Fundamental shifts in the structure and generation profile of electrical grids are occurring amidst increased demand for resilience. These two simultaneous trends create the need for new planning and operational practices for modern grids that account for the compounding uncertainties inherent in both resilience assessment and increasing contribution of variable inverter-based renewable energy sources. This work reviews the research work addressing the changing generation profile, state-of-the-art practices to address resilience, and research works at the intersection of these two topics in regards to electrical grids. The contribution of this work is to highlight the ongoing research in power system resilience and integration of variable inverter-based renewable energy sources in electrical grids, and to identify areas of current and further study at this intersection. Areas of research identified at this intersection include cyber-physical analysis of solar, wind, and distributed ene... [more]

The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is mainly due to the mitigation measures that include lockdown and work from home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-lockdown business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result in a net benefit for flexible stakeholders, such as consumers on variable tariffs. Furthermore, the analysis illustrate... [more]

Social housing corporations play an important role in society as they provide affordable and good-quality housing for vulnerable citizens. Yet, the sector has to deal with the historical legacy of a high number of old and poorly insulated buildings. While research into the processes that drive or hinder business model innovation in this sector is scarce, this paper draws upon multiple qualitative case studies of social housing in the Netherlands to identify critical success factors for the transition to sustainable business models for new buildings and retrofits. Results show that there are four key attributes for a successful transition process: collaboration (both with supply chain partners as well as other social housing associations); continuous innovation; vision; and the role of the government (including subsidies and fiscal regulations). While economic performance was an important boundary condition, sustainability was not always seen as a strategic organizational objective, a f... [more]

Microgrid has flexible composition, a complex operation mechanism, and a large amount of data while operating. However, optimization methods of microgrid scheduling do not effectively accumulate and utilize the scheduling knowledge at present. This paper puts forward a microgrid optimal scheduling method based on Deep Deterministic Policy Gradient (DDPG) and Transfer Learning (TL). This method uses Reinforcement Learning (RL) to learn the scheduling strategy and accumulates the corresponding scheduling knowledge. Meanwhile, the DDPG model is introduced to extend the microgrid scheduling strategy action from the discrete action space to the continuous action space. On this basis, this paper holds that a microgrid optimal scheduling TL algorithm on the strength of the actual supply and demand similarity is proposed with a purpose of making use of the existing scheduling knowledge effectively. The simulation results indicate that this paper can provide optimal scheduling strategy for micr... [more]

The aim of this paper is the optimization of velocity trajectories for electrical railway vehicles with the focus on total energy consumption. On the basis of four fundamental operating modes—acceleration, cruising, coasting, and braking—energy-optimal trajectories are determined by optimizing the sequence of the operating modes as well as the corresponding switching points. The optimization approach is carried out in two consecutive steps. The first step ensures compliance with the given timetable, regarding both time and position constraints. In the second step, the influence of different operating strategies, such as load distribution and the switch-off of traction components during low loads, are analyzed to investigate the characteristics of the energy-optimal velocity trajectory. A detailed simulation model has been developed to carry out the analysis, including an assessment of its capabilities and advantages. The results suggest that the application of load-distribution techniq... [more]

Pathways leading to a carbon neutral future for the German energy system have to deal with the expected phase-out of coal-fired power generation, in addition to the shutdown of nuclear power plants and the rapid ramp-up of photovoltaics and wind power generation. An analysis of the expected impact on electricity market, security of supply, and system stability must consider the European context because of the strong coupling—both from an economic and a system operation point of view—through the cross-border power exchange of Germany with its neighbors. This analysis, complemented by options to improve the existing development plans, is the purpose of this paper. We propose a multilevel energy system modeling, including electricity market, network congestion management, and system stability, to identify challenges for the years 2023 and 2035. Out of the results, we would like to highlight the positive role of innovative combined heat and power (CHP) solutions securing power and heat sup... [more]

In this work, an innovative sustainable spatial energy planning framework is developed on national scale for identifying and prioritizing appropriate, technically and economically feasible, environmentally sustainable as well as socially acceptable sites for the siting of large-scale onshore Wind Farms (WFs) and Photovoltaic Farms (PVFs) in Israel. The proposed holistic framework consists of distinctive steps allocated in two successive modules (the Planning and the Field Investigation module), and it covers all relevant dimensions of a sustainable siting analysis (economic, social, and environmental). It advances a collaborative and participatory planning approach by combining spatial planning tools (Geographic Information Systems (GIS)) and multi-criteria decision-making methods (e.g., Analytical Hierarchy Process (AHP)) with versatile participatory planning techniques in order to consider the opinion of three different participatory groups (public, experts, and renewable energy plan... [more]

