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The rapid growth of wind and solar energy sources in recent years has brought challenges to power systems. One challenge is surging wind and solar electric generation, understanding how to consume such generation is important. Achieving the complementarity of hydropower and renewable energies such as wind and solar power by utilizing the flexible regulation performance of hydropower is helpful to provide firm power to help renewable energy consumption. However, the multi-energy complementary operation mode will change the traditional hydropower operation mode, causing challenges to the comprehensive utilization of hydropower. In this paper, a multi-objective optimal scheduling model is built by considering coordinated hydro-wind-solar system peak shaving and downstream navigation. First, the Gaussian mixture model is adopted to quantify the uncertainty of wind and solar power. Then, a hydro-wind-solar coordinated model was built to obtain the standard deviation of the residual load and the standard deviation of the downstream water level. Finally, the ε-constraint method is used to solve for the Pareto optimality. The results demonstrate the following: 1) The proposed model can effectively determine hydropower output schemes that can coordinate wind and solar power output to reconcile peak shaving and navigation; 2) The downstream hydropower stations’ reverse regulation of the upstream hydropower station is a positive factor in reconciling conflicts; and 3) Reasonable planning of wind power and solar power is helpful for hydro-wind solar power complement operation.

The changing energy mix under the integration of wind and solar power widens the load peak-valley difference of the power grid and poses great challenges in power grid operation, especially for peak shaving. Hydropower has a crucial role to play in enabling the integration of more variable renewables. To explore a reasonable approach for the changing roles of hydropower and the development of wind and solar power in response to energy transition, a long-term typical daily aggregate model is developed for hydro-wind-solar power with peak shaving operation. Scenarios of integrated wind and solar power proportions under increasing penetrations are simulated by incrementally increasing the installed wind and solar power capacity. The model is converted to a mixed integer linear programming formulation. Groups of numerical experiments divided by influencing factors are used to analyze the complementary influence and optimization mechanisms. The results demonstrate that (1) the proposed model decouples hydro-wind-solar sources at temporal scales and conducts peak shaving effectively. (2) Different scales of wind and solar power have notable impacts on both long-term and short-term hydropower operation. (3) Developing wind and solar power in reasonable proportions could effectively utilize the complementarity and reduce hydropower output fluctuations on different time scales.

Extreme weather lasting for 5–15 days not only brings large inflows in the medium-term but also causes sharp fluctuations in the daytime load of the power grid which imposes considerable challenges to the modeling and solving of medium-term power grid scheduling. The complex nonlinear constraints and water delay times of cascade hydropower plants significantly increase the difficulty of finding solutions. Hence, this study first advocates an accurate optimization model for determining the medium-term peak shaving operation of cascade hydropower plants considering the water delay time. The objective is to minimize the variance of the remaining load and maximize energy production that aims to regulate the load fluctuation and enhance hydropower efficiency. Then, to describe the water delay time of cascade hydropower plants, a piecewise delay time method (PDTM) is proposed based on the water balance equation. Finally, cascade comprehensive benefits (CCB) are established to alleviate the deviations from the scheduling plan and actual plan affected by the lag time. The developed model is implemented on the eleven cascade hydropower plants of the Lancang River in China. Simulation results demonstrate that the model is able to provide more realistic and executable generation scheduling while obtaining larger hydropower generation benefits and good peak shaving performance.

The increasing peak-valley differences pose a major threat to safe operation of the thermal-dominant power grid in China. Cascade hydropower stations, especially for one-reservoir and multi-cascade hydropower stations (OMHS), as the second largest power supply in China form an important part of peak shaving operation. However, nonconvex and nonlinear constraints have made it deeply difficult to perform the daily peak shaving operation (DPSO) of OMHS. In this paper, an improved DPSO model of OMHS considering water delay time is formulated and solved. Firstly, the confluence coefficient method (CCM) is developed to accurately describe the water delay time of OMHS. Secondly, to reduce the errors and water spillage, aggregation of nonlinear constraints, selection of the linearized area and periodic hypothesis are performed. Finally, an MIQP model integrated CCM is established by considering one-reservoir and six-cascade hydropower stations from the Hongshui River of China as a case study. The optimization results demonstrate that the proposed model can accurately simulate real-world system, and the optimized peak-valley differences on typical days in dry season and wet season reduce by 25% and 33%, respectively. Moreover, the CCM can produce more realistic and executable generation scheduling than the state-of-the-art description method of water delay time.

