In this study, a daily distribution management system (DMS) is developed, which utilises the potential of soft open points (SOPs) to enhance the renewable share in power provision task. The active and reactive power c...
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In this study, a daily distribution management system (DMS) is developed, which utilises the potential of soft open points (SOPs) to enhance the renewable share in power provision task. The active and reactive power contributions of SOPs, as a flexible interconnection, amend the voltage security issues due to high penetration of photovoltaics (PVs). A complete model for SOP is developed to address the conduction and switching losses that affect the technical and economic operation indices. The optimal scheduling framework of DMS is linearly modelled within mixed-integer linear programming to lower the computational burden of the problem. Case studies are carried out on IEEE 33-node system to illustrate and verify the effectiveness of the proposed model. The comparative analysis shows a lower operation cost besides the improved voltage profile and the reduced losses of the whole system. The effective collaboration of PVs and dispatchable distributed generations (DDGs) assisted by the SOPs power processes causes higher penetration of PVs and enhanced the exploitation of DDGs results in a 9.8% increment of PV power exploitation and improved power factor of DDGs operation.
Distribution service restoration (DSR) is critical for improving the resilience and reliability of modern distribution systems by strategically and sequentially energizing the system components and customer loads. Res...
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Distribution service restoration (DSR) is critical for improving the resilience and reliability of modern distribution systems by strategically and sequentially energizing the system components and customer loads. Restoring electricity service to affected customers also requires multiple crews with different skill sets to perform multiple tasks that are procedurally interdependent with safety guaranteed. However, in existing DSR practices, switch operations and crew dispatch are scheduled separately, and their interdependence is not fully considered. As advanced technologies are enabling remote communication, control, and dispatch, utilities now desire an integrated DSR framework to achieve seamless coordination among multiple DSR tasks such as switch operation, crew dispatch, and component repair. In this paper, we introduce a synthetic model that integrates the service restoration model and the crew dispatch model based on a universal routing model. The proposed model can provide the estimated time of restoration for each load, the switching sequence for safely operating remotely/manually operated switches, and dispatch solutions for crews with different skill sets. The proposed synthetic model is formulated as a mixed-integer linear programming model, and its effectiveness is evaluated via the IEEE 123 bus test feeder and several large-scale test feeders (EPRI Ckt5, Ckt7, Ckt24, and IEEE 8500 node test feeder.
This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust...
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This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first-stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second-stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data-driven approach is applied to construct the ambiguity set based on principal component analysis and first-order deviation functions. By taking advantage of affine decision rules, a tractable mixed-integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a small-scale illustrative example and a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models, including the deterministic and stochastic programming counterparts, are investigated as well. (c) 2018 American Institute of Chemical Engineers AIChE J, 65: 947-963, 2019
In this paper we propose a model for the in-port ship scheduling problem that consists in scheduling the movement of ships inside a canal harbor. Our model, which we name RECIP-MILP, is inspired by a model for schedul...
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In this paper we propose a model for the in-port ship scheduling problem that consists in scheduling the movement of ships inside a canal harbor. Our model, which we name RECIP-MILP, is inspired by a model for scheduling trains, to exploit the analogies between a canal harbor and a single track railway network. Moreover, we show how to translate spatial safety constraints into time ones. We apply our model to instances representing ship movements in the Port of Venice. We test the performance of both the exact RECIP-MILP model and a heuristic solution algorithm based on it. We show that we can exactly solve most instances in few minutes.
Renewable energy systems (RES) play a key role in sustainable energy supply systems. Due to the volatile nature of RES, storage capacities and demand shifting strategies must be implemented. In remote areas like islan...
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Renewable energy systems (RES) play a key role in sustainable energy supply systems. Due to the volatile nature of RES, storage capacities and demand shifting strategies must be implemented. In remote areas like islands, the water supply sector mainly consisting of desalination plants and wells has a significant impact on the energy system. The objective of this paper is the evaluation of the demand shifting potential within this sector with respect to its robustness against probabilistic influencing variables. mixedintegerlinearprogramming is used to simulate the optimised dispatch. Compared to the total annual electricity demand of the island, the study shows that up to 2.1% (1.5 GWh) positive and 5.0% (3.6 GWh) negative demand shifting potential can be assumed if only existing plants are used. Utilisation of micro pumped hydro storage can increase these potentials to 5.5% (4.2 GWh) in positive and 10.3% (7.9 GWh) in negative direction. Furthermore, probabilistic solar radiation has no significant influence on the DR potential while for wind power a saturation of the DR potential for higher degrees of self-sufficiency can be found. Nevertheless, the absolute highest degrees of self-sufficiency come along with the utilisation of both photovoltaic (PV) and wind. (C) 2019 Elsevier Ltd. All rights reserved.
