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Optimal design and operation of integrated microgrids under intermittent renewable energy sources coupled with green hydrogen and demand scenarios

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第65期47卷 27848-27865页

作者： Tatar, Su Meyra Akulker, Handan Sildir, Hasan Aydin, Erdal Koc Univ Dept Chem & Biol Engn TR-34457 Istanbul Turkey Bogazici Univ Dept Chem Engn TR-34342 Istanbul Turkey Gebze Tech Univ Dept Chem Engn TR-41400 Kocaeli Turkey Koc Univ Koc Univ TUPRAS Energy Ctr KUTEM TR-34450 Istanbul Turkey

Renewable energy integration into existing or new energy hubs together with Green technologies such as Power to Gas and Green Hydrogen has become essential because of the aim of keeping the average global temperature rise within 2 degrees C with regard to the Paris Agreement. Hence, all energy markets are expected to face substantial transitions world-wide. On the other hand, investigation of renewable energy systems integrated with green chemical conversion, and in particular combination of green hydrogen and synthetic methanation, is still a scarce subject in the literature in terms of optimal and simultaneous design and operation for integrated energy grids under weather intermittency and demand uncertainty. In fact, the integration of such promising new technologies has been studied mainly in the operational phase, without considering design and management simultaneously. Thus, in this work, a multi-period mixed-integer linear programming (MILP) model is formulated to deal with the aforementioned challenges. Under current carbon dioxide limitations dictated by the Paris Agreement, this model computes the best configuration of the renewable and non-renewable-based generators, their optimal rated powers, capacities and scheduling sequences from a large candidate pool containing thirty-nine different equipment simultaneously. Moreover, the effect of the intermittent nature of renewable resources is analyzed comprehensively under three different scenarios for a specific location. Accordingly, a practical scenario generation method is proposed in this work. It is observed that photovoltaic, oil co-generator, reciprocating ICE, micro turbine, and bio-gasifier are the equipment that is commonly chosen under the three different scenarios. Results also show that concepts such as green hydrogen and power-to-gas are currently not preferable for the investigated location. On the other hand, analysis shows that if the emission limits are getting tightened, it is expected tha

关键词： Optimal renewable energy integration mixed-integer linear programming Scenario generation Green hydrogen Renewable energy management

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Two-Scale Model Predictive Control for Resource Optimization Problems With Switched Decisions

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IEEE ACCESS 2022年 10卷 57824-57834页

作者： Balaguer-Herrero, Pedro Carlos Alfonso-Gil, Jose Itzame Martinez-Marquez, Camilo Martinez-Navarro, German Orts-Grau, Salvador Segui-Chilet, Salvador Univ Jaume I UJI Ind Engn & Design Dept Castellon De La Plana 12071 Spain Univ Politecn Valencia Inst Interuniv Invest Reconocimiento Mol & Desarr Valencia 46022 Spain

Model predictive control (MPC) is widely used in resource optimization problems because it naturally deals with bounded controls and states and allows predictive information to be included. However, at each sampling instant, an optimization problem must be solved. Resource optimization problems with switching control actions naturally lead to optimization problems with integer decision variables, which are computationally costly, particularly when the number of variables is large. As a result, the approach of directly discretizing (DD) the problem to derive a mixed-integer linear program (MILP) sets fundamental limitations on the MPC sampling rate owing to the computational time required to solve the optimization problem. In this paper, we propose a two-scale optimization algorithm (TSOA) for MPC. On the first-scale, the entire prediction horizon is considered and the algorithm provides the optimal resources to be used at each interval with a constant weighting cost. This optimization problem may be cast as a linear program (LP);thus, it is computationally tractable even for a large number of variables and constraints. In the second-scale, the switching nature of the decision variable is recovered by posing an MILP to deploy the optimal resources computed in the previous scale. In this manner, the MILP is solved for a shorter time interval than the entire prediction horizon, thus reducing the number of variables in the optimization problem. The simulation results demonstrate the computational advantages of the proposed algorithm compared to direct problem discretization and optimization.

