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Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids

ENERGIES 2025年第7期18卷 1696-1696页

作者： Lim, Sungmin Lee, Jaekyu Lee, Sangyub Korea Elect Technol Inst Energy IT Convergence Res Ctr 25 Saenari Ro Seongnam Si 13509 Gyeonggi Do South Korea

This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). While the isolated operation of individual MGs limits system-wide optimization, the proposed approach enhances both stability and efficiency through integrated control. The system employs mixed-integer quadratic constrained programming (MIQCP) to model complex operational characteristics of MGs, facilitating the optimization of interactions among distributed energy resources (DERs) and power exchange within the MG network. The effectiveness of the proposed method was validated through a series of case studies. First, the performance of the algorithm was evaluated under various weather conditions. Second, its robustness against prediction errors was tested by comparing scenarios with and without disturbance prediction. Finally, the cooperative operation of MGs was compared with the independent operation of a single MG to analyze the impact of the cooperative approach on performance improvement. Quantitatively, integrating predictions reduced operating costs by 19.23% compared to the case without predictions, while increasing costs by approximately 3.7% compared to perfect predictions. Additionally, cooperative MG operation resulted in an average 46.18% reduction in external resource usage compared to independent operation. These results were verified through simulations conducted on a modified version of the IEEE 33-bus test feeder.

关键词： model predictive control energy management system renewable energy distributed energy resources mixed-integer quadratically constrained programming

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A MIQCP formulation for B-spline constraints

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OPTIMIZATION LETTERS 2018年第4期12卷 713-725页

作者： Grimstad, Bjarne Norwegian Univ Sci & Technol Dept Engn Cybernet N-7491 Trondheim Norway

This paper presents a mixed-integer quadratically constrained programming (MIQCP) formulation for B-spline constraints. The formulation can be used to obtain an exact MIQCP reformulation of any spline-constrained optimization problem problem, provided that the polynomial spline functions are continuous. This reformulation allows practitioners to use a general-purpose MIQCP solver, instead of a special-purpose spline solver, when solving B-spline constrained problems. B-splines are a powerful and widely used modeling tool, previously restricted from optimization due to lack of solver support. This contribution may encourage practitioners to use B-splines to model constraint functions. However, as the numerical study suggests, there is still a large gap between the solve times of the general-purpose solvers using the proposed formulation, and the special-purpose spline solver CENSO, the latter being significantly lower.

关键词： Piecewise polynomial B-spline mixed-integer nonlinear programming mixed-integer quadratically constrained programming

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Coordinated Planning of Transportation and Electric Power Networks With the Proliferation of Electric Vehicles

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IEEE TRANSACTIONS ON SMART GRID 2020年第5期11卷 4005-4016页

作者： Gan, Wei Shahidehpour, Mohammad Yan, Mingyu Guo, Jianbo Yao, Wei Paaso, Aleksi Zhang, Liuxi Wen, Jinyu Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China IIT Elect & Comp Engn Dept Chicago IL 60616 USA China Elect Power Res Inst Dept Power Syst Beijing 100192 Peoples R China ComEd Chicago IL 60181 USA

The interdependency of transportation and electric power networks is becoming tighter due to the proliferation of electric vehicles (EVs), which introduces additional difficulties in the planning of the two networks. This paper presents the enhanced solution for the coordinated planning of multiple facilities in the two networks, including electric power lines, transportation roads, energy storage systems and fast charging stations. In order to calculate the optimal solution for the proposed coordinated planning problem, we introduce the applications of linear optimization theory including Karush-Kuhn-Tucker conditions, the big M method, and a linear expression of power loss to transform the nonlinear planning problem into a mixed-integer quadratically constrained programming (MIQCP) formulation, which is solved by commercial solvers. The proposed MIQCP formulation is decomposed into two corresponding subproblems by Lagrangian relaxation to represent transportation and electric power networks. The case studies validate the proposed planning model and demonstrate that the proposed solution can enhance the coordinated network planning with the proliferation of EVs.

关键词： Planning Power systems Roads Electric vehicles Investment Traffic congestion Coordinated planning of transportation and electric power networks fast-charging station energy storage power loss mixed-integer quadratically constrained programming

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SCIP: global optimization of mixed-integer nonlinear programs in a branch-and-cut framework

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OPTIMIZATION METHODS & SOFTWARE 2018年第3期33卷 563-593页

作者： Vigerske, Stefan Gleixner, Ambros GAMS Software GmbH Zuse Inst Berlin Berlin Germany Zuse Inst Berlin Dept Math Optimizat Berlin Germany

This paper describes the extensions that were added to the constraint integer programming framework SCIP in order to enable it to solve convex and nonconvex mixed-integer nonlinear programs (MINLPs) to global optimality. SCIP implements a spatial branch-and-bound algorithm based on a linear outer-approximation, which is computed by convex over- and underestimation of nonconvex functions. An expression graph representation of nonlinear constraints allows for bound tightening, structure analysis, and reformulation. Primal heuristics are employed throughout the solving process to find feasible solutions early. We provide insights into the performance impact of individual MINLP solver components via a detailed computational study over a large and heterogeneous test set.

