2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Computational Intelligence and Applications (ICCIA), 2017 2nd IEEE International Conference on. :51-56 Sep, 2017
Yugang Wang, Udit Singhal, Yuanyuan Qiao, Tadas Kasputis, Jae-Seung Chung, Huiru Zhao, Farah Chammaa, Jacob A. Belardo, Therese M. Roth, Hao Zhang, Alexander B. Zaslavsky, Ganesh S. Palapattu, Kenneth J. Pienta, Arul M. Chinnaiyan, Russell S. Taichman, Frank C. Cackowski, and Todd M. Morgan
Translational Oncology, Vol 13, Iss 4, Pp - (2020)
Neoplasms. Tumors. Oncology. Including cancer and carcinogens and RC254-282
Wnt signaling has been implicated as a driver of prostate cancer-related osteoblast differentiation, and previous studies have linked modifications in Wnt function with the induction of tumor metastasis. A unique aspect of prostate cancer bone metastases in mouse models is their relative predilection to the hindlimb (femur) compared to the forelimb (humerus). Comparative gene expression profiling was performed within the humerus and femur from non–tumor-bearing mice to evaluate differences in the microenvironments of these locations. This revealed the relative overexpression of the Wnt signaling inhibitors WIF1 and SOST in the humerus compared to the femur, with increased WNT5A expression in femur bone marrow, suggesting a coordinated upregulation of Wnt signals within the femur compared to the humerus. Conditioned medium (CM) from bone marrow stromal cells (HS-5 cells) was used to mimic the bone marrow microenvironment, which strongly promoted prostate cancer cell invasion (3.3-fold increase in PC3 cells, P
Huiru Zhao, Hao Lu, Bingkang Li, Xuejie Wang, Shiying Zhang, and Yuwei Wang
Energies, Vol 13, Iss 5, p 1255 (2020)
stochastic optimization, microgrid operation, day-ahead market, demand response, energy storage system, and Technology
More and more attention has been paid to the development of renewable energy in the world. Microgrids with flexible regulation abilities provide an effective way to solve the problem of renewable energy connected to power grids. In this article, an optimization strategy of a microgrid participating in day-ahead market operations considering demand responses is proposed, where the uncertainties of distributed renewable energy generation, electrical load, and day-ahead market prices are taken into account. The results show that, when the microgrid implements the demand response, the operation cost of the microgrid decreases by 4.17%. Meanwhile, the demand response program can transfer the peak load of the high-price period to the low-price period, which reduces the peak valley difference of the load and stabilizes the load curve. Finally, a sensitivity analysis of three factors is carried out, finding that, with the increase of the demand response adjustable ratio or the maximum capacity of the electrical storage devices, the operation cost of the microgrid decreases, while, with the increase of the demand response cost, the operation cost of the microgrid increases and, finally, tends to the cost without the demand response. The sensitivity analysis reveals that the demand response cost has a reasonable pricing range to maximize the value of the demand response.
Yuwei Wang, Yuanjuan Yang, Liu Tang, Wei Sun, and Huiru Zhao
Energies, Vol 12, Iss 20, p 3983 (2019)
combined cooling, heating and power (cchp) micro-grid, electricity market, stochastic optimization, conditional value-at-risk (cvar), demand response program, and Technology
Combined cooling, heating and power (CCHP) micro-grids have the advantage of high energy efficiency, and can be integrated with renewable energies and demand response programs (DRPs). With the deepening of electricity market (EM) reforms, how to carry out operation optimization under EM circumstances will become a key problem for CCHP micro-grid development. This paper proposed a stochastic-CVaR (conditional value at risk) optimization model for CCHP micro-grid operation with consideration of EM participation, wind power accommodation and multiple DRPs. Specifically, based on the stochastic scenarios for EM clearing prices and wind power outputs uncertainties, the stochastic optimization method was applied to ensure the realization of operational cost minimization and wind power accommodation; the CVaR method was implemented to control the potential risk of operational cost increase. Moreover, by introducing multiple DRPs, the electrical, thermal and cooling loads can be transformed as flexible sources for CCHP micro-grid operation. Simulations were performed to show the following outcomes: (1) by applying the proposed stochastic-CVaR approach and considering multiple DRPs, CCHP micro-grid operation can reach better performance in terms of cost minimization, risk control and wind power accommodation etc.; (2) higher energy utilization efficiency can be achieved by coordinately optimizing EM power biddings; etc.
