Consumption Uncertainty Research Articles

Robust Optimization for Electric Vehicle Routing Problem Considering Time Windows Under Energy Consumption Uncertainty

Compared to fossil fuel-based internal combustion vehicles, electric vehicles with lower local pollution and noise are becoming more and more popular in urban logistic distribution. When electric vehicles are involved, high-quality delivery depends on energy consumption. This research proposes an electric vehicle routing problem considering time windows under energy consumption uncertainty. A mixed-integer programming model is established. The robust optimization method is adopted to deal with the uncertainty. Based on the modification of adaptive large neighborhood search algorithm, a metaheuristic procedure, called novel hybrid adaptive large neighborhood search, is designed to solve the problem, and some new operators are proposed. The numerical experiments show that the proposed metaheuristic can obtain high-performance solutions with high efficiency for large-scale instances. Furthermore, the robust solution based on the proposed model can achieve a satisfactory tradeoff between performance and risk.

Probabilistic load flow analysis for evaluation of photovoltaic grid connection applications

Uncertainty in power system analysis is increasing, mainly due to decentralized generation and the related spatial distribution of power plants, as well as the inherent volatility of renewable energy sources. In Austria, this development is reflected in a high number of photovoltaic grid connection applications in recent years. On the consumption side, there are changes in grid loads due to the increasing electrification of society, which is reflected, for example, in a rising number of electric vehicles. These aspects of uncertainty in generation and consumption must be considered in the planning process of distribution grids. A proper way to include uncertainty in the hosting capacity analysis of power grids are probabilistic load flow calculation methods. This paper provides an introductory overview of probabilistic load flow analysis, available calculation methods, required input parameters and the interpretation of results. This paper focuses on photovoltaics, so a correlation analysis of historical power generation measurements of spatially distributed photovoltaic power plants in Austria is presented. Furthermore, an insight into the development of Austrian photovoltaic grid connection applications from recent years is given.

Studies on effective solar photovoltaic integration in distribution network with a blend of Monte Carlo simulation and artificial hummingbird algorithm

AbstractIn this paper, the two level stochastic optimisation approach has been suggested. In the lower level, the probability distribution functions (pdfs) for bus voltages and branch currents have been determined using the Monte Carlo simulation (MCS) to be employed in chance‐constrained probabilistic optimisation by taking into account solar radiation and power consumption uncertainties in the distribution networks (DNs). In the upper level, artificial hummingbird algorithm (AHA) handles the expected power loss minimisation subjected to chance constraints, which are related to bus voltages and branch currents, by optimising photovoltaic (PV) system capacities. This research examines the effect of uncertainties in PV system performing under diverse solar radiation and varying PV penetration level scenarios on expected power losses with stochastic DN limits. The stochastic optimisation approach has been compared with the deterministic method for observing the efficiency with optimal power usage. This research improves the knowledge base for optimal PV installation in DN by combining AHA with MCS and emphasising chance‐constrained methods. To indicate the efficacy of proposed strategy, the optimisation outcomes are tested utilising MCS under various uncertainty circumstances and DN parameters are assessed in terms of probabilities of exceeding limitations. The results are compared with the application of firefly algorithm (FA) using stochastic assessment and simulations. The simulation results show that the AHA technique outperforms the FA method in terms of effectively minimising power losses with less simulation time.

Investigating the influence of uncertainty on office building energy simulation through occupant-centric control and thermal comfort integration

Buildings with occupant-centric control (OCC) systems can adjust heating, ventilation, and air conditioning (HVAC) operations based on occupant status to minimize unnecessary energy use. However, because occupant behavior is stochastic rather than constant, and preferred setpoints can vary across occupant groups, this randomness significantly impacts energy use in buildings with OCC systems.This study used a stochastic simulation approach to model the uncertainty inherent in occupancy patterns and evaluate its impact on the energy consumption of an office building with OCC. In this case, the setpoint temperature was determined based on the Predicted Mean Vote (PMV) method so that each zone could be controlled to the preferred temperature according to the occupant group.The results showed that the uncertainty of the annualized sum of electricity consumption for heating and cooling was 4.3 % and 1.8 %, respectively, with 95 % confidence intervals. Notably, a negative correlation was observed between the number of occupants and the uncertainty of energy consumption. Thus, as the uncertainty in the setpoint temperature increased during periods of low occupancy, the uncertainty in the energy consumption of the HVAC system also tended to increase. These insights underscore the significant influence of occupancy-related factors on building energy utilization.

