•We quantify the potential of EVs to utilize fluctuating RES through optimized charging.•We use empirical driving data to model the behavior of key sociodemographic groups.•Optimized charging can ...double the utilization of RES compared to simple charging.•Trip information is more relevant than charger availability to utilize EV flexibility.
Electric vehicles (EVs) are a new load type with considerable temporal flexibility. This work evaluates to what extent EV fleets (based on empirical driving profiles from two distinct sociodemographic groups) can cover their charging requirements by means of variable renewable generation (wind or solar-PV). For this purpose we formulate a mixed-integer optimization problem minimizing the amount of conventional generation employed. The results indicate that the usage of variable renewable generation can be more than doubled as compared to uncoordinated charging. Furthermore, we analyze how the utilization of renewable generation by EV fleets is affected through different portfolios of renewable generation sources, charging infrastructure specifications as well as a reduced optimization horizon.
The behavior and chemical reactivity of a group of molecules are typically illustrated using intricate and extensive chemical reaction networks. These networks consist of a group of species and a ...series of reactions that detail their evolution. Although recent years have seen a surge in efforts to create numerical algorithms that generate dense chemical reaction networks with thousands of reactions and molecules, the simulation of these networks is a computationally demanding task, even for reaction mechanisms that describe the combustion of hydrocarbons. This paper introduces an innovative and unbiased approach to data-driven model reduction of extensive reaction networks called the SParse IdeNtification (SPIN) algorithm. SPIN combines tools from different domains to identify a set of crucial reactions using species concentrations and reaction rates, all while maintaining minimal computational costs and without requiring extra data or simulations. SPIN is successfully tested for large combustion networks of propane and n-heptane. The study demonstrates that, despite containing only one-fifth of the reactions found in the full mechanism, the SPIN reduced mechanism for n-heptane combustion serves as a highly accurate approximation of the original mechanism, with an average deviation of only 8.4% in ignition delay. Notably, this outstanding performance is achieved without bias towards any particular target property, such as ignition delay, as the reduction and model parameters are optimized to obtain the best possible results. We demonstrate that SPIN can operate as a standalone method or be hybridized with existing species-based reduction methods to further enhance its ability to identify the most significant reactions. This capability is particularly beneficial in comprehending the intricate mechanisms of combustion.
•SPIN massively simplifies complex reaction networks using an unbiased approach.•SPIN reduces mechanisms (up to 80%) with minimal loss in ignition delay accuracy.•Combining SPIN with DRGP produces even smaller but still accurate mechanisms.
•We study single or bilevel models, respectively, for flexible electricity tariffs to appropriately capture the incentives of retailers and consumers.•We consider the following flexible electricity ...tariffs: fixed-price, time-of-use, real-time-pricing, and critical-peak-pricing tariffs.•The tariffs are analyzed in a detailed computational study.•Real-time-pricing increases retailer profits, however, shifts all price risk to the prosumer.•Time-of-use and critical-peak-pricing only yield mild additional retailer profits.
We compare various flexible tariffs that have been proposed to cost-effectively govern a prosumer’s electricity management—in particular time-of-use (TOU), critical-peak-pricing (CPP), and a real-time-pricing tariff (RTP). As the outside option, we consider a fixed-price tariff (FP) that restricts the specific characteristics of TOU, CPP, and RTP, so that the flexible tariffs are at least as profitable for the prosumer as the FP tariff. We propose bilevel models to determine the optimal interplay between the retailer’s tariff design and the prosumer’s decisions on using the storage, on consumption, and on electricity purchases from as well as electricity sales to the grid. The single-level reformulations of the considered bilevel models are computationally highly challenging optimization problems since they combine bilinearities and mixed-integer aspects for modeling certain tariff structures. Based on a computational study using real-world data, we find that RTP increases retailer profits, however, leads to the largest price volatility for the prosumer. TOU and CPP only yield mild additional retailer profits and, due to the multiplicity of optimal plans on the part of the prosumer, imply uncertain revenues for the retailer.
State-of-the-art clustering algorithms provide little insight into the rationale for cluster membership, limiting their interpretability. In complex real-world applications, the latter poses a ...barrier to machine learning adoption when experts are asked to provide detailed explanations of their algorithms’ recommendations. We present a new unsupervised learning method that leverages Mixed Integer Optimization techniques to generate interpretable tree-based clustering models. Utilizing a flexible optimization-driven framework, our algorithm approximates the globally optimal solution leading to high quality partitions of the feature space. We propose a novel method which can optimize for various clustering internal validation metrics and naturally determines the optimal number of clusters. It successfully addresses the challenge of mixed numerical and categorical data and achieves comparable or superior performance to other clustering methods on both synthetic and real-world datasets while offering significantly higher interpretability.
This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or ...inequality constraints. The proposed approach employs two recurrent neural networks operating concurrently at two timescales. In addition, particle swarm optimization is used to update the initial neuronal states iteratively to escape from local minima toward better initial states. In spite of its minimal system complexity, the approach is proven to be almost surely convergent to optimal solutions. Its superior performance is substantiated via solving five benchmark problems.
