The carbon neutrality policy aiming for net zero carbon emissions has led to a significant increase in the use of renewable energy sources (RES) globally. However, due to their uncertain nature, RES can cause imbalanc...
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The carbon neutrality policy aiming for net zero carbon emissions has led to a significant increase in the use of renewable energy sources (RES) globally. However, due to their uncertain nature, RES can cause imbalances in power demand. Recently, Power-to-X (P2X) station technology has gained attention as a solution to the uncertainties of RES and as a means to enhance the capacity and efficiency of RES operations. P2X stations can be utilized when power demand imbalances occur due to the uncertain output of RES or when the power system cannot accommodate the power supply from RES due to various stability issues. Specifically, when supply disruptions occur in the power system due to RES uncertainties, P2X stations contribute to preventing RES curtailment by supplying power to electric vehicle (EV) fuel sources, producing heat using electric heat pumps (EHP), or producing hydrogen using electrolyzers (ELZ), thus improving the uncertain financial benefits for independent power producers (IPP). This paper proposes a mixed-integer linear programming (MILP) based chance-constrained two-stage stochastic optimization (CCTS) approach to address imbalances in power demand from RES and to enhance the profitability of IPP by finding the optimal planning and operational solutions for P2X stations. The proposed method provides hierarchical level results, demonstrating that economic benefits can increase by up to 60.2% with the application of P2X stations and that curtailed energy from RES can be reduced by up to 76.5%. The proposed methodology is also validated for its superior performance by being compared with both the non-linear stochastic chance constraint method and the stochastic method.
This work proposes a tool that integrates a hierarchical mixed -integerlinearprogramming (MILP) model with a discrete event simulation (DES) model to simulate an annual mining plan discretized in shift -by -shift pe...
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This work proposes a tool that integrates a hierarchical mixed -integerlinearprogramming (MILP) model with a discrete event simulation (DES) model to simulate an annual mining plan discretized in shift -by -shift periods. The hierarchical MILP model has ten objectives and optimizes the shift schedule according to the available materials in the free faces at each simulation moment. The DES model simulates the realization of this schedule considering the uncertainties and interactions between the mine's equipment. Four scenarios from a Brazilian mining company were analyzed. These differ regarding the prioritization order of the MILP goals and the plants' grade tolerance. The results report that prioritizing the plant with the highest value-added product results in a 7.0% production gain. Additionally, prioritizing the particle size target of the plants leads to a 2.5% production gain compared to prioritizing the element grade targets.
The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because of its high operational flexibility. In the MES, the hybrid energy storage s...
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The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because of its high operational flexibility. In the MES, the hybrid energy storage system (HESS) composed of the battery and thermal storage tank plays an important role in enhancing reliability, economics, and operational flexibility. Hence, determining the optimal size of HESS in the MES is a critical problem but has not received enough attention. In light of this problem, this paper focuses on the optimal HESS planning problem in the community MES (CMES) under diverse uncertainties. Firstly, a two-stage stochastic planning model is proposed for the CMES to coordinate the optimal long-term HESS allocation and the short-term system operation. The thermal inertia in the heating network, space heating demand, and domestic hot water demand is utilized to reduce both the planning and operational cost. Secondly, a deterministic equivalence is proposed for the two-stage planning model to convert it into a mixed-integer linear programming model, which is then solved by off-the-shelf solvers. Finally, simulation results verify the effectiveness of the proposed method. The results reveal that the HESS can enhance the operational flexibility of the CMES but only needs a very few investment costs and prove that the thermal inertia in the CMES can reduce the investment cost of HESS, the fuel, and the operational maintenance cost.
We consider the context of a telecommunication company that is at the same time an infrastructure operator and a service provider. When planning its network expansion, the company can leverage over its knowledge of th...
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We consider the context of a telecommunication company that is at the same time an infrastructure operator and a service provider. When planning its network expansion, the company can leverage over its knowledge of the subscriber dynamic to better optimize the network dimensioning, therefore avoiding unnecessary costs. In this work, the network expansion represents the deployment and/or reinforcement of several technologies (e.g., 2G, 3G, 4G), assuming that subscribers to a given technology can be served by this technology or older ones. The operator can influence subscriber dynamic by subsidies. The planning is made over a discretized time horizon while some strategic guideline requirements are required at the end of the time horizon. Following classical models, we consider that the willingness of customers for shifting to a new technology follows anS-shape piecewise constant function. We propose a mixed-integer linear programming formulation, improved through several valid inequalities and a heuristic algorithm. We assess the formulation numerically on real instances.
The combinatorial integral approximation (CIA) decomposition suggests solving mixed-integer optimal control problems by solving one continuous nonlinear control problem and one mixed-integerlinear program (MILP). Unr...