A Stochastic Drone-Scheduling Problem with Uncertain Energy Consumption

In this paper, we present a stochastic drone-scheduling problem where the energy consumption of drones between any two nodes is uncertain. Considering uncertain energy consumption as opposed to deterministic energy consumption can effectively enhance the safety of drone flights. To address this issue, we developed a two-stage stochastic programming model with recourse cost, and we employed a fixed-sample sampling strategy based on Monte Carlo simulation to characterize uncertain variables, followed by the design of an efficient variable neighborhood search algorithm to solve the model. Case study results indicate the superiority of our algorithm over genetic algorithms. Additionally, a comparison between deterministic and stochastic models suggests that considering the uncertainty in energy consumption can significantly enhance the average returns of unmanned aerial vehicle scheduling systems.

Co-optimizing electric bus dispatching and charging considering limited resources and battery degradation

This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced from 20 (real-world case) to 12, considering the cost-effectiveness and robustness of the bus system. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge for different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30 %, and 10-minute charging is more recommended when the state-of-charge of the electric bus is between 30 % and 70 %.

Enhancing Coordination Efficiency with Fuzzy Monte Carlo Uncertainty Analysis for Dual-Setting Directional Overcurrent Relays Amid Distributed Generation.

In the contemporary context of power network protection, acknowledging uncertainties in safeguarding recent power networks integrated with distributed generation (DG) is imperative to uphold the dependability, security, and efficiency of the grid amid the escalating integration of renewable energy sources and evolving operational conditions. This study delves into the optimization of relay settings within distribution networks, presenting a novel approach aimed at augmenting coordination while accounting for the dynamic presence of DG resources and the uncertainties inherent in their generation outputs and load consumption-factors previously overlooked in existing research. Departing from conventional methodologies, the study proposes a dual-setting characteristic for directional overcurrent relays (DOCRs). Initially, a meticulous modeling of a power network featuring distributed generation is undertaken, integrating Weibull probability functions for each resource to capture their probabilistic behavior. Subsequently, the second stage employs the fuzzy Monte Carlo method to address generation and consumption uncertainties. The optimization conundrum is addressed using the ant lion optimizer (ALO) algorithm in the MATLAB environment. This thorough analysis was conducted on IEEE 14-bus and IEEE 30-bus power distribution systems, showcasing a notable reduction in the total DOCR operating time compared to conventional characteristics. The proposed characteristic not only achieves resilient coordination across a spectrum of uncertainties in both distributed generation outputs and load consumption, but also strengthens the resilience of distribution networks overall.

A data-driven robust optimization method based on scenario clustering for PVC production scheduling under uncertainty

In this paper, a data-driven robust optimization method based on scenario clustering is proposed for addressing energy consumption uncertainty characterized by correlation and multi-peaked distribution within the PVC production process. Firstly, principal component analysis (PCA) and kernel density estimation (KDE) methods are used to capture the correlation and distribution information effectively across multidimensional uncertain parameters; then a modified K-means clustering method based on density peaks is applied to cluster energy consumption scenarios and the flexible uncertainty subsets is established. A two-stage robust optimization model for the vinyl chloride production section is then proposed, and the column and constraint generation algorithm is applied to solved. Finally, the effectiveness of the proposed method is validated through a PVC production case study. Comparative results demonstrate that the proposed model reduces energy consumption under uncertainty and improves the robustness of PVC production scheduling.

Multi-objective placement and sizing of energy hubs in energy networks considering generation and consumption uncertainties

This paper presents the placement and sizing of energy hubs (EHs) in electricity, gas, and heating networks. EH is a coordinator framework for various power sources, storage devices, and responsive loads. For simultaneous modeling of economic, operation, reliability, and flexibility indices, the proposed scheme is expressed as a three-objective optimization in the form of Pareto optimization based on the sum of weighted functions. The objective functions of this problem respectively minimize the planning cost of EHs (equal to the total cost of construction of hubs and their expected operating cost), the expected energy loss of the mentioned networks, and the expected energy not-supplied (EENS) of these networks in the case of an N − 1 event. The problem is constrained by power flow equations and operation and reliability constraints of these network together with the EH planning and operation model, and flexibility constraints of the EHs. Then, to achieve unique optimal solution in the shortest possible time, a linear approximation model is extracted for the proposed scheme. Moreover, scenario-based stochastic programming (SBSP) is employed to model uncertainties of load, energy cost, renewable power, and accessibility of the mentioned network equipment. Finally, the obtained numerical results indicate the capability of the proposed scheme in enhancing the economic and flexibility situation of EHs and improving the reliability and operation status of energy networks along with achieving optimal planning and operation for EHs.

