Considering the complexity of the three-dimensional environment and the flexibility of the quadrotor aircraft, using the traditional A* algorithm for global path planning has the disadvantages of less search direction, more expanded nodes, and a longer planning path. Therefore, an improved A* algorithm is proposed, which is improved from two aspects. Firstly, a two-layer extended neighborhood strategy is proposed, which can increase the search direction and make better use of the flexibility of the aircraft. Secondly, the heuristic function is improved to make the heuristic function value closer to the actual planning path distance, which can reduce the expansion nodes and optimize the planning path. Finally, the path planning simulation of the improved A* algorithm is carried out and the results show that the path planned by the improved algorithm is shorter and the expanded nodes are fewer, which can guide the quadrotor to reach the destination better.

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

To improve the efficiency of loading operation by researching the optimization of the pre-marshalling operation scheme in the export container block between the time when the ship stowage chart was published and the beginning time of loading, a two-stage mixed integer programming model was established. The first stage established an optimization model of the container reshuffling location, based on the objective function of the least time-consuming operation of a single-bay-yard crane, and designed an improved artificial bee colony algorithm to solve it. Based on the first stage, an optimization model of yard crane configuration and scheduling was built to minimize the maximum completion time of the yard crane in the export block, and an improved genetic algorithm was designed to solve the built model. Through comparative analysis, the performance of our algorithm was better than CPLEX and traditional heuristic algorithms. It could still solve the 30 bays quickly, and the solving quali... [more]

While carbon emissions reduction brings about environmental benefits, it can also create financial pressure on many manufacturing enterprises. Many manufacturing enterprises have begun to pledge their own carbon emissions right quotas for financing and the funds from this financing are being used to implement energy savings and emissions reduction strategies. To investigate the impact of carbon emissions right pledge financing on supply chains, this study constructed a two-echelon low-carbon supply chain, which consisted of a capital-constrained manufacturer and a retailer. The manufacturer invested in carbon reduction technologies using carbon emissions right pledge financing. On this basis, we analyzed the carbon emissions reduction levels and profits of the supply chain in three different power structures. The results showed that the manufacturer pledged the most carbon emissions rights to finance emissions reduction in the Nash model and, in this case, the carbon emissions reductio... [more]

The increasing use of high shares of renewable energy sources (RESs) in the current electricity network introduces challenges to the design and management of the electricity network due to the variation and uncertainty nature of the RESs. Some existing energy infrastructures, such as heat, gas, and transport, all have some level of inbuilt storage capacity and demand response (DR) potentials that can be exploited in an energy system integration to give the electricity network some level of flexibility and promote an efficient transition to a low-carbon, resilient, and robust energy system. The process of integrating different energy infrastructure is known as multi-vector energy systems (MESs). This paper reviews different studies on the planning of MESs using the energy hubs (EHs) approach. The EHs model used in this paper links different energy vectors such as gas, electricity, and heat energy vectors in its planning model, as opposed to planning each energy vector independently, in... [more]

The framework of energy transition poses significant challenges in subtransmission network development, where the increased renewable energy generation is collected, in order to efficiently convey power production, avoiding limitations in a range of operating conditions. In this paper, a method to evaluate possible margins for further renewable penetration due to electric network development is assessed, by means of scenario evaluation for the concretisation of renewable initiatives, combined producibility analysis, and load flow studies, accounting for operation islands in subtransmission network organisation, carried out in N and N-1 conditions. The method is applied to a provisional model of the southern part of the Italian power system.

After a large number of electric vehicles (EVs) are connected to the integrated energy system, disorderly charging and discharging of EVs will have a negative impact on the safe and stable operation of the system. In addition, EVs’ uncertain travel plans and the stochastic fluctuation of renewable energy output and load power will bring risks and challenges. In view of the above problems, this paper establishes a hierarchical stochastic optimal scheduling model of an electric thermal hydrogen integrated energy system (ETH-IES) considering the EVs vehicle-to-grid (V2G) mechanism. The EVs charging and discharging management layer aims to minimize the variance of the load curve and minimize the dissatisfaction of EV owners participating in V2G. The multi-objective sand cat swarm optimization (MSCSO) algorithm is used to solve the proposed model. On this basis, the daily stochastic economic scheduling of ETH-IES is carried out with the goal of minimizing the operation cost. The simulation... [more]

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in deal... [more]

With the expansion of global trade and the deterioration of the marine environment, research on the sustainability of marine transport has drawn increasing scientific attention. This study takes the marine supply chain composed of Maersk and ports in 17 coastal cities in China as decision-making units (DMUs). It then chooses indicators from the three dimensions of economy, environment and society to evaluate the sustainable efficiency of the marine supply chain, Maersk and ports. In order to deal with the uncertain variables of the sustainability evaluation index, this study develops an uncertain network DEA model based on the uncertainty theory, and the computable equivalent form and proof are also provided. In addition, this study divides the decentralized marine supply chain into two modes, i.e., Maersk as leader and the port as leader, and it calculates their sustainable efficiency, respectively. These results suggest that the sustainable performance of ports is superior to that of... [more]