In China, there are numerous single-reservoir and multicascade hydropower plants (SMHPs), which provide high-quality peak-shaving power supply due to their characteristics of rapid load tracking and flexible regulation. However, the short-term peak-shaving operation (SPSO) of SMHPs serving multiple power grids faces difficulties from complicated hydraulic and electrical connections, dimensional disasters and complex solution difficulties. To effectively address the above problems, this paper presents a new method combining a typical peak-shaving output curve and a spillage adjustment strategy for the SPSO of SMHPs. First, the typical peak-shaving output curves of SMHPs are determined by the fuzzy membership function, flat-to-peak ratio and valley-to-peak ratio to reduce the dimension of SPSO. The fuzzy membership function is applied to identify the peak, flat and valley periods of multigrid loads. Then, taking the maximum total final energy storage of the scheduling period as the objective function, an SPSO model of SMHPs serving multiple power grids is established to balance the energy storage and peak-shaving requirements. Energy storage refers to the maximum hydropower generation that a hydropower plant can theoretically produce under a certain storage state. Finally, a spillage adjustment strategy is adopted to modify the power outputs of the SMHPs to minimize water spillage. The results from a real-world hydropower system in China demonstrate that the proposed method can allow SMHPs to play an important role in peak shaving while obtaining relatively high energy storage with negligible water spillage.

Uncertainties from neighboring rival’s reservoirs challenge hydropower companies in participating in competitive markets. Cooperative behaviors are generally impractical due to stakeholders’ self-interest and regulatory requirements. Considering this obstacle, this paper proposes a data-driven bilevel model, in a competitive context, to estimate the operational information of the neighboring rival’s reservoir, including its historical operating states and operational functions. The proposed bilevel model is an inverse problem of the conventional hydropower scheduling model. The upper-level model is designed to find the most appropriate operational parameters of the estimated reservoir that fit its historical generation volumes. The lower-level model simulates the profit-maxing operation of the estimated reservoir. Since the lower simulating model is nonconvex, an Enhanced Parallel Genetic Algorithm (EPGA) is proposed. It avoids infeasible situations through several strategies and uses multiple CPU threads simultaneously in solving. A case study in China’s market demonstrates that the proposed model and solving method can efficiently obtain accurate state series and (near-)optimal operational parameters. More experiments are also taken to validate the parallel design.

Short-term hydro-unit load dispatching (SHULD) refers to the determination of the power output of each unit within a hydropower station over a planning horizon to minimize the operational cost or maximize the power-generation profit while satisfying hydraulic and electrical constraints. In China, huge hydropower stations, such as the Three Gorges (TG) and Xiluodu (XLD) stations, are composed of a large number of hydro units, which feature a high installed capacity and a high water head. SHULD models of these stations are more complex and difficult to solve compared with those of traditional stations, especially when the stations serve multiple power grids. This study develops a practical method for optimizing SHULD models by considering the XLD hydropower station as a case study. First, a SHULD model for huge hydropower stations with multiple vibration zones and multiple receiving power grids is presented. Second, classical and sophisticated dynamic programming (DP) is applied to the SHULD model, and a practical strategy is proposed to balance the available water in a reservoir’s left and right banks to satisfy their load demands. Finally, the Fork/Join framework is used to parallelize DP to reduce the computation time and fully utilize the computer resources. The wet and dry season results demonstrate that the approach is efficient and suitable for huge hydropower stations with a high water head and multiple receiving power grids, thereby demonstrating its potential practicability and validity for solving the SHULD problem.

