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Comprehensive approach for distribution system planning with uncertainties

IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第24期13卷 5467-5477页

作者： Seta, Felipe da Silva de Oliveira, Leonardo W. de Oliveira, Edimar J. Fed Ctr Technol Educ Dept Elect Engn Angra Dos Reis RJ Brazil Fed Univ Juiz Fora UFJF Dept Elect Engn Juiz De Fora MG Brazil

This study presents a comprehensive approach for the distribution system expansion planning (DSEP) that considers investment, operation, carbon dioxide emission and reliability costs, as well as uncertainties over load demand and wind-based distributed generation. A restoration strategy is taken into account for obtaining the energy not supplied under the 'N - 1' criterion and the corresponding reliability cost over a planning horizon. The problem is modelled as mixed-integer non-linear programming by using the artificial immune system algorithm. Also, two methods to represent uncertainties are applied and compared: an interval technique through an interval power flow and a scenario-based approach. Network constraints are considered, as the limits of current, voltage and power from substations, as well as the obtaining of radial and connected topology. The novelty of the proposed interval DSEP consists of handling the uncertainties over operation in an efficient manner through a single step, instead of the several deterministic evaluations of the scenario-based approach. Numerical results are presented for well known test systems, which show the potentials of the proposed approach.

关键词： power distribution planning distributed power generation investment load flow artificial immune systems power distribution economics nonlinear programming power distribution faults integer programming power distribution reliability mixed-integer nonlinear programming artificial immune system algorithm interval technique interval power flow scenario-based approach interval DSEP known test systems distribution system expansion planning carbon dioxide emission reliability costs uncertainties overload demand wind-based distributed generation restoration strategy planning horizon

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Light robustness in the optimization of Markov decision processes with uncertain parameters

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COMPUTERS & OPERATIONS RESEARCH 2019年 108卷 69-81页

作者： Buchholz, Peter Scheftelowitsch, Dimitri TU Dortmund Informat 4 D-44221 Dortmund Germany

Markov decision processes are often specified with limited knowledge of the real behavior or are part of a partially unknown environment such that transition rates and rewards are not exactly known. Different models to describe this uncertainty in a formal way have been proposed. In all cases it is important to consider uncertainty during the computation of optimal control policies. Usually this is done by computing robust solutions which are optimal in the worst realization of uncertainty. However, such solutions tend to be very conservative. In this paper, we develop an approach to mitigate robustness by computing policies that are optimal in a predefined situation, like the average case, but also guarantee a minimal gain in all other situations, including the worst case. We present algorithms based on policy iteration that solve subproblems using Mixed Integer Linear programming (MILD) or nonlinear programming (NLP). (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Markov decision process Parameter uncertainty Robust optimization Mixed Integer Linear programming nonlinear programming

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Berge-acyclic multilinear 0-1 optimization problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期273卷 102-107页

作者： Buchheim, Christoph Crama, Yves Rodriguez-Heck, Elisabeth TU Dortmund Fak Math Vogelpothsweg 87 D-44227 Dortmund Germany Univ Liege HEC Management Sch QuantOM Rue Louvrex 14 B-4000 Liege Belgium

The problem of optimizing a multilinear polynomial f in 0-1 variables arises in applications from many different areas. We are interested in resolution methods based on reformulating the polynomial problem into an equivalent linear one, an approach that attempts to draw benefit from the extensive literature in integer linear programming. More precisely, we characterize instances for which the classical standard linearization procedure guarantees integer optimal solutions. We show that the standard linearization polytope P-H is integer if and only if the hypergraph H defined by the higher-degree monomials off is Berge-acyclic, or equivalently, when the matrix defining P-H is balanced. This characterization follows from more general conditions that guarantee integral optimal vertices for a relaxed formulation depending on the sign pattern of the monomials of f. (C) 2018 Elsevier B.V. All rights reserved.

关键词： nonlinear programming Integer programming Standard linearization Balanced matrix Signed hypergraph

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Indefinite multi-constrained separable quadratic optimization: Large-scale efficient solution

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期278卷 49-63页

作者： Edirisinghe, Chanaka Jeong, Jaehwan Rensselaer Polytech Inst Lally Sch Management Troy NY 12180 USA Radford Univ Coll Business & Econ Radford VA 24142 USA

Multi-constrained indefinite separable quadratic optimization occurs in many practical applications. However, it is an NP-hard problem and its solution even for problems of moderate size is computationally tedious. Extending our previous work on singly constrained problems, we develop the necessary theory and computational procedures for problems with multiple linear constraints, by employing iterative constraint aggregation, known as surrogation. The surrogate dual solution is obtained using a cutting plane technique over the multiplier search space, which then is used to develop a monotonic sequence of upper bounds that yields a near-global optimal solution. A detailed numerical analysis indicates the method is extremely efficient for quadratic programs with hundreds of thousands of variables. While the number of constraints affects the computational efficiency, it is still an order of magnitude superior, both in speed and quality, compared to leading commercial global optimization software. Preliminary results with application to mixed integer quadratic indefinite optimization further reveal the performance superiority of the proposed methodology relative to the standard techniques. (C) 2019 Elsevier B.V. All rights reserved.