关键词： mixed-integer nonlinear programming global optimization nonconvex constraints mixed-integer quadratically constrained programming MINLP MIQCP

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An islanding partition method of active distribution networks based on chance-constrained programming

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APPLIED ENERGY 2019年第May 15 Pt.1/1284期242卷 78-91页

作者： Zhao, Jinli Zhang, Mengzhen Yu, Hao Ji, Haoran Song, Guanyu Li, Peng Wang, Chengshan Wu, Jianzhong Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Cardiff Univ Sch Engn Inst Energy Cardiff CF24 3AA S Glam Wales

With the high integration of uncontrollable distributed generators (NDGs) comprising photovoltaic arrays (PVs) and wind turbines (WTs), many technical issues have become increasingly prominent in the islanding operation of active distribution networks (ADNs). Such problems including branch overloading and voltage violations threaten the secure operation of distribution systems and a continuous power supply. To address the uncertainties of NDG outputs when implementing islanding partition strategies, this paper proposes a new method of the islanding partition of ADNs based on chance-constrained programming. First, power generation scenarios and probability distributions for these scenarios are generated according to historical data considering the time series characteristics and uncertainties of NDGs. Then, the chance-constrained description of islanding operation is expanded to consider the effect of uncertainties related to NDGs. Moreover, an islanding partition model of ADNs based on chance-constrained programming is established. By applying convex relaxation and introducing auxiliary variables, the model is converted to a mixed-integer quadratically constrained programming model that can be effectively solved. Finally, case studies involving the modified IEEE 33-node system, IEEE 123-node system and an actual distribution system are conducted to verify the effectiveness and scalability of the proposed method.

关键词： Active distribution network Islanding partition Uncertainty Chance constrained mixed-integer quadratically constrained programming

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Undercover: a primal MINLP heuristic exploring a largest sub-MIP

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MATHEMATICAL programming 2014年第1-2期144卷 315-346页

作者： Berthold, Timo Gleixner, Ambros M. Zuse Inst Berlin Dept Optimizat D-14195 Berlin Germany

We present Undercover, a primal heuristic for nonconvex mixed-integer nonlinear programs (MINLPs) that explores a mixed-integer linear subproblem (sub-MIP) of a given MINLP. We solve a vertex covering problem to identify a smallest set of variables to fix, a so-called cover, such that each constraint is linearized. Subsequently, these variables are fixed to values obtained from a reference point, e.g., an optimal solution of a linear relaxation. Each feasible solution of the sub-MIP corresponds to a feasible solution of the original problem. We apply domain propagation to try to avoid infeasibilities, and conflict analysis to learn additional constraints from infeasibilities that are nonetheless encountered. We present computational results on a test set of mixed-integer quadratically constrained programs (MIQCPs) and MINLPs. It turns out that the majority of these instances allows for small covers. Although general in nature, we show that the heuristic is most successful on MIQCPs. It nicely complements existing root-node heuristics in different state-of-the-art solvers and helps to significantly improve the overall performance of the MINLP solver SCIP.

关键词： Primal heuristic mixed-integer nonlinear programming Large neighborhood search mixed-integer quadratically constrained programming Nonconvex optimization

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Optimal Routing and Charging of an Electric Vehicle Fleet for High-Efficiency Dynamic Transit Systems

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IEEE TRANSACTIONS ON SMART GRID 2018年第4期9卷 3563-3572页

作者： Chen, Tao Zhang, Bowen Pourbabak, Hajir Kavousi-Fard, Abdollah Su, Wencong Univ Michigan Dept Elect & Comp Engn Dearborn MI 48126 USA

This paper proposes a framework and its mathematical model for optimal routing and charging of an electric vehicle fleet for high-efficiency dynamic transit systems, while taking into account energy efficiency and charging price. Based on an extended pickup and delivery problem, an optimization model is formulated from the transit service providers' perspective and is applied to an electric vehicle (EV) fleet with economically efficient but small batteries in very urbanized areas. It aims to determine the best route from the origin to the final destination for each EV to satisfy the welfare of all passengers (e.g., travel time and passengers' travel distance), while maximizing the energy efficiency (e.g., by reducing fuel and charging cost), subject to local/global constraints (e.g., EV charging station availability and battery state-of-charge dynamics). This optimization model is solved as a mixed-integer quadratically constrained programming problem. This paper also explores the potential impact of EV fleet of dynamic commuter transit services on electric distribution systems, such as increased average load.

关键词： Electric vehicle charging battery sizing mixed-integer quadratically constrained programming

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