Liner shipping is a vital component of the world trade. Liner shipping companies usually operate fixed routes and announce their schedules. However, disruptions in sea and/or at ports affect the planned vessel schedules. Moreover, some liner shipping routes pass through the areas, designated by the International Maritime Organization (IMO) as emission control areas (ECAs). IMO imposes restrictions on the type of fuel that can be used by vessels within ECAs. The vessel schedule recovery problem becomes more complex when disruptions occur at such liner shipping routes, as liner shipping companies must comply with the IMO regulations. This study presents a novel mixed-integer nonlinear mathematical model for the green vessel schedule recovery problem, which considers two recovery strategies, including vessel sailing speed adjustment and port skipping. The objective aims to minimize the total profit loss, endured by a given liner shipping company due to disruptions in the planned operation... [more]

Obstacle avoidance systems for autonomous driving vehicles have significant effects on driving safety. The performance of an obstacle avoidance system is affected by the obstacle avoidance path planning approach. To design an obstacle avoidance path planning method, firstly, by analyzing the obstacle avoidance behavior of a human driver, a safety model of obstacle avoidance is constructed. Then, based on the safety model, the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt. Finally, based on the improved artificial potential field, a collision-free path for autonomous driving vehicles is generated. To verify the performance of the proposed algorithm, co-simulation and real vehicle tests are carried out. Results show that the generated path satisfies the constraints of roads, dynamics, and kinematics. The real time performance, effectiveness, and feasibility of the proposed path planning approach for obstacle avoidance scenarios are a... [more]

Fossil fuels have been heavily exploited since the Industrial Revolution. The resulting carbon emissions are widely regarded as being the main cause of global warming and climate change. Key mitigation technologies for reducing carbon emissions include carbon capture and storage (CCS) and renewables. According to recent analysis of the International Energy Agency, renewables and CCS will contribute more than 50% of the cumulative emissions reductions by 2050. This paper presents a new mathematical programming model for multi-footprint energy sector planning with CCS and renewables deployment. The model is generic and considers a variety of carbon capture (CC) options for the retrofit of individual thermal power generation units. For comprehensive planning, the Integrated Environmental Control Model is employed in this work to assess the performance and costs of different types of power generation units before and after CC retrofits. A case study of Taiwan’s energy sector is presented t... [more]

This paper presents a mixed receding horizon control (RHC) strategy for the optimal scheduling of a battery energy storage system (BESS) in a hybrid PV and wind power plant while satisfying multiple operational constraints. The overall optimisation problem was reformulated as a mixed-integer linear programming (MILP) problem, aimed at minimising the total operating cost of the entire system. The cost function of this MILP is composed of the profits of selling electricity, the cost of purchasing ancillary services for undersupply and oversupply, and the operation and maintenance cost of each component. To investigate the impacts of day-ahead and hour-ahead forecasting for battery optimisation, four forecasting methods, including persistence, Elman neural network, wavelet neural network and autoregressive integrated moving average (ARIMA), were applied for both day-ahead and hour-ahead forecasting. Numerical simulations demonstrated the significant increased efficiency of the proposed mi... [more]

As the penetration of intermittent renewable energy increases and unexpected market behaviors continue to occur, new challenges arise for system operators to ensure cost effectiveness while maintaining system reliability under uncertainties. To systematically address these uncertainties and challenges, innovative advanced methods and approaches are needed. Motivated by these, in this paper, we consider an energy integrated system with renewable energy and pumped-storage units involved. In addition, we propose a data-driven risk-averse two-stage stochastic model that considers the features of forbidden zones and dynamic ramping rate limits. This model minimizes the total cost against the worst-case distribution in the confidence set built for an unknown distribution and constructed based on data. Our numerical experiments show how pumped-storage units contribute to the system, how inclusions of the aforementioned two features improve the reliability of the system, and how our proposed d... [more]

