The recent advancements achieved in the development of a fluid-to-fluid similarity theory for heat transfer with fluids at supercritical pressures are summarised. The prime mover for the development of the theory was the interest in the development of Supercritical Water nuclear Reactors (SCWRs) in the frame of research being developed worldwide; however, the theory is general and can be applied to any system involving fluids at a supercritical pressure. The steps involved in the development of the rationale at the basis of the theory are discussed and presented in a synthetic form, highlighting the relevance of the results achieved so far and separately published elsewhere, with the aim to provide a complete overview of the potential involved in the application of the theory. The adopted rationale, completely different from the ones in the previous literature on the subject, was based on a specific definition of similarity, aiming to achieve, as much as possible, similar distributions... [more]

Transmission expansion planning (TEP), the determination of new transmission lines to be added to an existing power network, is a key element in power system planning. Using classical optimization to define the most suitable reinforcements is the most desirable alternative. However, the extent of the under-study problems is growing, because of the uncertainties introduced by renewable generation or electric vehicles (EVs) and the larger sizes under consideration given the trends for higher renewable shares and stronger market integration. This means that classical optimization, even using efficient techniques, such as stochastic decomposition, can have issues when solving large-sized problems. This is compounded by the fact that, in many cases, it is necessary to solve a large number of instances of a problem in order to incorporate further considerations. Thus, it can be interesting to resort to metaheuristics, which can offer quick solutions at the expense of an optimality guarantee.... [more]

Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the ag... [more]

Recent works have addressed the analysis of some situations that alter the gearbox oil results in wind energy conversion systems (WECS). This work contributes by completing the analysis of additional situations, based on key operational data collected from 10 different multi-megawatt wind turbines at two different locations with two top-tier technologies, and has demonstrated that the oil analysis results can be altered in practice. As important as detecting these situations is to verify how the data collected by the different operators and transferred to the laboratories, this relevant information is not included in most cases. The issues that can stem from this lack of valuable data can be mitigated with a new and more complete template. This paper proposes a detailed template that is ready for an industrial use and contributes to standardizing the information handled by all actors. The suggested template, which is designed based on extensive experimental results and an in-depth anal... [more]

The aim of the research is to identify and quantify the direct economic effects resulting from the improved seaport nautical access and capacity expansion. This case study considers a regional port located in the Baltic sea and relates to port users, i.e., shipping operators and shippers. The effects were identified for maritime transport by comparing transport performance in two scenarios: with-the-investment and without-the-investment. Incremental calculus addresses freights (containers, dry bulk, and cereals) traded to and from the given port, changes in size of vessels, and the shipping route alternatives vis-a-vis adjacent ports in the range. Sustainable impact concerns generalized maritime transport cost, i.e., shipping operating costs and port-to-port transit time, as well as energy consumption and external costs of maritime shipping. To capture effects, daily and unit dry bulk, as well as container shipping cost, values of time, and marginal external costs were revealed in frei... [more]

In recent years, the use of electric Autonomous Wheeled Mobile Robots (AWMRs) has dramatically increased in transport of the production chain. Generally, AWMRs must operate for several hours on a single battery charge. Since the energy density of the battery is limited, energy efficiency becomes a key element in improving material transportation performance during the manufacturing process. However, energy consumption is influenced by the navigation stages, because the type of motion necessary for the AWMR to perform during a mission is totally defined by these stages. Therefore, this paper analyzes methods of energy efficiency that have been studied recently for AWMR navigation stages. The selected publications are classified into planning and motion control categories in order to identify research gaps. Unlike other similar studies, this work focuses on these methods with respect to their implications for the energy consumption of AWMRs. In addition, by using an industrial Self-Guide... [more]

Based on data from 2083 wind turbines installed in Sweden from 1988 onwards, the accuracy of the predictions of the annual energy production (AEP) from the project planning phases has been compared to the actual wind-index-corrected production. Both the electricity production and the predicted AEP come from Vindstat, a database that collects information directly from wind turbine owners. The mean error for all analyzed wind turbines was 13.0%, which means that, overall, the predicted AEP has been overestimated. There has been an improvement of accuracy with time with an overestimation of 8.2% for wind turbines installed in the 2010s, however, the continuous improvement seems to have stagnated around 2005 despite better data availability and continuous refinement of methods. Dividing the results by terrain, the error is larger for wind turbines in open and flat terrain than in forest areas, indicating that the reason behind the error is not the higher complexity of the forest terrain. A... [more]