Flexographic printing is a highly sought-after technique within the realm of packaging and labeling due to its versatility, cost-effectiveness, high speed, high-quality images, and environmentally friendly nature. A major challenge in flexographic printing is the need to optimize energy usage, which requires diligent attention to resolve. This research combines lean principles and machine learning to improve energy efficiency in selected flexographic printing machines; i.e., Miraflex and F&K. By implementing the 5Why root cause analysis and Kaizen, the study found that the idle time was reduced by 30% for the Miraflex machine and the F&K machine, resulting in energy savings of 34.198% and 38.635% per meter, respectively. Additionally, a multi-linear regression model was developed using machine learning and a range of input parameters, such as machine speed, production meter, substrate density, machine idle time, machine working time, and total machine run time, to predict energy consum... [more]

The use of regenerative braking systems is an important approach for improving the travel mileage of electric vehicles, and the use of an auxiliary hydraulic braking energy recovery system can improve the efficiency of the braking energy recovery process. In this paper, we present an algorithm for optimizing the energy recovery efficiency of a hydraulic regenerative braking system (HRBS) based on fuzzy Q-Learning (FQL). First, we built a test bench, which was used to verify the accuracy of the hydraulic regenerative braking simulation model. Second, we combined the HRBS with the electric vehicle in ADVISOR. Third, we modified the regenerative braking control strategy by introducing the FQL algorithm and comparing it with a fuzzy-control-based energy recovery strategy. The simulation results showed that the power savings of the vehicle optimized by the FQL algorithm were improved by about 9.62% and 8.91% after 1015 cycles and under urban dynamometer driving schedule (UDDS) cycle conditi... [more]

This study describes the implementation of an energy sector community to examine the practice of cybersecurity for operational technology environments and their supply chains. Evaluating cybersecurity from the perspectives of different actors participating in the energy sector, the progress and challenges of operators and suppliers in delivering cybersecurity for the sector are explored. While regulatory frameworks incentivize individual organizations to improve their cybersecurity, operational services contain contributions from many organizations, and this supply chain of activity needs to be influenced and managed to achieve desired security and resilience outcomes. Through collaborations and systems engineering approaches, a reference model is created to facilitate improvements in managing the cybersecurity of supply chains for different actors, including service operators, maintainers, manufacturers, and systems integrators. This study provides an illustration of implementing a co... [more]

This paper investigates the design problem of an electric bus (E-bus) route with charging stations to smooth the operations between E-bus service and charging. The design variables include the locations of E-bus stops, number of charging piles at charging stations, fare, and headway. A mathematical programming model is proposed to maximize social welfare in consideration of the uncertain charging demand at charging stations. The model solution algorithm is also designed. The model and algorithm are demonstrated on the E-bus route 931 in the city of Suzhou, China. The results of the case studies show that (i) the right number of stops on a bus route can contribute to the highest social welfare; (ii) the pile−bus ratio decreases with the increase of E-bus fleet size, thereby improving the E-bus charging efficiency at charging stations; and (iii) deploying charging stations at one end of a bus route can achieve a shorter waiting time for E-bus compared with deployment at two ends.

This work addresses the internalization of externalities in energy decision making and in generation expansion planning (GEP). Although the linkage between externalities and energy is well recognized, the issue of the internalization in GEP models and from a sustainability perspective is still far from being fully explored. A critical literature review is presented, including scientific articles published in the period from 2011 to 2021 and selected from scientific databases according to a set of pre-defined keywords. The literature is vast and quite heterogeneous in the models and methods used to deal with these externalities, and therefore a categorization of these studies was attempted. This categorization was based on the methods used, the geographical scope, the externalities included in the planning model and the strategies for their inclusion. As a result, it was possible to perceive that most studies tend to focus on the internalization of externalities related to CO2 and equiv... [more]

Dealing with multi-objective problems has several interesting benefits, one of which is that it supplies the decision-maker with complete information regarding the Pareto front, as well as a clear overview of the various trade-offs that are involved in the problem. The selection of such a representative set is, in and of itself, a multi-objective problem that must take into consideration the number of choices to show the uniformity of the representation and/or the coverage of the representation in order to ensure the quality of the solution. In this study, day-ahead scheduling has been transformed into a multi-objective optimization problem due to the inclusion of objectives, such as the operating cost of multi-energy multi-microgrids (MMGs) and the profit of the Distribution Company (DISCO). The purpose of the proposed system is to determine the best day-ahead operation of a combined heat and power (CHP) unit, gas boiler, energy storage, and demand response program, as well as the tra... [more]

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applications. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.50... [more]

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective.

