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]

Penetration of equipment such as photovoltaic power generations (PV), heat pump water heaters (HP), and electric vehicles (EV) introduces voltage unbalance issues in distribution systems. Controlling PV and energy storage system (ESS) outputs or coordinated EV charging are investigated for voltage unbalance compensation. However, some issues exist, such as dependency on installed capacity and fairness among consumers. Therefore, the ideal way to mitigate unbalanced voltages is to use grid-side equipment mainly. This paper proposes a voltage unbalance compensation based on optimal tap operation scheduling of three-phase individual controlled step voltage regulators (3ϕSVR) and load ratio control transformer (LRT). In the formulation of the optimization problem, multiple voltage unbalance metrics are comprehensively included. In addition, voltage deviations, network losses, and coordinated tap operations, which are typical issues in distribution systems, are considered. In order to inves... [more]

This study determines the relationship between the increase in size of dry bulk carriers and container ships and the changes in sustainable shipping performance. It measures the elasticities of shipping costs for bulk carriers and container ships. Using regression, it derives the functions of the daily and unit costs of shipping with respect to the size of dry bulk carriers and container ships. The estimated daily and unit cost elasticities and cost models reveal significant but diversified impacts of vessel size on dry bulk and container shipping cost and its components, other operating capital, and fuel costs. Findings: Dry bulk carriers and containership size mean elasticities of daily operating costs estimates respectively: total operating costs 0.291 and 0.552, other operating cost (labor cost included) 0.238 and 0.328, capital costs 0.329 and 0.765, fuel costs 0.289 and 0.462; dry bulker and container ship unit shipping mean elasticity respectively: full operating costs (−0.751)... [more]

In this article, a novel dynamic economic load dispatch with emission based on a multi-objective model (MODEED) considering demand side management (DSM) is presented. Moreover, the investigation and evaluation of impacts of DSM for the next day are considered. In other words, the aim of economical load dispatch is the suitable and optimized planning for all power units considering different linear and non-linear constrains for power system and generators. In this model, different constrains such as losses of transformation network, impacts of valve-point, ramp-up and ramp-down, the balance of production and demand, the prohibited areas, and the limitations of production are considered as an optimization problem. The proposed model is solved by a novel modified multi-objective artificial bee colony algorithm (MOABC). In order to analyze the effects of DSM on the supply side, the proposed MODEED is evaluated on different scenarios with or without DSM. Indeed, the proposed MOABC algorithm... [more]

The introduction of autonomous cars will help to improve road traffic safety, and the use of a cargo trailer improves the energy efficiency of transport. One of the critical (collision) road situations has been considered, where immediate counteraction is required in a space that has been only partly defined. This research work was aimed at determining the impact of the trajectory planning method and the values of some parameters of the control system on the feasibility of safe avoidance of an obstacle that has suddenly appeared. The obstacle is assumed to be a motor vehicle moving on a road intersection along a collision path in relation to the autonomous car-trailer unit (CT unit) travelling at high speed. Analysis of cooperation between several non-linear models (representing the car, trailer, tyre−road interaction, and driving controller) has been carried out. Mathematical models of the control system and the CT unit have been built. The process of selection of temporary and variab... [more]

Biogas technology is an important alternative energy source worldwide. Blackwater and kitchen refuse represent ideal waste streams for bioenergy recovery through anaerobic co-digestion. Modeling of the biokinetics of anaerobic digestion on several aspects, such as microbial activity, substrate degradation, and methane production, from co-digestion of black water (BW) and kitchen refuse (KR) was the objective of this research. A mathematical model was developed towards a simulation based on mass balances on biomass, the organic substrate, and biogas. The model was implemented in INSEL and experimental data from the literature were used for model validation. The study shows that the simulation results fit well with the experimental data. The energy consumption and generation potential of anaerobic co-digestion of BW and KR were calculated to investigate if the produced biogas could supply the digester’s energy demand. This study can be used to pre-design anaerobic digestion systems... [more]

