A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

Resilience Enhancement with Sequentially Proactive Operation Strategies

Extreme weather events, many of which are climate change related, are occurring with increasing frequency and intensity and causing catastrophic outages, reminding the need to enhance the resilience of power systems. This paper proposes a proactive operation strategy to enhance system resilience during an unfolding extreme event. The uncertain sequential transition of system states driven by the evolution of extreme events is modeled as a Markov process. At each decision epoch, the system topology is used to construct a Markov state. Transition probabilities are evaluated according to failure rates caused by extreme events. For each state, a recursive value function, including a current cost and a future cost, is established with operation constraints and intertemporal constraints. An optimal strategy is established by optimizing the recursive model, which is transformed into a mixed integer linear programming by using the linear scalarization method, with the probability of each state as the weight of each objective. The IEEE 30-bus system, the IEEE 118-bus system, and a realistic provincial power grid are used to validate the proposed method. The results demonstrate that the proposed proactive operation strategies can reduce the loss of load due to the development of extreme events.postprin

A three‐stage stochastic planning model for enhancing the resilience of distribution systems with microgrid formation strategy

In recent years, severe outages caused by natural disasters such as hurricanes have highlighted the importance of boosting the resilience level of distribution systems. However, due to the uncertain characteristics of natural disasters and loads, there exists a research gap in the selection of optimal planning strategies coupled with provisional microgrid (MG) formation. For this purpose, this study proposes a novel three‐stage stochastic planning model considering the planning step and emergency response step. In the first stage, the decisions on line hardening and Distributed Generation (DG) placement are made with the aim of maximising the distribution system resilience. Then, in the second stage, the line outage uncertainty is imposed via the given scenarios to form the provisional MGs based on a master‐slave control technique. In addition, the non‐anticipativity constraints are presented to guarantee that the MG formation decision is based on the line damage uncertainty. Last, with the realisation of the load demand, the cost of load shedding in each provisional MG is minimised based on a demand‐side management program. The proposed method can consider the step‐by‐step uncertainty realisation that is near to the reality in MG formation strategy. Two standard distribution systems are utilised to validate the correctness and effectiveness of the presented model

インターネットプロトコルネットワークにおけるリンク故障に対するリンク重み最適化モデル

　As Internet traffic grows with little revenue for service providers, keeping the same service level agreements (SLAs) with limited capital expenditures (CAPEX) is challenging. Major internet service providers backbone traffic grows exponentially while revenue grows logarithmically. Under such situation, CAPEX reduction and an improvement of the infrastructure utilization efficiency are both needed.　Link failures are common in Internet Protocol (IP) backbone networks and are an impediment to meeting required quality of service (QoS). After failure occurs, affected traffic is rerouted to adjacent links. This increase in network congestion leads to a reduction of addable traffic and sometimes an increase in packet drop rate. In this thesis network congestion refers to the highest link utilization over all the links in the network. An increase of network congestion may disrupt services with critical SLAs as allowable traffic becomes restricted and packet drop rate increases. Therefore, from a network operator point of view keeping a manageable congestion even under failure is desired　A possible approach to deal with congestion increase is to augment link capacity until meeting the manageable congestion threshold. However CAPEX reduction is required. Therefore a minimization of the additional capacity is necessary. In IP networks where OSPF is widely used as a routing protocol, traffic paths are determined by link weights which are preconfigured in advance. Since traffic paths are decided by link weights, links weights therefore decide the links that will get congested. As result they determine the network congestion. Link weights can be optimized in order to minimize the additional capacity under the worst case failure. The worst case failure is the link failure case which generates the highest congestion in the network.　In the basic model of link weight optimization, a preventive start-time optimization (PSO) scheme that determines a link weight set to minimize the worst congestion under any single-link failure was presented. Unfortunately, when there is no link failure, that link weight set leads to a congestion that may be higher than the manageable congestion. This is a penalty that will be carried on and thus become a burden especially in networks with few failures.　The first part of this thesis proposes a penalty-aware (PA) model that determines a link weight set which reduces that penalty while also reducing the worst congestion by considering both failure and non-failure scenarios. In our PA model we present two simple and effective schemes: preventive start-time optimization without penalty (PSO-NP) and strengthen preventive start-time optimization (S-PSO). PSO-NP suppresses the penalty for the no failure case while reducing the worst congestion under failure, S-PSO minimizes the worst congestion under failure and tries to minimize the penalty compared to PSO for the no failure case. Simulation results show that in several networks, PSO-NP and S-PSO achieve substantial penalty reduction while showing a congestion closed to that of PSO under worst case failure.　Despite these facts, PSO-NP and S-PSO do not guarantee an improvement of both the penalty and the worst congestion at the same time as they focus on fixed optimization conditions which restrict the emergence of upgraded solutions for that purpose. A relaxation of these fixed conditions may give us sub-optimal link weight sets that reduce the worst congestion under failure to nearly match that of PSO with a controlled penalty for the no failure case.　To determine these sub-optimal sets we expand the penalty-aware model of link weight optimization. We design a scheme where the network operator can set a manageable penalty and find the link weight set that reduces most the worst congestion while maintaining the penalty. This enable network operators to choose more flexible link weight sets accordingly to their requirements under failure and non-failure scenarios. Since setting the penalty to zero would give the same results as PSO-NP, and not setting any penalty condition would give S-PSO, this scheme covers PSO-NP and S-PSO. For this reason we denote it: general preventive start-time optimization (GPSO). Simulation results show that GPSO determines link weight sets with worst congestion reduction equivalent to that of PSO under reduced penalty for the no failure case. GPSO is effective in finding a link weight set that reduces the congestion under both failure and non-failure cases. However it does not guarantee the manageable congestion as it considers penalty.　In the second part of this thesis we propose a link-duplication (LD) model that aims to suppress link failure in the first place in order to always meet the manageable congestion. For this purpose we consider the duplication or reinforcement of links which is broadly used to make network reliable.　Link duplication provides fast recovery as only switching from the failed link to the backup link will hide the failure at upper layers. However, due to capital expenditure constraints, every link cannot be duplicated. Giving priority to some selected links makes sense. As mentioned above, traffic routes are determined by link weights that are configured in advance. Therefore, choosing an appropriate set of link weights may reduce the number of links that actually need to be duplicated in order to keep a manageable congestion under any single-link failure scenario. Now, PSO also determines the link failure which creates the worst congestion after failure. Since by duplicating this link we can assume it no more fails, PSO can be used to find the smallest number of links to protect so as to guarantee a manageable congestion under any single link failure. The LD model considers multiple protection scenarios before optimizing link weights for the reduction of the overall number of protected links with the congestion of keeping the congestion below the manageable threshold. Simulation results show the LD model delivers a link weight set that requires few link protections to keep the manageable congestion under any single-link failure scenario at the cost of a computation time order L times that of PSO. L represents the number of links in the network.　Since the LD model considers additional resources, a fair comparison with the PA model would require considering additional capacity in the PA mode as well. In the third part of this thesis we incorporate additional capacity in the PA model. For the PA model we introduce a mathematical formulation that aims to determine the minimal additional capacity to provide in order to maintain the manageable congestion under any single-link failure scenario. We then compare the LD model to the PA model that incorporates additional capacity features. Evaluation results show that the performance difference between the LD model and the PA model in terms of the required additional capacity depends on the network characteristics. The requirements of latency and continuity for traffic and geographical restriction of services should be taken into consideration when deciding which model to use.電気通信大学201

