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]

District heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems. Optimization-based models provide near optimal solutions, while it takes a while to generate solutions due to the characteristics of the underlying solution mechanism. When prompt re-planning due to any parameter changes is necessary, the traditional approaches might be inefficient to generate modified solutions quickly. In this study, we developed a two-phase solution mechanism, where deep learning algorithm is applied to learn optimal production patterns from optimization module. In the first training phase, the optimization module generates optimal production plans for the input scenarios derived from operations history, which are provided to the deep learning module for training. In th... [more]

Battery energy storage is becoming an important asset in modern power systems. Considering the market prices and battery storage characteristics, reserve provision is a tempting play fields for such assets. This paper aims at filling the gap by developing a mathematically rigorous model and applying it to the existing and future electricity market design in Europe. The paper presents a bilevel model for optimal battery storage participation in day-ahead energy market as a price taker, and reserve capacity and activation market as a price maker. It uses an accurate battery charging model to reliably represent the behavior of real-life lithium-ion battery storage. The proposed bilevel model is converted into a mixed-integer linear program by using the Karush−Kuhn−Tucker optimality conditions. The case study uses real-life data on reserve capacity and activation costs and quantities in German markets. The reserves activation quantities and activation prices are modeled by a set of credibl... [more]

Cold-chain products are time-sensitive and perishable and pose the risk of failure if they are transported to a distant location. Thus, there is a need to analyze their quality during distribution so that the customers may receive optimal-quality products. To address this issue, this study integrates inspection strategies with the sustainable distribution system of multi-quality multiple-cold-chain products. A bi-objective model of cost and emission is proposed under the constraints of heterogeneous vehicle and time window. Furthermore, this study intends to address the following questions: which inspection strategy helps to ensure the potency of delivered products, and what is the impact of quality differentiation on the value of objective functions? A set of meta-heuristics is used for implementing the model using a rich panel of experiments. The results reveal that the quality conditions of different products impact the solutions of cost and emissions. Moreover, the conformity strat... [more]

Occupant behavior and business processes in a building environment constitute an inseparable set of important factors that drives energy consumption. Existing methodologies for building energy management lag behind in addressing these core parameters by focusing explicitly on the building’s structural components. Additional layers of information regarding indoor and outdoor environmental conditions and occupant behavior patterns, mostly driven by everyday business processes (schedules, loads, and specific business activities related to occupancy patterns and building operations), are necessary for the effective and efficient modeling of building energy performance in order to establish a holistic energy efficiency management framework. The aim of this paper was to develop a context-driven framework in which multiple levels of information regarding occupant behavior patterns resulting from everyday business processes were incorporated for efficient energy management in buildings. A prel... [more]

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is ben... [more]

Due to rapid economic development and population growth, environmental pollution problems such as urban pollution and depletion of natural resources have become increasingly prominent. Municipal solid waste is part of these problems. However, waste is actually an improperly placed resource. As a part of green supply chain management, remanufacturing can turn waste products into remanufactured products for resale. Based on the development status of China’s remanufacturing industry, this paper establishes three Stackelberg game models, namely the free recycling model (model N), the government regulation model based on the reward−penalty mechanism (model G), and the government dual-intervention model (model GF). In this study, the standard solution method for the Stackelberg game method, namely the backward induction method, is applied to solve the dynamic game equilibrium. For comparison, a further numerical analysis is also carried. The research results show that: (1) in the closed-loop... [more]

This work presents a review that aims to characterize the policy evaluation practices regarding the public policies on energy, with a focus on the metrics: concerns, objectives, and indicators. As key novelty, emphasis was put into finding attributes and metrics that can be used to assess effectiveness, not only efficacy or efficiency. The concerns and objectives were organized into four categories: Institutional, Environmental, Economic, and Social. For every category, detailed and condensed concerns were identified. It was attempted to find indicators for every condensed concern, which resulted in 15 core indicators.

A model developed at the University of Tomsk, Russia, for high latitudes (over 55° N) is proposed and applied to the analysis and observation of the solar resource in the state of Sonora in the northwest of Mexico. This model utilizes satellite data and geographical coordinates as inputs. The objective of this research work is to provide a low-cost and reliable alternative to field meteorological stations and also to obtain a wide illustration of the distribution of solar power in the state to visualize opportunities for sustainable energy production and reduce its carbon footprint. The model is compared against real-time data from meteorological stations and satellite data, using statistical methods to scrutinize its accuracy at local latitudes (26−32° N), where a satisfactory performance was observed. An annual geographical view of available solar radiation against maximum and minimum temperatures for all the state municipalities is provided to identify the photovoltaic electricity g... [more]

This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for... [more]

The Time-Stepped Linear Superposition Method (TLSM) has been used previously to model and analyze the propagation of multiple competitive hydraulic fractures with constant internal pressure loads. This paper extends the TLSM methodology, by including a time-dependent injection schedule using pressure data from a typical diagnostic fracture injection test (DFIT). In addition, the effect of poro-elasticity in reservoir rocks is accounted for in the TLSM models presented here. The propagation of multiple hydraulic fractures using TLSM-based codes preserves infinite resolution by side-stepping grid refinement. First, the TLSM methodology is briefly outlined, together with the modifications required to account for variable time-dependent pressure and poro-elasticity in reservoir rock. Next, real world DFIT data are used in TLSM to model the propagation of multiple dynamic fractures and study the effect of time-dependent pressure and poro-elasticity on the development of hydraulic fracture n... [more]