Energy systems in cities need to be decarbonized and are becoming more integrated via energy sector coupling. Today, cities often use simple methods to assess their low carbon targets, e.g., carbon calculators, and these methods use annualized carbon reduction potentials. For example, reductions from heat savings in buildings or fuel demand in transport. This is done because it is simple and fast. This paper describes a methodology that goes beyond carbon calculators and assesses highly renewable energy systems. The methodology is carried out for a case city—Sønderborg, Denmark. Using a national 100% renewable energy study and a suitable energy system analysis tool (EnergyPLAN), the method accounts for inter-sector coupling and energy system dynamics. The energy system is assessed by comparing the results from the analysis tool against numerous key sustainability factors for a Smart Energy System. The paper illustrates how the method delivers a sustainable 100% renewable Smart Energy S... [more]

The optimal scheduling of multi-energy hub systems plays an important role in the safety, stability, and economic operation of the system. However, due to the strong uncertainty of renewable energy access, serious coupling, and the interaction among energy hubs of multi-energy hub systems, it is difficult for the traditional optimal scheduling method to solve these problems. Therefore, game theory was used to solve the optimal scheduling problem of multi-energy hub systems. According to the internal connection mode and energy conversion relationship of energy hubs, along with the competitive and cooperative relationship between multi-energy hubs, the game theoretic optimal scheduling model of the multi-energy hub system was established. Then, two cases and 50 groups of wind speed series were used to test the robustness of the proposed method. Simulation results show that the total power injection is −16,805.8, 104.1847, and −865.561 and the natural gas injection is 46,046.81, 27,727.65... [more]

The large-scale cascade hydropower plants in southwestern China now challenge a multi-market environment in the new round of electricity market reform. They not only have to supply the load for the local provincial market, but also need to deliver electricity to the central and eastern load centers in external markets, which makes the generation scheduling much more complicated, with a correlated uncertain market environment. Considering the uncertainty of prices and correlation between multiple markets, this paper has proposed a novel optimization model of long-term generation scheduling for cascade hydropower plants in multiple markets to seek for the maximization of overall benefits. The Copula function is introduced to describe the correlation of stochastic prices between multiple markets. The price scenarios that obey the Copula fitting function are then generated and further reduced by using a scenario reduction strategy that combines hierarchical clustering and inconsistent valu... [more]

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is th... [more]

This study examined the association between digital transformation (DT), supply chain integration (SCI), and overall sustainable supply chain performance (OSSCP). The current literature has preliminarily explored the concepts of DT and SCI and their relationship with sustainable supply chain performance. However, real empirical evidence of the direct impact of DT and SCI on OSSCP has been missing so far. To fill this gap, data were collected from 134 professionals working in international manufacturing companies operating in Morocco through a questionnaire-based survey from August 2022 to November 2022. A conceptual framework was developed based on DT, SCI, and OSSCP and analyzed by partial least squares structural equation modeling (PLS-SEM) with the assistance of SmartPLS 4.0 software. The findings revealed that DT has a significant positive influence on SCI and OSSCP. Furthermore, SCI directly and positively impacts OSSCP with a partial mediation effect on the relationship between D... [more]

The aim of the article is the development of methods for optimal overhaul planning of compressor station equipment. Nowadays, due to uncertainties in the forecast of gas supply flow rates, increasing the reliability and energy efficiency of main gas pipelines is an urgent problem. The dependence of operating costs for major repairs on the maintenance periodicity is extreme. Reducing equipment’s maintenance period leads to an increase in repair costs. It also increases the reliability of equipment operation. Overall, all these facts reduce the probability of emergency failures and related expenses for emergency recovery, gas losses, and undersupply to consumers. Therefore, an optimal maintenance frequency exists, at which the total operating costs will be minimal. A procedure for optimizing the periodicity of repairs and equipment replacement is proposed. It was realized by constructing an objective function as a dependence of exploitation costs on the inter-repair period of major repai... [more]

The policy decision of extending electric power transmission lines to connect a remote area to a primary grid vs. developing local electricity generation resources must be informed by studies considering both alternatives’ economic and environmental outcomes. Such analysis must also consider the uncertainty of several factors such as fuel prices, the cost and performance of renewable and conventional power generation technologies, and the value of environmental benefits. This paper presents a method for this analysis, making two main contributions to the literature. First, it shows how to characterize the two alternatives (i.e., main-grid extension vs. local power generation) in detail for precise quantification of their capital and operating costs while guaranteeing that they are both adequate to meet forecast demand and operating reserves. Second, it shows how to properly account for the economic and environmental implications of renewable energy intermittency and uncertainty through... [more]

