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•A trading framework is designed enabling the exchange of energy and carbon allowance.•Smart contract is exploited to automate standardised auction procedure.•The bidding/selling ...prices directly target on reshaping prosumption behaviours.•Results prove that the proposed framework facilitates regional energy balance and carbon saving.
Prosumers are active participants in future energy systems who produce and consume energy. However, the emerging role of prosumers brings challenges of tracing carbon emissions behaviours and formulating pricing scheme targeting on individual prosumption behaviours. This paper proposes a novel blockchain-based peer-to-peer trading framework to trade energy and carbon allowance. The bidding/selling prices of prosumers can directly incentivise the reshaping of prosumption behaviours to achieve regional energy balance and carbon emissions mitigation. A decentralised low carbon incentive mechanism is formulated targeting on specific prosumption behaviours. Case studies using the modified IEEE 37-bus test feeder show that the proposed trading framework can export 0.99 kWh of daily energy and save 1465.90 g daily carbon emissions, outperforming the existing centralised trading and aggregator-based trading.
A highly selective catalyst based on mesoporous zeolites for the production of C5–C11 isoparaffins from syngas has been developed. The selectivity to C5–C11 hydrocarbons over Ru/meso‐ZSM‐5 reaches ...about 80 % with a ratio of isoparaffins to n‐paraffins of 2.7:1. The mesoporous structure and the unique acidity of meso‐ZSM‐5 play key roles in tuning the product selectivity by controlling the secondary hydrocracking reactions.
► The history and current status of industrial eco-parks in China are reviewed. ► The symbiosis in several unique chemical industrial parks is delineated. ► The critical factors for the development ...of chemical industrial ecosystems in China are discussed. ► The outlook for the path forward is presented.
Development of eco-industrial parks (EIP) is an effective method for recycling, reuse and conservation. Facing resource shortage as well as stringent energy saving and emission reduction targets, the importance of EIPs are getting ever-increasing attention in China. In this paper, the history and current status of EIPs in China are reviewed. The synergies in several unique chemical industrial parks are delineated. The critical factors for the development of chemical industrial parks in China are discussed and the outlook for the path forward is presented.
Excessive carbon emissions have posed a threat to sustainable development. An appropriate market‐based low carbon policy becomes the essence of regulating strategy for reducing carbon emissions in ...the energy sector. This study proposes a Stackelberg game‐theoretic model to determine an optimal low carbon policy design in energy market. To encourage fuel switching to low‐carbon generating sources, the effects of varying carbon price on generator's profit are evaluated. Meanwhile, to reduce carbon emissions caused by energy consumption, carbon tracing and billing incentive methods for consumers are proposed. The efficiency of low carbon policy is ensured through maximising social welfare and the overall carbon reductions from economic and environmental perspectives. A bi‐level multiobjective optimisation immune algorithm is designed to dynamically find optimal policy decisions in the leader level, and optimal generation and consumption decisions in the followers level. Case studies demonstrate that the designed model leads to better carbon mitigation and social welfare in the energy market. The proposed methodology can save up to 26.41% of carbon emissions from the consumption side for the UK power sector and promote 31.45% of more electricity generation from renewable energy sources.
Chlorine and chlorine-containing compounds have been widely used in various reactions but the atom efficiency of chlorine is usually very low. In polyurethane industry, the synthesis of isocyanates ...generates significant amount of by-product HCl gas and waste water containing salt. How to recycle and reuse them is a critical concern of the Polyurethane (PU) industry. Especially, the rapid expansion of PU industry in developing countries urges cost-effective solutions for chlorine recycling.
The authors led a group of scientists and engineers and developed two advanced technologies to realize the closed-loop recycling of chlorine. Using the hydrogen chloride catalytic oxidation technology, the by-product HCl gas can be converted to Cl2, with much lower energy consumption than that of the traditional chlor-alkali process. After treatment, the waste brine generated from the Methylene Diphenyl Diisocyanate (MDI) plant can be used as the electrolyte in chlor-alkali industry. The application of these two technologies brings significant economic, environmental and societal benefits.
► Two advanced technologies realizing closed-loop chlorine recycling are introduced. ► A new copper-based catalyst and a fluidized-bed process was developed for converting the by-product HCl gas to Cl2. ► The waste brine generated from the isocyanate plant is recycled as the electrolyte in chlor-alkali industry. ► These two technologies bring significant economic, environmental and societal benefits.
