The paper aims to propose a method of reconfiguring the train timetable, taking into account minimising the globally consumed energy for traction purposes. This is a very important issue in the context of rising electricity prices, alarming climate changes and the “Fit for 55” policy introduced in Europe. Each unit of energy saved contributes to improving the state of the planet and reducing the negative human impact on it. In this paper, the authors propose a model that, when applied, will reconfigure the timetable in terms of energy intensity and, as a result, reduce the impact of railways on the burden on the environment. It is proposed to introduce an interdependence between trajectories of electrical train movement. This interdependence is to take place so that it is possible to efficiently transfer the energy recovered during the braking of one train to another train, moving on the same section of the railway line and at the same time (i.e., without using energy storage devices).... [more]

With the increasingly customized product requirements of customers, the manufactured products have the characteristics of multi-variety and small-batch production. A high-quality production scheduling scheme can reduce energy consumption, improve production capacity and processing quality of the enterprise. The high-dimensional many-objective green flexible job shop scheduling problem (Ma-OFJSSP) urgently needs to be solved. However, the existing optimization method are difficult to effectively optimize the Ma-OFJSSP. This study proposes a many-objective flexible job shop scheduling model. An optimization method SV-MA is designed to effectively optimize the Ma-OFJSSP model. The SV-MA memetic algorithm combines an improved strength Pareto evolution method (SPEA2) and the variable neighborhood search method. To effectively distinguish the better solutions and increase the selection pressure of the non-dominated solutions, the fitness calculation method based on the shift-based density es... [more]

Local path planning is a key task for the motion planners of autonomous vehicles since it commands the vehicle across its environment while avoiding any obstacles. To perform this task, the local path planner generates a trajectory and a velocity profile, which are then sent to the vehicle’s actuators. This paper proposes a new local path planner for autonomous vehicles based on the Attractor Dynamic Approach (ADA), which was inspired by the behavior of movement of living beings, along with an algorithm that takes into account four acceleration policies, the ST dynamic vehicle model, and several constraints regarding the comfort and security. The original functions that define the ADA were modified in order to adapt it to the non-holonomic vehicle’s constraints and to improve its response when an impact scenario is detected. The present approach is validated in a well-known simulator for autonomous vehicles under three representative cases of study where the vehicle was capable of gene... [more]

Meeting the generation schedule in a wind farm is a major issue. This work utilized battery energy storage systems (BESS) integrated wind farms (WF) to supply energy to the power grid at a pre-determined generation schedule, which was set previously based on the meteorological forecast and BESS characteristics. This study proposed the integration of two independently controlled BESS into the WF to balance stochastic power deviations between actual wind power and scheduled power. By utilizing linear optimization and solving in MATLAB, simulation models of the operations of BESS-integrated WF have been developed. The technical performance of the BESS-integrated wind farm on meeting the generation schedule, along with the cost benefits and profit attributed to the BESS, is therefore measured by a series of indices. The simulation on a practical wind farm, i.e., Adama-I WF, Ethiopia shows that even though it depends on the type of state exchanging strategy adopted, the developed methodolog... [more]

Smart grid technology has given users the ability to regulate their home energy use more efficiently and effectively. Home Energy Management (HEM) is a difficult undertaking in this regard, as it necessitates the optimal scheduling of smart appliances to reduce energy usage. In this research, we introduce a metaheuristic-based HEM system which incorporates Earth Worm Algorithm (EWA) and Harmony Search Algorithms (HSA). In addition, a hybridization based on the EWA and HSA operators is used to optimize energy consumption in terms of electricity cost and Peak-to-Average Ratio (PAR) reduction. Hybridization has been demonstrated to be beneficial in achieving many objectives at the same time. Extensive simulations in MATLAB were used to test the performance of the proposed hybrid technique. The simulations were run for multiple homes with multiple appliances, which were categorized according to the usage and nature of the appliance, taking advantage of appliance scheduling in terms of the... [more]

Worldwide, the use of fossil fuels covers almost 80% of the entire energy needs. In the European Union (EU), 2020 represents a watershed: for the first time, renewables were the main source of electricity. In Italy, the latest surveys demonstrate an increase in the use of alternative energy sources. European legislative framework highlights the importance of these new trends encouraging (and imposing) the use of renewables. The necessity to become more proactive in the energy production−consumption process and in the achievement of sustainability targets brings people to create Energy Communities (ECs) to manage their own energy supply chain. The authors present an overview of the main legislative framework in the EU and Italy in relation to ECs, analyzing the energy consumption, the electricity and heat energy production potentiality, and the energy balance in a portion of an Italian middle-size city in the Milan metropolitan area. Moreover, they underline the technical, regulatory, a... [more]