Sino-US film collaboration in practice: Mulan

ABSTRACT This article examines recent Sino-US film collaborations, focusing on Mulan (2020) against the backdrop of geopolitical shifts and the COVID-19 pandemic. Through in-depth interviews and a case study, it investigates the lukewarm reception of Mulan (2020) in China. The study explores how external pressures have disrupted the previously thriving partnership, leading to investment uncertainty, intensified censorship, and shifts in audience consumption towards online platforms, which have exacerbated piracy issues. It also addresses the political tensions surrounding Mulan (2020), including controversies related to Xinjiang and Hong Kong, which hindered its box-office success in Mainland China despite domestic support. A comparative analysis with Mulan (1998) assesses the evolution of feminist themes and filial piety in the 2020 adaptation. The findings indicate that while efforts were made to enhance feminist narratives and cultural motifs, the execution was superficial, treating complex cultural themes as mere embellishments rather than integral elements of the narrative. The film’s portrayal of these themes within a predominantly Western framework highlights ongoing challenges in bridging cultural narratives in Sino-US film productions. The findings underscore the profound impact of geopolitics and reveal the contentious portrayal of feminism and cultural integration in Mulan (2020), emphasizing the challenges in Sino-US film collaborations.

Accounting for BEV Users’ Risk Attitudes and Charging Inertia in En Route Charging Choice Behavior

This paper innovatively explores BEV (battery electric vehicle) users’ risk attitudes and charging inertia, examining their effects on en route charging and charging route choice behavior. An attitudinal survey was conducted to explore the two latent variables of risk attitudes and charging inertia in relation to socioeconomic and travel-related characteristics. ICLV (Integrated choice and latent variable) models are adopted to estimate the latent variables and the charging choice behavior simultaneously. Specifically, uncertainty in energy consumption is first considered in the ICLV model, which is represented by the available range (AR) uncertainty. Multinomial logit (MNL) models directly incorporating socioeconomic attributes are employed as a reference for comparison with ICLV models. Results illustrate that risk attitudes and charging inertia both play significant roles in modeling en route charging choice behavior. Risk-averse users and users having charging inertia value AR uncertainty more. Battery range, charging frequency, and income emerge as the most crucial factors influencing users’ intention to charge en route. The results show significant heterogeneity of BEV users in attitudes and charging choice behavior, underscoring the importance of accounting for the heterogeneity in en route charging demand estimation and deployment optimization of public charging stations, particularly for medium-to long-distance trips.

Power Sharing at Rooftop Solar PV System Based Community Microgrid Using Helioscope Software

Due to increase in use of rooftop solar PV system and increase of electricity price, the power sharing among the prosumers of community microgrid become an interesting research area. This study proposes a power sharing mechanism that captures the interaction within a community microgrid. The efficient management of energy sharing is crucial for the efficient operation of community microgrids. To design rooftop solar PV systems, Helioscope software is employed. One of the key features of Helioscope is its ability to efficiently arrange arrays and blocks of solar panels based on the designated location within the software. The power sharing is obligated to coordinate the sharing of PV energy with maximization of the own profit, while the prosumers are autonomous to maximize their utilities with demand response availability. Finally, a load demand and PV generation mechanism is designed to deal with the uncertainty of PV energy and load consumption.

Study of metrological characteristics of the state primary measurement standard of volume flow and mass consumption of liquid in preparation for participation in international comparisons

