This paper looks into the issue of optimal power scheduling of multiple microgrids using hierarchical imitation learning. The system is designed to be a hierarchical learning model towards a two-level microgrid commun...
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ISBN:
(纸本)9798350354416;9798350354409
This paper looks into the issue of optimal power scheduling of multiple microgrids using hierarchical imitation learning. The system is designed to be a hierarchical learning model towards a two-level microgrid community (MGC) structure. The upper-level MGC agent uses an imitation learning algorithm to schedule exchange power between different microgrids, while lower-level microgrid agents are controlled by individual energy management systems using mixed-integer linear programming (MILP). In this paper, we focus on achieving economic dispatch in a large microgrid community while maintaining the privacy of the local microgrids. A simulation study of hierarchical imitation learning is provided based on an MGC system. Our results show the outstanding performance of the designed algorithm with a cost close to the centralized optimal results, about 10% improvement compared to the offline method, and very fast execution, which would be suitable for online power scheduling.
The envisioned concept of Urban Air Mobility (UAM) introduces new challenges for computing safe trajectories. A safe trajectory must ensure that a vertiport to conduct a safety landing is always within reach in the ev...
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ISBN:
(数字)9781624107160
ISBN:
(纸本)9781624107160
The envisioned concept of Urban Air Mobility (UAM) introduces new challenges for computing safe trajectories. A safe trajectory must ensure that a vertiport to conduct a safety landing is always within reach in the event of a contingency. Moreover, the trajectory must avoid any no-fly zones. Computing optimal trajectories that fulfill both requirements is particularly complicated for areas with numerous no-fly zones and vertiports because the resulting optimization problem involves continuous and discrete variables. As a consequence, the problem cannot be optimized efficiently using collocation- or shooting-based methods. Instead, we propose a linearmixed-integer-based formulation of the problem, which can be solved using mixed-integer linear programming (MILP) solvers. Our approach considers electric Vertical Take-Off and Landing aircraft (eVTOL), which can transition between vertical and wingborne flight. The capabilities of the approach are demonstrated with a two-dimensional example.
Due to the increasing share of renewable energies, the role of demand response and production planning is becoming more important. Power-to-X (PtX) technologies, especially power-to-methanol (PtM), are particularly su...
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ISBN:
(纸本)9798350386509;9798350386493
Due to the increasing share of renewable energies, the role of demand response and production planning is becoming more important. Power-to-X (PtX) technologies, especially power-to-methanol (PtM), are particularly suitable for storing excess energy, as these processes are highly flexible as they can be ramped up and down quickly. In this work, an optimization framework for the flexibilization of a novel power to methanol process that uses decentralized carbon dioxide point sources is developed. Based on simulation data of the stationary operation of a power to methanol process, a mixed-integer Nonlinear Program (MINLP) considering buffer storages, an electrolyser and a power to methanol plant is constructed. The optimal operation is determined taking into account variable electricity costs, varying renewable electricity supply and various production constraints. It has been shown that a suitable scheduling of the process can achieve cost savings up to twenty percent, depending on the boundary conditions and configuration.
This study proposes a home healthcare routing and scheduling problem, where perishable products such as medicines, vaccines, or meals must be provided for some patients' treatments. The problem is formulated as a ...
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ISBN:
(纸本)9798350358513;9798350358520
This study proposes a home healthcare routing and scheduling problem, where perishable products such as medicines, vaccines, or meals must be provided for some patients' treatments. The problem is formulated as a mixedintegerlinearprogramming (MILP). A two-stage matheuristic is then developed as the solution approach. The first stage is a local search to solve the nurse routing problem, and the second stage is run as the relaxed MILP to solve the scheduling problem. The matheuristic is tested on newly generated instances and compared with the results of CPLEX. It is able to obtain CPLEX solutions within shorter computational times for small instances and achieve feasible solutions for larger instances.
The rapid growth of Internet-of-Things (IoT) systems demands higher throughput to process sensor data. Existing data processing platforms use simple heuristics for task placement, which perform poorly. We proposed a P...
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ISBN:
(纸本)9798350386066;9798350386059
The rapid growth of Internet-of-Things (IoT) systems demands higher throughput to process sensor data. Existing data processing platforms use simple heuristics for task placement, which perform poorly. We proposed a Permutation-based Task Placement Optimizer (PTPO) that constructs a set of valid task placement permutations to formulate a mixed-integer linear programming problem. PTPO enables efficient real-time task placement for multiple dynamic applications. Our study highlights three key design factors: joint consideration of compute and network constraints, accurate profiling of resource needs, and fine-grained splitting of tasks across nodes. We demonstrate more than 80% throughput gain compared to state-of-the-art schemes using real-world IoT Applications.
In this paper, a multi-objective optimization problem of sizing and siting power systems with the integration of electric vehicles in a distribution system is addressed. Vehicle-to-grid contribution, charging stations...