Construction of the Włocławek Barrage on the Vistula River in 1970 became an important source of energy; however, it discontinued sediment transport in the Vistula River. This phenomenon resulted in accumulation before and erosion below the barrage; a similar problem can be expected due to the planned construction of the Lower Vistula Cascade. This study is dedicated to finding an effective and feasible solution to sediment transport management, acceptable to a wide group of users (e.g., navigation, energy producers, and ecologists). A numerical 1D model was applied to carry out analysis of hydrodynamics and sediment transport in the river section from Włocławek Barrage to the Vistula River mouth in the Baltic Sea. For calibration, historical data describing the accumulation and erosion created by the Włocławek Barrage were used. The proposed model allowed incorporation of new barrages chronologically, in accordance with the proposed time schedules. Analysis was carried out in a 50-yea... [more]

An essential challenge in generation scheduling (GS) problems of hydrothermal power systems is the inclusion of adequate modeling of the hydroelectric production function (HPF). The HPF is a nonlinear and nonconvex function that depends on the head and turbined outflow. Although the hydropower plants have multiple generating units (GUs), due to a series of complexities, the most attractive modeling practice is to represent one HPF per plant, i.e., a single function is built for representing the plant generation instead of the generation of each GU. Furthermore, due to the computation time constraints and representation of nonlinearities, the HPF must be given by a piecewise linear (PWL) model. This paper presented some continuous PWL models to include the HPF per plant in GS problems of hydrothermal systems. Depending on the type of application, the framework allows a choice between the concave PWL for HPF modeled with one or two variables and the nonconvex (more accurate) PWL for HPF... [more]

The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total CO2 emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey−Clark−Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs’ aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we ob... [more]

Countries’ emission reduction commitments under the Paris Agreement have significant implications for lifestyles. National planning to meet emission targets is based on modelling and analysis specific to individual countries, whereas global integrated assessment models provide scenario projections in a consistent framework but with less granular output. We contribute a novel methodology for translating global scenarios into lifestyle implications at the national and household levels, which is generalisable to any service or country and versatile to work with any model or scenario. Our 5Ds method post-processes Integrated Assessment Model projections of sectoral energy demand for the global region to derive energy-service-specific lifestyle change at the household level. We illustrate the methodology for two energy services (mobility, heating) in two countries (UK, Sweden), showing how effort to reach zero carbon targets varies between countries and households. Our method creates an ana... [more]

To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed-integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison... [more]

Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the ef... [more]

Time synchronization is one of the most fundamental problems on the internet of things (IoT). The IoT requires low power and an efficient synchronization protocol to minimize power consumption and conserve battery power. This paper introduces an efficient method for time synchronization in the IoT called low-power multi-hop synchronization (LPSRS). It employs a reference node scheduling mechanism to avoid packet collisions and minimize the communication overhead, which has a big impact on power consumption. The performance of LPSRS has been evaluated and compared to previous synchronization methods, HRTS and R-Sync, via real hardware networks and simulations. The results show that LPSRS achieves a better performance in terms of power consumption (transmitted messages). In particular, for a large network of 450 nodes, LPSRS reduced the total number of transmitted messages by 53% and 49% compared to HRTS and R-Sync, respectively.

One of the big concerns when planning the expansion of power distribution systems (PDS) is reliability. This is defined as the ability to continuously meet the load demand of consumers in terms of quantity and quality. In a scenario in which consumers increasingly demand high supply quality, including few interruptions and continuity, it becomes essential to consider reliability indices in models used to plan PDS. The inclusion of reliability in optimization models is a challenge, given the need to estimate failure rates for the network and devices. Such failure rates depend on the specific characteristics of a feeder. In this context, this paper discusses the main reliability indices, followed by a comprehensive survey of the methods and models used to solve the optimal expansion planning of PDS considering reliability criteria. Emphasis is also placed on comparing the main features and contributions of each article, aiming to provide a handy resource for researchers. The comparison i... [more]

Wireless rechargeable sensor networks (WRSN) are utilized in environmental monitoring, traffic video surveillance, medical services, etc. In most existing schemes, WRSNs provide sustainable energy for sensor nodes by employing one or more wireless charging vehicles (WCVs). However, two essential drawbacks, regional limitations and traveling speed limitations, constrain these schemes when applied in hostile and large-scale environments. On the other hand, benefiting from the intrinsic flexibility, high flight speed, low cost, and small size of drones, some works have used drones to charge sensor nodes. However, suffering from limited battery capacities, it is also hard to only use drones in large-scale WRSNs. To overcome the drawbacks of WCVs and drones, we proposed a novelty wireless charging system that deploys WCV, WCV-carried drones, and wireless charging pads (pads) in a large-scale wireless sensor network. Based on this new wireless charging system, we first formulated a pad deplo... [more]

