A trading optimization model for virtual power plants in day-ahead power market considering uncertainties

Background: The day-ahead power market is an important part of the spot market. In the day-ahead market, participants make short-term forecasts of the load and output to propose the bidding curve more precisely. As energy aggregators that have regulatory resources, virtual power plants (VPPs) need to consider the uncertainty of distributed renewable energy output when participating in power market transactions.Methods: This paper analyzes the uncertainty and built an optimization model for VPP in day-ahead power market considering the uncertainty from both inner parts and the market environment. To verify the model, a simulation study is ran.Results: And the study results show the following: 1) the forecasting model is more efficient than the traditional algorithm in terms of accuracy, and 2) the confidence levels are not fully positive with the benefit of VPPs.Discussion: Improving the confidence level could reduce the uncertainty brought by renewable energy, but could also cause conservative trading behavior and affect the consumption of renewable energy

Characterization of the complete mitochondrial genome and phylogenetic analysis of Pelodiscus sinensis, a mutant Chinese soft-shell turtle

The Chinese soft-shell turtle (Pelodiscus sinensis, Testudines: Pelodiscus) shows geographical variation, and one strain is the inked turtle. Wild population numbers have dropped substantially during the past decades, and the species is now classed as vulnerable. However, little genetic data exists so this study aimed to sequence and analyze the complete mitochondrial genome. The circular double-stranded genome is 17,145 bp in length and contains 13 protein-coding genes (PCGs), two rRNA genes, 22 tRNA genes, an L-strand replication origin and a control region. The base composition is 35.5% A, 27.3% T, 11.8% G and 25.4% C, with an AT content of 62.8%. Trionychidae species were divided into two clades based on phylogenetic analysis, and the closest genetic distance was between Trionyx axenaria and P. sinensis. This study provides basic genetic data for future studies on conservation biology, phylogenetic and evolutionary analysis of this inked strain of the Chinese soft-shell turtle.</p

Delayed lethal central nervous system toxicity induced by a low-dose intrathecal administration of bupivacaine: case report

Spinal anesthesia by intrathecal administration of local anesthetic (LA) is a routine practice. Local anesthetic system toxicity, occurring in the central nervous system (CNS) and cardiovascular system, is a common and life-threatening adverse event of LA through a variety of routes, but is rarely encountered in spinal anesthesia when a very low dose of LA is injected into the subarachnoid space. Here, we report a case with manifestations of delayed lethal CNS toxicity after spinal anesthesia. A 55-year-old man underwent elective repair surgery for a chronic ulcer after receiving 10 mg intrathecal administration of bupivacaine. He developed nausea, agitation, paresthesia and myoclonus on the arms, legs, and trunk, as well as a gradually reduced level of consciousness one hour after intrathecal administration. He was sedated, intubated, and transferred to the intensive care unit. Both CT and MRI scans of the brain and assessments of blood showed no abnormalities. The electroencephalogram showed spike waves occurring at electrodes C3, C4, P3, P4, and T5. The patient was sedated continuously and treated with valproate. These symptoms were completely resolved in the following days without residual neurological complications. No cardiovascular complications were observed during the entire process. The delayed lethal symptoms in this case were most likely to be CNS toxicity induced by intrathecal bupivacaine administration. CNS toxicity after spinal anesthesia may be underestimated and unpredictable and should be vigilantly cared for in clinical settings

A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting

Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND) method, fruit fly optimization algorithm (FOA), and least square support vector machine (LSSVM) model, namely BND-FOA-LSSVM model. Firstly, the original electricity price time series were decomposed into deterministic term, periodic term, and stochastic term by using BND model. Then, these three decomposed terms were forecasted by employing LSSVM model, respectively. Meanwhile, to improve the forecasting performance, a new swarm intelligence optimization algorithm FOA was used to automatically determine the optimal parameters of LSSVM model for deterministic term forecasting, periodic term forecasting, and stochastic term forecasting. Finally, the forecasting result of electricity price can be obtained by multiplying the forecasting values of these three terms. The results show the mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the proposed BND-FOA-LSSVM model are respectively 3.48%, 11.18 Yuan/MWh and 9.95 Yuan/MWh, which are much smaller than that of LSSVM, BND-LSSVM, FOA-LSSVM, auto-regressive integrated moving average (ARIMA), and empirical mode decomposition (EMD)-FOA-LSSVM models. The proposed BND-FOA-LSSVM model is effective and practical for electricity price forecasting, which can improve the electricity price forecasting accuracy

C-161 IPN Y CANACINTRA IMPULSARÁN EL PROGRAMA BECA EMPRESARIAL

C-161 IPN Y CANACINTRA IMPULSARÁN EL PROGRAMA BECA EMPRESARIA

Cost-Benefit Analysis for the Concentrated Solar Power in China

In 2016, the first batch of concentrated solar power (CSP) demonstration projects of China was formally approved. Due to the important impact of the cost-benefit on the investment decisions and policy-making, this paper adopted the static payback period (SP), net present value (NPV), net present value rate (NPVR), and internal rate of return (IRR) to analyze and discuss the cost-benefit of CSP demonstration plants. The results showed the following. (1) The SP of CSP systems is relatively longer, due to high initial investment; but the cost-benefit of CSP demonstration plants as a whole is better, because of good expected incomes. (2) Vast majority of CSP projects could gain excess returns, on the basis of meeting the profitability required by the benchmark yield of 10%. (3) The cost-benefit of solar tower CSP technology (IRR of 12.33%) is better than that of parabolic trough CSP technology (IRR of 11.72%) and linear Fresnel CSP technology (IRR of 11.43%). (4) The annual electricity production and initial costs have significant impacts on the cost-benefit of CSP systems; the effects of operation and maintenance costs and loan interest rate on the cost-benefit of CSP systems are relatively smaller but cannot be ignored

Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization

Evolutionary algorithms (EAs) were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA) with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective

Continuous Reinforcement Algorithm and Robust Economic Dispatching-Based Spot Electricity Market Modeling considering Strategic Behaviors of Wind Power Producers and Other Participants

In a spot wholesale electricity market containing strategic bidding interactions among wind power producers and other participants such as fossil generation companies and distribution companies, the randomly fluctuating natures of wind power hinders not only the modeling and simulating of the dynamic bidding process and equilibrium of the electricity market but also the effectiveness about keeping economy and reliability in market clearing (economic dispatching) corresponding to the independent system operator. Because the gradient descent continuous actor-critic algorithm is demonstrated as an effective method in dealing with Markov’s decision-making problems with continuous state and action spaces and the robust economic dispatch model can optimize the permitted real-time wind power deviation intervals based on wind power producers’ bidding power output, in this paper, considering bidding interactions among wind power producers and other participants, we propose a gradient descent continuous actor-critic algorithm-based hour-ahead electricity market modeling approach with the robust economic dispatch model embedded. Simulations are implemented on the IEEE 30-bus test system, which, to some extent, verifies the market operation economy and the robustness against wind power fluctuations by using our proposed modeling approach