The undersupplies of feedstock and high costs have hindered the development of China’s biomass power generation. In this paper, the noncooperative game, farmer−broker cooperative game, and broker−biomass power plant cooperative game, under government incentives, are constructed and analyzed. The optimal decision strategies and profits for these three cases are obtained, while numerical examples and sensitivity analysis are conducted, aiming at illustrating some specific features of the games. It is shown that the government plays a critical role in the development of utilizing agribiomass for power generation and can work better in cooperative games. In addition, both agribiomass supply quantity and profits of supply chain members are higher in cooperative than in noncooperative game. Meanwhile, farmers can get the maximum profit in the broker−biomass power plant cooperative game, while biomass power plant makes the maximum profit in the farmer−broker cooperative game. To guide the hea... [more]

As renewable penetration increases in microgrids (MGs), the use of battery energy storage systems (BESSs) has become indispensable for optimal MG operation. Although BESSs are advantageous for economic and stable MG operation, their life degradation should be considered for maximizing cost savings. This paper proposes an optimal BESS scheduling for MGs to solve the stochastic unit commitment problem, considering the uncertainties in renewables and load. Through the proposed BESS scheduling, the life degradation of BESSs is minimized, and MG operation becomes economically feasible. To address the aforementioned uncertainties, a scenario-based method was applied using Monte Carlo simulation and the K-means clustering algorithm for scenario generation and reduction, respectively. By implementing the rainflow-counting algorithm, the BESS charge/discharge state profile was obtained. To formulate the cycle aging stress function and examine the life cycle cost (LCC) of a BESS more realistical... [more]

The scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of pro... [more]

This paper proposes the use of decomposition analysis to assess the effect of local energy-related actions towards climate change mitigation, and thus improve policy evaluation and planning at the local level. The assessment of the impact of local actions has been a challenge, even from a strictly technical perspective. This happens because the total change observed is the result of multiple factors influencing local energy-related greenhouse gas (GHG) emissions, many of them not even influenced by local authorities. A methodology was developed, based on a recently developed decomposition model, that disaggregates the total observed changes in the local energy system into multiple causes/effects (including local socio-economic evolution, technology evolution, higher-level governance frame and local actions). The proposed methodology, including the quantification of the specific effect associated with local actions, is demonstrated with the case study of the municipality of Malmö (Swede... [more]

The enormous cost of handling the challenges of flow assurance in subsea wells, flowlines, and risers, especially in deepwater applications, has necessitated a proactive approach to prevent their risk of occurrence. To ensure that transportation of the hydrocarbon is economical and efficient from the subsea wellhead to the processing units, a flow assurance heat management system is relevant in the design and planning of a fluid transport system. Consequently, the advancement of new technologies to serve the increasing need by exploring the technologically challenging and hostile subsea fields is of great importance. A comparative study on heat management systems in flowlines was conducted from the top five publishers (Elsevier, Springer, Taylor & Francis, Wiley, and Sage) based on the number of publications to determine the level of work done by researchers in the last decade, the figures from the study showed the need for scientific research in the field of active heating. Additional... [more]

The tropical climate with its high average temperatures throughout the year affects the thermal comfort of buildings, especially for naturally ventilated spaces. The government’s move to turn hospitals into green buildings is seen in line with the global commitment to conserve the environment and the country’s current policy of supporting sustainable development. To achieve this goal, energy efficiency and thermal comfort need to be given priority in the focus on hospital planning and implementation for a better quality of the indoor environment. This literature review has led to the need to improve thermal comfort in natural ventilated wards in government hospitals. Some wards are built without air conditioning to save on construction costs, reduce utility costs through low energy consumption, as well as the need for infection control and airborne infections. However, current climate change requires a special study of thermal comfort in wards that use natural ventilation. An innovativ... [more]