To meet the rapid growth of electricity demand and reduce carbon intensity, China is developing renewable energies rapidly including hydropower, wind and solar power. Due to the geographical mismatch of energy sources and demands in China, many long-distance and large-scale UHVDC and HVDC transmission projects have been built to transmit electric power from the western renewable bases to eastern coastal load centers. Some provincial power sources serve both local demands and deliver power to multiple regional power grids via HVDC transmission lines. As large capacity HVDC power transmission projects have great impacts on receiving-end power grids. Thus, the local exporting power grid should consider both local demands and energy importing area demands. A mixed-integer linear programming day-ahead peak shaving model to minimize the peak-valley difference in residual load after renewable generation of multiple power grids is developed. The model uses chance constraints to compensate for forecast errors of wind and solar power with hydropower, and introduces maximum daily power regulation times and stair-like power curve constraints of HVDC tie lines to avoid frequent HVDC power change and ensure power grid safety. The case studies in Yunnan province, which has large scale hydro, wind and solar power sources and delivers power to multiple regional power grids via HVDC transmission lines, shows the proposed model can shave peaks from multiple power grids effectively, hydropower can compensate for wind and solar forecast error and obtain satisfying results for multiple power grids, and that HVDC constraints can avoid their frequent power change and ensure the power grid safety.

For low construction cost, the hydropower plant with multiple units sharing a common tunnel (HPMUST) has been widely used, which plays an essential role in the short-term scheduling of powe...

With the expansion of wind power grid integration, the challenges of sharp fluctuations and high uncertainty in preparing the power grid day-ahead plan and short-term dispatching are magnified. These challenges can be overcome through accurate short-term wind power process prediction based on mining historical operation data and taking full advantage of meteorological forecast information. In this paper, adopting the ERA5 reanalysis dataset as input, a short-term wind power prediction framework is proposed, combining light gradient boosting machine (LightGBM), mutual information coefficient (MIC) and nonparametric regression. Primarily, the reanalysis data of ERA5 provide more meteorological information for the framework, which can help improve the model input features. Furthermore, MIC can identify effective feature subsets from massive feature sets that significantly affect the output, enabling concise understanding of the output. Moreover, LightGBM is a prediction method with a stronger ability of goodness-of-fit, which can fully mine the effective information of wind power historical operation data to improve the prediction accuracy. Eventually, nonparametric regression expands the process prediction to interval prediction, which significantly improves the utility of the prediction results. To quantitatively analyze the prediction results, five evaluation criteria are used, namely, the Pearson correlation coefficient (CORR), the root mean square error (RMSE), the mean absolute error (MAE), the index of agreement (IA) and Kling–Gupta efficiency (KGE). Compared with support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost) models, the present framework can make full use of meteorological information and effectively improve the prediction accuracy, and the generated output prediction interval can also be used to promote the safe operation of power systems.

Climate change could have dire effects on hydropower systems, especially in southwest China, where hydropower dominates the regional power system. This study examines two large cascade hydropower systems in Yunnan province in southwest China for 10 climate change projections made with 5 global climate models (GCMs) and 2 representative concentration pathways (RCPs) under Coupled Model Intercomparison Project Phase 5 (CMIP5). First, a back propagation neural network rain-runoff model is built for each hydropower station to estimate inflows with climate change. Then, a progressive optimality algorithm maximizes hydropower generation for each projection. The results show generation increasing in each GCM projection, but increasing more in GCMs under scenario RCP8.5. However, yearly generation fluctuates more: generation decreases dramatically with potential for electricity shortages in dry years and more electricity as well as spill during wet years. Average annual spill, average annual inflow and average storage have similar trends. The analysis indicates that a planned large dam on the upper Jinsha River would increase seasonal regulation ability, increase hydropower generation, and decrease spill. Increased turbine capacity increases generation slightly and decreases spill for the Lancang River. Results from this study demonstrate effects of climate change on hydropower systems and identify which watersheds might be more vulnerable, along with some actions that could help adapt to climate change.