The paper discusses the results of a study of metrological characteristics of the state primary measurement standard of volume flow and mass consumption of liquid, as well as the volume and mass of liquid flowing through pipelines (DETU 03-04-04). The study was conducted at the National Scientific Centre “Institute of Metrology” in preparation for international comparisons. Metrological characteristics of the components of the state measurement standard were studied, including the stability of the flow by experimentally determining the standard measurement uncertainty of the mass consumption of liquid at the measurement section of the standard. Based on the obtained results, the measurement standard was modernized. In addition, the paper discusses possible directions for further research aimed at improving measurement methods and analysing the measurement uncertainty of Type A. Further work in this direction can contribute to increasing the competitiveness across the national and international metrological community. The paper also examines the procedure for processing measurement results during international comparisons and identifies basic requirements for complying with international standards for further research of the measurement standard to meet these requirements.
 Metrological comparisons are an important part of metrology, as they ensure the equivalence of measurement units, which is essential for ensuring uniformity of measurements in various industrial and scientific areas. Such measures do not only promote scientific progress, but also ensure high standards of quality and safety in modern society. The results of this research will help to prepare for international comparisons, which are important for comparing measurement units. This, in turn, will contribute to more efficient transfer of measurement units to secondary measurement standards and measuring instruments.

Drone-based hybrid charging for multiple sensors: A distributionally robust optimization approach

The drone-based charging is emerging as a promising way to supply electricity to sensors that keep the Internet of Things working. One-to-one and one-to-many charging strategies can both be adopted. In this background, how to dispatch drones and decide the charging strategy becomes an important problem. In this paper, we first investigate the electricity consumption uncertainty during drone travel, and then develop a distributionally robust hover location and routing optimization model with hybrid charging strategies. After that, we transform the established model into a tractable mixed-integer linear programming model based on dual theory. In order to solve it, we introduce a pre-screening process based on greedy algorithm to select the candidate hover locations. Finally, we conduct extensive numerical experiments and sensitive analysis to verify the efficacy and advantages of the proposed method. We find that the optimized charging strategies and drone dispatching schemes can vary with different sensor distribution patterns. Valuable managerial insights are also provided for drone-based hybrid charging in practice.

Research on economic dispatching strategy of electric vehicles participating in frequency regulation ancillary service

Electric vehicles (EVs) are demand-side resources capable of rapid power adjustments. When clustered as aggregators in frequency regulation (FR) services, EVs have the advantages of low deployment cost and fast response speed. This passage studies description of uncertain scenarios when aggregators participate in FR services. With a focus on the optimization of charging and frequency reserve capacity for aggregators participating in FR, a conversion method from FR signal sequence to FR demand is proposed based on historical data of FR signals. The intent is to address the uncertainty of both system FR and load electricity consumption. This passage provides an uncertain characterization of FR demand, which performs as a key indicator of proposed economic dispatching strategy. The result shows how different levels of risk appetite affects the real-time responsiveness, thus mentoring the strategic decision of how much reserve capacity should be provided for FR.

Optimal Energy Distribution in a Smart Micro-Grid Containing a Combined Electricity with Heat System Based on Fuel Cell and Energy Storage

Abstract- Aim of the study: Today, electrical and thermal loads have become one of integral components of smart micro-grids, along with the combined sources of electricity and heat production. Methods: the optimal distribution of energy in these smart micro-grids is always one of the basic challenges of managers and operators of these micro-grids. Hence, the optimal management of power generation in a residential micro-grids based on heat and electricity generation is discussed. In this hybrid system, a fuel cell is used to generate electricity and heat, and the battery is also used as a source of energy storage. Results: In the adopted method, at each time step, according to the amount of electrical and thermal consumption, the optimal production amount of this hybrid system is used in such a way that the cost of providing the power of the smart residential micro-grids (the cost of electricity and gas consumption) is minimized. In this regard, the variable costs of electricity prices in low-load, mid-load, and peak consumption hours along with the uncertainty of production and consumption are also considered, which have been ignored in previous researches. Conclusion: In this research, an objective function was formed first, and this objective function is composed of different parts such as the costs of fuel cells, batteries, etc. After that, the objective function is optimized using the advanced gravity search optimization algorithm in such a way that the production costs of the fuel cell hybrid system battery, electricity, and gas consumption are minimized.

Coordination of protection and operation in micro-grids with inverter based distributed generation using SAOA.

In the proposed protection coordination scheme, the depreciation of the operation time of the entire relay in the primary and backup protection modes for all possible fault locations is considered as the objective function. The limitations of this problem include the equations for calculating the operation time of the relays in both forward and reverse directions, the limitation of the coordination time interval, the limitation of the setting parameters of the proposed relays, the restriction of the size of the reactance that limits the fault current, and the limitation of the standing time of distributed generation per small signal fault. The operation time of the relays depends on the short circuit current passing through them, so it is necessary to calculate the network variables before the fault occurs. For this purpose, optimal daily power distribution should be used in the micro-grid, because micro-grids consist of storage and renewable resources. The proposed plan includes the uncertainties of consumption and generation capacity of renewable resources. Then, to achieve a reliable answer with a low standard deviation, the refrigeration optimization algorithm is used to solve the proposed problem. Finally, the proposed design is implemented on the standard test system in the MATLAB software, and then the capabilities of the proposed design are examined.