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ISBN:
(纸本)9798350387186;9798350387179
In this paper, a multi-objective optimization problem of sizing and siting power systems with the integration of electric vehicles in a distribution system is addressed. Vehicle-to-grid contribution, charging stations, renewable energy sources in the form of photovoltaic and wind turbines, and energy storage systems are considered in the proposed approach. A mixed-integer linear programming model is presented for this problem, which minimizes the total cost associated with infrastructure development and power generation while considering operational efficiency and sustainability considerations. A strategic placement of such infrastructural arrangements and their sizes can improve grid resiliency and optimally host a high penetration of electric vehicles, along with integrating renewable energy sources, is also proposed. The suitability of the proposed model is proven through a case study presented for an IEEE 24-bus distribution power system, drawing improvements in cost effectiveness, energy efficiency, and renewable energy utilization within the power grid.
This paper introduces a new formulation that finds the optimum for the Moving-Target Traveling Salesman Problem (MT-TSP), which seeks to find a shortest path for an agent, that starts at a depot, visits a set of movin...
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ISBN:
(纸本)9798350377712;9798350377705
This paper introduces a new formulation that finds the optimum for the Moving-Target Traveling Salesman Problem (MT-TSP), which seeks to find a shortest path for an agent, that starts at a depot, visits a set of moving targets exactly once within their assigned time-windows, and returns to the depot. The formulation relies on the key idea that when the targets move along lines, their trajectories become convex sets within the space-time coordinate system. The problem then reduces to finding the shortest path within a graph of convex sets, subject to some speed constraints. We compare our formulation with the current state-of-the-art mixedinteger Conic Program (MICP) formulation for the MT-TSP. The experimental results show that our formulation outperforms the MICP for instances with up to 20 targets, with up to two orders of magnitude reduction in runtime, and up to a 60% tighter optimality gap. We also show that the solution cost from the convex relaxation of our formulation provides significantly tighter lower-bounds for the MT-TSP than the ones from the MICP.
PurposeWith the changing landscape of the globalised business world, business-to-business supply chains face a turbulent ocean of disruptions. Such is the effect that supply chains are disrupted to the point of failur...
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PurposeWith the changing landscape of the globalised business world, business-to-business supply chains face a turbulent ocean of disruptions. Such is the effect that supply chains are disrupted to the point of failure, supply is halted and its adverse effect is seen on the consumer. While previous literature has extensively studied risk and resilience through mathematical modelling, this study aims to envision a novel supply chain model that integrates blockchain to support visibility and recovery resilience ***/methodology/approachThe stochastic bi-objective (cost and shortage utility) optimisation-based mixed-integer linear programming model integrates blockchain through a binary variable, which activates at a particular threshold risk-averse level of the ***, visibility is improved, as identified by the average reduction of penalties by 36% over the different scenarios. Secondly, the average sum of shortages over different scenarios is consequently reduced by 36% as the recovery of primary suppliers improves. Thirdly, the feeling of shortage unfairness between distributors is significantly reduced by applying blockchain. Fourthly, unreliable direct suppliers resume their supply due to the availability of timely information through blockchain. Lastly, reliance on backup suppliers is reduced as direct suppliers recover *** limitations/implicationsThe findings indicate that blockchain can enhance visibility and recovery even under high-impact disruption conditions. Furthermore, the study introduces a unique metric for measuring visibility, i.e. penalty costs (lower penalty costs indicate higher visibility and vice versa). The study also improves upon shortages and recoveries reported in prior literature by 6%. Finally, blockchain application caters to the literature on shortage unfairness by significantly reducing the feeling of shortage unfairness among *** implicationsThis study establi
There are numerous studies on the unmanned vehicle routing problem (VRP) considering battery constraints in the areas of 1) path-planning problem based on intelligent task allocation and 2) determination of routes acc...
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There are numerous studies on the unmanned vehicle routing problem (VRP) considering battery constraints in the areas of 1) path-planning problem based on intelligent task allocation and 2) determination of routes according to defined objectives and constraints. However, in most previous literature, only a simple linear approximation of battery energy consumption is considered, producing unrealistic results. In this study, a cost and time-competitive VRP is established and solved using mixed-integer linear programming (MILP), considering the relationship between the cost and electricity consumption of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In particular, the maximum flyable and drivable ranges of the UAV and UGV were calculated by setting a linear capacity degradation equation based on the state of health, considering a limited number of (dis)charge cycles. This approach guarantees more realistic optimization results due to the adaptation of the detailed characteristics of battery-related information. Numerical analyses using two solvers based on MILP, 1) COIN-OR Branch and Cut (CBC) and 2) Gurobi, were performed with four different scenarios and four corresponding cases for each scenario by varying the number of demanders. The results show that using a combination of UAVs and UGVs slightly reduces the cost by approximately 1% but significantly reduces the delivery completion time by approximately 79%. The simulation running time was approximately 1.1 s for all the cases, and the CBC solver operates faster than the Gurobi solver by approximately 0.93%.
This report presents a novel approach to quantifying and comparing national efforts towards the United Nations Sustainable Development Goals (UNSDGs) by developing a Sustainable Development Index (SDI) using Mixe...
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