In this paper, we propose a novel mixed-integer Non-linear Optimization formulation to construct a risk score, where we optimize the logistic loss with sparsity constraints. Previous approaches are typically designed ...
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A fundamental problem in combinatorial optimization is identifying equivalent formulations, which can lead to more efficient solution strategies and deeper insights into a problem’s computational complexity. The need...
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This note addresses the signed-digit representation of non-negative integer binary numbers. We review and revisit popular literature methods for canonical signed-digit representation. A method based on string substitu...
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Different from traditional secure communication that focuses on symbolic protection at the physical layer, semantic secure communication requires further attention to semantic-level task performance at the application...
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The transition to sustainable energy systems has highlighted the critical need for efficient sizing of renewable energy resources in microgrids. In particular, designing photovoltaic (PV) and battery systems to meet r...
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For any number field K with DK = |Disc(K)| and any integer ≥ 2, we improve over the commonly cited trivial bound |ClK[]| ≤ |ClK| [K:Q]Ε DK1/2+Ε on the -torsion subgroup of the class group of K by showing that |ClK...
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This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vac-...
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This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vac-cines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model in-cludes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncer-tainty in supply, and in the worst case, approximately 30% of the demand cannot be met.
In Smart Cities (SC), the efficient management of services such as health, transport, public safety, and especially the electricity ensures the welfare of citizens. In recent years, the insertion of renewable sources ...
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In Smart Cities (SC), the efficient management of services such as health, transport, public safety, and especially the electricity ensures the welfare of citizens. In recent years, the insertion of renewable sources (RSs) (e.g., solar and wind) in the power grid (PG) of SCs has contributed to meeting the electricity needs of the various consumer units. However, the large-scale integration of these RSs can fatigue the assets, leading to their premature aging and, consequently, compromising the quality of electricity supply. To overcome these challenges, the implementation of Neighboring Energy Storage Communities (NESCs) employing demand response (DR) strategies along with efficient coordination of storage batteries (SBs) could be a promising alternative. In this sense, the present work proposes a mixed-integer linear programming (MILP) model to efficiently manage SBs and the set of household appliances, including charging electric vehicles (EVs), in an NESC provided solely by PG. The proposed model aims to minimize: the total costs related to energy consumption, the peak rebound effect on the total consumption profile, energy wastage through load factor (LF) improvement, and the deep discharges in the SBs during their daily operational cycle. Operational constraints related to the home appliances, such as average usage time, the number of times that the appliance is used daily, etc., are taking into account. The EV state-of-charge (SOC), EV charging rate limits, and initial and final SOC of the SBs, are also considered. A Monte Carlo Algorithm (MCA) is used to simulate the habitual consumption patterns of each customer. The proposed model was implemented in AMPL and solved using CPLEX. The performance of this proposed model is evaluated considering two NESCs differentiated by the number of consumer communities. A first NESC (small-scale) is analyzed considering only two consumer communities. In this NESC, two case studies (Case 1 and 2) are discussed. Next, the sec
Fossil fuel power plants continue to contribute significantly to carbon emissions, necessitating a transition towards cleaner energy sources. Despite the growing presence of renewables within the power systems, the in...
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Fossil fuel power plants continue to contribute significantly to carbon emissions, necessitating a transition towards cleaner energy sources. Despite the growing presence of renewables within the power systems, the incorporation of carbon capture technologies into the traditional thermal power plants holds great potential in emissions reduction. In this paper, the integration of renewable energy sources (RES) and coal-fired power generation units outfitted with carbon capture schemes is addressed. Multiple demand response (DR) programs and hydropower plants are strategically utilized to increase the power system flexibility. To effectively plan the day-ahead (DA) operation of the power system, a presumed market-clearing framework is adopted and modelled as a risk-constrained two-objective stochastic mixed-integer linear programming problem. The proposed framework helps to tackle the uncertainties related to RES and demand variations by employing a hidden Markovian process (HMP) technique. To simultaneously minimize the system's operational costs and CO2 emissions, an enhanced version of the augmented..-constraint method is employed. To prove its value, the proposed framework is devoted to the 24-bus IEEE reliability test system (IEEE-RTS). The system features substantial penetration of RES (exceeding 87% of peak load) and standard DR options capacities (less than 25% of peak load). The results show a 24% reduction in load peaks, an over 63% decrease in emissions, and a 17% reduction in the overall operation costs.
Considering the electricity market, data analytics paves the way for completely new strategies regarding demand and supply-side policies. In this manner, predictive analysis of the demanded power accuracy is carried o...
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Considering the electricity market, data analytics paves the way for completely new strategies regarding demand and supply-side policies. In this manner, predictive analysis of the demanded power accuracy is carried out to boost profits and increase the penetration of similar demand response (DR) programs across all levels of end -user categories. Residential loads experience stiff spikes and unpredictable variations due to occupancy activities and environmental factors. To address this, we first propose a robust short-term multivariate -multistep forecasting framework that is resilient to missing or erroneous data, employing temporal convolution networks (TCNs). We then incorporate two distinct valley -filling indices to optimize the charging of electric vehicle loads according to DR requirements, showcasing the efficacy of leveraging artificial intelligence to enhance the utilization of clean energy resources. Simulation studies are conducted using real -world nodal residential loads with hourly granularity. The results demonstrate that the forecasting method is reliable for residential locations, even when dealing with highly damaged data. The case studies effectively fill the load into the valleys and minimize fluctuations in residential locations. Through the integration of emission -aware forecasting and optimization strategies, our study lays the groundwork for a comprehensive approach that not only improves economic outcomes and grid stability but also advances the imperative of reducing carbon emissions.
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