In this study, a mathematical model that integrates spatial and temporal dimensions is developed for strategic planning of future bioethanol supply chain systems. The planning objective is to ...minimize the cost of the entire supply chain of biofuel from biowaste feedstock fields to end users over the entire planning horizon, simultaneously satisfying demand, resource, and technology constraints. This model is used to evaluate the economic potential and infrastructure requirements for bioethanol production from eight waste biomass resources in California as a case study. It is found that, through careful supply chain design, biowaste-based ethanol production can be sustained at a compatible cost around $1.1 per gallon.
It has been shown that any 9 by 9 Sudoku puzzle must contain at least 17 clues to have a unique solution. This paper investigates the more specific question: given a particular completed Sudoku grid, ...what is the minimum number of clues in any puzzle whose unique solution is the given grid? We call this problem the Minimum Sudoku Clue Problem (MSCP). We formulate MSCP as a binary bilevel linear program, present a class of globally valid inequalities, and provide a computational study on 50 MSCP instances of 9 by 9 Sudoku grids. Using a general bilevel solver, we solve 95% of instances to optimality, and show that the solution process benefits from the addition of a moderate amount of inequalities. Finally, we extend the proposed model to other combinatorial problems in which uniqueness of the solution is of interest.
•A PEM fuel cell integrated with an ORC using twenty zeotropic mixtures is proposed.•Considering mixture type as a variable, a newly optimization mode is applied.•Comparative single/multi ...optimizations are performed to find the optimal mixtures.•Low-temperature PEM fuel cell works better from multi-objective optimization facet.•(13.32/86.68) R601a/Hexane is the best fluid from multi-objective aspect.
In the present study, a comparative optimization analysis of a hydrogen-based proton exchange membrane (PEM) fuel cell integrated with an organic Rankine cycle (ORC) using twenty different zeotropic mixtures is accomplished. Accordingly, considering the mixture type as a qualitative decision variable, a novel method of integer single/multi-objective optimization is implemented from thermodynamic and economic aspects. Using a developed genetic algorithm code in MATLAB software, histogram and scatter distributions are presented to determine the density of optimum points and optimum fraction for each mixture. The optimal solution points of exergy efficiency and total cost rate for each mixture are extracted via a Pareto frontier diagram. Eventually, to assess the influence of major decision variables on system performance, a comparative parametric study on five optimal mixtures is carried out. Referring to single-objective optimization results of the ORC unit and the overall system, the use of R601/Cis-2-Butene (2/98) and R601a/Cis-2-Butene (1.32/98.68), respectively, lead to the highest exergy efficiency. Also, considering exergy efficiency as objective, the results of optimization indicates that at optimal condition, the temperature difference between the PEM fuel and evaporator temperature is 13 K. Results further indicate that while a high-temperature PEM fuel cell is a suitable option from an exergy maximization aspect, a low-temperature PEM fuel cell is superior from multi-objective optimization viewpoint. Results of multi-objective optimization reveal that R601a/Hexane (13.32/86.68) and R601a/C-2-Butene (20.14/79.86) are the best mixtures. Furthermore, what stands out from scatter distribution is that most of the optimal points of evaporator temperature are between 305 K and 380 K. Comparative parametric study results demonstrate that in the selected range of major decision variables, R601a/Cis-2-Butene (20.14/79.86) and R601a/Hexane (13.32/86.68) are the best optimum mixtures from an economic facet.
Modern Formula 1 race cars are hybrid electric vehicles equipped with an internal combustion engine and an electric energy recovery system. In order to achieve the fastest possible lap time, the ...components’ operation must be carefully optimized, and the energy management must account for the impact of the gearshift strategy on the overall performance. This paper presents an algorithm to calculate the time-optimal energy management and gearshift strategies for the Formula 1 race car. First, we leverage a convex modeling approach to formulate a mathematical description of the powertrain including the gearbox, preserving convexity for a given engine speed trajectory. Second, we devise a computationally efficient algorithm to compute the energy management and gearshift strategies for minimum lap time, under consideration of given fuel and battery consumption targets. In particular, we combine convex optimization, dynamic programming and Pontryagin’s minimum principle in an iterative scheme to solve the arising mixed-integer optimization problem. We showcase our algorithm with a case study for the Bahrain racetrack, underlining the interactions between energy management and gear selection. Finally, we use our approach as a benchmark to evaluate the sub-optimality of a heuristic gearshift rule. Our results show that using an optimized engine speed threshold for upshifts can yield close-to-optimal results. However, already deviations smaller than 4% from the best possible threshold can increase lap time by more than 100ms, highlighting the importance of jointly optimizing energy management and gearshift strategies.
•We propose an engine-speed-dependent model of a hybrid electric race car.•We jointly optimize the energy management and gear strategies for minimum lap time.•Our iterative algorithm combines convex optimization and dynamic programming.•An entire lap can be optimized with a computation time of around 90 s.•Deviating by 4% from best upshift threshold can increase lap time by more than 100 ms.
PDF
