Optimal operation of dams/reservoirs emphasizing potential environmental and climate change impacts

Mahdi studied the potential ecological and climate change impacts on management of dams. He developed several new optimization frameworks in which benefits of dams are maximized, while above impacts are mitigated. Governments and consulting engineers can use the proposed frameworks for managing dams considering environmental challenges in river basins

Reviewing energy system modelling of decentralized energy autonomy

Research attention on decentralized autonomous energy systems has increased exponentially in the past three decades, as demonstrated by the absolute number of publications and the share of these studies in the corpus of energy system modelling literature. This paper shows the status quo and future modelling needs for research on local autonomous energy systems. A total of 359 studies are roughly investigated, of which a subset of 123 in detail. The studies are assessed with respect to the characteristics of their methodology and applications, in order to derive common trends and insights. Most case studies apply to middle-income countries and only focus on the supply of electricity in the residential sector. Furthermore, many of the studies are comparable regarding objectives and applied methods. Local energy autonomy is associated with high costs, leading to levelized costs of electricity of 0.41 $/kWh on average. By analysing the studies, many improvements for future studies could be identified: the studies lack an analysis of the impact of autonomous energy systems on surrounding energy systems. In addition, the robust design of autonomous energy systems requires higher time resolutions and extreme conditions. Future research should also develop methodologies to consider local stakeholders and their preferences for energy systems

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

A modelling approach for evaluating impacts of hydropeaking in a sub-arctic river

Abstract. The release of pulses of water to increase hydroelectric power production at hydropower dams to meet daily peaks in electricity demands is called hydropeaking. Due to energy supply and demand fluctuations, the energy markets direct hydropower companies to balance load fluctuations through variations in power generation which result in flow regulation. More recently, this regulation is being carried out at shorter time intervals i.e., intra-daily and intra-hourly levels. The hydropeaking phenomenon increases drastically at shorter time intervals, severely impacting the riverine and riparian ecosystem. Social, economic, and ecological impacts arise from short-term hydropeaking. Furthermore, recreational services offered by the river are also impacted. This research develops a novel methodology for assessing these impacts in a strongly regulated sub-arctic river in Finland, i.e., Kemijoki River, Ossauskoski-Tervola reach. The methodology combines assessment of seasonal variations in sub-daily hydropeaking, two-dimensional hydrodynamic modelling, and a high-resolution land cover map developed through supervised land use classification via a machine learning algorithm. The results obtained include; the identification of a zone of influence of hydropeaking at sub-daily levels during each season, the total and class-wise area affected during each peaking event, and vulnerability zonation for water-based recreation in the river reach. The overall area of reach affected by peaking in Winter was (1.05 km2), Spring (0.96 km2), Summer (1.39 km2), and Autumn (0.66 km2). A vulnerability mapping was also carried out for the suitability of water-based recreation in the study reach. The novel methodology developed in this research which defines the vulnerable zone of hydropeaking can be used as the first step in detailed impacts assessment studies such as those for impacts on fish habitat and sediment transport processes in the river. The hydropeaking-influenced zone can be used to set thresholds for ecological flows and ramping rates downstream of power stations and opens avenues for future research, development, and policy endeavors for riparian ecosystem impact assessment and mitigation

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Optimal sizing for a grid-connected hybrid renewable energy system.

Masters Degree. University of KwaZulu- Natal, Durban.Hybrid renewable energy systems (HRESs) refer to power generating systems that integrate several sources of energy, including renewables, to provide electricity to consumers. HRESs can either work as standalone or grid-connected systems. Since wind and solar have complementary characteristics and are available in most areas, they are considered as suitable energy sources to be combined in an HRES. Moreover, the maturity of technologies needed for generating electricity from wind and solar has turned them into more economical options in many locations. Many countries, including South Africa, have introduced policies and incentives to increase their renewable energy capacities in order to address environmental concerns and reduce pollutant emissions into the atmosphere. In addition, consumers in South Africa have faced the ever-increasing price of electricity and unreliability of the grid since 2007 due to the lack of sufficient electricity production. As a result, employing HRESs has gained popularity among consumers in different sectors. This research is focused on grid-connected hybrid energy systems based on solar photovoltaic (PV) panels and wind turbines as a potential solution to reduce the dependency of residential sector consumers on the grid in Durban. The aim of the research is to identify the optimal sizing of such a HRES to be cost-effective for consumers over a certain period of time. Since the energy supplied by renewable sources are intermittent and dependent on the geographical location of the system, identifying optimal sizing becomes a challenging task in HRESs. In this research, Durban’s meteorological data and eThekwini municipality tariff rates have been considered. Moreover, two artificial intelligence methods have been used to obtain the optimal sizing for different types of available PV panels, wind turbines and inverters in the market. The results have shown that the combination of PV panels and battery storage (BS) can become a profitable option for Durban area. Moreover, the systems using higher rated power PV panels can start to become profitable in a shorter lifetime. Considering BS in a system can only become a cost-effective choice if we consider a long enough lifespan for the system

Hydraulic-hydrologic model for the Zambezi River using satellite data and artificial intelligence techniques

The development of hydraulic-hydrologic models is a challenge in the case of large catchment areas with scarce or erroneous measurement data and observations. With his study Mr. Dr. José Pedro Matos made several original contributions in order to overcome this challenge. The scientific developments were applied at Zambezi River basin in Africa in the framework of the interdisciplinary African Dams research project (ADAPT). First of all, procedures and selection criteria for satellite data regarding topography, rainfall, land use, soil types and cover had to be developed. With the goal to extend the time scope of the analysis, Dr. Matos introduced a novel Pattern-Oriented Memory (POM) historical rainfall interpolation methodology. When POM interpolated rainfall is applied to hydrologic models it effectively opens up new possibilities related to extended calibration and the simulation of historical events, which would otherwise be difficult to exploit. A new scheme for rainfall aggregation was proposed, based on hydraulic considerations and easily implemented resorting to remote sensing data, which was able to enhance forecasting results. Dr. Matos used machine-learning models in an innovative way for discharge forecast. He compared the alternative models (e.g. Autoregressive Moving-Average (ARMA), Artificial Neural Networks (ANN) and Support-Vector Regression (SVR)). Dr. Matos made then significant contributions to the enhancement of rainfall aggregation techniques and the study of limitations inherent to SVR forecasting model. He proposed also a novel method for developing empirical forecast probability distributions. Finally Dr. Matos could successfully calibrate, probably for the first time, a daily hydrological model covering the whole Zambezi River basin (ZRB)

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC