Energy-saving scheduling is a well-known issue in the manufacturing system. The flexibility of the workshop increases the difficulty of scheduling. In the workshop schedule, considering the collaborative optimization of multi-level structure product production and energy consumption has certain practical significance. The process sequence of parts and components should be consistent with the assembly sequence. Additionally, the non-production energy consumption (NPEC) (such as the energy consumption of workpiece handling, equipment standby, and workpiece conversion) generated by the auxiliary machining operations, which make up the majority of the total energy consumption, should not be ignored. A sub-batch priority is set according to the upper and lower coupling relationship in the product structure. A bi-objective batch scheduling model that minimizes the total energy consumption and the total completion time is developed, and the multi-objective gray wolf optimizer (MOGWO) is emplo... [more]

Improving the efficiency of renewable energy and electricity utilization is an urgent problem for China under the objectives of carbon peaking and carbon neutralization. This paper proposes an optimization scheduling method of electric vehicles (EV) combined with wind and photovoltaic power based on the Frank-Copula-GlueCVaR. First, a joint output model based on copula theory was built to describe the correlation between wind and photovoltaic power output. Second, the Frank-Copula-GlueCVaR index was introduced in a novel way. Operators can now predetermine the future wind-photovoltaic joint output range based on this index and according to their risk preferences. Third, an optimal scheduling model aimed at reducing the group charging cost of EVs was proposed, thereby encouraging EV owners to participate in the demand response. Fourth, this paper: proposes the application of a Variant Roth−Serve algorithm; regards the EV group as a multi-intelligent group; and finds the Pareto optimal s... [more]

This paper presents a multi-agent Double Deep Q Network (DDQN) based on deep reinforcement learning for solving the transmission network expansion planning (TNEP) of a high-penetration renewable energy source (RES) system considering uncertainty. First, a K-means algorithm that enhances the extraction quality of variable wind and load power uncertain characteristics is proposed. Its clustering objective function considers the cumulation and change rate of operation data. Then, based on the typical scenarios, we build a bi-level TNEP model that includes comprehensive cost, electrical betweenness, wind curtailment and load shedding to evaluate the stability and economy of the network. Finally, we propose a multi-agent DDQN that predicts the construction value of each line through interaction with the TNEP model, and then optimizes the line construction sequence. This training mechanism is more traceable and interpretable than the heuristic-based methods. Simultaneously, the experience re... [more]

Planning and management of urban space that involves the local community the process is key to optimal management of the surroundings, in line with social needs. Social isolation imposed because of the COVID-19 pandemic considerably reduces the possibility of conducting public consultations. This study hypothesized that such consultations can be carried out using new visualisation technologies in the virtual reality (VR) area. Owing to the development of new technologies, innovative services can be created which make it easier for recipients to absorb new content. To this end, the ArchitektVR application was developed, which uses enhanced reality for public consultations concerning planned land development. 3D visualisation with VR enables the presentation of various aspects of area development in a clear form, understandable to an average user with no specialist qualifications. It facilitates the presentation and creation of multiple variants/scenarios for the future shape of the area... [more]

The efficiency of photovoltaic (PV) cells has improved significantly in the last decade, making PV generation a common feature of the sustainable microgrid. As the PV-powered microgrid reaches high penetrations of intermittent PV power, optimum scheduling of over-production is necessary to minimize energy curtailment. Failure to include an accurate assessment of curtailed energy costs in the scheduling process increases wasted energy. Moreover, applying an objective function without considering the cost coefficients results in an inefficient concentration of curtailed power in a specific time interval. In this study, we provide an optimization method for scheduling the microgrid assets to evenly distribute curtailment over the entire daily period of PV generation. Each of the curtailment intervals established in our optimization model features the application of different cost coefficients. In the final step, curtailment costs are added to the objective function. The proposed cost mini... [more]

The main purpose of this paper is to investigate the contributions of building-integrated photovoltaic (BIPV) systems to the notion of nearly zero-energy cities in the capitals of the European Union member states (EU), Norway, and Switzerland. Moreover, an in-depth investigation of the barriers and challenges ahead of the widespread rollout of BIPV technology is undertaken. This study investigates the scalability of the nearly zero-energy concept using BIPV technology in moving from individual buildings to entire cities. This study provide a metric for architects and urban planners that can be used to assess how much of the energy consumed by buildings in Europe could be supplied by BIPV systems when installed as building envelope materials on the outer skins of buildings. The results illustrate that by 2030, when buildings in the EU become more energy-efficient and the efficiency of BIPV systems will have improved considerably, BIPV envelope materials will be a reasonable option for b... [more]

The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced... [more]

The promising power-to-gas (P2G) technology makes it possible for wind farms to absorb carbon and trade in multiple energy markets. Considering the remoteness of wind farms equipped with P2G systems and the isolation of different energy markets, the scheduling process may suffer from inefficient coordination and unstable information. An automated scheduling approach is thus proposed. Firstly, an automated scheduling framework enabled by smart contract is established for reliable coordination between wind farms and multiple energy markets. Considering the limited logic complexity and insufficient calculation of smart contracts, an off-chain procedure as a workaround is proposed to avoid complex on-chain solutions. Next, a non-linear model of the P2G system is developed to enhance the accuracy of scheduling results. The scheduling strategy takes into account not only the revenues from multiple energy trades, but also the penalties for violating contract items in smart contracts. Then, th... [more]

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential instal... [more]

In modern systems, there is a tendency to model issues more accurately with low computational cost and considering multiscale decision-making which increases the complexity of the optimization. Therefore, it is necessary to develop tools to cope with these new challenges. Supply chain management of enterprise-wide operations usually involves three decision levels: strategic, tactical, and operational. These decision levels depend on each other involving different time scales. Accordingly, their integration usually leads to multiscale models that are computationally intractable. In this work, the aim is to develop novel clustering methods with multiple attributes to tackle the integrated problem. As a result, a clustering structure is proposed in the form of a mixed integer non-linear program (MINLP) later converted into a mixed integer linear program (MILP) for clustering shape-based time series data with multiple attributes through a multi-objective optimization approach (since differ... [more]

The unit commitment problem (UCP) is one of the key and fundamental concerns in the operation, monitoring, and control of power systems. Uncertainty management in a UCP has been of great interest to both operators and researchers. The uncertainties that are considered in a UCP can be classified as technical (outages, forecast errors, and plugin electric vehicle (PEV) penetration), economic (electricity prices), and “epidemics, pandemics, and disasters” (techno-socio-economic). Various methods have been developed to model the uncertainties of these parameters, such as stochastic programming, probabilistic methods, chance-constrained programming (CCP), robust optimization, risk-based optimization, the hierarchical scheduling strategy, and information gap decision theory. This paper reviews methods of uncertainty management, parameter modeling, simulation tools, and test systems.

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The artificial potential field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of artificial potential field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel artificial potential field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augme... [more]

Unlike a general commercial building, heating for a building with an indoor swimming pool is highly energy-intensive due to the high energy demand for swimming water heating. In Korea, the conventional heating method for this kind of building is to use boilers and heat storage tanks that have high fuel costs and greenhouse gas emissions. In this study, a combined heat and power (CHP) system for such a building using the electricity and waste heat from a Phosphoric Acid Fuel Cell (PAFC) system was designed and analyzed in terms of its primary energy saving, CO2 reduction, fuel cell and CHP efficiency, and economic feasibility. The mathematical model of the thermal load evaluation was used with the 3D multi-zone building model in TRNSYS 18 software (Thermal Energy System Specialists, LLC, Madison, MI, USA) to determine the space heating demand and swimming pool heat losses. The energy efficiency of the fuel cell unit was evaluated as a function of the part-load ratio from the operating d... [more]

Electrifying public bus transportation is a critical step in reaching net-zero goals. In this paper, the focus is on the problem of optimal scheduling of an electric bus (EB) fleet to cover a public transport timetable. The problem is modelled using a mixed integer program (MIP) in which the charging time of an EB is pertinent to the battery’s state-of-charge level. To be able to solve large problem instances corresponding to real-world applications of the model, a metaheuristic approach is investigated. To be more precise, a greedy randomized adaptive search procedure (GRASP) algorithm is developed and its performance is evaluated against optimal solutions acquired using the MIP. The GRASP algorithm is used for case studies on several public transport systems having various properties and sizes. The analysis focuses on the relation between EB ranges (battery capacity) and required charging rates (in kW) on the size of the fleet needed to cover a public transport timetable. The results... [more]

This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers’ robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN’s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance... [more]

The aim of this paper is to present a model of energy efficient scheduling for series production systems during operation, including setup and shutdown activities. The flow shop system together with setup, shutdown times and energy consumption are considered. Production tasks enter the system with exponentially distributed interarrival times and are carried out according to the times assumed as predefined. Tasks arriving from one waiting queue are handled in the order set by the Multi Objective Immune Algorithm. Tasks are stored in a finite-capacity buffer if machines are busy, or setup activities are being performed. Whenever a production system is idle, machines are stopped according to shutdown times in order to save energy. A machine requires setup time before executing the first batch of jobs after the idle time. Scientists agree that turning off an idle machine is a common measure that is appropriate for all types of workshops, but usually requires more steps, such as setup and s... [more]

Battery energy storage systems (BESSs) are key components in efficiently managing the electric power supply and demand in microgrids. However, the BESSs have issues in their investment costs and operating lifetime, and thus, the optimal sizing of the BESSs is one of the crucial requirements in design and management of the microgrids. This paper presents a problem framework and its solution method that calculates the optimal size of the BESSs in a microgrid, considering their cooperative operations with the other components. The proposed framework is formulated as a bi-level optimization problem; however, based on the Karush−Kuhn−Tucker approach, it is regarded as a type of operation scheduling problem. As a result, the techniques developed for determining the operation schedule become applicable. In this paper, a combined algorithm of binary particle swarm optimization and quadratic programming is selected as the basis of the solution method. The validity of the authors’ proposal is ve... [more]

In recent years electricity sectors worldwide have undergone major transformations, referred to as the “energy transition”. This has required energy planning to quickly adapt to provide useful inputs to the regulation activity so that a cost-effective electricity market emerges to facilitate the integration of renewables. This paper analyzes the role of system planning and regulations on two specific elements in the energy market design: the concept of firm capacity and the presence of distributed energy resources, both of which can be influenced by regulation. We assess the total cost of different regulatory mechanisms in the Brazilian and Mexican systems using optimization tools to determine optimal long-term expansion for a given regulatory framework. In particular, we quantitatively analyze the role of the current regulation in the total cost of these two electricity systems when compared to a reference “efficient” energy planning scenario that adopts standard cost-minimization pri... [more]

Universal access to electricity is a crucial challenge in many developing countries. Establishing the electrification agenda of an underserved region is a complicated task where computer models play a critical role in calculating geospatial plans that efficiently allocate resources. Such plans should include—among other things—reasonable estimations of the designs and economic costs of standalone systems, mini-grids, and grid extensions. This implies that computer models need to estimate the network cost for many potential mini-grids. To that end, most planning tools apply quick rules of thumb or geometric methods that ignore power flows and electric constraints, which play a significant role in network designs. This paper presents a methodology that rapidly estimates any low-voltage mini-grid network cost without neglecting the impact of electrical feasibility in such cost. We present a case study where we evaluate our method in terms of accuracy and computation time. We also compare... [more]

This paper develops the coordination structure and method for utilizing flexibilities in a Micro-Grid (MG), an Active Distribution Network (ADN) and a Transmission Grid (TG), which can play an essential role in addressing the uncertainties caused by renewable energy power generation (REPG). For cooperative dispatching, both flexibilities and uncertainties on the interface of MG−ADN and ADN−TG are portrayed in unified forms utilizing robust optimization (RO), based on the modified equipment-level model of flexible resources. The Constraint-and-Column Generation method is adopted to solve the RO control problems. Simulations on the modified IEEE case-6 and case-33 systems are carried out. The results suggest that the proposed algorithm can exploit flexible resources in both an MG and an ADN, improving the economy and promoting REPG consumption within each level (MG, ADN and TG) while reducing uncertainties and providing flexibilities for superior operators.