This article aims to determine the specificity of spatial conflicts related to spatial plans concerning wind power plants. To achieve the aim of the article, all spatial plans in force in Poland were analysed, distinguishing those which determine the possibility of realisation of wind power plants. The research concerns the whole country. The literature review carried out for this article verifies approaches to spatial conflicts and identifies how planning barriers to the implementation of wind power investments are defined. The results identified Polish municipalities where spatial plans containing provisions for implementing wind power plants have been enacted. Then, through survey research, an attempt was made to identify critical spatial conflicts occurring in these municipalities. The last part of the research involved identifying and analysing Polish court decisions concerning spatial plans permitting wind power plants. These were recognised as a particular stage of spatial confl... [more]

Soiling of PV modules is an issue causing non-negligible losses on PV power plants, between 3 and 4% of the total energy production. Cleaning is the most common way to mitigate soiling. The impact of the cleaning activity can be significant, both in terms of cost and resources consumption. For these reasons, it is important to monitor and predict soiling profiles and establish an optimal cleaning schedule. Especially in locations where raining is irregular or where desert winds carry a high concentration of particles, it is also important to know how precipitation and dust events affect the soiling ratio. This paper presents a new model based on environmental conditions that helps the decision-making process of the cleaning schedule. The model was validated by the analysis of five large grid-connected PV plants in Spain over two years of operation, with a total power of 200 MW. The comparison between the model and soiling sensors at the five locations was included. Excellent results we... [more]

The supply logistics of energy biomasses generally involves a complex system of supply chains, which aim to achieve timely and cost-efficient feedstock deliveries to biomass demand points. The performance of supply chains is often examined in case studies where spatial data about biomass sources and transportation networks are deployed in varying resolutions and to different geographical extents. In this paper, we have reviewed 94 publications, in which spatial data were used in case studies that focused on analysing and optimising energy biomass supply chains. The reviewed publications were classified into 16 categories, according to the publication year, study methods and objectives, biomass types, supply system complexity and the spatial features of each study area. This review found that the use of geographical information systems in this context has increased in popularity in recent years, and that and the multiformity of the applied methods, study objectives and data sources have... [more]

High-performance computing (HPC), according to its name, is traditionally oriented toward performance, especially the execution time and scalability of the computations. However, due to the high cost and environmental issues, energy consumption has already become a very important factor that needs to be considered. The paper presents a survey of energy-aware scheduling methods used in a modern HPC environment, starting with the problem definition, tackling various goals set up for this challenge, including a bi-objective approach, power and energy constraints, and a pure energy solution, as well as metrics related to the subject. Then, considered types of HPC systems and related energy-saving mechanisms are described, from multicore-processors/graphical processing units (GPU) to more complex solutions, such as compute clusters supporting dynamic voltage and frequency scaling (DVFS), power capping, and other functionalities. The main section presents a collection of carefully selected a... [more]

Load flow solutions refer to the steady-state stability of power systems and have a crucial role in the design and planning of slow-changing elements; e.g., in online tab changing actions, automatic generation control, over-excitation limiters and the power recovery characteristics of a load. Therefore, the purpose of this work was to show the connectivity between load flow analysis and long-term voltage stability using a generator model by introducing a novel voltage stability assessment based on the multi-machine dynamic model along with the load flow study for a power grid. The Euclidean distance (ED) was used to introduce a new voltage stability index based on the voltage phasor profile for real-time monitoring purposes. The effects of reactive power compensation, in addition to load-generation patterns and network topology changes in the system behavior, could be seen clearly on the voltage profiles of the buses. Thus, the increased values for the EDs of the buses’ voltage amplitu... [more]

In view of the current situation of the global energy crisis and environmental pollution, the energy industry transition and environmental governance are urgently needed. To deal with the problem above, the construction of a power system dominated by renewable energy (PSDRE) with wind turbine (WT), photovoltaic (PV), biomass power (BP), and other clean, low-carbon, renewable energy sources as the principal part has become a consensus all over the world. However, the random and uncertain power output of renewable energy will not only put pressure on the power system but also lead to the unreasonable and insufficient usage of renewable energy. In this context, the energy storage (ES) effects of flexible resources, such as physical energy storage of batteries and demand response (DR), are analyzed first. Next, a modeling method for the operational characteristics of physical and virtual shared energy storage (PVSES) in regional PSDRE (RPSDRE) is proposed. Finally, an optimal scheduling st... [more]