The use of parallel applications in High-Performance Computing (HPC) demands high computing times and energy resources. Inadequate scheduling produces longer computing times which, in turn, increases energy consumption and monetary cost. Task scheduling is an NP-Hard problem; thus, several heuristics methods appear in the literature. The main approaches can be grouped into the following categories: fast heuristics, metaheuristics, and local search. Fast heuristics and metaheuristics are used when pre-scheduling times are short and long, respectively. The third is commonly used when pre-scheduling time is limited by CPU seconds or by objective function evaluations. This paper focuses on optimizing the scheduling of parallel applications, considering the energy consumption during the idle time while no tasks are executing. Additionally, we detail a comparative literature study of the performance of lexicographic variants with local searches adapted to be stochastic and aware of idle ener... [more]

The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative to traditional expensive reinforcements to network infrastructure, due to its flexibility, decreasing costs and fast deployment capabilities. In this work, an end-to-end data-driven solution to optimally design the control of a battery unit with the aim of reducing the peak electricity demand is presented. The proposed solution uses state-of-the-art machine learning methods for forecasting electricity demand and PV generation, combined with an optimisation strategy to maximise the use of photovoltaic energy to charge the energy storage unit. To this end, historical demand, weather, and solar energy generation data collected at the Stentaway Primary substation near Plymouth, UK, and at other s... [more]

Electric power distribution networks plays a significant role in providing continuous electrical energy to different categories of customers. In the context of the present advancements, future load expansion in the active distribution networks (ADNs) poses the key challenge of planning to be derived as a multi-stage optimization task, including the optimal expansion planning scheme optimization (EPSO). The planning scheme optimization is a multi-attribute decision-making issue with high complexity and solving difficulty, especially when it involves a large-scale planning zone. This paper proposes a novel approach of a multi-year planning scheme for the effective solution of the EPSO problem in large planning zones. The proposed approach comprises three key parts, where the first part covers two essential aspects, i.e., (i) suggesting a project condition set that considers the elements directly related to a group of specific conditions and requirements (collectively referred to as condi... [more]

Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users’ patterns, goals, and needs, by creating energy consumption awareness and helping current change habits. In this way, energy manager systems (EMSs) monitor and manage electrical appliances, automate and schedule actions, and make suggestions regarding electrical consumption. Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money. Therefore, this paper proposes a personalized gamification strategy for an EMS through an adaptive neuro-fuzzy inference system (ANFIS) decision-making engine to classify the level of electrical consumption and pe... [more]

Energy communities, mostly microgrid based, are a key stakeholder of modern electrical power grids. Operating a microgrid based energy community is a challenging topic due to the involved uncertainties, complexities and often conflicting objectives. The aim of this paper is to present a novel methodology demonstrating that energy community flexibility can contribute to each community member’s wellbeing when a grid fault occurs. A three-house energy community will be modelled considering as consumption sources non-controllable and controllable devices in each house. As power supply sources, PV systems installed in a community’s houses are considered, as well as the power obtained from main grid. Each house’s flexibility inside the community will be studied to improve the management of loads during a fault occurrence. Moreover, three different scenarios will be considered with different available power in the community. With these simulations, it was possible to understand that houses’ e... [more]

Bidirectional coupling systems for electricity and natural gas composed of gas units and power-to-gas (P2G) facilities improve the interactions between different energy systems. In this paper, a combined optimization planning method for an electricity-natural gas coupling system with P2G was studied. Firstly, the characteristics of the component model of the electricity-natural gas coupling system were analyzed. The optimization planning model for the electricity-natural gas coupling system was established with the goal of minimizing the sum of the annual investment costs and the annual operation costs. Based on the established model, the construction statuses for different types of units, power lines, and pipelines and the output distribution values for gas units and P2G stations were optimized. Then, the immune algorithm was proposed to solve the optimization planning model. Finally, an electricity-natural gas coupling system composed of a seven-node natural gas system and a nine-nod... [more]

Full electrification of the transport sector is a necessity to combat climate change and a pressing societal issue: climate agreements require a fuel shift of all the modes of transport, but while uptake of passenger electric vehicles is increasing, long haul trucks rely almost completely on fossil fuels. Providing highways with proper charging infrastructure for future electric mobility demand is a problem that is not fully investigated in literature: in fact, previous work has not addressed grid planning and infrastructure design for both passenger vehicles and trucks on highways. In this work, the authors develop a methodology to design the electrical infrastructure that supplies static and dynamic charging for both modes of transport. An algorithm is developed that selects substations for the partial electrification of a highway and, finally, the design of the electrical infrastructure to be implemented is produced and described, assessing conductors and substations sizing, in orde... [more]