Some results reported in a recent paper (Chen, Y., Journal of Manufacturing Systems, 42:196-209, 2017) appear to be inconsistent. A property observed in the case study reported therein seems to also be questionable. T...
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Some results reported in a recent paper (Chen, Y., Journal of Manufacturing Systems, 42:196-209, 2017) appear to be inconsistent. A property observed in the case study reported therein seems to also be questionable. This note points such inconsistencies and questions the validity of the observed property, showing that it is more easily attributable to features of the employed algorithm, rather than to the problem's nature or instance data. This note also presents an alternative solution method which obtains solutions that dominate those reported by the recent paper and support the critiques hereby presented. These solutions are provided as supporting information.
This paper presents a new mixed-integer nonlinearprogramming (MINLP) model for the optimal operation of unbalanced three-phase droop-based microgrids. The proposed MINLP model can be seen as an extension of an optima...
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This paper presents a new mixed-integer nonlinearprogramming (MINLP) model for the optimal operation of unbalanced three-phase droop-based microgrids. The proposed MINLP model can be seen as an extension of an optimal power flow for microgrids operating in islanded mode, that aims to minimize the total amount of unsupplied demand and the total distributed generator (DG) generation cost. Since the slack bus concept is not longer valid, the proposed model considers the frequency and voltage magnitude reference as variables. In this case, DCs units operate with droop control to balance the system and provide a frequency and voltage magnitude reference. Additionally, a set of efficient linearizations are introduced in order to approximate the original MINLP problem into a mixed-integer linear programming (MILP) model that can be solved using commercial solvers. The proposed model has been tested in a 25-bus unbalanced three-phase microgrid and a large 124-node grid, considering different operational and time-coupling constraints for the DGs and the battery systems (BSs). Load curtailment and different modes of operation for the wind turbines have also been tested. Finally, an error assessment between the original MINLP and the approximated MILP model has been conducted.
The most effective software packages for solving mixed 0-1 linear programs use strong valid linear inequalities derived from polyhedral theory. We introduce a new procedure which enables one to take known valid inequa...
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The most effective software packages for solving mixed 0-1 linear programs use strong valid linear inequalities derived from polyhedral theory. We introduce a new procedure which enables one to take known valid inequalities for the knapsack polytope, and convert them into valid inequalities for the fixed-charge and single-node flow polytopes. The resulting inequalities are very different from the previously known inequalities (such as flow cover and flow pack inequalities), and define facets under certain conditions. (C) 2019 Elsevier B.V. All rights reserved.
When simulation studies involve many quantitative (i.e., discrete and continuous) and qualitative (i.e., categorical) input factors with different numbers of levels for each, meta-models of simulation responses can be...
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When simulation studies involve many quantitative (i.e., discrete and continuous) and qualitative (i.e., categorical) input factors with different numbers of levels for each, meta-models of simulation responses can benefit from the use of mixed-factor space-filling designs. The first-order nearly orthogonal-and-balanced (NOAB) design is a popular approach in these situations. This research develops second-order extensions for an existing construction method of NOAB designs, estimating the pairwise correlations between possible first-order and second-order terms. These extensions permit additional linear constraints in the mixed-integer linear programming (MILP) formulations previously developed for first-order NOAB designs. A case study is presented for NOAB designs of different sizes and construction approaches. The second-order MILP extensions show improvements in design performance measures for parameter estimation and prediction variance for an assumed second-order model as well as for model misspecification with respect to second-order terms for an assumed first-order model.
This paper considers two optimization problems commonly associated to mixed-model assembly lines: balancing task-station assignments and sequencing/scheduling different product models in a cyclical manner. Cyclical sc...
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This paper considers two optimization problems commonly associated to mixed-model assembly lines: balancing task-station assignments and sequencing/scheduling different product models in a cyclical manner. Cyclical scheduling for this particular problem variant is challenging, and multiple approaches have been previously employed by different authors. This paper presents a new mixed-integer linear programming formulation to optimize the steady-state of these lines. Tests on a 36-instance benchmark demonstrated that the proposed model significantly outperformed the previous literature formulation. Furthermore, it is shown that common scheduling rules (often used in simulators) do not necessarily converge to optimal cyclical schedules even when the optimal launch order is used. Tests have also demonstrated that parallelism can allow a marginally increasing value for workstations: doubling (tripling) stations in a line with parallelism can often offer more than double (triple) the optimal throughput of lines without parallelism.
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