关键词： Optimization Switches Costs Prediction algorithms Cost function Control systems Minimization Energy optimization linear programming mixed-integer linear programming model predictive control resource optimization switched controls

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linear Optimization Based Distribution Grid Flexibility Aggregation Augmented With OLTC Operational Flexibilities

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IEEE ACCESS 2022年 10卷 77510-77521页

作者： Majumdar, Neelotpal Sarstedt, Marcel Kluss, Leonard Hofmann, Lutz Leibniz Univ Hannover Elect Power Engn Sect Inst Elect Power Syst D-30167 Hannover Germany

Ancillary services e.g., voltage control, congestion management and frequency control, require to be compensated increasingly from the Distributed Energy Resources (DERs). DERs are predominantly wind and photo-voltaic power plants, the major share of which are installed at the distribution grid level. Therefore, the previously passive distribution grids require transformation towards a more active role. Provision of ancillary services from the distribution grid level, requires assessment of active and reactive power flexibility (PQ-flexibility) potentials. Furthermore, increased renewable penetration correlates to increased responsibility of the Distribution System Operators (DSOs) for assessing the flexibility potentials. An aggregation of distribution grid potentials, subject to technical grid constraints and technological power limitations, is termed as Feasible Operating Region (FOR) of the distribution grid. The FOR effectively serves as an interface between the DSOs and the Transmission System Operators (TSO), for flexibility exchanges and planning of ancillary services provision. The determination of the FOR is established in current research, using different algorithms e.g stochastic methods, meta-heuristic programming and mathematical optimization techniques. In this paper, an FOR determination algorithm using successive linear programming (sLP) is proposed and validated against established optimization approaches on a uniform medium voltage (MV) grid model. Comparisons reveal competitiveness with established methods and an added advantage of fast calculation times, suitable for real time assessments. Further enhancement of the FOR is proposed, by integrating discrete transformer tap-changing operational flexibilities using a successive mixed integer linear programming (sMILP). Results demonstrate an increase in the flexibility potential from the distribution grid.

关键词： Sensitivity Transformers Optimization Voltage Optimization methods linear programming Monte Carlo methods Ancillary services flexibility aggregation feasible operating region stochastics meta-heuristics successive linear programming mixed-integer linear programming

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Resiliency-Driven Multi-Step Critical Load Restoration Strategy Integrating On-Call Electric Vehicle Fleet Management Services

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IEEE TRANSACTIONS ON SMART GRID 2022年第4期13卷 3118-3132页

作者： Erenoglu, Ayse Kubra Sancar, Semanur Terzi, Idil Su Erdinc, Ozan Shafie-Khah, Miadreza Catalao, Joao P. S. Fatih Sultan Mehmet Vakyf Univ Dept Elect Elect Engn TR-34445 Istanbul Turkey Yildiz Tech Univ Dept Elect Engn TR-34220 Istanbul Turkey Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

In order to enhance the restoration capability of the distribution system during emergency conditions, a resiliency-driven critical load restoration strategy is propounded in this paper. Electric vehicles (EVs) are considered for the grid-support services to deal with challenges on such occasions, in order to maintain the power supply continuity of critical loads by reducing the number of outage periods. The collaboration between fleet operator and distribution system operator is considered in the proposed scheme, making it possible to direct available EVs to the damaged areas. The random characteristic of the seismic event is captured by generating numerous hazard scenarios using a probabilistic approach with the Monte Carlo Simulation (MCS) technique. Afterwards, the unavailability of overhead distribution branches is determined within the fragility curve concept. Besides, the uncertainties caused by EV mobility are considered by performing learning-based analyses for forecasting the location and amount of EVs in the related zone. The obtained data is processed as input parameters in a mixed-integer linear programming (MILP) framework-based stochastic model. Besides, the conceptually developed interfaces for all stakeholders in the proposed scheme are described in detail for bridging the gap between the theoretical background of the concept and practical real-world implementation.