This paper proposes a three-parameter gearshift scheduling strategy that has been implemented on both large and small electric vehicles with two-speed transmission systems. The new strategy evaluates vehicle performance under varying driving conditions on flat and hilly roads by assessing the vehicle speed, acceleration, and road grade. A heuristic approach is used to develop two gearshift schedules for vehicle acceleration and road grade, and gradient descent and pattern search methods are applied to optimize the gear ratios and primary gearshift schedules. The results show that the proposed gearshift strategy saves 16.5% of energy on hilly roads compared to conventional approaches. Optimal gearshift schedules for acceleration provide more room for second gear operation, while optimized gearshift schedules for the road grade increase the buffer zone for larger vehicles and allow more space for the second gear operating area. The experimental results validate the proposed approach’s pe... [more]

Autonomous electric vehicles (AEVs) have garnered increasing attention in recent years as they hold significant promise for transforming the transportation sector. However, despite advances in the field, effective vehicle drive control remains a critical challenge that must be addressed to realize the full potential of AEVs. This study presents a novel approach to AEV drive control for concurrently generating a suitable speed profile and controlling the vehicle drive speed along a planned path that takes into account various driving circumstances that mimic real-world driving. The designed strategy is divided into two parts: The first part presents a proposed speed planning algorithm (SPA) that works on developing an adequate speed profile for vehicle navigation; first, the algorithm uses an approach for identifying sharp curves on the predefined trajectory; secondly, based on the dynamic properties of these curves, it generates an appropriate speed profile to ensure smooth vehicle tra... [more]

Due to the lack of historical output data of new wind farms, there are difficulties in the scheduling and planning of power grid and wind power output scenario generation. The randomness and uncertainty of meteorological factors lead to the results of traditional scenario generation methods not having the ability to accurately reflect their uncertainty. This article proposes a RAC-GAN-based scenario generation method for a new wind farm output. First, the Pearson coefficient is adopted in this method to screen the meteorological factors and obtain the ones that have larger impact on wind power output; Second, based on the obtained meteorological factors, the Grey Relation Analysis (GRA) is used to analyze the meteorological correlation between multiple wind farms with sufficient output data and new wind farms (target power stations), so that the wind farm with high meteorological correlation is selected as the source power station. Then, the K-means method is adopted to cluster the met... [more]

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96... [more]

Under the background of “carbon peaking and carbon neutrality goals” in China, the Highway Self-Consistent Energy System (HSCES) with renewable energy as the main body has become a key research object. To study the operational status of the HSCES in a specific region and realize the economically optimal operation of the HSCES, an HSCES model in a low-load, abundant-renewable-energy and no-grid scenario is established, and a two-stage optimal scheduling method for the HSCES is proposed. Moreover, in the day-ahead stage, uncertainty optimization scenarios are generated by Latin hypercube sampling, and a definition of the self-consistent coefficient is proposed, which is used as one of the constraints to establish a day-ahead economic optimal scheduling model. Through the case comparison analysis, the validity of the day-ahead scheduling model is confirmed and the optimal day-ahead scheduling plan is attained. Furthermore, in the intra-day stage, an intra-day rolling optimization method i... [more]

Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in t... [more]

Shared energy storage systems (SESS) have been gradually developed and applied to distribution networks (DN). There are electrical connections between SESSs and multiple DN nodes; SESSs could significantly improve the power restoration potential and reduce the power interruption cost during fault periods. Currently, a major challenge exists in terms of how to consider both the efficiency of the operation and the reliability cost when formulating the SESS scheduling scheme. A SESS optimal scheduling method that considers the DN operation risk is proposed in this paper. First, a multi-objective day-ahead scheduling model for SESS is developed, where the user’s interruption cost is regarded as the reliability cost and it is the product of the occurrence probability of the expected accident and the loss of power outage. Then, an island partition model with SESS was established in order to accurately calculate the reliability cost. Via the maximum island partition and island optimal rectifi... [more]