Selection of the most suitable drill bit type is an important task for drillers when planning for new oil and gas wells. With the advancement of intelligent predictive models, the automated selection of drill bit type is possible using earlier drilled offset wells’ data. However, real-field well data samples naturally involve an unequal distribution of data points that results in the formation of a complex imbalance multi-class classification problem during drill bit selection. In this analysis, Ensemble methods, namely Adaboost and Random Forest, have been combined with the data re-sampling techniques to provide a new approach for handling the complex drill bit selection process. Additionally, four popular machine learning techniques namely, K-nearest neighbors, naïve Bayes, multilayer perceptron, and support vector machine, are also evaluated to understand the performance degrading effects of imbalanced drilling data obtained from Norwegian wells. The comparison of results shows that... [more]

According to the United Nations (UN), although cities occupy only 3% of Earth’s surface, they host more than half of the global population, are responsible for 70% of energy consumption, and 75% of carbon emissions. All this is a consequence of the massive urbanization verified since the 1950s and which is expected to continue in the coming decades. A crucial issue will therefore concern the management of existing cities and the planning of future ones, and this was also emphasized by the UN Sustainable Development Goals (SDGs), especially in Goal 11 (Sustainable Cities and communities). Smart Cities are often seen as ideal urban environments in which the different dimensions of a city (economy, education, energy, environment, etc.) are managed successfully and proactively. So, one of the most important challenges cities will have to face, is to guide citizens towards a form of “clean” energy consumption, and the dimension on which decision-makers will be able to work is the decarboniz... [more]

A new robust scheduling method for pumping water in a water distribution system under the uncertainty of activating regulation reserves is proposed in this paper. During the operation of power systems, utilizing the energy equipment of the customer to enhance supply-demand control is attracting attention. Because water pumps have been already installed, they can be regarded as a relatively inexpensive, operational, and flexible resource. Changes in the operation of the water pump can contribute to the power supply and demand control. The proposed method helps generate a robust daily schedule for pumping water and provides regulation reserves under the uncertainty of activating regulation reserves. It is based on electric energy prices and regulation reserves, hourly water demand profiles, and the properties of water flow quantity and the electricity consumption of water pumps. This method comprises an optimization model formulated using mixed integer linear programming, validated throu... [more]

The thirst of the Earth for energy is lurching towards catastrophe in an era of increasing water shortage where most of the power plants are hydroelectric. The hydro-based power systems are facing challenges in determining day-ahead generation schedules of cascaded hydropower plants. The objective of the current study is to find a speedy and practical method for predicting and classifying the future schedules of hydropower plants in order to increase the overall efficiency of energy by utilizing the water of cascaded hydropower plants. This study is significant for water resource planners in the planning and management of reservoirs for generating energy. The proposed method consists of data mining techniques and approaches. The energy production relationship is first determined for upstream and downstream hydropower plants by using multiple linear regression. Then, a cluster analysis is used to find typical generation curves with the help of historical data. The decision tree algorith... [more]

The European strategic long-term vision underlined the importance of a smarter and flexible system for achieving net-zero greenhouse gas emissions by 2050. Distributed energy resources (DERs) could provide the required flexibility products. Distribution system operators (DSOs) cooperating with TSO (transmission system operators) are committed to procuring these flexibility products through market-based procedures. Among all DERs, battery energy storage systems (BESS) are a promising technology since they can be potentially exploited for a broad range of purposes. However, since their cost is still high, their size and location should be optimized with a view of maximizing the revenues for their owners. Intending to provide an instrument for the assessment of flexibility products to be shared between DSO and TSO to ensure a safe and secure operation of the system, the paper proposes a planning methodology based on the non-dominated sorting genetic algorithm-II (NSGA-II). Contrasting obj... [more]

Power systems can be analyzed using either a deterministic or a probabilistic approach. The deterministic analysis centers on studying the quantities and indicators that characterize the operating states of the power system under strictly defined conditions. However, the long-term horizon of planning analyses, the changes of marketing mechanisms, the development of renewable electricity sources, the leaving from large-scale generation, the growth of smart technology and the increase in consumer awareness make the development of transmission networks a non-deterministic problem. In this article, we propose a planning procedure that takes the probabilistic elements into account. This procedure was developed to take into account the high variability of power flows caused by the generation of renewable sources and international exchange. Such conditions of the power system operation forced a departure from deterministic planning. The new probabilistic approach uses the existing tools and e... [more]

