Co-optimized bidding strategy of an integrated wind-thermal-photovoltaic system in deregulated electricity market under uncertainties

Clean Energy sources, such as wind and solar, have become an inseparable part of today's power grids. However, the intermittent nature of these sources has become the greatest challenge for their owners, which makes the bidding in the restructured electricity market more challenging. Hence, the main goal of this paper is to propose a novel multi-objective bidding strategy framework for a wind-thermal-photovoltaic system in the deregulated electricity market for the first time. Contrary to the existing bidding models, in the proposed model, two objective functions are taken into account that the first one copes with profit maximization while the second objective function concerns with emission minimization of thermal units. The proposed multi-objective optimization problem is solved using the weighted sum approach. The uncertainties associated with electricity market prices and the output power of renewable energy sources are characterized by a set of scenarios. Ultimately, in order to select the best-compromised solution among the obtained Pareto optimal solutions, two diverse approaches are applied. The proposed bidding strategy problem is being formulated and examined in various modes of joint and disjoint operation of dispatchable and non-dispatchable energy sources. Simulation results illustrate that not only the integrated participation of these resources increases the producer's expected profit, but also decreases the amount of the produced pollution by the thermal units.© 2019 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Probabilistic Multi Objective Optimal Reactive Power Dispatch Considering Load Uncertainties Using Monte Carlo Simulations

Optimal Reactive Power Dispatch (ORPD) is a multi-variable problem with nonlinear constraints and continuous/discrete decision variables. Due to the stochastic behavior of loads, the ORPD requires a probabilistic mathematical model. In this paper, Monte Carlo Simulation (MCS) is used for modeling of load uncertainties in the ORPD problem. The problem is formulated as a nonlinear constrained multi objective (MO) optimization problem considering two objectives, i.e., minimization of active power losses and voltage deviations from the corresponding desired values, subject to full AC load flow constraints and operational limits. The control variables utilized in the proposed MO-ORPD problem are generator bus voltages, transformers’ tap ratios and shunt reactive power compensation at the weak buses. The proposed probabilistic MO-ORPD problem is implemented on the IEEE 30-bus and IEEE 118-bus tests systems. The obtained numerical results substantiate the effectiveness and applicability of the proposed probabilistic MO-ORPD problem

The implications of Simpson's paradox for cross-scale inference among lakes

Using cross-sectional data for making ecological inference started as a practical means of pooling data to enable meaningful empirical model development. For example, limnologists routinely use sample averages from numerous individual lakes to examine patterns across lakes. The basic assumption behind the use of cross-lake data is often that responses within and across lakes are identical. As data from multiple study units across a wide spatiotemporal scale are increasingly accessible for researchers, an assessment of this assumption is now feasible. In this study, we demonstrate that this assumption is usually unjustified, due largely to a statistical phenomenon known as the Simpson's paradox. Through comparisons of a commonly used empirical model of the effect of nutrients on algal growth developed using several data sets, we discuss the cognitive importance of distinguishing factors affecting lake eutrophication operating at different spatial and temporal scales. Our study proposes the use of the Bayesian hierarchical modeling approach to properly structure the data analysis when data from multiple lakes are employed. © 2019 Elsevier Lt

Hourly Price-Based Demand Response for Optimal Scheduling of Integrated Gas and Power Networks Considering Compressed Air Energy Storage

Gas-fired plants are becoming an optimal and practical choice for power generation in electricity grids due to high efficiency and less emissions. Such plants with fast start-up capability and high ramp rate are flexible in response to stochastic load variations. Meanwhile, gas system constraints affect the flexibility and participation of such units in the energy market. Compressed air energy storage (CAES) as a flexible source with high ramp rate can be an alternative solution to reduce the impact of gas system constraints on the operation cost of a power system. In addition, demand response (DR) programs are expressed as practical approaches to overcome peak-demand challenges. This study introduces a stochastic unit commitment scheme for coordinated operation of gas and power systems with CAES technology as well as application of an hourly price-based DR. The introduced model is performed on a six-bus system with a six-node gas system to verify the satisfactory performance of the model

Categorization of the main descriptors of different ampicillin crystal habits

With the purpose of enabling the analysis by digital methods of particles of multisource pharmaceutical raw materials, this study analyzed different crystal habits of ampicillin particles, by grouping the external shapes obtained from 3 different solvents (acetonitrile, ethanol, and methanol), thereby reducing the number of descriptors necessary to adequately represent each shape. For this purpose, a selection of morphological descriptors was used including: circularity, roughness, roundness, compactness, aspect ratio, effective diameter, solidity, convexity, fractal dimension, and 10 Complex Fourier descriptors. These measures cover highly diverse morphological properties and define the crystal habit of a particle. Principal Component Analysis (PCA) and the Cluster Analysis (CA) were the grouping techniques used, which demonstrated the possibility of using between 2 and 4 descriptors instead of the 18 proposed initially.Com o objetivo de possibilitar a análise, por meio de métodos digitais, de partículas de matérias-primas farmacêuticas de múltiplas fontes, analisaram-se diferentes cristais de partículas de ampicilina através do agrupamento de formas externas obtidas de três diferentes solventes (acetonitrila, etanol e metanol), reduzindo, desse modo, o número de descritores necessários para representar adequadamente cada forma. Com esse propósito, utilizou-se seleção de descritores morfológicos, incluindo: circularidade, aspereza, arredondamento, compactação, relação de aspecto, diâmetro efetivo, solidez, convectividade, dimensão fractal e 10 descritores complexos de Fourier. Essas medidas cobrem diversas propriedades morfológicas e definem a cristalinidade de uma partícula. As análises do componente principal (PCA) e por grupamento (CA) foram as técnicas de agrupamento utilizadas, que demonstraram a possibilidade de utilizar entre 2 e 4 descritores ao invés dos 18, inicialmente propostos