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Design and development of symmetrical super-lift DCa??AC converter using firefly algorithm for solar-photovoltaic applications
摘要： Since dynamic economic dispatch with wind power uncertainty poses great challenges for power system operation due to its non-linear and uncertain characteristics, this paper proposes a robust optimization model with different levels of uncertainty budget. The dynamic economic dispatch problem is converted into the robust optimization model with an uncertainty budget, which transforms the non-deterministic model into a deterministic optimization problem. Differential evolution is improved by the sequential quadratic programming method and utilized to solve the robust optimization model. Due to the complex-coupled constraints among thermal units, several constraint-handling procedures are proposed to address those constraint limits, which have a significant impact on the efficiency of the whole optimization. The robust optimization model with an adjustable uncertainty budget is implemented in two test systems. The results obtained for the first test system prove the efficiency of differential evolution-based sequential quadratic programming and the constraint-handling procedures; the performance of the second test system reveals that the robust optimization method with different levels of uncertainty budget provides a promising method for solving the dynamic economic dispatch problem with wind power uncertainty.
关键词： robust optimization,Dynamic economic dispatch,uncertainty budget,differential evolution,sequential quadratic programming,wind power
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Influence of Front-Side Ag Metallization on High Temperature and High Humidity Test of Crystalline Silicon PV Module
摘要： The focus of most research in evolutionary dynamic optimization has been tracking moving optimum (TMO). Yet, TMO does not capture all the characteristics of real-world dynamic optimization problems (DOPs), especially in situations where a solution’s future ?tness has to be considered. To account for a solution’s future ?tness explicitly, we propose to ?nd robust solutions to DOPs, which are formulated as the robust optimization over time (ROOT) problem. In this paper we analyze two robustness de?nitions in ROOT and then develop two types of benchmark problems for the two robustness de?nitions in ROOT, respectively. The two types of benchmark problems are motivated by the inappropriateness of existing DOP benchmarks for the study of ROOT. Additionally, we evaluate four representative methods from the literature on our proposed ROOT benchmarks, in order to gain a better understanding of ROOT problems and their relationship to more popular TMO problems. The experimental results are analyzed, which show the strengths and weaknesses of different methods in solving ROOT problems with different dynamics. In particular, the real challenges of ROOT problems have been revealed for the ?rst time by the experimental results on our proposed ROOT benchmarks.
关键词： dynamic optimization problems (DOPs),evolutionary algorithms (EAs),robust optimization over time (ROOT),Benchmarking
更新于2025-09-19 17:13:59
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Power Management in Active Distribution Systems Penetrated by Photovoltaic Inverters: A Data–Driven Robust Approach
摘要： Under the smart grid paradigm, distribution systems with large penetrations of photovoltaic–based power generation are called to optimize their operational resources to achieve a more ef?cient and reliable performance. In this context, this paper proposes a multiperiod mixed integer second order cone formulation to optimize distribution feeders operation. The model takes into account the feeder physical behavior; discrete control equipment (tap changers and capacitors banks) with a maximum allowable daily switching operation number; photovoltaic inverters operation; and the uncertain nature of solar energy and loads. A two–stage robust optimization framework is used to include the uncertainty into the model, where discrete and continuous control actions are assumed to be part of the ?rst and second stage of this model, respectively. The conservativeness level of the robust model is controlled by an polyhedral uncertainty set whose vertexes are adaptively adjusted in a data–driven fashion in order to better capture complex spatiotemporal dependencies among uncertain parameters. Extensive computational experiments are performed utilizing modi?ed versions of various IEEE test feeders. The performance of the proposed data–driven model is contrasted against traditional deterministic and robust budget–constrained models, using a rolling horizon out–of–sample evaluation methodology. When compared to the deterministic model, the data–driven approach yields a reduction in power losses of approximately 15% and a reduction up to 98% in hourly voltage violations. Results also suggests that the proposed approach exhibits better performance in terms of both average and conditional–value–at–risk metrics in comparison to budget–constrained models.
关键词： Distribution Systems,Data-Driven Optimization,Optimal Power Flow,Robust Optimization,Volt/VAR Control
更新于2025-09-12 10:27:22
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Robust Profit Maximization in Multiperiod Energy Markets with Interval Prediction of Photovoltaics
摘要： In this paper, we formulate an optimal bidding problem in a multiperiod electricity market with the consideration of the uncertain PV output. Based on the proposed strategy, the aggregator of interest operates his dispatchable assets to pursue the largest pro?t by submitting one optimal prosumption curve to the day-ahead market. To deal with a non-convexity issue stemming from the robust optimization problem, we reformulate the problem as its Lagrange dual counterpart. Then, we describe the uncertain range of the PV output by one polyhedral set, with which a new decision variable is introduced. We implement a constraints-generation method to solve the newly formulated problem by splitting it into another two subproblems. Based on one iterative scheme, the two subproblems are solved in sequence, and the solution of the robust optimization problem is obtained when the optimal results of the two subproblems are equal. In the numerical simulation, the pro?ts under di?erent PV lower bounds are presented.
关键词： Smart grid,robust optimization,polyhedral set,optimal bidding,Lagrange duality
更新于2025-09-12 10:27:22
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Robust Optimization of Nanoslit Array Sensor Based on Extraordinary Optical Transmission
摘要： A robust optimization approach for plasmonic periodic array sensor based on extraordinary optical transmission is proposed using Kriging surrogate models to reduce the effects of uncertainty in various manufacturing processes while maintaining sensor performance. For systematic design with reasonable computation cost, the author adopt the universal Kriging models whose regression function is a polynomial. The gradient index and the multiobjective genetic algorithm are chosen as a robustness measure and a global optimization tool, respectively. The figure of merit and the gradient index are set as two objective functions, and the design variables are the slit width and height, respectively. The optical properties of interest are investigated using the finite-element method. The numerical optimization results show the proposed scheme to be powerful and efficient in designing nanoslity array sensors based on extraordinary optical transmission with fabrication uncertainty.
关键词： gradient index,robust optimization,Plasmonics,surface plasmon polariton,multiobjective optimization,Kriging model
更新于2025-09-09 09:28:46
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An optimization model for robust FSO network dimensioning
摘要： FSO (Free Space Optics) is a well established wireless optical transmission technology considered as an alternative to radio communications, for example in metropolitan wireless mesh networks. An FSO link is established by means of a laser beam connecting the transmitter and the receiver placed in the line of sight. A major disadvantage of FSO links (with respect to fiber links) is their sensitivity to weather conditions such as fog, rain and snow, causing substantial loss of the transmission power over the optical channel due mostly to absorption and scattering. Thus, although the FSO technology allows for fast and low-cost deployment of broadband networks, its operation will be affected by this sensitivity, manifested by substantial losses in links’ transmission capacity with respect to the nominal capacity. Therefore, a proper approach to FSO network dimensioning should take such losses into account so that the level of carried traffic is satisfactory under all observed weather conditions. In the paper we describe such an approach. We introduce a relevant dimensioning problem and present a robust optimization algorithm for such enhanced dimensioning. A substantial part of the paper is devoted to present a numerical study of two FSO network instances that illustrates the promising effectiveness of the proposed approach.
关键词： Free space optics,Multicommodity flows,Linear and mixed-integer programming,Robust optimization,Resilient and survivable networks,Variable link capacity
更新于2025-09-09 09:28:46