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Among various bioreactors examined in anaerobic digestion and dark fermentation, UASB bioreactor has shown to be a promising alternative. However, mass transfer resistance and biomass washout have been the issues that reported as draw backs of the granular system in the literature. Another problem associated with such a system is its long start-up period as a result of biomass washout and long microbial granulation stage. In this paper, the results obtained from an UASFF bioreactor in methane (AD process) and hydrogen (DF process) production from POME, are presented to assess mass transfer of substrate into the granules and also study the role of internal packing used in the middle part of reactor in the process stability. The value of effectiveness factor, η, for AD and DF processes were calculated to be 0.96 and 0.94, respectively, indicating that there was no mass transfer resistance due to internal and/or external factors. The results showed that the packing material could retain biomass in the reactor and had outstanding contribution in the granulation enhancement. Its role as a supplementing treatment stage was more significant at low HRTs and up-flow velocities.

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

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.

Abstract In many visions and roadmaps, there is a broad agreement that fuel cells – both for stationary and mobile applications – are the key technology to allow the development of a hydrogen infrastructure. Furthermore, this development is generally thought to be based on a gradual, decentralised evolution. Nevertheless, in this paper it is argued that, taking into account the entire hydrogen chain (production, transport, storage, distribution and end-use), this decentralised fuel-cell based philosophy shows some serious flaws. Therefore, a new hydrogen-transition approach was pushed forward: mixing in of hydrogen into the natural-gas bulk. Using Flanders – the Northern part of Belgium – as a case study, the development of a transitory hydrogen infrastructure has been studied, taking into account the entire hydrogen chain and its dynamics, from production to end use. In a next step, this transition is being quantified. An optimisation model has been developed using Matlab and the commercial solvers GAMS and CPLEX. Following a mixed-integer linear-programming approach, this model is able to determine the economically optimal hydrogen-production mix and operational behaviour of each hydrogen-production plant separately. The model then allows gaining valuable insights in the importance of storage and the influence of fuel prices and carbon taxes with regard to the development of an early hydrogen economy.

Abstract In the new competitive electricity supply industry, there is a renewed interest in algorithms that can provide savings in operation costs. An optimal scheduling of generators can provide substantial annual savings in fuel costs, but this highly constrained non-linear mixed integer optimisation problem can only be full v solved by complete enumeration, a process which is not computationally feasible for realistic power systems. In the recent past, evolutionary computation techniques have been applied to the solution of the unit commitment problem, but when implemented as stand alone systems, they suff er from computational time limitations, especially when the systems are scaled up. This paper proposes a hybridgenetic algorithm incorporating a priority list unit ordering scheme to solve the generator scheduling problem. Test results on networks with up to 110 generators are presented and the results demonstrate the viability of the hybrid GA method for unit commitment in realistic power systems.

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.

Abstract Electricity dispatch is a difficult optimisation problem that aims at minimising total fuel cost while satisfying system power demand and certain thermal unit constraints. Modelling valve point effects, multiple fuel options or transmission losses brings non-convexity and non-smoothness into the mathematical models. This paper proposes mathematical programming-based models for valve point effects, multiple fuel options and transmission loss problems respectively. Mathematical programming leads to robust and rigorous optimisation methods for these problems. The applicability of the proposed methods is demonstrated through a number of case studies. For all the case studies, the corresponding methods identify power schedules at least as good as or better than the best known in literature.

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.

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.