The use of renewable energy sources (RES) has increased exponentially worldwide, as an alternative to the indiscriminate use of fossil fuels and to mitigate their effects on the environment. Cuba is not lagging behind in this development since the government’s plan until 2030 includes the contribution of renewable sources as a fundamental component in the national energy mix. This paper models possible scenarios based on 2019 statistics for achieving a 25% and 100% penetration of renewable sources by 2030 in the Isla de la Juventud’s (an island south of the main island of Cuba) electrical power system (EPS). This modeling is carried out utilizing and open source Excel-based accounting framework Long-range Integrated Development Analysis (LINDA). For this purpose, international and national trends in the use and development of renewable energy sources and the influence of the characteristics of each renewable source (wind, solar, biodiesel, battery storage) were analyzed. The analysis o... [more]

Renewable energy sources including wind farms and solar sites, have been rapidly integrated within power systems for economic and environmental reasons. Unfortunately, many renewable energy sources suffer from variability and uncertainty, which may jeopardize security and stability of the power system. To face this challenge, it is necessary to develop new methods to manage increasing supply-side uncertainty within operational strategies. In modern power system operations, the optimal power flow (OPF) is essential to all stages of the system operational horizon; underlying both day-ahead scheduling and real-time dispatch decisions. The dispatch levels determined are then implemented for the duration of the dispatch interval, with the expectation that frequency response and balancing reserves are sufficient to manage intra-interval deviations. To achieve more accurate generation schedules and better reliability with increasing renewable resources, the OPF must be solved faster and with... [more]

Biomass is an abundant energy source, particularly in Canada, as an alternative or primary source for electricity generation. However, low economy of scale could cause a loss of efficiency for bioenergy adoption in small remote communities. In this sense, coordination among the players could promote the efficiency and profitability of bioenergy supply chains for these communities. There are different coordination strategies with varying impacts on supply chain players’ profit or cost. Therefore, analyzing and comparing them could provide insights on how to decide about the choice of coordination strategy. In doing so, this study considers the coordination strategies of quantity discounts and cost-sharing. The study adopts a system dynamics approach for simulating these coordination scenarios, obtaining their corresponding optimal supply chain decisions, followed by a comparative analysis. For a case study, the study considers multiple suppliers providing biomass for electricity generat... [more]

As microgrids have gained increasing attention over the last decade, more and more applications have emerged, ranging from islanded remote infrastructures to active building blocks of smart grids. To optimally manage the various microgrid assets towards maximum profit, while taking into account reliability and stability, it is essential to properly schedule the overall operation. To that end, this paper presents an optimal scheduling framework for microgrids both for day-ahead and real-time operation. In terms of real-time, this framework evaluates the real-time operation and, based on deviations, it re-optimises the schedule dynamically in order to continuously provide the best possible solution in terms of economic benefit and energy management. To assess the solution, the designed framework has been deployed to a real-life microgrid establishment consisting of residential loads, a PV array and a storage unit. Results demonstrate not only the benefits of the day-ahead optimal schedul... [more]

This work is the earliest attempt to propose an integrated resource planning for distributed hybrid microgrids considering virtual-inertia support (VIS) and demand-response support (DRS) systems. Initially, three-distributed sustainable energy-based unequal hybrid microgrids are envisioned with the availability of solar/wind/bioenergy resources. In order to overcome the effects of intermittency in renewable resources and low inertia, each microgrid is incorporated with DRS and VIS units for demand- and supply-side management, respectively. The proposed system is simulated in MATLAB considering real-time recorded solar/wind data with realistic loading for 12 months. A novel quasi-oppositional chaotic selfish-herd optimization (QCSHO) algorithm is proposed by hybridizing quasi-opposition-based learning and chaotic linear search techniques into the selfish-herd optimization, for optimal regulation of voltage and frequency in microgrids. Then, the system responses are compared with 7 algor... [more]

Recently, multi-energy systems (MESs), whereby different energy carriers are coupled together, have become popular. For a more efficient use of MESs, the optimal operation of these systems needs to be considered. This paper focuses on the day-ahead optimal schedule of an MES, including a combined heat and electricity (CHP) unit, a gas boiler, a PV system, and energy storage devices. Starting from a day-ahead PV point forecast, a non-parametric probabilistic forecast method is proposed to build the predicted interval and represent the uncertainty of PV generation. Afterwards, the MES is modeled as mixed-integer linear programming (MILP), and the scheduling problem is solved by interval optimization. To demonstrate the effectiveness of the proposed method, a case study is performed on a real industrial MES. The simulation results show that, by using only historical PV measurement data, the point forecaster reaches a normalized root-mean square error (NRMSE) of 14.24%, and the calibration... [more]