STGNet: Short‐term residential load forecasting with spatial–temporal gated fusion network

AbstractWith the continuous transformation of the global power system, ensuring the flexibility and stability of the power system has become a direction that the global energy industry has been striving for. This puts forward higher requirements for load forecasting, especially high‐precision short‐term residential load forecasting. However, the high volatility and uncertainty of electric consumption make this problem challenging. Existing methods usually focus on capturing temporal correlations and ignore the underlying spatial correlations in historical loads, which is crucial for accurate forecasting. In this paper, we propose a novel spatial–temporal gated fusion network (STGNet) for short‐term residential load forecasting including not only individual residential load but also aggregated load. By seeking both adaptive neighbors and temporal similarity neighbors, we construct a dynamic graph based on a data‐driven method. On this basis, we provide an adaptive graph gated fusion mechanism to capture fine‐grained node‐specific spatial dependence more accurately and comprehensively. Finally, a bidirectional gated recurrent unit (BiGRU) is utilized to learn the temporal dependence of load data. Extensive experiments on real‐world datasets are conducted and demonstrate the superiority of STGNet over the existing approaches.

Multi-objective global dynamic optimal scheduling of smart building loads considering carbon emissions

In recent years, there has been a significant increase in electricity consumption and carbon emissions from households in smart buildings. This increase has had a profound impact on the economic aspects of residential electricity consumption and the achievement of the national dual carbon goal. In this paper, an optimal scheduling model for household loads in smart buildings is established. It considers the user demand side, grid-side supply, and the impact of the PV energy storage system (PESS) on carbon emissions. The optimization objectives are the economy of household electricity consumption and the total deviation of electricity consumption. Second, the adaptive mixed inverse learning strategy multi-objective beluga optimization algorithm (AMOBWO) is adopted to enhance its overall dynamic optimization. This optimization addresses challenges such as the uneven distribution of electricity consumption, load timing constraints, power limitations, and uncertainty in electricity consumption during the scheduling of smart building loads. Finally, the results demonstrate that AMOBWO decreases the total cost of household electricity consumption in smart buildings by 59.32% (based on the selected maximum PV capacity). Additionally, it reduces the total deviation by 14.28% after optimal scheduling. As the PESS capacity increases, there is a significant reduction in the indirect carbon emissions from smart buildings, decreasing from 881.279 kg CO2 to 431.299 kg CO2.

Managing material shortages in project supply chains: Inventories, time buffers, and supplier flexibility

We consider a two‐stage project supply chain with a downstream project firm producing an engineer‐to‐order (ETO) complex product or a make‐to‐order (MTO), low‐volume, customized industrial product as a project, and an upstream contract supplier supplying a key material to the project. The project faces two uncertainties: project activity time uncertainty and material consumption uncertainty, which may be positively or negatively correlated. In anticipation of these uncertainties, the project firm has to carefully decide its promised project due date to its project customer, against which harsh penalties will be assessed, and his material order quantity to commit to the contract supplier in advance. In most practical settings, project firms order from contracted suppliers via a flexible wholesale price contract consisting of a discounted advance order price and a risk‐premium adjusted expedite order price. The discounted advance order price encourages the project firm to take more inventory risk in the supply chain, and the expedite order price incentivizes the supplier to bear more inventory risk by carrying safety stock in excess of the project firm's advance material order. We formulate an optimization model that solves the project firm's project due date and material order problem, which takes into account the supplier's strategic reaction to the project firm's material order under the flexible wholesale price contract. We show that for MTO projects, risk‐sharing with suppliers on project materials is less important to the project firm, with the project firm assuming ownership of all material inventory in the channel and setting a deliberate project due date being the key. On the other hand, for ETO projects, risk‐sharing with contracted suppliers assumes critical importance. Project firms managing ETO projects should fully exploit the flexibility in the material supply contract to optimally drive the supplier's safety stock level and set the project due date reflecting the shared risk in the supply chain.