Topology identification is crucial for advanced analysis such as state estimation in hybrid AC/DC distribution networks. Traditional topology identification methods based on data statistics lack accuracy, and optimiza...
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ISBN:
(数字)9798350373318
ISBN:
(纸本)9798350373325
Topology identification is crucial for advanced analysis such as state estimation in hybrid AC/DC distribution networks. Traditional topology identification methods based on data statistics lack accuracy, and optimization-based methods exhibit low efficiency. To address these issues, a topology identification method based on mixed-integerlinearprogramming is proposed. Initially, the normalized Lagrange multiplier is utilized to identify suspicious measurements, forming a set of suspicious measurements to reduce the number of integer variables to be determined. Subsequently, a linearization method for network constraints in hybrid AC/DC distribution networks is introduced to transform the topology identification problem into a mixed-integerlinearprogramming model. The simulation results demonstrate that by constructing a set of suspicious measurements and linearizing network constraints, the efficiency of solving the problem is significantly improved while ensuring the accuracy of identification.
Several studies have been focused on developing the distribution system planning techniques, varying from classical to nontraditional soft computing techniques, to solve the distribution system planning problem. This ...
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ISBN:
(数字)9798350379648
ISBN:
(纸本)9798350379655
Several studies have been focused on developing the distribution system planning techniques, varying from classical to nontraditional soft computing techniques, to solve the distribution system planning problem. This paper presents a new planning model for accurate mathematical planning to get optimum feeder routing of radial distribution systems using a mixed integer linear programming (MILP) technique. The model treats the loads at different load buses as injected power. The model also considers the ohmic power loss of various branches as a variable for optimization. All cost items (capital cost, energy loss cost, cost of bays, and line interruption cost) are considered. Also, bus voltage and line capacity limits are considered in addition to the different line sizes. Model verification has been made using two test examples. Lingo software is used to verify the proposed planning model. The developed model can be extended to deal with the general distribution planning problem in which both feeding substations and feeders can be optimized. In addition, it is very important for getting the optimal feeder routing of the system.
We consider the problem of single link failure in an elastic optical network, (also known as flex-grid WDM network). The task is to reroute optical connections that go through the broken link using free capacity of ot...
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ISBN:
(数字)9798350351859
ISBN:
(纸本)9798350351866
We consider the problem of single link failure in an elastic optical network, (also known as flex-grid WDM network). The task is to reroute optical connections that go through the broken link using free capacity of other links of the network. Nowadays, dynamic restoration gains popularity, in which the possiblity of rerouting is only inspected after a link failure is detected. Since the problem of recovery is NP-hard, heuristic algorithms are used to either find such routes, or suggest that the routes do not exist. In order to understand the quality of these heuristics, often mixed integer linear programming is used to obtain exact positive and negative answers. We present a detailed such model that checks whether restoration is possible without the use of additional regenerators. This means, that the new light paths need to satisfy a length constraint. As preprossing we apply a trimming procedure that takes advantage of this length constraint, and significantly speeds up the evaluation of these models. Our model is more general, and besides solving the problem of link restoration, also solves the full problem of wavelength and spectrum assignment.
A mixed integer linear programming (MILP)–based distributed optimization of three-phase unbalanced active distribution network is proposed. Modern distribution networks have becoming more and more active with increas...
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ISBN:
(数字)9798350372403
ISBN:
(纸本)9798350372410
A mixed integer linear programming (MILP)–based distributed optimization of three-phase unbalanced active distribution network is proposed. Modern distribution networks have becoming more and more active with increasing deployment of microgrids, distributed energy resources (DERs) and flexible loads. Considering various ownership and control models of microgrids, DERs and loads, a distributed optimization was formulated and solved using the alternating direction method of multipliers (ADMM) algorithm. By ADMM, the distribution management system (DMS) and these active components are coordinated through price signals, which are adjusted from the nodal power unbalance per node per phase. To enable resolution of the ADMM-based distributed optimization using MILP solver, various linearization methods were proposed to linearize the augmented Lagrangian and other nonlinear terms. Results of case studies using a three-phase active distribution system with three microgrids and several DERs and flexible loads validated the effectiveness of proposed MILP-based distributed optimization. In addition, the capability of proposed method in mitigating phase power unbalance has been demonstrated.
mixed integer linear programming (MILP) is an important problem in the combinatorial optimization domain, which has wide applications in practical optimization scenarios. Given that most MILP problems fall into the NP...
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mixed integer linear programming (MILP) is an important problem in the combinatorial optimization domain, which has wide applications in practical optimization scenarios. Given that most MILP problems fall into the NP-hard category, which the traditional methods may fail to solve, recent research has tried to derive MILP solutions using machine learning techniques. The whole MILP-solving procedure involves lots of modules, such as pre-solving, cut selection, node section, etc., and these modules are closely related and influence each other. However, the previous machine learning-based approaches neglect the connections between these modules, and focus on single-module learning techniques. To address this, we propose an initial step towards a more comprehensive multi-agent learning framework that allows different modules to interact and collaborate. Specifically, our current implementation involves two key modules: HEM for cut selection applied at the root node and GCNN for variable selection. By employing HEM to influence the training of GCNN, these two agents thus work in unison. Through extensive experiments on four MILP datasets in diverse scenarios, we observe significant improvements in solving time and PD integral metrics compared with the state-of-the-art learning-based MILP solving methods. This work lays the groundwork for future development of a fully integrated multi-agent framework.
