Groundwater pollution is a serious threat to the ecological environment and human life. It is necessary to determine the characteristics of pollution sources accurately and efficiently after the occurrence of pollutio...
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Groundwater pollution is a serious threat to the ecological environment and human life. It is necessary to determine the characteristics of pollution sources accurately and efficiently after the occurrence of pollution. The main basis for determining the characteristics of pollution sources is the pollutant concentration data of each observation well. However, due to the layout of the monitoring wells, the noise intensity of observation well concentration and unknown aquifer parameters, inversion results of groundwater pollution sources will be influenced significantly. Therefore, this article focuses on how to reduce its influence on the inversion results. In this article, a stochasticprogramming optimization model is introduced to explore its ability to control errors in complex situations. Results show that: in homogeneous aquifer, the normalized error (NE%) produced by simulation-optimization method is stable at about 2%, and the NE generated by stochastic programming method in confidence interval of 60%similar to 95% is between 0.20% and 5.42%. Moreover, stochasticprogramming model can effectively control the influence of noise. In the simulation-optimization model, when the noise intensity is 0.1 similar to 0.5, the NE value is 2.01%similar to 12.68%, and the corresponding NE of stochasticprogramming model is 0.12%similar to 11.87%. Finally, this article considers the case that aquifer parameters are unknown (simultaneous identification of aquifer parameters and groundwater pollution sources). The results show that with the increase of the number of unknown aquifer parameters, the NE of the simulation-optimization model gradually increases from 1.16% to 8.75%. The NE value of the stochasticprogramming model decreases by 30% compared with the simulation-optimization model when the confidence level is 80%.
This paper proposes an Electric Vehicle (EV) aggregator bidding strategy in the reserve market. The EV aggregator determines the charging/discharging operations of EVs in providing reserve service for profits maximiza...
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This paper proposes an Electric Vehicle (EV) aggregator bidding strategy in the reserve market. The EV aggregator determines the charging/discharging operations of EVs in providing reserve service for profits maximization. In the Day-Ahead Market (DAM), the EV aggregator submits a bidding plan to the Independent Systems Operator (ISO) including base-load and reserve up/down capacities plans. In the Real-Time Market (RTM), the EV aggregator should deploy reserve based on the ISO's requirements, and the EV aggregator could receive income by deploying reserve or penalty for reserve shortage. The stochastic programming method is applied to address the uncertain reserve deployment requirements in RTM. In addition, Energy Storage Systems (ESS) are utilized by the EV aggregator to enhance the ability in providing reserve service. The aggregator-owner contract is designed to guarantee EV owners' economic benefits. Case studies show the expected profits of the EV aggregator are maximized and the risk of the reserve shortage is well managed, i.e., penalty is minimized. With the utilization of ESS, the performance of the EV aggregator in making response to the ISO's requirements is improved. That is, the required reserve percentage increases from 5.68% to 7.85%, and the deployed reserve percentage increases from 69.71% to 88.47%.
Due to increase in population and occurrence of extreme droughts in recent years, correct management and planning of water resources are essential and considered vital needs in the Middle East countries. Optimal water...
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Due to increase in population and occurrence of extreme droughts in recent years, correct management and planning of water resources are essential and considered vital needs in the Middle East countries. Optimal water release from reservoirs is part of water resource management. For optimization of water release from reservoirs, different methods can be applied. In this research, dynamic programming (DP) method (a discrete method for optimization) and stochastic discrete programming (SDP) method (a stochastic discrete method for optimization) are considered for optimal operation of Dez dam reservoir. The Dez dam locates in the southwest of Iran. Useful storage of the reservoir of the Dez dam is 2993.27 Mcm. This dam was constructed in 1963. This research shows that reliability and resiliency of SDP method are higher than those of DP method, whereas vulnerability of SDP method is less than that of DP method. Also, SDP method can show months in which deficits occur correctly. The number of deficits in SDP method is less than that in DP method. In addition, in this research, variations of the number of deficits are evaluated in relation to variations of inflow to the reservoir. Sensitivity of DP method to variations of inflow to the reservoir is higher than that of SDP method.
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