1,138 research outputs found
The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks
Cyber-Physical Systems (CPS) are increasingly complex and frequently
integrated into modern societies via critical infrastructure systems, products,
and services. Consequently, there is a need for reliable functionality of these
complex systems under various scenarios, from physical failures due to aging,
through to cyber attacks. Indeed, the development of effective strategies to
restore disrupted infrastructure systems continues to be a major challenge.
Hitherto, there have been an increasing number of papers evaluating
cyber-physical infrastructures, yet a comprehensive review focusing on
mathematical modeling and different optimization methods is still lacking.
Thus, this review paper appraises the literature on optimization techniques for
CPS facing disruption, to synthesize key findings on the current methods in
this domain. A total of 108 relevant research papers are reviewed following an
extensive assessment of all major scientific databases. The main mathematical
modeling practices and optimization methods are identified for both
deterministic and stochastic formulations, categorizing them based on the
solution approach (exact, heuristic, meta-heuristic), objective function, and
network size. We also perform keyword clustering and bibliographic coupling
analyses to summarize the current research trends. Future research needs in
terms of the scalability of optimization algorithms are discussed. Overall,
there is a need to shift towards more scalable optimization solution
algorithms, empowered by data-driven methods and machine learning, to provide
reliable decision-support systems for decision-makers and practitioners
A Novel Decomposition Solution Approach for the Restoration Problem in Distribution Networks
The distribution network restoration problem is by nature a mixed integer and
non-linear optimization problem due to the switching decisions and Optimal
Power Flow (OPF) constraints, respectively. The link between these two parts
involves logical implications modelled through big-M coefficients. The presence
of these coefficients makes the relaxation of the mixed-integer problem using
branch-and-bound method very poor in terms of computation burden. Moreover,
this link inhibits the use of classical Benders algorithm in decomposing the
problem because the resulting cuts will still depend on the big-M coefficients.
In this paper, a novel decomposition approach is proposed for the restoration
problem named Modified Combinatorial Benders (MCB). In this regard, the
reconfiguration problem and the OPF problem are decomposed into master and sub
problems, which are solved through successive iterations. In the case of a
large outage area, the numerical results show that the MCB provides, within a
short time (after a few iterations), a restoration solution with a quality that
is close to the proven optimality when it can be exhibited
Energy and Route Optimization of Moving Devices
This thesis highlights our efforts in energy and route optimization of moving devices. We have focused on three categories of such devices; industrial robots in a multi-robot environment, generic vehicles in a vehicle routing problem (VRP) context, automatedguided vehicles (AGVs) in a large-scale flexible manufacturing system (FMS). In the first category, the aim is to develop a non-intrusive energy optimization technique, based on a given set of paths and sequences of operations, such that the original cycle time is not exceeded. We develop an optimization procedure based on a mathematical programming model that aims to minimize the energy consumption and peak power. Our technique has several advantages. It is non-intrusive, i.e. it requires limited changes in the robot program and can be implemented easily. Moreover,it is model-free, in the sense that no particular, and perhaps secret, parameter or dynamic model is required. Furthermore, the optimization can be done offline, within seconds using a generic solver. Through careful experiments, we have shown that it is possible to reduce energy and peak-power up to about 30% and 50% respectively. The second category of moving devices comprises of generic vehicles in a VRP context. We have developed a hybrid optimization approach that integrates a distributed algorithm based on a gossip protocol with a column generation (CG) algorithm, which manages to solve the tested problems faster than the CG algorithm alone. The algorithm is developed for a VRP variation including time windows (VRPTW), which is meant to model the task of scheduling and routing of caregivers in the context of home healthcare routing and scheduling problems (HHRSPs). Moreover,the developed algorithm can easily be parallelized to further increase its efficiency. The last category deals with AGVs. The choice of AGVs was not arbitrary; by design, we decided to transfer our knowledge of energy optimization and routing algorithms to a class of moving devices in which both techniques are of interest. Initially, we improve an existing method of conflict-free AGV scheduling and routing, such that the new algorithm can manage larger problems. A heuristic version of the algorithm manages to solve the problem instances in a reasonable amount of time. Later, we develop strategies to reduce the energy consumption. The study is carried out using an AGV system installed at Volvo Cars. The results are promising; (1)the algorithm reduces performance measures such as makespan up to 50%, while reducing the total travelled distance of the vehicles about 14%, leading to an energy saving of roughly 14%, compared to the results obtained from the original traffic controller. (2) It is possible to reduce the cruise velocities such that more energy is saved, up to 20%, while the new makespan remains better than the original one
Solving the Integrated Bin Allocation and Collection Routing Problem for Municipal Solid Waste: a Benders Decomposition Approach
The municipal solid waste system is a complex reverse logistic chain which
comprises several optimisation problems. Although these problems are
interdependent, i.e., the solution to one of the problems restricts the
solution to the other, they are usually solved sequentially in the related
literature because each is usually a computationally complex problem. We
address two of the tactical planning problems in this chain by means of a
Benders decomposition approach: determining the location and/or capacity of
garbage accumulation points, and the design and schedule of collection routes
for vehicles. Our approach manages to solve medium-sized real-world instances
in the city of Bah\'{i}a Blanca, Argentina, showing smaller computing times
than solving a full MIP model.Comment: 29 pages, 6 figure
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Transmission Expansion Planning : computational challenges toward real-size networks
The importance of the transmission network for supplying electricity demand is undeniable, and Transmission Expansion Planning (TEP) studies is key for a reliable power system. Due to increasing sources of uncertainty such as more intermittent energy resources, mobile and controllable demands, and fast technology improvements for PVs and energy storage devices, the need for using systematic ways for solving this complex problem is increased. One of the main barriers for deploying optimization-based TEP studies is computationally intractability, which is the main motivation for this research.