Freight movement has always been, and always will be an essential activity. Freight transport is one of the most challenging sectors to transition to net-zero carbon. Traffic assignment, mode allocation, network planning, hub location, train scheduling and terminal design problem-solving have previously been used to address cost and operation efficiencies. In this study, the interdisciplinary transition innovation, management and engineering (InTIME) methodology was used for the conceptualization, redesign and redevelopment of the existing freight systems to achieve a downshift in fossil energy consumption. The fourth step of the InTIME methodology is the conceptualization of a long-term future intermodal transport system that can serve the current freight task. The novelty of our approach stands in considering the full range of freight supply chain factors as a whole, using an optimization-simulation approach as if we were designing the low-carbon system of 2121. For the optimization,... [more]

High profitability and high costs have stiffened competition in the airline industry. The main purpose of the study is to propose a computationally efficient algorithm for integrated fleet assignments and aircraft routing problems for a real-case hub and spoke airline planning problem. The economic concerns of airline operations have led to the need for minimising costs and increasing the ability to meet rising demands. Since fleets are the most limited and valuable assets of airline carriers, the allocation of aircraft to scheduled flights directly affects profitability/market share. The airline fleet assignment problem (AFAP) addresses the assignment of aircraft, each with a different capacity, capability, availability, and requirement, to a given flight schedule. This study proposes a mathematical model and heuristic method for solving a real-life airline fleet assignment and aircraft routing problem. We generate a set of problem instances based on real data and conduct a computatio... [more]

Studies on the energy−water−CO2 synergetic relationship is an effective way to help achieve the peak CO2 emission target and carbon neutral goal in global countries. One of the most valid way is to adjust through the electric power structure transformation. In this study, a mixed-integer linear resource planning model is proposed to investigate the energy−water−CO2 synergetic optimization relationship, concerning the uncertainties in the fuel price and power demand prediction process. Coupled with multiple CO2 emissions and water policy scenarios, Beijing, the capital city of China, is chosen as a case study. Results indicate that the demand-side management (DSM) level and the stricter environmental constraints can effectively push Beijing’s power supply system in a much cleaner direction. The energy−water−CO2 relationship will reach a better balance under stricter environmental constraints and higher DSM level. However, the achievement of the energy−water−CO2 synergetic optimization w... [more]

The AC/DC hybrid distribution network is composed of a medium-voltage DC bus, a low-voltage DC bus, and a power electronic transformer, and has the characteristics of multi-voltage level, multi-DC bus, and multi-converter, so its operation mode and optimal scheduling strategy are more complex. Firstly, this paper constructs the AC/DC hybrid distribution network using an power electronic transformer. Then, a two-layer control structure including a scheduling management layer and a bus control layer is proposed, which simplifies the control structure and gives full play to the role of “energy routing” function of the power electronic transformer. Moreover, the minimum operation cost of the AC/DC hybrid distribution network in the whole scheduling cycle is taken as the optimization objective, considering the characteristics of various distributed generations, the structure of AC/DC hybrid distribution network, and the interaction of “source−load−storage”. Finally, the optimal scheduling m... [more]

This paper presents a novel scheduling scheme for the real-time home energy management systems based on Internet of Energy (IoE). The scheme is a multi-agent method that considers two chief purposes including user satisfaction and energy consumption cost. The scheme is designed under environment of microgrid. The user impact in terms of energy cost savings is generally significant in terms of system efficiency. That is why domestic users are involved in the management of domestic appliances. The optimization algorithms are based on an improved version of the rainfall algorithm and the salp swarm algorithm. In this paper, the Time of Use (ToU) model is proposed to define the rates for shoulder-peak and on-peak hours. A two-level communication system connects the microgrid system, implemented in MATLAB, to the cloud server. The local communication level utilizes IP/TCP and MQTT and is used as a protocol for the global communication level. The scheduling controller proposed in this study... [more]

