Springer Science and Business Media LLC

While climate and energy policy targets require fundamental changes and expansions in the energy infrastructure, hydropower systems across Europe remain essential for low-carbon energy systems. With renewable fuel import prices being subject to large uncertainties, this work aims to substantiate the relationship between these fuel import prices and multireservoir hydropower systems in a climate-neutral energy system. To that end, three green hydrogen import price scenarios are combined with two aggregated modelling approaches for pan-European hydropower assets. Using the integrated energy system model SCOPE SD, the analysis shows that import prices for green hydrogen have a significant impact on European electricity generation (+ 595 GW $$_\text {el}$$ and + 650 TWh $$_\text {el}$$ /yr), domestic hydrogen production (+ 396 TWh $$_\text {th}$$ /yr), and water values of European hydropower assets (+ 33 % of average value in Norway). The results further indicate that the different aggregation methods only have a minor impact, suggesting that the computationally more efficient approach with up to 90% reductions in solution time provides suitable approximations of hydropower generation and flexibility in future analyses.

This paper takes a renewed look at the impacts of energy consumption (fossil fuels and renewable) and economic output on carbon dioxide emissions. Using country-level data from 103 countries from 1990 to 2011, a random-parameters model was developed, to account for unobserved heterogeneity across countries, and create potential strategies for reducing carbon dioxide emissions across countries. In order to reduce carbon dioxide emissions, two broad strategies were found to be feasible in this paper: systematic reduction in fossil fuels use through energy conservation and an expansion in the consumption of renewable energy sources that are free from CO $$_{2}$$ emissions. On average, a 1 % decrease in fossil fuels consumption would decrease carbon dioxide emissions by an average of 1.46 %, varying from 0.17 to 2.44 % across countries. While a 1 % increase in renewable energy consumption would decrease CO $$_{2}$$ emissions by 0.01 to 0.68 %, with an average of 0.18 %. Newly industrialized countries can significantly improve economic output by creating opportunities for industries to invest in green energy, which will significantly boost economic growth. The strategy of conserving fossil fuels through increasing efficiencies in vehicle fuels, power plants, buildings, and travel demand management are feasible to ensure sustainable reductions in global carbon dioxide emissions.

This paper proposes the design of a multivariable robust control strategy for a variable-speed WECS based on a SCIG. Optimal speed control of the SCIG is achieved by a conventional PI controller combined with a MPPT strategy. DTC-SVM technique based on a simple Clarke transformation is used to control the generator-side three-level converter in the variable speed WECS. The flow of real and reactive power between the inverter and the grid is controlled via the grid real and reactive currents and the DC link voltage using multivariable H $$_{\infty }$$ control. The overall WECS and control scheme are developed in Matlab/Simulink and the performance of the proposed control strategy is evaluated via a set of simulation scenarios replicating various operating conditions of the WECS such as variable wind speed and asymmetric single grid faults. The power quality of the WECS system under H $$_{\infty }$$ control control approach is assessed and the results show a significant improvement in the total harmonic distorsion as compared to that achieved with a classical PI control.

Electricity systems are subject to delays in construction and outages during operation. These stochastic events have probabilities and costs that depend on local variables and system design. We present a methodology to analyze the cost and performance of different electricity infrastructure development paths in the presence of stochastic events and probabilistic parameters. Of primary interest are the impacts of long construction horizons for centralized infrastructures, variable commodity prices, random generation and transmission outages and budget instability. The methodology is demonstrated through a case study analysis of two different development paths for rural electrification in Rwanda, one that is primarily centralized and one that is primarily decentralized. We show that conventional modeling, which aggregates the effects of random events and probabilistic parameters, may understate the true costs of centralized infrastructure, as compared to explicit simulation of individual stochastic events. We show the extent to which the centralized development path can be negatively affected by disruptions to the available development budget. We also show that when consumer discount rates are high, the decentralized path can become a relatively lower cost option on a levelized basis.