The benefits of networked microgrids in terms of economics and resilience are investigated and validated in this work. Considering the stochastic unintentional islanding conditions and conventional forecast errors of both renewable generation and loads, a two-stage adaptive robust optimization is proposed to minimize the total operating cost of networked microgrids in the worst scenario of the modeled uncertainties. By coordinating the dispatch of distributed energy resources (DERs) and responsive demand among networked microgrids, the total operating cost is minimized, which includes the start-up and shut-down cost of distributed generators (DGs), the operation and maintenance (O&M) cost of DGs, the cost of buying/selling power from/to the utility grid, the degradation cost of energy storage systems (ESSs), and the cost associated with load shedding. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The results of case studies demonstrate... [more]

The penetration of renewable energy sources (RESs) is increasing in modern power systems. However, the uncertainties of RESs pose challenges to distribution system operations, such as RES curtailment. Demand response (DR) and battery energy storage systems (BESSs) are flexible countermeasures for distribution-system operators. In this context, this study proposes an optimization model that considers DR and BESSs and develops a simulation analysis platform representing a medium-sized distribution system with high penetration of RESs. First, BESSs and DR were employed to minimize the total expenses of the distribution system operation, where the BESS model excluding binary state variables was adopted. Second, a simulation platform based on a modified IEEE 123 bus system was developed via MATLAB/Simulink for day-ahead scheduling analysis of the distribution system with a high penetration of RESs. The simulation results indicate the positive effects of DR implementation, BESS deployment, a... [more]

Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (... [more]

Long-term energy scenarios (LTES) have been serving as an important planning tool by a wide range of institutions. This article focuses on how LTES have been used (and also devised in some cases) in the government sector, and specifically how the new challenges and opportunities brought by the aspiration for the clean energy transition change the way that governments use LTES. The information tends to remain tacit, and a gap exists in understanding the way to enhance LTES use and development at the government level. To address this gap, we draw on the experience from national institutions that are leading the improvement in official energy scenario planning to articulate a set of overarching best practices to (i) strengthen LTES development, (ii) effectively use LTES for strategic energy planning and (iii) enhance institutional capacity for LTES-based energy planning, all in the context of new challenges associated with the clean energy transition. We present implementation experience... [more]

China announced a target of achieving carbon neutrality by 2060. As one of the most promising pathways to minimize carbon emissions, the low-carbon electricity supply is of high consideration in China’s future energy planning. The main purpose of this study is to provide a comparative overview of the regional siting potential of various low-carbon power plants in the Yangtze River Delta of China. First, unsuitable zones for power plants are identified and excluded based on national regulations and landscape constraints. Second, we evaluate the spatial siting potential of the seven low-carbon energy power plants by ranking their suitability with geographic information system (GIS)-based hierarchical analysis (AHP). The results revealed that around 78% of the area is suitable for power plant siting. In summary, biomass power plants have high siting potential in over half of the spatial areas. Solar photovoltaic and waste-to-electricity are encouraged to establish in the long-term future.... [more]

Recently, it has been suggested that microgrids (MGs) can improve the resilience of distribution systems. However, predictions about future faults are uncertain. This makes calculating the exact value of the benefits of system resilience enhancement close to impossible at the time of MG planning. Therefore, this paper proposes a framework for MG planning, which focuses on resilience estimation. To consider the uncertainties of future failure events, the proposed method for estimating the resilience utilized the Monte Carlo simulation. In addition, an optimal scenario was estimated using a cost−benefit analysis and constraints on the expected value of resilience enhancement. In the case study, an actual MG installation at D-university was evaluated to obtain the optimal MG planning scenario. The results show that the capacity and installation locations of the distributed generators (DGs) impact the resilience enhancement. The proposed method can effectively derive the optimal MG plannin... [more]

Given the ongoing transformation of the transport sector toward electrification, expansion of the current charging infrastructure is essential to meet future charging demands. The lack of fast-charging infrastructure along highways and motorways is a particular obstacle for long-distance travel with battery electric vehicles (BEVs). In this context, we propose a charging infrastructure allocation model that allocates and sizes fast-charging stations along high-level road networks while minimizing the costs for infrastructure investment. The modeling framework is applied to the Austrian highway and motorway network, and the needed expansion of the current fast-charging infrastructure in place is modeled under different future scenarios for 2030. Within these, the share of BEVs in the car fleet, developments in BEV technology and road traffic load changing in the face of future modal shift effects are altered. In particular, we analyze the change in the requirements for fast-charging inf... [more]

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The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experime... [more]

Recently, smart energy hubs with hydrogen conversion and storage have received increased attention in the Netherlands. The hydrogen is to be used for vehicle filling stations, industrial processes and heating. The scientific problem addressed in this paper is the proper sizing of capacities for renewable energy generation, hydrogen conversion and storage in relation to a feasible business case for the energy hub while achieving security of supply. Scenario analysis is often used during the early stages of the energy planning process, and for this an easy-to-use analysis model is required. This paper investigates available modelling approaches and develops an algorithmic modelling method which is worked out in Microsoft Excel and offers ease of use for scenario analysis purposes. The model is applied to case study, which leads to important insights such as the expected price of hydrogen and the proper sizing of electrolyser and hydrogen storage for that case. The model is made available... [more]