Mathematical Problems in Engineering, Vol 2019 (2019)
Engineering (General). Civil engineering (General), TA1-2040, Mathematics, and QA1-939
With the increase of wind power installed capacity and the development of energy storage technologies, it is gradually accepted that integrating wind farms with energy storage devices to participate in spot electricity market (EM) is a promising way for improving wind power uncertainty accommodation and bringing considerable profit. Hence, research on reasonable offering and operating strategies for integrated wind farm-energy storage system (WF-ESS) under spot EM circumstances has important theoretical and practical significance. In this paper, a newly progressive stochastic-robust hybrid optimization model series is proposed for yielding such strategies. In the day-ahead stage, day-ahead and balancing prices uncertainties are formulated by applying joint stochastic scenarios, and real-time available wind power uncertainties are modeled by using the seasonal auto-regression (AR) based dynamic uncertainty set. Then, the first model of this model series is established and utilized for cooptimizing both the day-ahead offering and nominal real-time operating strategies. In the balancing stages, wind power uncertainty set and balancing prices stochastic scenarios are dynamically updated with the newly realized data. Then, each model from the remaining of this model series is established and utilized period by period for obtaining the optimal balancing/real-time offering/operating strategies adjusted from the nominal ones. Robust optimization (RO) in this progressive framework makes the operation of WF-ESS dynamically accommodate wind power uncertainties while maintaining relatively low computational complexity. Stochastic optimization (SO) in this progressive framework makes the WF-ESS avoid pursuing profit maximization strictly under the worst-case scenarios of prices uncertainties. Moreover, by adding a risk-aversion term in form of conditional value at risk (CVaR) into the objective functions of this model series, the optimization models additionally provide flexibility in reaching a trade-off between profit maximization and risk management. Simulation and profit comparisons with other existing methods validate the scientificity, feasibility, and effectiveness of applying our proposed model series.
Xueliang Li, Bingkang Li, Long Zhao, Huiru Zhao, Wanlei Xue, and Sen Guo
Sustainability, 2019, 11, 10, 1.
air pollution prevention and control policy, short-term load forecasting, BWM-GRA approach, and SSA-LSSVM technique
Since 2013, a series of air pollution prevention and control (APPC) measures have been promulgated in China for reducing the level of air pollution, which can affect regional short-term electricity power demand by changing the behavior of power users electricity consumption. This paper analyzes the policy system of the APPC measures and its impact on regional short-term electricity demand, and determines the regional short-term load impact factors considering the impact of APPC measures. On this basis, this paper proposes a similar day selection method based on the best and worst method and grey relational analysis (BWM-GRA) in order to construct the training sample set, which considers the difference in the influence degree of characteristic indicators on daily power load. Further, a short-term load forecasting method based on least squares support vector machine (LSSVM) optimized by salp swarm algorithm (SSA) is developed. By forecasting the load of a city affected by air pollution in Northern China, and comparing the results with several selected models, it reveals that the impact of APPC measures on regional short-term load is significant. Moreover, by considering the influence of APPC measures and avoiding the subjectivity of model parameter settings, the proposed load forecasting model can improve the accuracy of, and provide an effective tool for short-term load forecasting. Finally, some limitations of this paper are discussed.
Wanlei Xue, Bingkang Li, Yongqi Yang, Huiru Zhao, and Nan Xu
Energies, 2019, 12, 6, 1.
NOKEC, effectiveness evaluation, electric power economics, DEMATEL-ANP, and DQ-GRA
This paper proposes a hybrid model for evaluating the effectiveness of new and old kinetic energy conversion (NOKEC), China’s major strategic move aiming to transform the mode of economic growth and improvie the quality of economic development. Considering the goals of NOKEC and the supporting roles of power industry to NOKEC, this paper constructs an index system for NOKEC effectiveness evaluation from an electric power economics perspective, involving three dimensions and 17 secondary indicators. Furthermore, a hybrid evaluation model based on DEMATEL-ANP and DQ-GRA techniques is developed to accomplish the evaluation of Shandong’s NOKEC effectiveness. The results show that Shandong’s NOKEC effectiveness increased from 2015–2017, indicating that Shandong’s NOKEC policies have achieved remarkable results. According to the evaluation results, this paper puts forward the indicators that should be paid close attention to and the following work priorities in Shandong’s future NOKEC process, which has certain practical value for the promotion of Shandong’s NOKEC. In addition, the evaluation model proposed in this paper considers the interrelationships between indicators and overcomes the shortcomings of traditional GRA, showing good applicability to similar effectiveness evaluation issues. Finally, the limitations and universality of the model are discussed and the improvement direction is put forward.
International Journal of Environmental Research and Public Health, Vol 16, Iss 16, p 2926 (2019)
PM2.5 concentrations, GDP per capita, economic structure, urbanization rate, civil vehicles amount, panel data model, and Medicine
With the rapid development of China’s economy, the environmental problems are becoming increasingly prominent, especially the PM2.5 (particulate matter with diameter smaller than 2.5 μm) concentrations that have exerted adverse influences on human health. Considering the fact that PM2.5 concentrations are mainly caused by anthropogenic activities, this paper selected economic growth, economic structure, urbanization, and the number of civil vehicles as the primary factors and then explored the nexus between those variables and PM2.5 concentrations by employing a panel data model for 31 Chinese provinces. The estimated model showed that: (1) the coefficients of the variables for provinces located in North, Central, and East China were larger than that of other provinces; (2) GDP per capita made the largest contribution to PM2.5 concentrations, while the number of civil vehicles made the least contribution; and (3) the higher the development level of a factor, the greater the contribution it makes to PM2.5 concentrations. It was also found that a bi-directional Granger causal nexus exists between PM2.5 concentrations and economic progress as well as between PM2.5 concentrations and the urbanization process for all provinces. Policy recommendations were finally obtained through empirical discussions, which include that provincial governments should adjust the economic and industrial development patterns, restrict immigration to intensive urban areas, decrease the successful proportion of vehicle licenses, and promote electric vehicles as a substitute to petrol vehicles.