A Mixed-Integer SDP Solution Approach to Distributionally Robust Unit Commitment with Second Order Moment Constraints

A power system unit commitment (UC) problem considering uncertainties of renewable energy sources is investigated in this paper, through a distributionally robust optimization approach. We assume that the first and second order moments of stochastic parameters can be inferred from historical data, and then employed to model the set of probability distributions. The resulting problem is a two-stage distributionally robust unit commitment with second order moment constraints, and we show that it can be recast as a mixed-integer semidefinite programming (MI-SDP) with finite constraints. The solution algorithm of the problem comprises solving a series of relaxed MI-SDPs and a subroutine of feasibility checking and vertex generation. Based on the verification of strong duality of the semidefinite programming (SDP) problems, we propose a cutting plane algorithm for solving the MI-SDPs; we also introduce a SDP relaxation for the feasibility checking problem, which is an intractable biconvex optimization. Experimental results on a IEEE 6-bus system are presented, showing that without any tunings of parameters, the real-time operation cost of distributionally robust UC method outperforms those of deterministic UC and two-stage robust UC methods in general, and our method also enjoys higher reliability of dispatch operation

Synthesis, Biological Evaluation and Mechanism Studies of Deoxytylophorinine and Its Derivatives as Potential Anticancer Agents

Previous studies indicated that (+)-13a-(S)-Deoxytylophorinine (1) showed profound anti-cancer activities both in vitro and in vivo and could penetrate the blood brain barrier to distribute well in brain tissues. CNS toxicity, one of the main factors to hinder the development of phenanthroindolizidines, was not obviously found in 1. Based on its fascinating activities, thirty-four derivatives were designed, synthesized; their cytotoxic activities in vitro were tested to discover more excellent anticancer agents. Considering the distinctive mechanism of 1 and interesting SAR of deoxytylophorinine and its derivatives, the specific impacts of these compounds on cellular progress as cell signaling transduction pathways and cell cycle were proceeded with seven representative compounds. 1 as well as three most potent compounds, 9, 32, 33, and three less active compounds, 12, 16, 35, were selected to proform this study to have a relatively deep view of cancer cell growth-inhibitory characteristics. It was found that the expressions of phospho-Akt, Akt, phospho-ERK, and ERK in A549 cells were greater down-regulated by the potent compounds than by the less active compounds in the Western blot analysis. To the best of our knowledge, this is the first report describing phenanthroindolizidines alkaloids display influence on the crucial cell signaling proteins, ERK. Moreover, the expressions of cyclin A, cyclin D1 and CDK2 proteins depressed more dramatically when the cells were treated with 1, 9, 32, and 33. Then, these four excellent compounds were subjected to flow cytometric analysis, and an increase in S-phase was observed in A549 cells. Since the molecular level assay results of Western blot for phospho-Akt, Akt, phospho-ERK, ERK, and cyclins were relevant to the potency of compounds in cellular level, we speculated that this series of compounds exhibit anticancer activities through blocking PI3K and MAPK signaling transduction pathways and interfering with the cell cycle progression

Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones

Thermal Stability of Retained Austenite and Properties of A Multi-Phase Low Alloy Steel

In this work, we elucidate the effects of tempering on the microstructure and properties in a low carbon low alloy steel, with particular emphasis on the thermal stability of retained austenite during high-temperature tempering at 500–700 °C for 1 h. Volume fraction of ~14% of retained austenite was obtained in the studied steel by two-step intercritical heat treatment. Results from transmission electron microscopy (TEM) and X-ray diffraction (XRD) indicated that retained austenite had high thermal stability when tempering at 500 and 600 °C for 1 h. The volume fraction was ~11–12%, the length and width remained ~0.77 and 0.21 μm, and concentration of Mn and Ni in retained austenite remained ~6.2–6.6 and ~1.6 wt %, respectively. However, when tempering at 700 °C for 1 h, the volume fraction of retained austenite was decreased largely to ~8%. The underlying reason could be attributed to the growth of austenite during high-temperature holding, leading to a depletion of alloy contents and a decrease in stability. Moreover, for samples tempered at 700 °C for 1 h, retained austenite rapidly transformed into martensite at a strain of 2–10%, and a dramatic increase in work hardening was observed. This indicated that the mechanical stability of retained austenite decreased