关键词： nonlinear programming Large scale optimization Integer programming Global optimization Constraint aggregation

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Impact of scaled fitness functions on a floating-point genetic algorithm to optimise the operation of standalone microgrids

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IET RENEWABLE POWER GENERATION 2019年第8期13卷 1280-1290页

作者： Batool, Munira Shahnia, Farhad Islam, Syed M. Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA Australia Murdoch Univ Sch Engn & Informat Technol Perth WA Australia Federat Univ Sch Sci Engn & Informat Technol Ballarat Vic Australia

Standalone hybrid remote area power systems, also known as microgrids (MGs), can provide reasonably priced electricity in geographically isolated and the edge of grid locations for their operators. To achieve the reliable operation of MGs, whilst consuming minimal fossil fuels and maximising the penetration of renewables, the voltage and frequency should be maintained within acceptable limits. This can be accomplished by solving an optimisation problem. Floating-point genetic algorithm (FP-GA) is a heuristic technique that has a proven track record of effectively identifying the optimal solutions. However, in addition to needing appropriate operators, the solver needs a fitness function to yield the most optimal control variables. In this study, a suitable fitness function is formulated, by including the operational, interruption and technical costs, which are then solved with an FP-GA, with different combinations of operators. The developed fitness function and the considered operators are tested for the non-linear optimisation problem of a 38-bus MG. Detailed discussions are provided on the impact, which different operators have upon the outcomes of the fitness function.

关键词： distributed power generation genetic algorithms floating point arithmetic power markets power generation economics power generation reliability costing nonlinear programming standalone hybrid remote area power systems floating-point genetic algorithm FP-GA optimal control variables nonlinear optimisation problem scaled fitness functions standalone microgrids MG electricity pricing fossil fuels

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MILP model for integrated expansion planning of multi-carrier active energy systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第7期13卷 1177-1189页

作者： Ghasemi, Hosein Aghaei, Jamshid Gharehpetian, Gevork Babamalek Safdarian, Amir Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Amirkabir Univ Technol Elect Engn Dept Tehran Iran Sharif Univ Technol Elect Engn Dept Tehran Iran

In growing economic energy systems, the interdependency of various energy infrastructures has led to a change in countries' policies in their expansion planning of energy networks. In this study, a mixed-integer non-linear programming (MINLP) model is proposed for expansion planning of the multi-carrier systems including electricity and gas distribution networks. The optimal planning determines the best location, time, and alternative for network assets in order to minimise investment costs and reduce losses. Also, as another distinctive feature of this study, given the integration of electricity and gas distribution networks and the complexity of the problem, a new MILP model using linearisation methods is presented. In this planning model, several types of alternative plans and a set of candidates for the new placements or increase of the capacity of transformers, feeders, distributed generation, gas pipelines, and city gate stations are considered. The proposed MILP model provides the convergence of the problem to a global optimum response using the powerful commercial software. In addition, a solution based on Benders decomposition algorithm is finally proposed to reduce the solution time of the problem. Finally, the efficiency of the proposed model is evaluated by means of some simulation results.

关键词： natural gas technology investment power generation planning distribution networks integer programming power transmission planning pipelines linear programming nonlinear programming energy infrastructures countries energy networks mixed-integer nonlinear programming gas distribution networks optimal planning network assets electricity MILP model planning model alternative plans distributed generation gas pipelines integrated expansion planning multicarrier active energy systems economic energy systems

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A matheuristic approach to the orienteering problem with service time dependent profits

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期273卷 488-503页

作者： Yu, Qinxiao Fang, Kan Zhu, Ning Ma, Shoufeng Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China

