Mixed-integer nonlinear programming 2018

Sahinidis, Nikolaos V.

doi:10.1007/s11081-019-09438-1

Cited by 71 publications

(54 citation statements)

References 36 publications

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“…The optimization problem, in this case, is of type Pure Integer Non-Linear Program, due to the non-linearity of the objective function and binary values of the [ ] array, contains linear and non-linear equations [38] [39]. We map the optimization problem to a variant of the decision problem of the Knapsack problem [40], which aims to determine the maximum value V that can be packed in a knapsack without exceeding the maximum allowed weight W of the knapsack (see Table II).…”

Section: Virtual Power Plant Modelmentioning

confidence: 99%

Blockchain-Based Decentralized Virtual Power Plants of Small Prosumers

et al. 2021

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The deployment of small-scale renewable energy sources will transform the management of energy grids towards more decentralized solutions in which the prosumers will have a more active role. Regulatory and market barriers are driving the implementation of virtual aggregation models in which the small-scale prosumers work together on a larger scale to gain benefits that could not be obtained on an individual basis. In this paper, we propose to use public blockchain and self-enforcing smart contracts to construct Virtual Power Plants (VPPs) of prosumers to provide energy services. A model has been defined for capturing the prosumer level constraints in terms of available energy profiles and energy service requirements enabling their optimal aggregation in hierarchical structures. A lightweight decentralized solution for VPPs construction is implemented using smart contracts enabling its efficient running on the public blockchain. Smart contracts are encoding the model constraints and are defining functionalities for prosumers to initiate or join a VPP implementing the complete chain of Offer-Operate-Measure-Remunerate actions. The VPP will be managed on top of a distributed ledger technology offering decentralized functionality for tracking and validating the delivery of energy based on the blockchain transactions and for energy and financial settlement, the remuneration being done according to the amount of energy provided by individual prosumers. Experimental results show that the proposed solution runs successfully on the public blockchain with good execution time and can address Balancing Responsible Party requests for additional generation. The overhead in terms of gas consumption and transactional throughput stays within reasonable boundaries.

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Section: Virtual Power Plant Modelmentioning

confidence: 99%

Blockchain-Based Decentralized Virtual Power Plants of Small Prosumers

et al. 2021

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“…Rezekne, Latvia Proceedings of the 13 th International Scientific and Practical Conference. Volume 3,[330][331][332][333][334][335] More effective, although more complex, is the implementation of the heating process with feedback on the temperature of the heater (see [9] about theory). Here, the model uses implicit (implicit) event management.…”

Section: A Heating Model With Periodic Timer Activationmentioning

confidence: 99%

Modeling the Operation of a Galvanic Bath With Electric Heaters and Optimizing Its Design Based on Numerical Simulation

Samarkin

Дмитриев

Dementyev

et al. 2021

ETR

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A model of the processes in a galvanic bath during heating is based on the finite element method. The processes of heating the air chamber and directly the reagent solution are simulated. Based on the constructed model, an automatic control system for heating elements is being developed, which provides a sufficient heating rate, a stable temperature and protects the heating elements from burnout. An application program has been created that allows performing the calculations without studying complex modeling systems.

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“…Equation () guarantees that each predictor

j

should be included at most one of the

k

groups. The mathematical model in (8) is a large‐scale mixed‐integer nonlinear programming (MINLP) model, and they are considered as difficult to solve problems in the literature [16]. In most cases, instead of using off‐the‐shelf solvers researchers develop tailored solutions for their problems such as new decomposition approaches, multi‐staged optimization methods and/or heuristics to make them faster.…”

Section: Coefficient Tree Regression (Ctr) For Generalized Linear Models (Glm)mentioning

confidence: 99%

Coefficient tree regression for generalized linear models

Sürer

Apley

Malthouse

2021

Statistical Analysis

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Large regression data sets are now commonplace, with so many predictors that they cannot or should not all be included individually. In practice, derived predictors are relevant as meaningful features or, at the very least, as a form of regularized approximation of the true coefficients. We consider derived predictors that are the sum of some groups of individual predictors, which is equivalent to predictors within a group sharing the same coefficient. However, the groups of predictors are usually not known in advance and must be discovered from the data. In this paper we develop a coefficient tree regression algorithm for generalized linear models to discover the group structure from the data. The approach results in simple and highly interpretable models, and we demonstrated with real examples that it can provide a clear and concise interpretation of the data. Via simulation studies under different scenarios we showed that our approach performs better than existing competitors in terms of computing time and predictive accuracy.

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Mixed-integer nonlinear programming 2018

Cited by 71 publications

References 36 publications

Blockchain-Based Decentralized Virtual Power Plants of Small Prosumers

Blockchain-Based Decentralized Virtual Power Plants of Small Prosumers

Modeling the Operation of a Galvanic Bath With Electric Heaters and Optimizing Its Design Based on Numerical Simulation

Coefficient tree regression for generalized linear models

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