The growing need for automation of factories and their processes determines a constant increase in the use of robots to carry out various tasks. Multiple robots can do different tasks simultaneously, speeding up proce...
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ISBN:
(数字)9798350340266
ISBN:
(纸本)9798350340273
The growing need for automation of factories and their processes determines a constant increase in the use of robots to carry out various tasks. Multiple robots can do different tasks simultaneously, speeding up processes. This practice raises the question of how many robots are needed to service the entire process, given all the limitations of the operation. One of these limitations is due to the navigation systems used by the several robots in the environment. Line-followers robots, for example, can only meet some tasks but are cheaper than free-movement ones. In this work, we develop an optimization model for task allocation among the available robots to minimize execution delays using mixed integer linear programming, considering their navigation systems. With the model, it is possible to obtain the best task allocation for each robot and the order to perform such tasks, thus avoiding delays as much as possible and getting zero delays for the presented case, that emulates the process of a vehicle assembler factory line.
Optimization methods for long-horizon, dynamically feasible motion planning in robotics tackle challenging nonconvex and discontinuous optimization problems. Traditional methods often falter due to the nonlinear chara...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Optimization methods for long-horizon, dynamically feasible motion planning in robotics tackle challenging nonconvex and discontinuous optimization problems. Traditional methods often falter due to the nonlinear characteristics of these problems. We introduce a technique that utilizes learned representations of the system, known as Polytopic Action Sets, to efficiently compute long-horizon trajectories. By employing a suitable sequence of Polytopic Action Sets, we transform the long-horizon dynamically feasible motion planning problem into a linear Program. This reformulation enables us to address motion planning as a mixedintegerlinear Program (MILP). We demonstrate the effectiveness of a Polytopic Action-Set and Motion Planning (PAAMP) approach by identifying swing-up motions for a torque-constrained pendulum as fast as 0.75 milliseconds. This approach is well-suited for solving complex motion planning and long-horizon Constraint Satisfaction Problems (CSPs) in dynamic and underactuated systems such as legged and aerial robots.
This paper delves into the optimization and economic benefits of wind-solar energy storage systems in park microgrids. By constructing and refining multiple mathematical models, the study provides scientific decision ...
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ISBN:
(数字)9798350373646
ISBN:
(纸本)9798350373653
This paper delves into the optimization and economic benefits of wind-solar energy storage systems in park microgrids. By constructing and refining multiple mathematical models, the study provides scientific decision support for system configuration, aiming to meet the increasing demand for load and enhance overall economic benefits. Firstly, the paper proposes the Photovoltaic and Energy Storage Coordination Optimization Model (PCSO-Model), which combines mixed integer linear programming (MILP) with Monte Carlo simulation, effectively reducing the total supply cost of the park. Subsequently, considering the aging effect of energy storage systems, load forecasting errors at different time scales, and the impact of electricity price fluctuations on economic viability, the PCSO model is improved and solved using an improved genetic algorithm. Simulation and optimization results demonstrate that the introduction of energy storage systems reduces costs by approximately 10
%
on average, decreases the curtailment of wind and solar power by about 15%, and increases the utilization rate of wind and solar power generation by 20%.
Optimal sizing of microgrids is achieving higher importance in the current era of energy transition driven by renewable sources. Due to the intermittence of the renewable sources i.e. PV, wind assisted by energy stora...
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ISBN:
(数字)9798331529765
ISBN:
(纸本)9798331529772
Optimal sizing of microgrids is achieving higher importance in the current era of energy transition driven by renewable sources. Due to the intermittence of the renewable sources i.e. PV, wind assisted by energy storages i.e. battery the optimal sizing of the microgrids alonf with cost minimization is a great challenge. This study presents a methodological framework for determining the optimal configuration of a hybrid microgrid, integrating various energy resources such as photovoltaic (PV) generation, battery storage, a genset, and grid interconnection. Employing Pyomo optimization tool with a mixed-integerlinearprogramming algorithm, the research focuses on sizing components within a 20 kW PV microgrid. Through meticulous examination of load demand and installed PV capacity, the algorithm strategically optimizes the sizing of PV panels, batteries, gensets, and grid interactions to minimize both capital expenditure (CAPEX) and operational expenditure (OPEX). The microgrid's performance is analyzed under three pricing schemes: net metering, selling excess energy to the grid (selling price less than buying price), and giving it away (zero price). Additionally, in off-grid mode, the total CO2 emissions are minimized. The proposed methodology offers a systematic approach for designing economically viable and efficient hybrid microgrids, essential for sustainable energy systems.
This paper discusses Lyapunov stability verification methods for continuous-time nonlinear systems. Traditional mathematical methods require a lot of manual calculations, which consume a lot of time and energy. To add...
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ISBN:
(数字)9798350372694
ISBN:
(纸本)9798350372700
This paper discusses Lyapunov stability verification methods for continuous-time nonlinear systems. Traditional mathematical methods require a lot of manual calculations, which consume a lot of time and energy. To address the problem of the low efficiency in traditional methods, this paper introduces neural networks into the design of the Lyapunov function to achieve independent verification. First, a neural network is used to represent the Lyapunov function. Then, the Lyapunov stability condition is converted into a mixed integer linear programming (MILP) problem, and the solution to the optimization problem is solved through the MILP solver to verify whether the output of the neural network satisfies the Lyapunov stability condition. In addition, this paper gives the training loss function of the Lyapunov neural network, which mainly consists of optimization problems. Finally, a simulation example is given to illustrate the effectiveness of this method.
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