7% gain in electricity and its correlation with the COD removal in the effluent. Furthermore, the electrochemical oxidation of EG was studied in detail under real circumstances and compared with other EG-derived organic substances possibly present in the waste effluent to glean the possible oxidation mechanism. This work provides some new insights for designing both energy-effective electrochemical systems and sustainable water-energy nexus.
Research on radiative cooling windows suitable for hot climate regions is a hot topic of building energy conservation. Most existing researches default that windows should be ideal radiators with high emissivity (~1) in wide infrared band (2.5-20Î¼m). However, the universality of such an assumption is unproven. To explore the optimal design scheme of emissivity of radiative cooling windows in infrared band, Comsol Multiphysics software was used to simulate the building with radiative cooling windows, and the reliability of the model was verified by experiments. First, based on the spectral properties of ordinary glass, the window with different outer surface emissivity in the atmospheric window (AW,8-13Î¼m) and remaining infrared bands (RIB,2.5-8Î¼mï¼†13-20Î¼m) were simulated by traversal. Second, the influence of emissivity of the above two bands on radiative cooling effect of the window and the building is studied. Finally, the radiative cooling potential of the window in these two bands was compared. The results show that the lower the RIB emissivity of the window outer surface in daytime, the more favorable the radiative cooling of the window. As the solar radiation decreases, it gradually changes to the larger the RIB emissivity is, the more favorable the radiative cooling of the window. At different time periods, the adjustment trend of the RIB emissivity of the window outer surface to radiative cooling may be reversed. Through data analysis, compared with the high emissivity in wide infrared band design, it is more beneficial to reduce the window and indoor air temperature by reducing the RIB emissivity of the window outer surface as much as possible. The smaller RIB emissivity reduces the heat flow into the room and extends the time that the window becomes a “cooler” during the day. In addition, the temperature regulating effect of the window emissivity in AW is almost 8-9 times that in RIB, and the energy regulating effect is almost 9-11 times that in RIB. This work clarifies the default assumption of non-universality of building radiative cooling windows in infrared band and quantifies the importance of the atmospheric window in radiative cooling of building windows, which can provide valuable guidelines for material developers.
The rapid urbanization has led to a significant increase in building energy consumption, with air-conditioning systems being a major contributor. Liquid desiccant air-conditioning systems (LDAS) have been proposed as an effective solution for energy saving. In this study, the feasibility of using a deep eutectic solvent, ethaline, in LDAS is investigated for its energy-saving benefits and CO2 concentration (CCO2) regulation. Results show that LDAS using ethaline exhibits higher coefficient of performance (COP) than conventional systems by over 16%, particularly at low fresh air ratios. However, the effect of ethaline on CCO2 control is not significant due to limited absorption with insufficient ethaline flow rate, and the regulation of CCO2 indoors still heavily depends on the inclusion of fresh air. Thereafter, an advanced air-conditioning system that coordinates energy-saving, CO2 capture, and energy recovery is proposed. The findings of this study can contribute to the further integration and design of air-conditioning systems, particularly for sustainable buildings in urban areas.
Great effort has been made to restructure the traditional monopolization of power industries to introduce fair competition. The deregulation of the electricity market allows the price of electricity to be formulated based on the bidding price. Nevertheless, it is still challenging to derive an optimal bidding strategy with many factors that need to be considered. This paper proposes a reinforcement learning (RL) based method to devise an optimal bidding strategy for maximizing the profit, taking the risk preferences in the spot electricity market into consideration. The problem is formulated based on Markov decision process (MDP), which is a discrete stochastic optimization method. The objective function is to optimize the cumulative profit over the span. This method also employs temporal difference technique and actor-critic learning algorithm for strategy optimization. In addition, the study introduces smart-market market-clearing method and a Gaussian distribution to formulate the strategy. Two different environmental conditions of the spot electricity market, static and dynamic, are applied in the simulation for analysis completeness. Only the target plant can adjust the bidding strategy in the static environment while all plants can adjust the bidding strategy in the dynamic environment. Simulation cases of nine participants are considered and the obtained results are analyzed.
As a transitional layer between the electricity market and prosumers, Virtual Power Plants (VPPs) can effectively integrate distributed resources of prosumers to participate in the electricity market to improve the energy economy for prosumers. This study aggregates distributed resources such as photovoltaics, energy storage, and flexible loads into a VPP within the same community microgrid. A two-layer peer-to-peer (P2P) energy sharing model within and among VPPs is established to consume PV power and construct a stable power supply system. At the VPP-layer, a comprehensive energy management model is created to optimize the scheduling of flexible loads to achieve optimal energy economic performance of the community. At the market-layer, a VPP bidding model is established to organize P2P energy sharing among VPPs. The VPP-layer scheduling provides initial information for the market-layer to participate in energy sharing, and energy sharing results of the market-layer are fed back to the VPP-layer as boundary conditions for re-scheduling. The energy economy analysis of the proposed system shows that the communityâ€™s cost is reduced by participating in P2P sharing, and the two-layer interactive mechanism can further reduce the communityâ€™s cost by increasing the quantity of shared energy in the P2P market, achieving dual technological and economic benefits.
Hybrid electricity/heat/hydrogen energy system is a potential solution for the future low-carbon residential energy system. This paper studies the efficient energy scheduling of such system, including hydrogen production, utilization and storage processes. To overcome the problems of coupling among the multi-energy flow and the uncertainties on both sides of power and load, a deep reinforcement learning (DRL) algorithm, namely deep deterministic policy gradient (DDPG), is used to realize adaptive energy scheduling of the system. The scheduling results of simulation experiment under typical winter day scenario illustrate that, based on the pre-trained DDPG framework, the system can achieve a rapid response to the environment and optimize energy efficiency. Additionally, by appropriate power charging and discharging, the states of energy storage devices can essentially recover to their initial states, enabling the sustainable operation of the hybrid energy system.