Windfall profit-aware stochastic scheduling strategy for industrial virtual power plant with integrated risk-seeking/averse preferences

The increasing penetration of renewable energy in power grids introduces higher levels of uncertainty, while current decision-making models typically favour either a risk-averse or risk-neural strategy, and the research works related to windfall profit-aware risk-seeking strategies are quite limited. In this paper, a novel concept of integrated risk-seeking/averse preference is proposed, and a windfall profit-aware stochastic scheduling model for an industrial virtual power plant (IVPP) is developed based on this type of risk preference, which can realize the joint management of potential high profits and extreme losses. Firstly, the potential best- and worst-case results are incorporated into the holistic decision-making framework, which are quantified by the value at best (VaB) and the conditional value at risk (CVaR) measures, respectively. Next, the windfall profit-aware stochastic scheduling model is developed for the optimal operation of IVPP in day-ahead and real-time electricity markets, where multi-type flexible resources, such as energy conversion and storage devices, industrial production workstations, material storages and financial instruments, are utilized to improve expected profits and minimize the potential risks. Specifically, two types of credit-based virtual transactions, increment and decrement bids, are employed by the IVPP to increase its trading flexibility in electricity markets. Finally, simulation studies are conducted for a multi-energy IVPP to validate the proposed windfall profit-aware framework and model, showcasing that the risk-seeking and risk-averse preferences of decision-makers can be fully satisfied simultaneously under actual market environment. Moreover, risk parameters can be adjusted accordingly to manage windfall profits, expected profits, and extreme losses flexibly in electricity markets

Multi-objective optimal dispatch of integrated heat and electricity energy systems considering heat load energy quality

As the energy crisis and consumption revolution advance, traditional energy service providers gradually transform into customer-centric integrated energy service providers. However, the customer’s experience with energy depends on the quality. This paper proposes an energy quality model for heat load of the integrated heat and electricity energy systems, from which the derived energy quality factor for heat load participates as one of the objectives of the multi-objective optimization model. The considered heat transfer dynamics accurately describe the heat transmission through the heat network. Finally, the effectiveness of the proposed multi-objective optimal dispatching model is verified by numerical simulations, in which a decision-making model established based on exergy economics enables the selection of a compromise solution from the Pareto front as the basis for dispatching

Electrochemical Determination of Iron(III) in River Water by Differential Pulse Voltammetry (DPV) Using a Poly(3,4-Ethylenedioxythiophene)-Polystyrene Sulfonate co-Polymer/Gold Nanoparticle Modified Gold Microelectrode

An electrochemical sensor based upon a poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT-PSS) co-polymer film with gold nanoparticles (AuNPs) modified microelectrode is reported for the determination of Fe(III). Scanning electron microscopy images showed that the AuNPs were immobilized on gold (Au) microelectrode by formation of a nanofilm with PEDOT-PSS. The individual particles and their cluster-like aggregates provide a larger active electrochemical area and higher conductivity, which facilitates the determination of Fe(III). Electrochemical impedance spectroscopy (EIS) shows the AuNPs/PEDOT-PSS/Au microelectrode possesses high conductivity. A low detection limit of 0.01 & mu;M Fe(III) was obtained using the modified microelectrode by differential pulse voltammetry (DPV) due to its large specific surface area, high conductivity, and the presence of more active sites. The optimized pH of the supporting acetate buffer was 2.0. The AuNPs/PEDOT-PSS/Au provided a linear range from 0.0119 & mu;M to 7.14 & mu;M. The modified electrode was successfully employed for the the determination of total dissolved iron in river water from Three Gorges with satisfactory results