This paper presents a planning approach for unbalanced radial distribution systems using differential evolution algorithm (DE) so as to determine the optimal phase balancing and conductor sizes. The objective functions used in the planning are minimization of: (1) total complex power unbalance, (2) total power loss, (3) average voltage drop, (4) voltage unbalance factor, and (5) total neutral current. The optimization is done under the constraints of minimum and maximum voltage limits for each bus voltage and thermal limit of each line. A three phase forward–backward sweep load flow algorithm is developed and used for the determination of these objective functions. The effectiveness of the proposed planning algorithm is corroborated on 19-bus and IEEE 25-bus unbalanced radial distribution systems. The results show that significant improvements in power loss and voltage drop with simultaneous optimization for phase balancing and conductor sizes. The performance of DE is found to be better and consistent as compared to some other meta-heuristic algorithms studied here.

The mixed-integer linear programming (MILP) model is introduced in this article for the formulation of the bi-objective problem of minimizing total delivery time and greenhouse gas (GHG) emissions in multimodal and intermodal freight transportation for the delivery of a full container unit from Ningbo, China to Kasur, Pakistan. In this study, a multiobjective genetic algorithm is used to address the optimization problem (MOGA). The Pareto optimum solution from MOGA helps decision-makers to make the best trade-off between environmentally friendly and time-saving modes of transportation. We chose the clothing industry for real-world data collecting using EXW incoterms and conditions from one of Pakistan’s leading logistics service providers. According to our research, direct shipping delivery from the Ningbo seaport to the Karachi seaport minimizes delivery time by 11% when compared to intermodal freight distribution. Furthermore, when moving a single container from China to Pakistan, our research on GHG emissions shows that multimodal freight transportation is 19% more environmentally friendly than intermodal freight transit. Second, we looked at how each mode of transportation affected overall emissions in multimodal and intermodal freight transportation.

This study suggests and discusses a new maximum power point tracking (MPPT) approach based on an adjustable step size for photovoltaic system applications to extract the real MPP under various atmospheric conditions. The suggested adjustable step size Theta approach (ASSTA) is essentially based on the Theta angle (θ), which is the arctangent of the change in photovoltaic power derived by the variation in the photovoltaic voltage (arctan(dPpv/dVpv)) and its derivation in relation to the change in the photovoltaic voltage (dθ/dVpv) with the use of a new adaptable step size (dPpv + dVpv × dIpv). MATLAB/Simulink® software is selected for the implementation of the overall photovoltaic system, and the simulation results in different scenarios indicate that the novel ASSTA guarantees a fast convergence speed to the real MPP under a rapid change in operating conditions (insolation and temperature) with a time fewer than 0.017 s. Furthermore, the ASSTA shows better accuracy in tracking the MPP in the case of sudden insolation and temperature change and tracks the real MPP with neglected fluctuation in steady-state operation. Its average tracking efficiency in all simulation scenarios can be between 99.10 and 99.81%. Additionally, in light of the comparative simulation results of the new ASSTA with other methods such as INC, P&O, variable step size INC (VSSINC), and PSO MPPT techniques under various climatic scenarios, it can be concluded that the novel ASSTA outperforms the other MPPT approaches and significantly boosts the tracking performance and reduces power loss.

The largest oil deposits discovered in the world are located in places far from developed countries that are major consumers of energy. This situation is the reason why, for two decades, huge quantities of hydrocarbons are transported everywhere in the world either by sea or by road. The oil trade and the market are subject to an inexorable competitive pricing. In reality, the age of the tanker intervenes in the decision-making process; it is often the cheapest available tonnage offered by the oldest ships that controls prices. It is therefore difficult to ensure that quality pays for; this phenomenon probably involves some risk for the maritime security as a whole: human (accidents, shipwrecks) and environmental (vegetation pollution e.g. fauna and flora), and on health).