This paper proposes a mathematical model in order to simulate Day-ahead markets of large-scale multi-energy systems with a high share of renewable energy. Furthermore, it analyses the importance of including unit commitment when performing such analysis. The results of the case study, which is performed for the North Sea region, show the influence of massive renewable penetration in the energy sector and increasing electrification of the district heating sector towards 2050, and how this impacts the role of other energy sources, such as thermal and hydro. The penetration of wind and solar is likely to challenge the need for balancing in the system as well as the profitability of thermal units. The degree of influence of the unit commitment approach is found to be dependent on the configuration of the energy system. Overall, including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of considerably increasing the computationa... [more]

The control and improvement of energy-environmental quality in buildings are responsible for almost 40% of the emissions related to energy and processes, and are essential to achieve the commitment of the Paris Agreement and the Sustainable Development Goals (SDGs) United Nations (UN). This paper provides a support tool to planners and administrators of the territory for the identification of interventions aimed at the energy requalification of the existing Italian building heritage, mainly for residential use. The purpose of this tool is to reduce energy consumption by intervening on the building envelope with specific solutions that are identified through a matrix resulting from the study. In the first part of the study, an analysis was carried out on various factors such as the existing residential building, the building and construction types and the materials of the envelope typical of each construction period, which are critical for energy efficiency issues. In the second part of... [more]

In traditional ecological scheduling, a single monthly or daily model will lead to the incomplete transmission of ecological information or increase the complexity of solving problems. Therefore, a multi-timescale nested model (MTNM) is proposed. Although the MTNM can express the daily flow process of environmental flow, the quadratic nested calculation method cannot obtain the optimal solution for the daily scheduling scheme. Targeting the problem that long and short-term objectives cannot obtain the optimal solution at the same time, this paper proposes a multi-timescale integrated model (MTIM) which considers the monthly, 10-day, and daily scale. The model is applied to the Liujiaxia reservoir. The scheduling results show that, compared with the MTNM, the MTIM can better meet the multi-objective demand. In a wet year, when both models can guarantee water supply and ecological demand, the MTIM increases electricity generation by 0.91%. In a dry year, electricity generation can still... [more]

A mathematical model for the design of small-scale supply chains for liquefied natural gas (LNG) has been developed. It considers the maritime delivery of LNG from supply ports to satellite terminals and land-based transports from the terminals to consumers on or off the coast. Both tactical and strategic aspects in the supply chain design are addressed by optimizing the maritime routing of a heterogeneous fleet of ships, truck connections, and the locations of the satellite terminals. The objective is to minimize the overall cost, including operation and investment costs for the selected time horizon. The model is expressed as a mixed-integer linear programming problem, applying a multi-period formulation to determine optimal storage sizes and inventory at the satellite terminals. Two case studies illustrate the model, where optimal LNG supply chains for a region with sparsely distributed island (without land transports) and a coastal region at a gulf (with both sea and land transport... [more]

During satellite development, engineers need to simulate and understand the satellite’s behavior in orbit and minimize failures or inadequate satellite operation. In this sense, one crucial assessment is the irradiance field, which impacts, for example, the power generation through the photovoltaic cells, as well as rules the satellite’s thermal conditions. This good practice is also valid for CubeSat projects. This paper presents a numerical tool to explore typical irradiation scenarios for CubeSat missions by combining state-of-the-art models. Such a tool can provide the input estimation for software and hardware in the loop analysis for a given initial condition and predict it along with the satellite’s lifespan. Three main models will be considered to estimate the irradiation flux over a CubeSat, namely an orbit, an attitude, and a radiation source model, including solar, albedo, and infrared emitted by the Earth. A case study illustrating the tool’s abilities is presented for a ty... [more]

The motivation for this paper is the enhanced role of power generation prediction in power plants and power systems in the smart grid paradigm. The proposed approach addresses the impact of the ambient temperature on the performance of an open cycle gas turbine when using the Kalman Filter (KF) technique and the power-temperature (P-T) characteristic of the turbine. Several Kalman Filtering techniques are tested to obtain improved temperature forecasts, which are then used to obtain output power predictions. A typical P-T curve of an open-cycle gas turbine is used to demonstrate the applicability of the proposed method. Nonlinear and linear discrete process models are studied. Extended Kalman Filters are proposed for the nonlinear model. The Time Varying, Time Invariant, and Steady State Kalman Filters are used with the linearized model. Simulation results show that the power generation prediction obtained using the Extended Kalman Filter with the piecewise linear model yields improved... [more]