Advancements in Enhancing Resilience of Electrical Distribution Systems: A Review on Frameworks, Metrics, and Technological Innovations

This comprehensive review paper explores power system resilience, emphasizing its evolution, comparison with reliability, and conducting a thorough analysis of the definition and characteristics of resilience. The paper presents the resilience frameworks and the application of quantitative power system resilience metrics to assess and quantify resilience. Additionally, it investigates the relevance of complex network theory in the context of power system resilience. An integral part of this review involves examining the incorporation of data-driven techniques in enhancing power system resilience. This includes the role of data-driven methods in enhancing power system resilience and predictive analytics. Further, the paper explores the recent techniques employed for resilience enhancement, which includes planning and operational techniques. Also, a detailed explanation of microgrid (MG) deployment, renewable energy integration, and peer-to-peer (P2P) energy trading in fortifying power systems against disruptions is provided. An analysis of existing research gaps and challenges is discussed for future directions toward improvements in power system resilience. Thus, a comprehensive understanding of power system resilience is provided, which helps in improving the ability of distribution systems to withstand and recover from extreme events and disruptions

A Simulation Based Approach for Determining Maintenance Strategies

Manufacturing organizations are continuously in the mode of identifying and implementing mechanisms to achieve a competitive edge. To this point manufacturers have recognized the critical role of equipment in the productivity of manufacturing operations. With the current trend of manufacturers attempting to lean out their production processes, primary and auxiliary equipment have become even more important to manufacturers as measured by productivity, quality, delivery, and cost metrics. As a result of the focus on lean manufacturing, maintenance management has found a new vigor and purpose to increase equipment capacity and capability. However, the most proactive maintenance strategy is not always the most effective utilization of resources. It is typical for manufacturers to integrate both reactive and proactive maintenance to define a cost effective maintenance strategy. A simulation-based approach is presented that allows an end user to develop such a maintenance strategy

Preventive Start-time Routing considering Failure Characteristics

電気通信大学201

Critical Infrastructures: Enhancing Preparedness & Resilience for the Security of Citizens and Services Supply Continuity: Proceedings of the 52nd ESReDA Seminar Hosted by the Lithuanian Energy Institute & Vytautas Magnus University

Critical Infrastructures Preparedness and Resilience is a major societal security issue in modern society. Critical Infrastructures (CIs) provide vital services to modern societies. Some CIs’ disruptions may endanger the security of the citizen, the safety of the strategic assets and even the governance continuity. The European Safety, Reliability and Data Association (ESReDA) as one of the most active EU networks in the field has initiated a project group on the “Critical Infrastructure/Modelling, Simulation and Analysis – Data”. The main focus of the project group is to report on the state of progress in MS&A of the CIs preparedness & resilience with a specific focus on the corresponding data availability and relevance. In order to report on the most recent developments in the field of the CIs preparedness & resilience MS&A and the availability of the relevant data, ESReDA held its 52nd Seminar on the following thematic: “Critical Infrastructures: Enhancing Preparedness & Resilience for the security of citizens and services supply continuity”. The 52nd ESReDA Seminar was a very successful event, which attracted about 50 participants from industry, authorities, operators, research centres, academia and consultancy companies.JRC.G.10-Knowledge for Nuclear Security and Safet