Ocean-going ships are one of the primary sources of Greenhouse Gas (GHG) emissions. Several actions are being taken to reduce the GHG emissions from maritime vessels, and integration of Renewable Energy Sources (RESs) is one of them. Ocean-going marine ships need a large amount of reliable energy to support the propulsive load. Intermittency is one of the drawbacks of RESs, and penetration of RESs in maritime vessels is limited by the cargo carrying capacity and usable area of that ship. Other types of reliable energy sources need to be incorporated in ships to overcome these shortcomings of RESs. Some researchers proposed to integrate fossil fuel-based generators like diesel generators and renewable energy in marine vessels to reduce GHG emissions. As the penetration of RESs in marine ships is limited, fossil fuel-based generators provide most of the energy. Therefore, renewable and fossil fuel-based hybrid energy systems in maritime vessels can not reduce GHG emissions to the desired... [more]

Sufficient and convenient fast-charging facilities are crucial for the effective integration of electric vehicles. To construct enough fast electric vehicle-charging stations, station owners need to earn a reasonable profit. This paper proposed an optimization framework for profit maximization, which determined the combined planning and operation of the charging station considering the vehicle arrival pattern, intermittent solar photovoltaic generation, and energy storage system management. In a planning horizon, the proposed optimization framework finds an optimal configuration of a grid-connected charging station. Besides, during the operation horizon, it determines an optimal power scheduling in the charging station. We formulated an optimization framework to maximize the expected profit of the station. Four types of costs were considered during the planning period: the investment cost, operational cost, maintenance cost, and penalties. The penalties arose from vehicle customers’ di... [more]

Modern distribution mechanisms within the smart grid paradigm are considered both reliable in nature and interconnected in topology. In this paper, a multiple-criteria-based sustainable planning (MCSP) approach is presented that serves as a future planning tool for interconnected distribution mechanisms and aims to find a feasible solution among conflicting criteria of various genres. The proposed methodology is based on three stages. In the stage 1, a weighted voltage stability index (VSI_W) and loss minimization condition (LMC) based approach aims at optimal asset optimization (sitting and sizing). In this stage, an evaluation of alternatives (solutions) is carried out across four dimensions (technical, economic, environmental, and social) of performance metrics. The assets considered in the evaluations include distributed generation (DG), renewable DGs, i.e., photovoltaic (PV), wind, and distributed static compensator (D-STATCOM) units. In the stage 2, various multicriteria decision... [more]

Modern mobility and adaptation of transport and communication systems to the requirements of the inhabitants are both inseparable elements of the developed concept of smart cities. One of the important stages in the implementation of this concept is the planning stage, taking into account the complexity and a large number of determinants that impact the effectiveness of decisions related to the planning of transport, communication and logistic systems. The purpose of the article is to lay out a model of a system based on the integration of selected Internet of Things tools used in smart cities to support urban development planning processes in the scope of the ongoing modification of transport, public communication and logistic systems. The model was developed on the basis of the cooperation between the authors and the boards of selected cities’ observations made during the authors’ travels and on the basis of identification and analysis of IT systems types and ICT (Information and Com... [more]

Local flexibility markets (LFMs) are a market-based concept to integrate distributed energy resources into congestion management. However, the activation of flexibility for storage-based flexibility changes the respective state of charge. Compensation in later points of time is needed to regain the original flexibility potential. Therefore, we propose a LFM bid formulation including both flexibility and compensation. Furthermore, flexibility market participation might lead to inc-dec-gaming, i.e., congestion-increasing behavior to maximize profits. However, this inc-dec-gaming might lead to electricity market schedule deviations if LFM offers are not activated. We propose a risk-averse modeling formulation considering the potential non-activation of LFM bids to provide a framework for the assessment of LFM participation comparing different approaches. Our exemplary case studies demonstrate the proposed LFM bid formulation and show the impact of LFM participation modeling on inc-dec-gam... [more]

Deep learning has already been successfully used in the development of decision support systems in various domains. Therefore, there is an incentive to apply it in other important domains such as agriculture. Fertilizers, electricity, chemicals, human labor, and water are the components of total energy consumption in agriculture. Yield estimates are critical for food security, crop management, irrigation scheduling, and estimating labor requirements for harvesting and storage. Therefore, estimating product yield can reduce energy consumption. Two deep learning models, Long Short-Term Memory and Gated Recurrent Units, have been developed for the analysis of time-series data such as agricultural datasets. In this paper, the capabilities of these models and their extensions, called Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Units, to predict end-of-season yields are investigated. The models use historical data, including climate data, irrigation scheduling, and... [more]