• Energy Research
• 2021-2025close
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The path congestion and communication risks of electric power communication network have emerged as the carried businesses are becoming more and more heavy. In this paper, a multi-objective optimization method for power communication network routing considering the importance of the carried businesses is proposed to reduce load imbalance and transmission risk. Firstly, information entropy is introduced as the objective of communication network load balancing optimization. And a risk assessment model for power communication network nodes and links is established. Then fuzzy membership functions are adopted to singularize the two objectives to jointly optimize path congestion and communication risks. The device nodes are dynamically divided according to the carried businesses to pursue the global optimization of the power communication network in which the Yen's algorithm is adopted. Finally, this paper uses the IEEE 30-bus communication network to carry out case simulations and analyzes the proposed method to verify the effectiveness and feasibility.

AbstractShipbuilding is an energy-intensive industrial sector that produces a significant amount of waste, pollution and air emissions. However, the International Maritime Organization concentrates only on reducing emissions during the operational phase. In order to completely phase out emissions from the shipping industry, a life-cycle approach must be taken. The study implemented the proposed transdisciplinary energy management framework in a Bangladeshi shipyard. The framework aims to support shipyard decision makers in making rational and optimized decisions to make shipyards sustainable, while maintaining good product quality and reducing relative cost. This is achieved by applying the Fuzzy Analytical Hierarchy Process and Fuzzy Order of Preference by Similarity to Ideal Solution methods to identify optimal solutions. In addition to making shipyards more sustainable, the framework can enhance both the business and socio-economic prospects of the shipyard and promote the reputation of the shipyard and improve its competitiveness and, in line with this, lead to the promotion of nationally determined contributions under the Paris Agreement for States. The implementation of the framework shows that the political and legal discipline, the social criteria and the implementation of ISO 14001 and cyber security were the most important criteria and options for the yard's decision makers.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.

With the continuous development of intelligent transportation technologies, new ways of energy usage in transportation continue to emerge, which puts forward new requirements for the planning and design of energy systems. However, comprehensive analyses on the characteristics of transportation energy systems and the development trend of energy usage patterns brought by intelligent technologies have rarely been carried out so far. This paper explores this subject by reviewing the recent development and utilization of intelligent technologies in the transportation system and its impacts on energy usage. This review is carried out from three aspects, covering the representative intelligent transportation and energy technologies on vehicles, infrastructures and systems. The scope is limited within road, railway and water transport domains, with a focus on the recent developments in China as a representative. In terms of vehicles, the development trend of the power systems for new energy vehicles, the characteristics of energy usages in electric vehicles and the effects on energy saving and emission reduction are summarized. In terms of infrastructures, new technologies on smart road, smart port, intelligent railway energy system and the usage of clear energy on electric grid for transportation are reviewed, with the consideration of their potential influences on energy usages and the energy consumption characteristics of typical facilities also being analyzed. As for the transportation system, this review has focused on intelligent and connected transportation systems, train control and autonomous systems, and intelligent shipping system, with the emphasis on the energy saving and emission reduction effects of applying these intelligent technologies. The overall development trend of the transportation energy system is then analyzed based on the above materials, in particular, the future energy usage patterns in transportation system are given and the major challenges and obstacles approaching the future scenarios are also identified.

This paper is concerned with the risk management practices of an electricity retailer motivated by the Dutch electricity market. We examine the effectiveness of the existing base- and peak-load futures contracts as a risk management tool for the electricity retailers. We analytically characterize the retailer’s optimal hedging policy as a function of the serial correlation of the prices and the demand profiles of its customers. We find that the retailer typically over-hedges in the futures market, and the over-hedging amount increases when both base- and peak-load contracts are used. Our findings indicate that although the existing contracts in the futures market are quite efficient to replicate the exposure from profiled customers, when industrial consumers and renewable generation are included to the retailer’s portfolio, the effectiveness of such contracts decreases substantially. In our motivating example, hedging the risk of the profiled customers with base-load contracts, the firm may reduce the variance of its cash flows by 85.9%. In addition to the base-load contracts, including peak-load contracts into the hedging portfolio of the retailer increases the efficiency of hedging to 89.3%. However, when we consider the aggregate portfolio of the retailer including profiled customers, industrial consumers and renewable contracts, the efficiency of hedging through the existing futures contracts goes down as low as 32.8% during certain periods. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

AbstractThe integration of power plants and desalination systems has attracted increasing attention over the past few years as an effective solution to tackle sustainable development and climate change issues. In this light, this paper introduces a novel modelling and optimization approach for a combined-cycle power plant (CCPP) integrated with reverse osmosis (RO) and multi-effect distillation (MED) desalination systems. The integrated CCPP and RO–MED desalination system is thermodynamically modelled utilizing MATLAB and EES software environments, and the results are validated via Thermoflex software simulations. Comprehensive energy, exergic, exergoeconomic, and exergoenvironmental (4E) analyses are performed to assess the performance of the integrated system. Furthermore, a new multi-objective water cycle algorithm (MOWCA) is implemented to optimize the main performance parameters of the integrated system. Finally, a real-world case study is performed based on Iran's Shahid Salimi Neka power plant. The results reveal that the system exergy efficiency is increased from 8.4 to 51.1% through the proposed MOWCA approach, and the energy and freshwater costs are reduced by 8.4% and 29.4%, respectively. The latter results correspond to an environmental impact reduction of 14.2% and 33.5%. Hence, the objective functions are improved from all exergic, exergoeconomic, and exergoenvironmental perspectives, proving the approach to be a valuable tool towards implementing more sustainable combined power plants and desalination systems.

Abstract In the context of climate change, global industrialised nations are grappling with transforming energy networks to support a low carbon future. Using an energy justice framework this work aims to understand holistic outcomes of one low-carbon energy network intervention: demand-side response enacted on domestic heat pumps. By exploring participants’ lived experience of a pilot project, from recruitment to installation and use, this work reveals how injustices were reduced, introduced and amplified. Choice, consent, cost, comfort, disruption, and control are highlighted as key aspects of interest when considering the distributive, procedural, and recognition implications of this domestic innovation. For a net reduction of energy injustices to be realised, we highlight the need for project designers to work in partnership with end users to optimise the benefits for the household and the electricity system. Whilst this is a UK study, the themes and findings are internationally applicable for interventions that aim to harness the flexibility of heating, the largest global energy end-use.