This paper addresses the orienteering problem with service time dependent profits (OPSTP), in which the profit collected at each vertex is characterized by a nonlinear function of service time, and the objective is to maximize the total profits by determining a subset of the vertices to be visited and assigning appropriate service time to each of them within a given time budget. To solve this problem, a mixed integer nonlinear programming model is formulated, and a two-phase matheuristic algorithm that consists of a tabu search method and a nonlinear programming is implemented. Extensive computational experiments are conducted on both randomly generated instances and instances that are adapted from the TSPLIB. The results show that our proposed matheuristic algorithm could be quite effective in finding good-quality solutions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Routing Orienteering problem Service time dependent profits Matheuristic algorithm nonlinear programming

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Elliptical Orbit Proximity Operations Differential Games

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2019年第7期42卷 1458-1472页

作者： Prince, Eric R. Hess, Joshuah A. Cobb, Richard G. Carr, Ryan W. Air Force Inst Technol Dept Aeronaut & Astronaut Wright Patterson AFB OH 45433 USA US Air Force Res Lab Wright Patterson AFB OH 45433 USA AIAA Reston VA 20191 USA

Differential games are formulated and solved for an inspector satellite operating nearby a resident space object (RSO) in an elliptical orbit. For each differential game, the goal of the inspector satellite is to minimize the time to achieve an inspection goal, whereas the goal of the RSO is to delay that condition as long as possible. Thus, it is assumed that the RSO can maneuver, and that both the inspector satellite and the RSO use a constant, steerable thruster (for example, an electric engine) during each game. The following games are formulated and solved via an indirect heuristic method, where each game corresponds to an inspection goal of the inspector satellite: 1)intercept;2)rendezvous;3)obtain sun vector;4)match energy;5)obtain sun vector and match energy;and 6)match energy and remain in close proximity during the ensuing orbit. The open-loop strategies obtained for these games represent worst-case scenarios, and they may inform requirements for future satellites as well as be actual open-loop strategies for a given scenario.

关键词： Satellite Maneuvers Resident Space Object Satellites Open Loop Transversality Condition Particle Swarm Optimization Boundary Value Problems nonlinear programming Earth Specific Angular Momentum

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nonlinear Optimal Guidance for Intercepting a Stationary Target

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2019年第11期42卷 2418-2431页

作者： Chen, Zheng Shima, Tal Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Zhejiang Peoples R China Technion Israel Inst Technol Fac Aerosp Engn IL-3200003 Haifa Israel

The problem of optimally guiding an interceptor to a stationary target is studied in a nonlinear setting. First of all, it is shown that a global solution does not exist for the typical free-time minimum-effort nonlinear optimal intercept problem. This leads to consideration of the linear combination of the control effort and engagement duration as the objective function. The necessary conditions for the optimal intercept problem with the new objective function are found to be parameterized by a scalar, reducing the problem of deriving the optimal guidance law to the problem of finding the zeros of a real-valued function. Moreover, a semianalytical form for the real-valued function is devised, and the interval for its zeros is restricted, allowing the use of a brute-force search to efficiently find all the zeros. As a result, the nonlinear optimal guidance law can be efficiently established. Finally, the characteristics of the guidance law are exemplified and studied through simulations, showing that the nonlinear optimal guidance law performs better than the conventional proportional navigation, especially for cases with large initial heading errors.

关键词： Guidance Laws Brute Force Search Proportional Navigation Heading Angle Pontryagin's Maximum Principle Feedback Control nonlinear programming IMU Numerical Simulation Genetic Algorithm

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Fuel-Optimal Rocket Landing with Aerodynamic Controls

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2019年第1期42卷 65-77页

作者： Liu, Xinfu Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China

Aerodynamic forces are not negligible for a reusable rocket returning back to Earth. How the aerodynamic controls and propulsion should be coordinated to realize fuel-optimal precise landing is addressed in this paper. To this end, a model-based optimal control problem is formulated with the rocket's angle of attack and thrust as control inputs, and constraints on the controls are included to reflect the capabilities of the vehicle. Precise landing requires the (highly nonlinear and nonconvex) problem to be solved onboard in real time. This ability of online computation is becoming increasingly desired in aerospace guidance and control for autonomous missions. Hence, this paper presents how to solve the rocket landing problem via convex optimization that has guaranteed polynomial-time complexity. Specifically, a novel methodology of handling the rocket nonlinear dynamics is introduced, and a relaxation technique used to convexify nonconvex constraints is theoretically proved to be valid. High efficiency of the proposed method, with potential for online computation, is demonstrated by numerical examples and comparisons with other methods.

关键词： Rocket Trajectories Fuels Lift Coefficient Aerodynamic Force nonlinear programming Trajectory Optimization nonlinear Dynamics Earth Flight Path Angle Numerical Algorithms

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