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The increased flood risk linked to global warming affects the safety of cascade reservoirs, with direct effects on the stable operation of power systems. In this article, an optimal scheduling framework for hydro-thermal power systems considering the flood risk of cascade reservoirs is presented. First, the extreme value theory-based peak over threshold model is adopted to build a generalized Pareto distribution of extreme flood inflow for a single reservoir. Then, the Copula function is used to build a joint probability distribution function of extreme inflow for cascade reservoirs during a flood period. Based on the superior performance of the conditional value at risk (CVaR) in characterizing the tail risk of the cascade reservoir spillway safety margin, a CVaR constraint for cascade reservoir flood prevention is proposed, and a scheduling model for hydro-thermal power systems considering the flood prevention risk of head-dependent cascade reservoirs is presented. Secondly, Rockafeller and Uryasey reformulation and sample average approximation are employed to transform the proposed model with a CVaR constraint into a convex solvable optimization model. Finally, a modified IEEE 14-node system is used to verify the better performance of the proposed model than that of the models with the chance constraint and with the independent normal distribution of extreme flood inflow. The impacts of flood prevention confidence level and Monte Carlo sample size on the optimal scheduling results are analysed quantitatively. [ABSTRACT FROM PUBLISHER]

Power system daily operational planning has become a difficult task with increasing wind penetrations into power system due to the variable and uncontrollable natures of wind resources. Although remarkable progress has been made in the development of wind power technology, wind speed prediction error still exists. In order to mitigate the negative effects caused by forecasting errors, several candidate daily operational plans should be scheduled ahead of time, with different wind power output scenarios taken into consideration. The key issue of this research work is to develop flexible operation framework that has the least cost adaption cost on the basis of receding horizon optimization. The selected operation plan may not be the cheapest one for one-time interval, but it takes the lowest cost considering the change process with most updated wind power information. Detailed case studies are performed to verify the feasibility of the proposed flexible operational planning framework. [ABSTRACT FROM PUBLISHER]

As an important way of addressing energy and environmental challenges, the market share of wind power generation has increased dramatically in the past decade and has introduced significant challenges to power system operation. In this article, the tail correlation between multiple wind farms is studied. The joint probability distribution of multiple wind farms is estimated by employing the Gumbel copula function. Based on the estimated joint probability distribution, a stochastic optimal dispatch model is proposed to take into account the chance constraints of energy utilization from multiple wind farms in the power system. The sample average approximation method is employed to handle the chance constraints in the proposed model, so as to transform stochastic optimal dispatch into a deterministic non-linear optimization problem. The quantum-inspired evolutionary algorithm is used to solve the proposed model. The proposed model and algorithm are tested with comprehensive case studies to demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

China experienced a rapid economic growth in the past few decades, whereas it has been facing a critical environmental situation. It is palpable that creating a sustainable energy structure in China is urgently required. As reported in China's 12th Five-year (2011–2015) Plan (which is a five-year development plan that outlines the development focus and strategic plan for different sectors in the next five-year period, published by the State Council of the PRC every five years), integrating more renewable energy, especially from solar and wind sources, becomes an essential part of their smart grid development. This article comprehensively reviews the existing achievements of renewable technology development and current status including relevant policies from central government and some on-going demonstration projects. Promoting new energy vehicles (e.g., electric vehicles) as an effective way of reducing carbon emissions is addressed in this article as well. Finally, the difficulties at the current stage are analyzed and future trends for a subsequent phase of development are also outlined. [ABSTRACT FROM AUTHOR]

Abstract—This article proposes a transmission pricing method that integrates the transmission loss component with the distribution factors enhanced transmission pricing method for the pool electricity market. Two new schemes are proposed: (1) New Scheme 1—the losses are allocated only for the locational charges and (2) New Scheme 2—the losses are allocated for both locational and non-locational charges. Both methods use justified distribution factors to evaluate the transmission line flows more accurately. The transmission losses are allocated among the market users by integrating the generalized generation justified distribution factors and generalized load justified distribution factors with the modified pro-rata method. The proposed approach is tested on the 10-machine IEEE 39-bus (New England) system to prove its effectiveness. Results show that the proposed method is able to reconcile transmission service cost fairly. [ABSTRACT FROM PUBLISHER]

In the smart grid environment, a power system restoration plan with flexibility is needed to improve reliability and efficiency in an automated way. Traditional system restoration methods based on fixed time intervals may not meet the requirement. This article proposes a new permutation-based model for power system restoration within an optimized flexible duration considering available generator capability and load prioritization. By utilizing this new model, power system restoration is formulated as a permutation-based combinatorial optimization problem to maximize the restored load per unit time. Finally an action-by-action flexible time schedule is obtained for generator startup. A novel quantum-inspired evolutionary algorithm, called the quantum-inspired differential evolutionary algorithm, has been applied to solve this problem due its to high population diversity and fast convergence. The effectiveness of the proposed restoration approach has been validated using IEEE 39- and 57-bus systems. [ABSTRACT FROM PUBLISHER]

Maintaining transient stability is a basic requirement for secure power system operations. Preventive control deals with modifying the system operating point to withstand probable contingencies. In this article, a decision tree (DT)-based on-line preventive control strategy is proposed for transient instability prevention of power systems. Given a stability database, a distance-based feature estimation algorithm is first applied to identify the critical generators, which are then used as features to develop a DT. By interpreting the splitting rules of DT, preventive control is realised by formulating the rules in a standard optimal power flow model and solving it. The proposed method is transparent in control mechanism, on-line computation compatible and convenient to deal with multi-contingency. The effectiveness and efficiency of the method has been verified on New England 10-machine 39-bus test system. [ABSTRACT FROM AUTHOR]

DNA Microarray is a powerful tool to measure the level of a mixed population of nucleic acids at one time, which has great impact in many aspects of life sciences research. In order to distinguish nucleic acids with very similar composition by hybridization, it is necessary to design microarray probes with high specificities and sensitivities. Highly specific probes correspond to probes having unique DNA sequences; whereas highly sensitive probes correspond to those with melting temperature within a desired range and having no secondary structure. The selection of these probes from a set of functional DNA sequences (exons) constitutes a computationally expensive discrete non-linear search problem. We delegate the .search task to a simple yet effective Evolution Strategy algorithm. The computational efficiency is also greatly improved by making use of an available bioinformatics tool. [ABSTRACT FROM AUTHOR]

The article reports that evolutionary computation includes stochastic search and optimization algorithms originated from the natural evolution principles. They are robust, adaptive and have found their application in a wide variety of theoretical and practical problems involving search and optimization tasks. Different from the traditional calculus based optimization techniques, evolutionary algorithms are based on a population of encoded tentative solutions which are processed with some evolutionary operators to find a good acceptable solution, if not the global optimum one.