Shape design of arch dams under load uncertainties with robust optimization

Due to an increased need in hydro-electricity, water storage, and flood protection, it is assumed that a series of new dams will be build throughout the world. The focus of this paper is on the non-probabilistic-based design of new arch-type dams by applying means of robust design optimization (RDO). This type of optimization takes into account uncertainties in the loads and in the material properties of the structure. As classical procedures of probabilistic-based optimization under uncertainties, such as RDO and reliability-based design optimization (RBDO), are in general computationally expensive and rely on estimates of the system’s response variance, we will not follow a full-probabilistic approach but work with predefined confidence levels. This leads to a bi-level optimization program where the volume of the dam is optimized under the worst combination of the uncertain parameters. As a result, robust and reliable designs are obtained and the result is independent from any assumptions on stochastic properties of the random variables in the model. The optimization of an arch-type dam is realized here by a robust optimization method under load uncertainty, where hydraulic and thermal loads are considered. The load uncertainty is modeled as an ellipsoidal expression. Comparing with any traditional deterministic optimization method, which only concerns the minimum objective value and offers a solution candidate close to limit-states, the RDO method provides a robust solution against uncertainty. To reduce the computational cost, a ranking strategy and an approximation model are further involved to do a preliminary screening. By this means, the robust design can generate an improved arch dam structure that ensures both safety and serviceability during its lifetime.

Sensitivity analysis and probability modelling of the structural response of a single-layer reticulated dome subjected to an external blast loading

The structural response of a single-layer reticulated dome to external explosions is shaped by many variables,and the associated uncertainties imply non-deterministic results.Existing deterministic methods for predicting the consequences of specific explosions do not account for these uncertainties.Therefore,the impact of the uncertainties associated with these input variables on the structures’response needs to be studied and quantified.In this study,a parametric uncertainty analysis was conducted first.Then,local and global sensitivity analyses were carried out to identify the drivers of the structural dynamic response.A probabilistic structural response model was established based on sensitive variables and a reasonable sample size.Furthermore,some deterministic empirical methods for explosion-resistance design,including the plane blast load model of CONWEP,the curved blast load model under the 50%assurance level,and the 20%mass-increased method,were used for evaluating their reliability.The results of the analyses revealed that the structural response of a single-layer reticulated dome to an external blast loading is lognormally distributed.Evidently,the MB0.5 method based on the curved reflector load model yielded results with a relatively stable assurance rate and reliability,but CONWEP did not;thus,the 1.2MB0.5 method can be used for making high-confidence simple predictions.In addition,the results indicated that the structural response is very sensitive to the explosion parameters.Based on these results,it is suggested that for explosion proofing,setting up a defensive barrier is more effective than structural strengthening.

Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

A New Framework for Employing Responsive End-Users Using FAHP and PSO Algorithm

The capacitor bank and synchronous condenser have been the only available sources of reactive power.Nowadays,most of the appliances use a power electronic interface for their connection.Applying a power electronic interface addsmany features to these appliances.One of the promising features is their capability to interact with Volt-VAR programs.In this paper was investigated the reactive power interaction of the end-user appliances.For this purpose,the distribution network buses are ranked based on their effectiveness,followed by studying their interaction in the Volt-VAR program.To be able to consider the uncertainties,Probability Density Function(PDF)curve was discretized to represent different scenarios,and the reduction method was utilized to reduce the situations.

Application of Interval Algorithm in Rural Power Network Planning

Rural power network planning is a complicated nonlinear optimized combination problem which based on load forecasting results, and its actual load is affected by many uncertain factors, which influenced optimization results of rural power network planning. To solve the problems, the interval algorithm was used to modify the initial search method of uncertainty load mathematics model in rural network planning. Meanwhile, the genetic/tabu search combination algorithm was adopted to optimize the initialized network. The sample analysis results showed that compared with the certainty planning, the improved method was suitable for urban medium-voltage distribution network planning with consideration of uncertainty load and the planning results conformed to the reality.

Impact of EV load uncertainty on optimal planning for electric vehicle charging station

With the development of electric vehicles(EV), there is a huge demand for electric vehicle charging stations(EVCS). The utilization of renewable energy sources(RES) in EVCS can not only decrease the energy fluctuation by participating in peakload reduction of the grid, but also reduce the pollution to the environment by cutting down the use of fossil fuels. In this paper,the optimal planning for grid-connected EVCS with RES is studied by considering EV load uncertainty. Nine scenarios are set based on a different characteristic of EV load to reveal the impact of EV load on net present cost(NPC) and to express the relationship between the optimal capacity and energy flow. Moreover, since electricity price also plays an important role in EVCS planning, an economic comparison between different cases with different electricity prices for peak-valley-flat period is carried out. The results reveal the economic benefits of applying RES in EVCS, and demonstrate that EV load with different characteristics would influence the capacity of each device(PV, battery, converter) in the EVCS optimal planning.

Probabilistic steady-state and dynamic security assessment of power transmission system

Two-level system model based probabilistic steady-state and dynamic security assessment model is introduced in this paper.Uncertainties of nodal power injection caused by wind power and load demand,steady-state and dynamic security constraints and transitions between system configurations in terms of failure rate and repair rate are considered in the model.Time to insecurity is used as security index.The probability distribution of time to insecurity can be obtained by solving a linear vector differential equation.The coefficients of the differential equation are expressed in terms of configuration transition rates and security transition probabilities.The model is implemented in complex system successfully for the first time by using the following effective measures:firstly,calculating configuration transition rates effectively based on component state transition rate matrix and system configuration array;secondly,calculating the probability of random nodal power injection belonging to security region effectively according to practical parts of critical boundaries of security region represented by hyper-planes;thirdly,locating non-zero elements of coefficient matrix and then implementing sparse storage of coefficient matrix effectively;finally,calculating security region off-line for on-line use.Results of probabilistic security assessment can be used to conduct operators to analyze system security effectively and take preventive control.Test results on New England 10-generators and 39-buses power system verify the reasonableness and effectiveness of the method.