关键词： Resilience Earthquakes Uncertainty Substations Hurricanes Routing Maintenance engineering Electric vehicles forecasting mixed-integer linear programming optimization resiliency restoration strategy

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A Comparison of Two mixed-integer linear Programs for Piecewise linear Function Fitting

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INFORMS JOURNAL ON COMPUTING 2022年第2期34卷 1042-1047页

作者： Warwicker, John Alasdair Rebennack, Steffen Karlsruhe Inst Technol Inst Operat Res Stochast Optimizat D-76185 Karlsruhe Raden Wurttembe Germany

The problem of fitting continuous piecewise linear (PWL) functions to discrete data has applications in pattern recognition and engineering, amongst many other fields. To find an optimal PWL function, the positioning of the breakpoints connecting adjacent linear segments must not be constrained and should be allowed to be placed freely. Although the univariate PWL fitting problem has often been approached from a global optimisation perspective, recently, two mixed-integer linear programming approaches have been presented that solve for optimal PWL functions. In this paper, we compare the two approaches: the first was presented by Rebennack and Krasko [Rebennack S, Krasko V (2020) Piecewise linear function fitting via mixed-integer linear programming. INFORMS J. Comput. 32(2):507-530] and the second by Kong and Maravelias [Kong L, Maravelias CT (2020) On the derivation of continuous piecewise linear approximating functions. INFORMS J. Comput. 32(3):531-5461 Both formulations are similar in that they use binary variables and logical implications modelled by big-M constructs to ensure the continuity of the PWL function, yet the former model uses fewer binary variables. We present experimental results comparing the time taken to find optimal PWL functions with differing numbers of breakpoints across 10 data sets for three different objective functions. Although neither of the two formulations is superior on all data sets, the presented computational results suggest that the formulation presented by Rebennack and Krasko is faster. This might be explained by the fact that it contains fewer complicating binary variables and sparser constraints. Summary of Contribution: This paper presents a comparison of the mixed-integer linear programming models presented in two recent studies published in the INFORMS Journal on Computing. Because of the similarity of the formulations of the two models, it is not clear which one is preferable. We present a detailed comparison of the two

关键词： piecewise linear function mixed-integer linear programming function fitting

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Optimal Eco-Routing for Hybrid Vehicles With Powertrain Model Embedded

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 14632-14648页

作者： Caspari, Adrian Fahr, Steffen Mitsos, Alexander Rhein Westfal TH Aachen Proc Syst Engn AVTSVT D-52074 Aachen Germany JARA ENERGY D-52056 Aachen Germany Forschungszentrum Julich Energy Syst Engn IEK 10 D-52425 Julich Germany

Exploiting the full potential of hybrid electric vehicles (HEVs) requires suitable (i) route selection and (ii) power management. Due to coupling of the two subproblems, an integrated optimization problem is desired, i.e., optimizing simultaneously the route selection and the split between combustion engine and electric motor over the entire route selection. The resulting optimal route and vehicle operation can be used as a basis for a subordinate vehicle controller. We present an eco-routing approach that embeds a hybrid (mechanistic/data-driven) model of the HEV powertrain in an integrated routing and power management optimization problem. Formulating the integrated routing problem with the hybrid model yields a mixed-integer bilinear program which we reformulate and solve a mixed-integer linear program using a state-of-the-art solver. The results show the validity of the developed hybrid powertrain model and demonstrate that the eco routing approach with the powertrain model embedded can be applied to large-scale problems. We consider optimization for minimal travel time and minimum fuel consumption. The latter results in fuel demand reductions up to 70 %. Alternatively, we minimize the fuel consumption while constraining the travel time to a maximum value resulting in up to 50 % fuel demand reductions. The highest fuel demand reductions are achieved in urban environments. The entire framework is written in python and provided as an open-source version (MIT License) under https://***/avt-svt/public/optimal-routing that can readily be applied.

关键词： Routing Mechanical power transmission Optimization Batteries State of charge Fuels Hybrid electric vehicles Optimal vehicle routing and operation hybrid vehicles eco routing mixed-integer linear programming mechanistic data-driven modeling carbon foot reduction

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Machine Learning-Based Initialization of Generalized Benders Decomposition for mixed integer Model Predictive Control

Machine Learning-Based Initialization of Generalized Benders...