With continuing development of hydropower in China, cascade hydropower system will account for more in the power grid, and may increase power grid operation risk under global climate change. This paper presents a parallel chance-constrained dynamic programming model to derive optimal operating policies for a cascade hydropower system in China. The innovation work of this paper is mainly embodied in two aspects. First, the reliabilities of meeting the firm power requirements of the cascade hydropower system and avoiding extreme system failure under extreme events are explicitly embedded in the model using Lagrangian duality theory and a penalty function. Multiple operating policies are generated by updating the values of Lagrangian multiplier and penalty coefficient for system disruption, then best operating rules are selected based on system performance and evaluated according to simulated reliability, extreme system failure, and maximum benefit. Second, the Fork/Join parallel framework is deployed to parallelize the chance-constrained dynamic programming in a multi-core environment for improving computational efficiency. Two computing platforms with contrasting configurations are employed to illustrate the parallelization performance. Results from a cascade hydropower system operation demonstrate that the proposed method is computationally efficient and can obtain satisfying operating policies, especially for extreme drought events.

China has the richest hydropower resources in the world. Yunnan's exploitable hydropower resources rank third in China, second only to Tibet and Sichuan Province. Yunnan has experienced soaring growth in installed hydropower capacity in the past several decades. The fast development of hydropower makes the province one of the main producers and exporters of hydroelectricity. However, with inconsistencies between hydropower development and various related resources, more and more hydropower generation has been curtailed in recent years. The increasing curtailment of hydropower generation not only caused large-scale waste of sustainable energy, but also impaired the potential higher contribution of hydropower for GHG emission reduction. This paper provides an overview of the hydropower resources and their development in Yunnan and systematically analyzes the constraints causing the curtailment of hydropower generation. We argue that there are several constraining factors such as slowdown of economic growth, inadequate transmission capacity due to a weak grid structure, inconsistency between power grid companies and local government, and uneven distribution of precipitation seasonally and annually. Recommendations are made to alleviate or eliminate hydropower curtailment in Yunnan and other hydropower rich areas.

Address space layout randomization (ASLR) is now widely adopted in modern operating systems to thwart code reuse attacks. However, an adversary can still bypass fine-grained ASLR by exploiting memory corruption vulnerabilities and performing memory disclosure attacks. Although Execute-no-Read schemes have been proven to be an efficient solution against read-based memory disclosures, existing solutions need modifications to kernel or hypervisor. Besides, the defense of execution-based memory disclosures has been ignored. In this paper, we propose BoundShield, a self-protection scheme that provides comprehensive protection against memory disclosure attacks, especially against those based on executing arbitrary code by leveraging Intel Memory Protection Extension . BoundShield protects code memory by defending not only read-based memory disclosure attacks but also execution-based memory disclosure attacks. On one hand, read-based memory disclosures can be eliminated by hiding all code sections and code pointers in a secret region separated from the user address space. On the other hand, BoundShield prevents return addresses from being corrupted and ensures that all function pointers point to the legitimate entries whenever they are dereferenced, which significantly reduces the attack surface for execution-based memory disclosures. We have implemented a prototype of BoundShield based on a set of modifications to compiler toolchain and the standard C library. Our evaluation results show that the BoundShield can provide strong defenses against memory disclosure attacks while incurring a small performance overhead.

A multi-core parallel Particle Swarm Optimization (MPPSO) algorithm is developed to improve computational efficiency for long-term optimal hydropower system operation, in response to rapidly increasing size and complexity of hydropower systems, especially in China. The MPPSO can be implemented in three steps with easily accessible multi-core hardware platforms. First, a multi-group parallel computing strategy is introduced to maintain the diversity of population for finding the global optima. Second, the fork/join framework based on divide-and-conquer strategy is adopted to distribute multiple populations to different CPU cores for parallel calculations to take full advantage of CPU performance. Third, the results generated in different CPUs are merged to achieve an improved acceleration effect on computational time cost and more accurate optimal scheduling solution. Results for a system of twelve hydropower stations in the Guizhou Power Grid in China demonstrate that the proposed algorithm makes full use of multi-core resources, and significantly improves the computational efficiency and accuracy of the optimal solution, in addition to its low parallelization cost and low implementation cost. These suggest that the proposed algorithm has great potential for future optimal operation of hydropower systems.

