This article thoroughly investigates the design and optimization of an off-grid hybrid renewable energy system for a remote town in the province of Ankara, whose traditional power infrastructure is lacking. The goal i...
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This article thoroughly investigates the design and optimization of an off-grid hybrid renewable energy system for a remote town in the province of Ankara, whose traditional power infrastructure is lacking. The goal is to provide an efficient and cost-effective energy system that can supply the village's power needs indefinitely. The optimization process entails selecting the best mix of renewable energy sources and sizing components to obtain the best economic and technical performance. To handle the complexity of the optimization problem, the Nelder-Mead simplex search method is used, taking into account the stochastic nature of renewable energy generation and the nonlinear features of RES-based power plants. The simulation results show that the suggested hybrid system outperforms traditional diesel power generators in terms of economic viability and environmental sustainability. The system assures consistent power supply by using reserve energy devices such as batteries, thereby minimizing the intermittent nature of renewable sources. With a competitive energy cost of 0.63/kWh, the optimized hybrid system ensures a dependable and continuous power supply, fulfilling the village's electrical requirement for an amazing 16 years.
We propose a novel adaptive stepsize for the gradient descent scheme to solve unconstrained nonlinear optimization problems. With the convex and smooth objective satisfying locally Lipschitz gradient we obtain the com...
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We propose a novel adaptive stepsize for the gradient descent scheme to solve unconstrained nonlinear optimization problems. With the convex and smooth objective satisfying locally Lipschitz gradient we obtain the complexity O(1/k) of f (x(k)) - f* at most. By using the idea of the new stepsize, we propose k another new algorithm based on the projected gradient for solving a class of nonconvex optimization problems over a closed convex set. The computational experiments show the efficiency of the new method. (c) 2024 Elsevier B.V. All rights reserved.
This paper presents a method to find the optimal topology, pipe sizing, and operational parameters of a district heating system under consideration of one design point. The current high costs of district heating syste...
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This paper presents a method to find the optimal topology, pipe sizing, and operational parameters of a district heating system under consideration of one design point. The current high costs of district heating systems set limits regarding the minimum heat demand density required for economic network expansions. Optimized routing with ideal pipe sizing and optimal operating parameters offers a potential for cost reduction. Therefore, this paper introduces a new two-phase method for district heating network expansion planning. This method consists of consecutive optimizations, starting with a mixed -integer linear programming followed by a nonlinear optimization. During the mixed -integer linear programming, the district heating system is optimized with continuous diameters, and the nonlinear pressure and temperature dependencies must be linearized. The resulting topology and the continuous diameters are afterward handed over to a nonlinear sparse sequential quadratic programming. The continuous diameters are discretized using a numerical continuation strategy that gradually forces the continuous diameter variables into discrete diameter choices. As a proof of concept, the district heating system for a small town with 400 consumers is optimized and analyzed. The two-phase optimization is performed in 251.68 sec, and in most cases, discrete or near discrete diameters are achieved in a nonlinear continuous optimization.
Buildings play a crucial role in ensuring power grid stability by providing energy flexibility. Existing research mainly improves the building energy flexibility through energy storage, but requires additional equipme...
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Buildings play a crucial role in ensuring power grid stability by providing energy flexibility. Existing research mainly improves the building energy flexibility through energy storage, but requires additional equipment costs. This study proposes a new source of building energy flexibility by properly managing indoor air quality with intermittent demand-controlled ventilation (iDCV), which requires no additional equipment investment. This study reveals the mechanisms of enhancing energy flexibility and develops a nonlinear programming algorithm to optimize the dynamic operation of the iDCV. The robustness of the proposed iDCV in enhancing energy flexibility is verified under different application scenarios in different climate zones. The results show that compared with the existing method, the proposed iDCV enhances the energy flexibility with a peak load reduction of 17.1 %-36.1 % under the scenario of constant electricity price and with a flexibility factor enhancement of 9.5 %-80.8 % under the scenario of time-of-use electricity price. Besides the benefit to the power grid with the enhanced energy flexibility, the proposed iDCV also delivers benefits to building users with energy savings of 19.6 %-22.0 % and cost savings of 21.5 %-26.1 %. Therefore, the proposed iDCV provides dual benefits for both the power grid and building users and contributes to developing energy-flexible, low-carbon, and healthy buildings.
State-of-the-art finite time convergence conditions for the sliding mode controllers rely on bounds on perturbation terms. These bounds are often over-approximated, leading to conservative designs, i.e., high gains th...
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ISBN:
(纸本)9781665467612
State-of-the-art finite time convergence conditions for the sliding mode controllers rely on bounds on perturbation terms. These bounds are often over-approximated, leading to conservative designs, i.e., high gains that amplify undesired behaviors such as chattering. This paper proposes to evaluate precisely the bounds on the perturbation terms to avoid conservative designs by using branch-and-bound algorithms dedicated to nonlinear programming. This leads to non-linear, a priori non-convex, non-differentiable constraints on the controller's gains, which is shown to be solvable using a modern black-box optimization algorithm. We propose a new methodology employing branch-and-bound and blackbox solvers to generate gains as small as possible ensuring finite time convergence for the twisting algorithm. It is investigated using both a classical and a recently proposed sufficient conditions for finite time convergence. The applicability of the approach is illustrated over a numerical example.