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American Control Conference (ACC)

作者： Mitrai, Ilias Daoutidis, Prodromos Univ Minnesota Dept Chem Engn & Mat Sci Minneapolis MN USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA USA

ISBN: (纸本)9798350382662;9798350382655

Model predictive control (MPC) has been widely used to control and operate complex systems. However, the efficient implementation of MPC depends on the efficient solution of the underlying optimization problem. In this work, we propose a machine learning (ML) based warm start initialization strategy for Generalized Benders Decomposition (GBD) to be used for the solution of mixed integer MPC problems. Specifically, the mixed integer MPC problem is first solved using an ML-based branch and check GBD algorithm, which provides a set of high-quality integer feasible solutions. These solutions are subsequently used to compute the exact Benders cuts, which are used to warm start the GBD algorithm. We apply the proposed approach to a case study on the operation of chemical processes where a mixed integer MPC problem is solved to compensate for disturbances. The results show that the proposed approach leads to 39% reduction in solution time compared to the standard application of GBD.

关键词： mixed-integer linear programming

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Optimization Approaches for a General Class of Single-Machine Scheduling Problems 15th

Optimization Approaches for a General Class of Single-Machin...

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15th International Conference of the Metaheuristics International Conference (MIC)

作者： Li, Haitao Alidaee, Bahram Univ Missouri Supply Chain & Analyt Dept St Louis MO 63121 USA Univ Mississippi Coll Business Oxford MS 38677 USA

ISBN: (纸本)9783031629211;9783031629228

We study a general class of single-machine scheduling problems with setup time/cost and no idle time in the schedule. It includes a variety of other scheduling and routing problems as special cases. Apolynomial size mixed-integer linear programming (MILP) formulation is presented. A tabu search (TS) algorithm, built upon a novel composite-move neighborhood structure, is developed for solving the addressed problem effectively and efficiently.

关键词： single machine scheduling earliness-tardiness setup time setup cost no idle time mixed-integer linear programming tabu search

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Chanced-Constrained Rolling-Horizon Optimization for the Green Hydrogen Microgrid System Operation

Chanced-Constrained Rolling-Horizon Optimization for the Gre...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Cho, Ha-Yeong Lee, Ho-Jun Lee, Jae-Hyung Yoon, Min-Han Kwangwoon University Dept. of Electric Engineering Seoul Korea Republic of Energy technology team Samsumg C&t Korea Republic of

ISBN: (纸本)9798350350821

In this paper, we introduce the problems with long-term period optimization of green hydrogen microgrid system operation and variability inherent in renewable energy. To solve the problems, we proposed a Rolling-Horizon Optimization(RHO) with Chanced-Constrained Optimization(CCO). The validity of our approach is confirmed by comparative analysis with conventional mixed-integer linear programming(MILP) scheduling by using real PV generation data. © 2024 IEEE.

关键词： mixed-integer linear programming

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VIRAS: Conflict-Driven Quantifier Elimination integer-Real Arithmetic 25th

VIRAS: Conflict-Driven Quantifier Elimination Integer-Real A...

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25th Conference on Logic for programming, Artificial Intelligence and Reasoning, LPAR 2024

作者： Schoisswohl, Johannes Kovács, Laura Korovin, Konstantin TU Wien Austria The University of Manchester United Kingdom

We introduce Virtual integer-Real Arithmetic Substitution (Viras), a quantifier elimination procedure for deciding quantified linear mixed integer-real arithmetic problems. Viras combines the framework of virtual substitutions with conflict-driven proof search and linear integer arithmetic reasoning based on Cooper’s method. We demonstrate that Viras gives an exponential speedup over state-of-the-art methods in quantified arithmetic reasoning, proving problems that SMT-based techniques fail to solve. © 2024, EasyChair. All rights reserved.

关键词： mixed-integer linear programming

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