Electric vehicles (EVs) are playing an important role in power systems due to their significant mobility and flexibility features. Nowadays, the increasing penetration of renewable energy resources ...has been observed in modern power systems, which brings many benefits for improving climate change and accelerating the low-carbon transition. However, the intermittent and unstable nature of renewable energy sources introduces new challenges to both the planning and operation of power systems. To address these issues, vehicle-to-grid (V2G) technology has been gradually recognized as a valid solution to provide various ancillary service provisions for power systems. Many studies have developed model-based optimization methods for EV dispatch problems. Nevertheless, this type of method cannot effectively handle the highly dynamic and stochastic environment due to the complexity of power systems. Reinforcement learning (RL), a model-free and online learning method, can capture various uncertainties through numerous interactions with the environment and adapt to various state conditions in real-time. As a result, using advanced RL algorithms to solve various EV dispatch problems has attracted a surge of attention in recent years, leading to many outstanding research papers and important findings. This paper provides a comprehensive review of popular RL algorithms categorized by single-agent RL and multi-agent RL, and summarizes how these advanced algorithms can be applied to various EV dispatch problems, including grid-to-vehicle (G2V), vehicle-to-home (V2H), and V2G. Finally, key challenges and important future research directions are discussed, which involve five aspects: (a) data quality and availability; (b) environment setup; (c) safety and robustness; (d) training performance; and (e) real-world deployment.
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•State-of-the-art reinforcement learning algorithms are reviewed.•Both single-agent and multi-agent reinforcement learning frameworks are discussed.•Three key applications of reinforcement learning to EV dispatch problems are reviewed.•Challenges and future directions of applying reinforcement learning to EV problems are discussed.
Governments’ net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy ...supply and demand. These will be achieved by the advanced information and control infrastructures of smart grids which allow the interoperability among various stakeholders. Under this circumstance, increasing number of consumers produce, store, and consume energy, giving them a new role of prosumers. The integration of prosumers and accommodation of incurred bidirectional flows of energy and information rely on two key factors: flexible structures of energy markets and intelligent operations of power systems. The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems. This paper attempts to address how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets. To achieve this objective, first, this paper reviews how policy designs price carbon emissions caused by the fossil-fuel based generation so as to facilitate the integration of prosumers with renewable energy sources. Second, the potential structures of energy markets with the support of the blockchain technologies are discussed. Last, how to apply the AI for enhancing the state monitoring and decision making during the operations of power systems is introduced.
•The use of blockchain and artificial intelligence for facilitating prosumers to be integrated into smart grids and decarbonising the power systems.•Review how policy designs price of carbon emissions to facilitate the integration of prosumers with renewable energy sources.•Future energy markets with blockchain technologies.•How to apply artificial intelligence technologies for enhancing the control and decision-making in smart grids.
Coupling the vehicle-to-grid (V2G) with integrated energy systems (IES) offers an emerging solution for decarbonisation of both energy and transport sectors. To evaluate the feasibility of coupling ...V2G with IES as a flexible storage, we propose an optimisation-based system planning framework embedding V2G into IES. Within this framework, stochastic features of electric vehicles (EV) fleets are simulated. The impacts of V2G on IES design are captured by assessing both economic and environmental benefits via multi-objective optimisations utilising an improved NSGA-II algorithm. Six case studies considering three cities with different climate conditions and two functional areas of residential and commercial are performed. The results manifest that Beijing-commercial case could achieve the largest mutual benefits. The EV fleets’ charging behaviour follows the time-of-use energy tariff in transition seasons while not during winter. Sensitivity analysis indicates the electricity and gas prices have significant impact on the system design. The benefits induced by growing EV penetration would gradually decrease and stabilise when the EV number reach 300, the growth of economic and environmental benefits stabilized at 1.3% and 1.8%, respectively. Overall, this study quantifies the benefits of enabling V2G in IES, and generates valuable insights for IES planners, V2G service providers, and relevant policymakers.
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•Co-optimisation of EV vehicle-to-grid (V2G) with integrated energy systems (IES).•Simulate stochastic features of EVs' charging patterns and initial battery states.•Capture V2G-IES design's trade-off by an efficient DM-NSGA-II algorithm.•Analyse parametric sensitivity of electricity price, gas price, and EV penetration.•V2G-IES achieves the largest mutual benefits in a Beijing-commercial demonstration.
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