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The combinatorial integral approximation (CIA) decomposition suggests solving mixed-integer optimal control problems by solving one continuous nonlinear control problem and one mixed-integerlinear program (MILP). Unrealistic frequent switching can be avoided by adding a constraint on the total variation to the MILP. Within this work, we present a fast heuristic way to solve this CIA problem and investigate in which situations optimality of the constructed feasible solution is guaranteed. In the second part of this article, we show tight bounds on the integrality gap between a relaxed continuous control trajectory and an integer feasible one in the case of two controls. Finally, we present numerical experiments to highlight the proposed algorithm's advantages in terms of run time and solution quality.
In this paper, a novel data envelopment analysis (DEA) model is proposed for ranking the association rules. In this regard, a mixed-integer linear programming (MILP) model is proposed to determine the most efficient a...
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In this paper, a novel data envelopment analysis (DEA) model is proposed for ranking the association rules. In this regard, a mixed-integer linear programming (MILP) model is proposed to determine the most efficient association rules where, an N-person bargaining game is used to create an interactive competition between the existing N-weights to get a better ranking. In addition, the proposed model is fuzzified by setting the ambiguous threshold of the indicators' weight in each rule to improve the overall ranking of the rules. Finally, the risk, resilience and decongestion factors are also considered to increase the responsiveness of the models to different real-world conditions. The proposed model is validated by some random problems and an illustrative example of market basket analysis where, the proposed model shows better results than the competing models in the literature. In addition, the applicability of the proposed model is illustrated using a real case-study. [Received: 2 February 2020;Accepted: 5 July 2020]
This article proposes a new mixed-integer linear programming formulation for the planning of active distribution networks and non-utility-owned electric vehicle charging stations (EVCSs). The approach uses multi-objec...
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ISBN:
(数字)9781665405577
ISBN:
(纸本)9781665405577
This article proposes a new mixed-integer linear programming formulation for the planning of active distribution networks and non-utility-owned electric vehicle charging stations (EVCSs). The approach uses multi-objective optimization to consider both the utility's and the EVCSs owner's economic interests. In this context, the utility decides on investments in network assets, such as replacing overloaded conductors and installing capacitor banks and voltage regulators, whereas the EVCSs owner decides on EVCSs infrastructure such as land size and location, as well as the number of chargers to be constructed. The model is designed to reduce the total expected cost for both owners. A travel simulation algorithm provides the EVCSs' load profiles. Scenario-based optimization is used to address uncertainties related to the energy price at the substation, wind speed, solar irradiation, electricity demand, EVCSs load profiles, and plug-in electric vehicle adoption rate. The effectiveness of the proposed model has been proved on a 69-node network. Results show that the objectives of both owners are in conflict, both depending on the location of the EVCSs.
We investigate the novel Two-stage Cutting Stock Problem with Flexible Length and Flexible Demand (2SCSP-FF): orders for rectangular items must be cut from rectangular stocks using guillotine cuts with the objective t...
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ISBN:
(数字)9783031080111
ISBN:
(纸本)9783031080111;9783031080104
We investigate the novel Two-stage Cutting Stock Problem with Flexible Length and Flexible Demand (2SCSP-FF): orders for rectangular items must be cut from rectangular stocks using guillotine cuts with the objective to minimize waste. Motivated by our industrial partner and different from problems in the literature, the 2SCSP-FF allows both the length of individual items and the total area of orders to vary within customer-specified intervals. We develop constraint programming (CP) and mixed-integerprogramming models, with the most successful coming from the adaptation of CP scheduling techniques. Numerical results show that this CP model has orders of magnitude smaller memory requirements and is the only model-based approach investigated that can solve industrial instances.
Hydro-Quebec (HQ) is a vertically integrated utility that produces, transmits, and distributes most of the electricity in the province of Quebec. The power grid it operates has a particular architecture created by lar...
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Hydrogen use is increasing in transportation, including within the railway sector. In collaboration with a governmental institution in the Netherlands, we study how to design an efficient hydrogen fueling infrastructu...
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Hydrogen use is increasing in transportation, including within the railway sector. In collaboration with a governmental institution in the Netherlands, we study how to design an efficient hydrogen fueling infrastructure for a railway system. The problem involves selecting yards in a network for hydrogen fueling, assigning trains to these yards, locating hydrogen storage and fueling stations, and connecting them via pipelines. This key planning phase must avoid oversizing costly fueling infrastructure while accounting for track availability at yards and costs due to fueling operations. We formulate this novel problem, which has the structure of a nested facility location problem, as a mixed-integerlinear program to minimize total annualized investment and operational costs. Due to the complexity of real-sized instances, we propose a matheuristic that estimates the infrastructural costs for each yard and train assignment by combining a constructive algorithm with a set covering model. It then solves a single-stage facility location problem to select yards and assign trains, followed by a yard-level improvement phase. Numerical experiments on a real Dutch case show that our approach delivers high-quality solutions quickly and offer insights into the optimal infrastructure design depending on the discretization of yard areas, number of trains, and other parameters.
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