Due to the rapid changes in the energy situation on a global scale, the amount of RES installed using clean renewable energy sources such as Photovoltaic (PV) and Wind-power Generators (WGs) is rapidly increasing. As a result, there has been a great deal of research aimed at promoting the adoption of renewable energy. Research on Demand-side Management (DSM) has also been important in promoting the adoption of RES. However, the massive introduction of PV has changed the shape of the demand curve for electricity, which significantly impacts the operational planning of thermal generators. Therefore, this paper proposes an Advanced Direct Load Control (ADLC) model to temporarily shutdown the electric connection between the power grid and Smart Houses (SHs). The most important feature of the proposed model is that it temporarily shuts down the electric connection with the power grid. The shutdown is performed twice to increase the load demand during daytime hours and reduce the peak load d... [more]

The main challenge with the penetration of variable renewable energy (VRE) in thermal-dominated systems has been the increase in the need for operating reserves, relying on dispatchable and flexible resources. In the case of hydro-dominated systems, the cost-effective flexibility provided by hydro-plants facilitates the penetration of VRE, but the compounded production variability of these resources challenges the integration of baseload gas-fired plants. The Brazilian power system illustrates this situation, in which the development of large associated gas fields economically depends on the operation of gas-fired plants. Given the current competitiveness of VRE, a natural question is the economic value and tradeoffs for expanding the system opting between baseload gas-fired generation and VRE in an already flexible hydropower system. This paper presents a methodology based on a multi-stage and stochastic capacity expansion model to estimate the optimal mix of baseload thermal power pl... [more]

In the European Green Deal, EU Commission has set a goal to reduce greenhouse gas emissions in the transport sector by 90% by 2050 compared to the 1990 level. Most likely, transport decarbonization will rely on a rapid expansion of electric and hydrogen vehicle fleet, which would seriously affect not just overall electricity demand, but also the shape of the electricity consumption curve. Consequently, our research focuses on integrated energy and transport modelling when analyzing its development pathways up to 2050 and beyond. This paper describes how already established transport modeling practices can be further improved by differentiating vehicles by age groups and setting vehicle age distributions to improve the representation of vehicle stock, fuel efficiencies and emissions, especially for countries that have non-declining vehicle age distributions. Modeling results using proposed and traditional approaches were compared for the Lithuanian case. It shows that the transport fuel... [more]

Assessment of gendered energy transition at an urban scale has emerged as a challenging issue for researchers, policy makers and practitioners. With municipalities becoming players in the energy markets, their involvement raises policy issues that need to be better assessed in supporting gendered energy transition. This paper, therefore, contributes to gendered energy transition assessments at urban household level from a policy maker perspective. We developed a system dynamics model to assess the effects of urban energy policy interventions on household energy consumption and gendered measures using Drakenstein Municipality as a case study. The study used secondary data from various sources for the model parameters. We tested three hypothetical policy scenarios: the business-as-usual, the energy subsidy policy and the energy efficiency policy. The results show that understanding the changes in urban household energy consumption and gendered measures due to energy transition interventi... [more]

Natural dimension stone processing generates large volumes of stone waste, which have a significant impact on the environment, as well as on the efficiency and profitability of the stone-processing plant. The article presents the characteristics of waste produced as a result of natural dimension stone processing and the structure of the waste production process. Solid stone scraps and sludge were distinguished. On the basis of the performed analyses, it was shown that stone waste constitutes 10−35% in relation to the quantity of the processed stone material, with the quantity of sludge being even threefold greater than the volume of solid scraps. According to the circular economy principles, the aim should be to reduce the amount of waste generated by reducing primary resources in favour of secondary material. Reducing the volume of stone waste is possible through rational planning of stone production while at the same time maximising the efficiency of stone material usage and introduc... [more]