Inflow forecasting plays an essential role in reservoir management and operation. The impacts of climate change and human activities make accurate inflow prediction increasingly difficult, especially for longer lead times. In this study, a new hybrid inflow forecast framework with ERA-Interim reanalysis data as input, adopting gradient boosting regression trees (GBRT) and the maximum information coefficient (MIC) was developed for multi-step ahead daily inflow forecasting. Firstly, the ERA-Interim reanalysis dataset provides enough information for the framework to discover inflow for longer lead times. Secondly, MIC can identify effective feature subset from massive features that significantly affects inflow so that the framework can avoid over-fitting, distinguish key attributes with unimportant ones and provide a concise understanding of inflow. Lastly, the GBRT is a prediction model in the form of an ensemble of decision trees and has a strong ability to capture nonlinear relationships between input and output in long lead times more fully. The Xiaowan hydropower station located in Yunnan Province, China is selected as the study area. Four evaluation criteria, the mean absolute error (MAE), the root mean square error (RMSE), the Nash-Sutcliffe efficiency coefficient (NSE) and the Pearson correlation coefficient (CORR), were used to evaluate the established models using historical daily inflow data (1/1/2017–31/12/2018). Performance of the presented framework was compared to that of artificial neural networks (ANN), support vector regression (SVR) and multiple linear regression (MLR) models. The experimental results indicate that the developed method generally performs better than other models and significantly improves the accuracy of inflow forecasting at lead times of 5–10 days. The reanalysis data also enhances the accuracy of inflow forecasting except for forecasts that are one-day ahead.

Short-term load dispatching (STLD) for a hydropower plant with multiple turbines in one tunnel (HPMTT) refers to determining when to startup or shutdown the units of different tunnels and scheduling the online units of each tunnel to obtain optimal load dispatch while simultaneously meeting the hydraulic and electric system constraints. Modeling and solving the STLD for a HPMTT is extremely difficult due to mutual interference between units and complications of the hydraulic head calculation. Considering the complexity of the hydraulic connections between multiple power units in one tunnel, a two-phase decomposition approach for subproblems of unit-commit (UC) and optimal load dispatch (OLD) is described and a two-stage model (TSM) is adopted in this paper. In the first stage, an on/off model for the units considering duration constraints is established, and the on/off status of the units and tunnels is determined using a heuristic searching method and a progressive optimal algorithm. In the second stage, a load distribution model is established and solved using dynamic programming for optimal load distribution under the premise of the on/off status of the tunnel and units in the first stage. The proposed method is verified using the load distribution problem for the Tianshengqiao-II reservoir (TSQII) in dry season under different typical load rates. The results meet the practical operation requirements and demonstrate the practicability of the proposed method.

Under the medium- and long-term electric markets, cascaded hydropower stations face a series of practical challenges due to the uncertainty of inflow and market price. For long-term dispatch scheduling, the allocation of power generation in multimarkets is critical, including clean energy priority consumption market, inter provincial market and intra provincial market in order to maximize the operator’s expected revenue and reduce the market operation risks. Based on the hydro-dominant electricity market structure and settlement rules, we propose a long-term optimal operation method for cascade hydropower stations considering the uncertainty of multiple variables. First, a seasonal autoregressive integrated moving average model is used to handle the time-varying and seasonal characteristics of inflow series by using a copula connect function to fit the joint distribution of the monthly inflow, the clearing price of the intra provincial market and the delivery volume of the inter provincial market. Then, uncertain chance-constrained programming is established. Finally, a developed particle swarm optimization algorithm embedded in a Monte Carlo simulation is solved for hydropower operation policies, and the maximum revenue, resource allocation and scheduling strategy are obtained under the corresponding risk tolerance. Taking the actual data of cascaded hydropower stations in Yunnan Province, China, as an example, a simulation analysis is carried out. The results show that the proposed method can reasonably describe the uncertainty and correlation between the variables, realizing the optimal allocation of resources among multimarkets, and provide references for the long-term optimal operation of cascade hydropower stations in a multimarket environment. The results also show that the decision strategies should be determined considering the decision-maker’s risk preference.