The aim of the research was to create a decision-making model, which would be able to support planning, organizing and conducting the dredging works in the port area. The proposed solution is a multiple element system which enables to verify, in a comprehensive way, the majority of the aspects determining the quality and the time of dredging enterprise realization. The paper presents an original approach to the decision-making process during the organization of dredging works, using the computer program. In order to achieve the main goal of the study, the conditions of dredging works were considered. Furthermore, the factors that have an influence on the schedule of the project were evaluated and algorithms, as well as process organization schemes, were developed. If it is not enough, the decision models corresponding to the discussed issue were analysed and the computer program was created. And last but not the least, the proposed project and equipment were verified using a simulation... [more]

The work presents a simulation-based Multi-Objective Optimization (MOO) framework for efficient production planning in Energy Supply Chains (ESCs). An Agent-based Model (ABM) that is more comprehensive than others adopted in the literature is developed to simulate the agent’s uncertain behaviors and the transaction processes stochastically occurring in dynamically changing ESC structures. These are important realistic characteristics that are rarely considered. The simulation is embedded into a Non-dominated Sorting Genetic Algorithm (NSGA-II)-based optimization scheme to identify the Pareto solutions for which the ESC total profit is maximized and the disequilibrium among its agent’s profits is minimized, while uncertainty is accounted for by Monte Carlo (MC) sampling. An oil and gas ESC model with five layers is considered to show the proposed framework and its capability of enabling efficient management of the ESC sustained production while considering the agent’s uncertain interact... [more]

This paper assesses the economic benefits of 57 World Bank Group-sponsored hydropower dam plant investments. Hydropower dams are among the main sources for producing electricity and the largest renewable source for power generation throughout the world. Hydropower dams are often a lower-cost option for power generation in Clean Energy Transition for addressing global climate change. Despite its conspicuous aspects, constructing hydropower dams has been controversial. Considering the World Bank’s long history as the largest hydropower development financier, this study investigates its performance in supporting hydropower dams. The outcomes of this study apply to the wider hydropower development community. Of the projects in this study, 70% experienced a cost overrun, and more than 80% of projects experienced time overruns, incurring potential additional costs as a result. Despite the high cost and time overruns, this hydropower portfolio of dams produced a present value of net economic... [more]

Climate change requires the reduction of energy consumption in transport and the associated fuel consumption, and emission of pollutants into the atmosphere. This issue is particularly relevant to air transport. Referring to the current legislative actions aimed at reducing the negative impact of air transport on the environment, the paper describes the possibilities of reducing energy consumption and related emissions. The Boeing 737 aircraft equipped with Snecma CFM 56C engines was adopted for the research. The research problem focused on determining a cruising trajectory characterized by the shortest cruising time and the lowest energy consumption by the aircraft during the flight between two selected European airports in given meteorological conditions. In the analysis, the Dijsktra’s algorithm was applied and built-in MATLAB functions were used. Based on the studied case, it was shown that it was possible to reduce both energy consumption and CO2 emissions by 10%. The novelty of t... [more]

With the increase in the electrical load and the progressive introduction of power generation from intermittent renewable energy sources, the power line operating conditions are approaching the thermal limits. The definition of thermal limits variable in time has been addressed under the concept of dynamic thermal rating (DTR), with which it is possible to provide a more detailed assessment of the line rating and exploit the electrical system more flexibly. Most of the literature on DTR has addressed overhead lines exposed to different weather conditions. The interest in the dynamic thermal rating of power cables is increasing, considering the evolution of computational methods and advanced systems for cable monitoring. This paper contains an overview of the concepts and methods referring to dynamic cable rating (DCR). Starting from the analytical formulations developed many years ago for determining the power cable rating in steady-state conditions, also reported in International Stan... [more]