Solar PhotoVoltaics (PV) integration into the electricity grids significantly increases the complexity of Transmission Expansion Planning (TEP) because solar PV power generation is uncertain and difficult to predict. Therefore, this paper proposes the optimal planning method for transmission expansion combined with uncertain solar PV generation. The problem of uncertain solar PV generation is solved by using Long Short-Term Memory (LSTM) for forecasting solar radiation with high accuracy. The objective function is to minimize total system cost, including the investment cost of new transmission lines and the operating cost of power generation. The optimal TEP problem is solved by the Binary Differential Evolution (BDE) algorithm. To investigate and demonstrate the performance of the proposed method, the IEEE 24-bus system and solar radiation data in Thailand are selected as a study case for TEP. The MATPOWER program written in MATLAB software is used for solving optimal power flow probl... [more]

The deployment of CO2 capture and storage (CCS) and negative emissions technologies (NETs) are crucial to meeting the net-zero emissions target by the year 2050, as emphasised by the Glasgow Climate Pact. Over the years, several energy planning models have been developed to address the temporal aspects of carbon management. However, limited works have incorporated CCS and NETs for bottom-up energy planning at the individual plant scale, which is considered in this work. The novel formulation is implemented in an open-source energy system software that has been developed in this work for optimal decarbonisation planning. The DECarbonation Options Optimisation (DECO2) software considers multiperiod energy planning with a superstructural model and was developed in Python with an integrated user interface in Microsoft Excel. The software application is demonstrated with two scenarios that differ in terms of the availabilities of mitigation technologies. For the more conservative Scenario 1... [more]

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-m... [more]

Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.

This paper presents a practical usability investigation of recurrent neural networks (RNNs) to determine the best-suited machine learning method for estimating electric vehicle (EV) batteries’ state of charge. Using models from multiple published sources and cross-validation testing with several driving scenarios to determine the state of charge of lithium-ion batteries, we assessed their accuracy and drawbacks. Five models were selected from various published state-of-charge estimation models, based on cell types with GRU or LSTM, and optimisers such as stochastic gradient descent, Adam, Nadam, AdaMax, and Robust Adam, with extensions via momentum calculus or an attention layer. Each method was examined by applying training techniques such as a learning rate scheduler or rollback recovery to speed up the fitting, highlighting the implementation specifics. All this was carried out using the TensorFlow framework, and the implementation was performed as closely to the published sources a... [more]

Energy is one of the most important aspects of urban development and technological advancements. As its production and consumption are connected to several environmental, social, and economic issues covering all three sustainability pillars, strategic and targeted energy planning is vital to the smooth transition towards a more efficient and greener society. In accordance with the specific priorities of every state, sustainable energy planning should also satisfy the international trends, requirements, and targets, including the global commitments for sustainable development. As of this time, energy transition with further deployment of renewable energy and energy efficiency improvement are the priorities for a sustainable future. However, due to recent global events, a new situation has been established. The COVID-19 pandemic and the ongoing war in Ukraine have caused new circumstances challenging the recognized approaches for an effective sustainable energy strategy. While the global... [more]

In order to better understand the complex pooling and vaporization of a liquid hydrogen spill, Sandia National Laboratories is conducting a highly instrumented, controlled experiment inside their Shock Tube Facility. Simulations were run before the experiment to help with the planning of experimental conditions, including sensor placement and cross wind velocity. This paper describes the modeling used in this planning process and its main conclusions. Sierra Suite’s Fuego, an in-house computational fluid dynamics code, was used to simulate a RANS model of a liquid hydrogen spill with five crosswind velocities: 0.45, 0.89, 1.34, 1.79, and 2.24 m/s. Two pool sizes were considered: a diameter of 0.85 m and a diameter of 1.7. A grid resolution study was completed on the smaller pool size with a 1.34 m/s crosswind. A comparison of the length and height of the plume of flammable hydrogen vaporizing from the pool shows that the plume becomes longer and remains closer to the ground with increa... [more]

One of the biggest issues impeding a country’s progress is the lack of power. To overcome this issue, hybrid renewable energy systems (HRES) play an important role. Due to rising consumption and diminishing resources globally, sustainability has recently attracted more attention. Bangladesh has access to renewable energy sources, including solar, micro-hydro, biomass, wind, and others. The objective of this research is to minimize the net present cost (NPC), cost of energy (COE), and CO2 emissions of the suggested electricity network using the Hybrid Optimization Model for Multiple Energy Resources (HOMER) Pro Software. This investigation explores the possible use of a hybridized energy system (i.e., solar, wind, and diesel) with battery storage in Bangladesh’s northern area. Utilizing HOMER Pro software, an optimal grid-connected system is chosen after evaluating the techno-economic viability of several configuration options. For the Rangpur metropolitan region, seven distinct grid-co... [more]