Short-term hydrothermal scheduling (STHS) can improve water use efficiency, reduce carbon emissions, and increase economic benefits by optimizing the commitment and dispatch of hydro and thermal generating units together. However, limited by the large system scale and complex hydraulic and electrical constraints, STHS poses great challenges in modeling for operators. This paper presents an improved proximal bundle method (IPBM) within the framework of Lagrangian relaxation for STHS, which incorporates the expert system (ES) technique into the proximal bundle method (PBM). In IPBM, initial values of Lagrange multipliers are firstly determined using the linear combination of optimal solutions in the ES. Then, each time PBM declares a null step in the iterations, the solution space is inferred from the ES, and an orthogonal design is performed in the solution space to derive new updates of the Lagrange multipliers. A case study in a large-scale hydrothermal system in China is implemented to demonstrate the effectiveness of the proposed method. Results in different cases indicate that IPBM is superior to standard PBM in global search ability and computational efficiency, providing an alternative for STHS.

There are many cascaded hydropower plants with poor regulation performance and sensitive water heads accompanied by water spillage during the wet season. Faced with the increasing load peak&ndash, valley differences, it is necessary to tap the peak-shaving potential of such head-sensitive cascaded hydropower plants (HSCHPs) because relying solely on hydropower plants with better regulation performance for peak shaving is inadequate. To address the modeling, solving, and water spillage treatment difficulties posed by HSCHPs, a new short-term peak-shaving method based on spillage adjustment is introduced. First, fuzzy cluster analysis is used to determine when to release more water spillage by automatically identifying valley periods of the daily load curve. Furthermore, a spillage adjustment strategy, implemented through an easy gate operation, is adopted to readjust the water release during each period of the load curve. The ratio of the water spillage released in advance in a certain period to its total water spillage is defined as the water spillage ratio (WSR) of the period. Finally, a mixed-integer linear programming model linearized by special ordered sets of type two is solved to determine the optimal WSRs, which achieves the optimal peak-shaving effect. HSCHPs in the Hongshui River Basin during the wet season were selected as case studies. The results demonstrate that the proposed method can achieve a good peak-shaving effect without significantly reducing the power generation and adding additional water spillage.

Booming renewable energy development, such as wind and solar power, with their intermittency and uncertainty characteristics, pose challenges for power grid dispatching, especially for power grid peak shaving. In this paper, coordinated operation of hydropower and renewable energy in a provincial power grid is explored to alleviate fluctuation and aid peak shaving. Considering their aggregate effect, this study aggregates wind power plants and solar power plants into a virtual wind power plant and a virtual solar power plant, respectively, and the forecasted error distribution of wind and solar power is analyzed with kernel density estimation. Then, based on the principles of using hydropower to compensate for fluctuating wind and solar power, a day ahead peak shaving model with the objective of minimizing residual load peak-valley difference is built, which introduces chance constraints for forecast errors and coordinate hydropower operation with wind and solar power. To simplify the solution, the proposed model is recast as a successive linear programming problem. Day-ahead scheduling case studies of a provincial power grid system indicate that the proposed model can conduct peak shaving effectively, hydropower can compensate wind and solar power fluctuation, improve the stability of combined output, and make better use of renewable energy. Therefore, this study provides an alternative approach for peak shaving operation of power system with hydropower and increasing integration of wind and solar power in China and other places worldwide.

With a huge energy demand, the small hydropower (SHP) has undertaken a rapid development in the past six decades in China. Especially in the recent three decades, the increasing environmental pressure has promoted its development rate and the significance of its management and operation. The number of SHP stations in China has exceeded 45,000 with a total installed capacity of more than 68 GW, ranked the first in the world. SHP provides approximately 5% of China׳s gross electricity generation and is the major backbone for rural electrification. However, how to manage and operate SHP effectively is still a difficult problem in China. In this paper, the current status of SHP in China is investigated. A special emphasis is given to their dispatching management. The southwestern province of Yunnan, ranked second in exploitable potential and first in established capacity of SHP in China, is taken as a typical example for the management and operation of SHP. The difficulties and challenges of management and operation of SHP in Yunnan province are depicted. A case system for the SHP management in Yunnan is introduced in details. It is believed that the system should be beneficial for regions with rich SHP resources in China and elsewhere as well.