The Energy Internet (EI) has been proposed as an evolution of the power system in order to improve its efficiency in terms of energy generation, transmission and consumption. It aims to make the use of renewable energy effective. Herein, the energy router has been considered the crucial element that builds the net structure between the different EI components by connecting and controlling the bidirectional power and data flow. The increased use of renewable energy sources in EI has contributed to the creation of a new competitive energy trading market known as peer-to-peer energy trading, which enables each component to be part of the trading process. As a consequence, the concept of energy routing is increasingly relevant. In fact, there are three issues that need to be taken into account during the energy routing process: the subscriber matching, the energy-efficient path and the transmission scheduling. In this work, we first proposed a peer-to-peer energy trading scheme to ensure a... [more]

This paper presents an electrical infrastructure planning method for transit systems that operate with partially grid-connected vehicles incorporating on-board batteries. First, the state-of-the-art of electric transit systems that combine grid-connected and battery-based operation is briefly described. Second, the benefits of combining a grid connection and battery supply in Bus Rapid Transit (BRT) systems are introduced. Finally, the planning method is explained and tested in a BRT route in Medellin, Colombia, using computational simulations in combination with real operational data from electric buses that are currently operating in this transit line. Unlike other methods and approaches for Battery Electric Bus (BEB) infrastructure planning, the proposed technique is system-focused, rather than solely limited to the vehicles. The objective of the technique, from the vehicle’s side, is to assist the planner in the correct sizing of batteries and power train capacity, whereas from the... [more]

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted bas... [more]

The variability of renewable energies and their integration into the grid via power electronics demands the design of robust control algorithms. This work incorporates two techniques to ensure the stability of a boost converter through its state equations, implementing the inverse optimal control and the gain-scheduling technique for robust control settings. In such a way that, under a single adjustment, it is capable of damping different changes such as changes in the parameters, changes in the load, the input voltage, and the reference voltage. On the other hand, inverse optimal control is based on a discrete-time control Lyapunov function (CLF), and CLF candidate depends on fixed parameters that are selected to obtain the solution for inverse optimal control. Once these parameters have been found through heuristic or artificial intelligence methods, the new proposed methodology is capable of obtaining a robust optimal control scheme, without having to search for new parameters throu... [more]

As the number of EVs increases, their impact on electrical systems will be substantial. Novel management schemes are needed to manage the electrical load they require when charging. Literature is rich with different techniques to manage and control this effect on the grid by controlling and optimizing power flow. Although these solutions heavily rely on communication lines, they mostly treat communication as a black box. It is important to develop communication solutions that can integrate EVs, charging stations (CSs), and the rest of the grid in an interoperable way. A standard approach would be indispensable as there are different EV models manufactured by different companies. The IEC 61850 standard is a strong tool used for developing communication models for different smart grid components. However, it does not have the necessary models for implementing smart EV management schemes that coordinate between EVs and CSs. In this paper, these missing links are addressed through the deve... [more]

Operation scheduling in electric power grids is one of the most practical optimization problems as it sets a target for the efficient management of the electric power supply and demand. Advancement of a method to solve this issue is crucially required, especially in microgrids. This is because the operational capability of microgrids is generally lower than that of conventional bulk power grids, and therefore, it is extremely important to develop an appropriate, coordinated operation schedule of the microgrid components. Although various techniques have been developed to solve the problem, there is no established solution. The authors propose a problem framework and a solution method that finds the optimal operation schedule of the microgrid components considering the uncertainty in the available data. In the authors’ proposal, the objective function of the target problem is formulated as the expected cost of the microgrid’s operations. Since the risk of imbalance in the power supply a... [more]

Operation and maintenance constitute a substantial share of the lifecycle expenditures of an offshore renewable energy farm. A noteworthy number of methods and techniques have been developed to provide decision-making support in strategic planning and asset management. Condition monitoring instrumentation is commonly used, especially in offshore wind farms, due to the benefits it provides in terms of fault identification and performance evaluation and improvement. Incorporating technology advancements, a shift towards automation and digitalisation is taking place in the offshore maintenance sector. This paper reviews the existing literature and novel approaches in the operation and maintenance planning and the condition monitoring of offshore renewable energy farms, with an emphasis on the offshore wind sector, discussing their benefits and limitations. The state-of-the-art in industrial condition-based maintenance is reviewed, together with deterioration models and fault diagnosis and... [more]