The aim of this work is to investigate the computational challenges associated with systematic TEP studies for large-scale problems, and develop algorithms to improve computational performance. In the first step, we investigate the impact of adding security constraints (as NERC standard requirement) into TEP optimization problem, and develop the Variable Contingency List (VCL) algorithm to pre-screen security constraints to only add those that may affect the feasible region. It significantly decreases the size of the problem compared to considering all security constraints. Then, we evaluate the impact of the size of candidate lines list (number of binary variables) on TEP, and developed a heuristic algorithm to decrease the size of this list.
In the next step, we integrate uncertainties into the TEP optimization problem and formulate the problem as a two-stage stochastic program. Adding uncertainties increases the size of the problem significantly. It leads us to develop a three-level filter that introduces important scenario identification index (ISII) and similar scenario elimination (SSE) technique to decrease the number of security constraints in stochastic TEP in a systematic and tractable way.
We then investigate the scalability of the
stochastic TEP formulation. We develop a configurable decomposition framework that allows us to decompose the original problem into subproblems that can be solved independently and in parallel. This framework can benefit from using both progressive hedging (PH) and Benders decomposition (BD) algorithms to decompose and parallelize a large-scale problem both vertically and horizontally. We have also developed a bundling algorithm that improves the performance of PH algorithm and the overall performance of the framework.
We have implemented our work on a reduced ERCOT network with more than 3000 buses to demonstrate the practicality of the proposed method in this work for large-scale problems.Electrical and Computer Engineerin
Distribution Network Planning and Operation With Autonomous Agents
With the restructured power system, different system operators and private investors are responsible for operating and maintaining the electricity networks. Moreover, with incentives for a clean environment and reducing the reliance on fossil fuel generation, future distribution networks adopt a considerable penetration of renewable energy sources. However, the uncertainty of renewable energy sources poses operational challenges in distribution networks. This thesis addresses the planning and operation of the distribution network with autonomous agents under uncertainty. First, a decentralized energy management system for unbalanced networked microgrids is developed. The energy management schemes in microgrids enhance the utilization of renewable energy resources and improve the reliability and resilience measures in distribution networks. While microgrids operate autonomously, the coordination among microgrid and distribution network operators contributes to the improvement in the economics and reliability of serving the demand. Therefore, a decentralized energy management framework for the networked microgrids is proposed. Furthermore, the unbalanced operation of the distribution network and microgrids, as well as the uncertainty in the operating modes of the microgrids, renewable energy resources, and demand, are addressed. The second research work presents a stochastic expansion planning framework to determine the installation time, location, and capacity of battery energy storage systems in the distribution network with considerable penetration of photovoltaic generation and data centers. The presented framework aims to minimize the capital cost of the battery energy storage and the operation cost of the distribution network while ensuring the security of energy supply for the data centers that serve end-users in the data network as well as the reliability requirements of the distribution network. The third research work proposes a coordinated expansion planning of natural gas-fired distributed generation in the power distribution and natural gas networks considering demand response. The problem is formulated as a distributionally robust optimization problem in which the uncertainties in the photovoltaic power generation, electricity load, demand bids, and natural gas demand are considered. The Wasserstein distance metric is employed to quantify the distance between the probability distribution functions. The last research work proposes a decentralized operation of the distribution network and hydrogen refueling stations equipped with hydrogen storage, electrolyzers, and fuel cells to serve hydrogen and electric vehicles. The uncertainties in the electricity demands, PV generation, hydrogen supply, and hydrogen demands are captured, and the problem is formulated as a Wasserstein distance-based distributionally robust optimization problem. The proposed framework coordinates the dispatch of the distributed generation in the distribution network with the hydrogen storage, electrolyzer, and fuel cell dispatch considering the worst-case probability distribution of the uncertain parameters. The proposed frameworks limit the information shared among these autonomous operators using Benders decomposition
Survey of Optimization Models for Power System Operation and Expansion Planning With Demand Response
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