A typical application normally includes the main program and some shared libraries. The main program can arbitrarily execute the code of those shared libraries due to the coexistence in the same memory space. This feature can be used by attackers to carry out code-reuse attacks. Meanwhile, the shared libraries are shared across multiple applications, which can help attackers to simplify the process of code-reuse attacks. Isolating shared library into a separate execution environment and restricting the access to this library is a promising countermeasure, while the existing isolation approaches need to either modify the main program, or break the shared feature of the library. In this paper, we present Libsec, an efficient and transparent approach to isolate shared libraries, without the need of source code of the main program or shared libraries. Libsec adopts commodity hardware virtualization extension to isolate shared libraries from the main program. Meanwhile, Libsec relies on static instrumentation and dynamic processing to provide interfaces for legitimate cross-domain invocations. By this way, Libsec can guarantee that the main program and shared libraries are executed in the corresponding execution environment respectively, while cross-domain invocation is only allowed via specific interfaces, thus preventing the main program from jumping directly to the shared library. We implement a prototype of Libsec in KVM. To demonstrate its effectiveness, we deploy it to some real-world applications and libraries, such as Nginx and OpenSSL libraries. Security evaluation shows that Libsec can prevent the attacker from utilizing the functions or instruction sequences of the shared library for code-reuse attack. Performance evaluation shows that the performance and space overhead caused by Libsec are appropriate.

This paper presents an integrated method to obtain optimal market operation and regulation with the objective of reducing the market price and increasing the electricity consumption in hydro-dominated electricity markets, in which giant cascaded hydropower facilities along different rivers are main power suppliers. To this end, a comprehensive indicator composed of market prices and electricity consumption is proposed to evaluate the situation of hydro-dominated market operation. Moreover, an iterative algorithm is proposed to investigate the strategic behaviors of power suppliers and to simulate the operation of the market. Furthermore, an integrated solution methodology based on a multi-core parallel tabu genetic algorithm (MPTGA) is proposed to provide the optimal assignment of bilateral contracts, considering the market simulation, in order to achieve the optimal market regulation. The results from the case study, with real data based on Yunnan’s electricity market, demonstrate that the proposed indicator and method are effective and efficient to simulate and regulate the market operation, and the effects of MPTGA are discussed last.

Background This paper is based on ethnomedicinal investigation conducted from 1999–2002 in Chuxiong, central Yunnan Province, Southwest China. The Yi medicine has made a great contribution to the ethnomedicinal field in China. Neither case studies nor integrated inventories have previously been conducted to investigate the traditional Yi plants. This paper aims to argue the status and features of medicinal plants used in traditional Yi societies through a case study. Methods The approaches of ethnobotany, anthropology, and participatory rural appraisal were used in the field surveys. Twenty-two informants in four counties were interviewed during eight field trips. Medicinal plant specimens were identified according to taxonomic methods. Results One hundred sixteen medicinal plant species were found to be useful by the local people in the treatment of various diseases or disorders, especially those relating to trauma, gastrointestinal disorders and the common cold. Among these 116 species, 25 species (21.55%) were found to have new curative effects and 40 species (34.48%) were recorded for their new preparation methods; 55 different species were used in treating wounds and fractures, and 47 were used to treat gastrointestinal disorders. Traditional Yi herbal medicines are characterized by their numerous quantities of herbaceous plants and their common preparation with alcohol. Conclusion Totally 116 species in 58 families of medicinal plants traditionally used by the Yi people were inventoried and documented. The characteristics of medicinal plants were analyzed. Some new findings (such as new curative effects and new preparation methods) were recorded These newly gathered ethnobotanical and medicinal data are precious sources for the future development of new drugs, and for further phytochemical, pharmacological and clinical studies.

Measuring mechanical characteristics of optical disk is very significant. This paper compares the existing measuring methods and gives a novel method we adopted in details. Some measuring results are also shown.

We raised a novel method to test radial run-out of optical disc. The method measures run-out by track-across signal when optical head keeps focusing on rotating disc and stops in radial direction. The track-across signal can be picked up from commercial disc drive. Then the distance of passed tracks is calibrated by linear gratings. The advantages of this method are low-cost and high accuracy.