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

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

Rethinking the lake trophic state index

Lake trophic state classifications provide information about the condition of lentic ecosystems and are indicative of both ecosystem services (e.g., clean water, recreational opportunities, and aesthetics) and disservices (e.g., cyanobacteria blooms). The current classification schemes have been criticized for developing indices that are single-variable based (vs. a complex aggregate of multi-variables), discrete (vs. a continuous), and/or deterministic (vs. an inherently random). We present an updated lake trophic classification model using a Bayesian multilevel ordered categorical regression. The model consists of a proportional odds logistic regression (POLR) that models ordered, categorical, lake trophic state using Secchi disk depth, elevation, nitrogen concentration (N), and phosphorus concentration (P). The overall accuracy, when compared to existing classifications of trophic state index (TSI), for the POLR model was 0.68 and the balanced accuracy ranged between 0.72 and 0.93. This work delivers an index that is multi-variable based, continuous, and classifies lakes in probabilistic terms. While our model addresses aforementioned limitations of the current approach to lake trophic classification, the addition of uncertainty quantification is important, because the trophic state response to predictors varies among lakes. Our model successfully addresses concerns with the current approach and performs well across trophic states in a large spatial extent

Offering strategy of thermal-photovoltaic-storage based generation company in day-ahead market

Designing appropriate strategies for the participation of generation companies (GenCos) in the electricity markets has always been a concern for researchers. Generally, a set of dispatchable and non-dispatchable units constitute GenCos. This chapter presents a coordinated offering structure for the participation of a GenCo consisting of thermal, photovoltaic (PV), and battery storage system (BSS) in the day-ahead (DA) electricity market. The proposed offering structure is formulated as a three-stage stochastic programming problem while a scenario-based technique is utilized to handle the uncertainty related to electricity prices and PV production. From another point of view, a compatible risk-measuring index with multi-stage stochastic programming problems, namely conditional value at risk (CVaR), is also considered in the proposed structure. The proposed offering model is not only able to derive the offering curves of GenCo but also is capable of applying various emission limitations pertaining to thermal units

Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model

Renewable energy resources such as wind, either individually or integrated with other resources, are widely considered in different power system studies, especially self-scheduling and offering strategy problems. In the current paper, a three-stage stochastic multi-objective offering framework based on mixed-integer programming formulation for a wind-thermal-energy storage generation company in the energy and spinning reserve markets is proposed. The commitment decisions of dispatchable energy sources, the offering curves of the generation company in the energy and spinning reserve markets, and dealing with energy deviations in the balancing market are the decisions of the proposed three-stage offering strategy problem, respectively. In the suggested methodology, the participation model of the energy storage system in the spinning reserve market extends to both charging and discharging modes. The proposed framework concurrently maximizes generation company's expected profit and minimizes the expected emission of thermal units applying lexicographic optimization and hybrid augmented-weighted ∊-constraint method. In this regard, the uncertainties associated with imbalance prices and wind power output as well as day-ahead energy and spinning reserve market prices are modeled via a set of scenarios. Eventually, two different strategies, i.e., a preference-based approach and emission trading pattern, are utilized to select the most favored solution among Pareto optimal solutions. Numerical results reveal that taking advantage of spinning reserve market alongside with energy market will substantially increase the profitability of the generation company. Also, the results disclose that spinning reserve market is more lucrative than the energy market for the energy storage system in the offering strategy structure