We present a review of available tools for solving mixed integer nonlinear programming problems. Our aim is to give the reader a flavor of the difficulties one could face and to discuss the tools one could use to try ...
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We present a review of available tools for solving mixed integer nonlinear programming problems. Our aim is to give the reader a flavor of the difficulties one could face and to discuss the tools one could use to try to overcome such difficulties.
In this paper, an optimal control problem is considered where a target vehicle aims to reach a desired location in minimum time while avoiding a dynamic engagement zone. Using simple motion, four potential approaches ...
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ISBN:
(纸本)9781624106316
In this paper, an optimal control problem is considered where a target vehicle aims to reach a desired location in minimum time while avoiding a dynamic engagement zone. Using simple motion, four potential approaches are considered. First, the min-time strategy which ignores the engagement zone is posed and solved. Second, the min-time strategy which avoids the engagement zone entirely is considered. Third, the min-time strategy which allows for some time in the engagement zone;but, still strives to stay away from the center of the engagement zone is posed. Lastly, a fixed final-time strategy is considered, wherein the target tries to avoid the engagement zone;but, is required to arrive at the desired location at a specific time. Using a nonlinear program solver, the optimal strategies are numerically solved. From the results of the numeric solutions, the optimal strategies are discussed and comparisons are drawn.
Amid concerns about freshwater scarcity, the agricultural sector faces challenges in water conservation and optimizing crop yields, highlighting the limitations of traditional irrigation scheduling methods. To overcom...
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Amid concerns about freshwater scarcity, the agricultural sector faces challenges in water conservation and optimizing crop yields, highlighting the limitations of traditional irrigation scheduling methods. To overcome these challenges, this paper introduces a unified, learning -based predictive irrigation scheduler that integrates machine learning and Model Predictive Control (MPC), while also incorporating multi -agent principles. The proposed framework incorporates a three -stage management zone delineation process, utilizing k -means clustering and hydraulic parameters estimates for optimized agro-hydrological modeling. Long Short -Term Memory (LSTM) networks are employed for accurate and computationally efficient root zone soil moisture modeling. The scheduler, formulated as a mixed -integer MPC with zone control, utilizes the identified LSTM networks to maximize root water uptake while minimizing overall water consumption and fixed irrigation costs. Additionally, the learning -based scheduler adopts a multi -agent MPC paradigm, where decentralized hybrid actor-critic agents and the concept of a limiting irrigation management zone are employed to enhance computational efficiency. Evaluating the performance on a 26.4 -hectare field in Lethbridge for the 2015 and 2022 growing seasons demonstrates the superiority of the proposed scheduler over the widely -used triggered scheduling approach in terms of Irrigation Water Use Efficiency (IWUE) and total prescribed irrigation. Notably, the proposed approach achieves water savings between 7 to 23%, coupled with IWUE increases ranging from 10 to 35%.
Inventory is the basis of the normal operation of enterprises. Managing the inventory of multi-variety small batch material production well is an important basis to maintain the normal operation of manufacturing indus...
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ISBN:
(纸本)9781665464680
Inventory is the basis of the normal operation of enterprises. Managing the inventory of multi-variety small batch material production well is an important basis to maintain the normal operation of manufacturing industry, which relates to the efficiency and profit of the enterprise. This paper takes the historical data of an enterprise as an example and establishes a mathematical model to help the enterprise reasonably arrange its material production. This study also provides management suggestions for manufacturing enterprises in this field.
This paper presents an automatic procedure to enhance the accuracy of the numerical solution of an optimal control problem (OCP) discretized via direct collocation at Gauss-Legendre points. First, a numerical solution...
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ISBN:
(纸本)9780791886304
This paper presents an automatic procedure to enhance the accuracy of the numerical solution of an optimal control problem (OCP) discretized via direct collocation at Gauss-Legendre points. First, a numerical solution is obtained by solving a non-linear program (NLP). Then, the method evaluates its accuracy and adaptively changes both the degree of the approximating polynomial within each mesh interval and the number of mesh intervals until a prescribed accuracy is met. The number of mesh intervals is increased for all state vector components alike, in a classical fashion. Instead, improving on state-of-the-art procedures, the degrees of the polynomials approximating the different components of the state vector are allowed to assume, in each finite element, distinct values. This explains the p(n)h definition, where n is the state dimension. Instead, in the literature, the degree is always raised to the highest order for all the state components, with a clear waste of resources. Numerical tests on three OCP problems highlight that, under the same maximum allowable error, by independently selecting the degree of the polynomial for each state, our method effectively picks lower degrees for some of the states, thus reducing the overall number of variables in the NLP. Accordingly, various advantages are brought about, the most remarkable being: (i) an increased computational efficiency for the final enhanced mesh with solution accuracy still within the specified tolerance, (ii) a reduced risk of being trapped by local minima due to the reduced NLP size.
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