In optimal wind bidding strategy related literatures, it is usually assumed that the full distribution information (for example, the cumulative distribution function or the probability density function) of wind power ...In optimal wind bidding strategy related literatures, it is usually assumed that the full distribution information (for example, the cumulative distribution function or the probability density function) of wind power output is known. In real world applications, however, only very limited distribution information can be obtained. Therefore, the “optimal bidding strategy” obtained based on the hypothetical distribution may be far away from the true optimal one. In this paper, an optimal bidding strategy is obtained based on the minimax regret criterion. The salient feature of the new approach is that it requires only partial information of wind power distribution, for example, the expectation and the support set. Numerical test is then performed and the results suggest that the method established in this paper is effective.展开更多
The technology investment strategy under uncertainty is the key subject. However, the expected utility maximization often employed as the decision process fails to consider the high risk with low probability cases. On...The technology investment strategy under uncertainty is the key subject. However, the expected utility maximization often employed as the decision process fails to consider the high risk with low probability cases. On the other hand, the existing min-max regret strategy tends to be dominated by the ＂worst assumption＂ regardless of its probability. This research proposes a new framework by formulating the regret by the Minkowski＇s generalized distance. The authors then apply the formulation to the IAM （integrated assessment model） MARIA. This study focuses on the uncertainties of CCS （carbon capture and storage） costs and the global warming damages. This formulation is then extended to the multi-stage decision frame, known as ATL （act-then-learn） method. The simulation results suggest that the substantial changes in CCS and nuclear deployment strategies depending on the future uncertainty scenarios. The results also suggest that the minimum regret strategy favors the capital accumulation in the early stage.展开更多
A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as mea...A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers' robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.展开更多
文摘In optimal wind bidding strategy related literatures, it is usually assumed that the full distribution information (for example, the cumulative distribution function or the probability density function) of wind power output is known. In real world applications, however, only very limited distribution information can be obtained. Therefore, the “optimal bidding strategy” obtained based on the hypothetical distribution may be far away from the true optimal one. In this paper, an optimal bidding strategy is obtained based on the minimax regret criterion. The salient feature of the new approach is that it requires only partial information of wind power distribution, for example, the expectation and the support set. Numerical test is then performed and the results suggest that the method established in this paper is effective.
文摘The technology investment strategy under uncertainty is the key subject. However, the expected utility maximization often employed as the decision process fails to consider the high risk with low probability cases. On the other hand, the existing min-max regret strategy tends to be dominated by the ＂worst assumption＂ regardless of its probability. This research proposes a new framework by formulating the regret by the Minkowski＇s generalized distance. The authors then apply the formulation to the IAM （integrated assessment model） MARIA. This study focuses on the uncertainties of CCS （carbon capture and storage） costs and the global warming damages. This formulation is then extended to the multi-stage decision frame, known as ATL （act-then-learn） method. The simulation results suggest that the substantial changes in CCS and nuclear deployment strategies depending on the future uncertainty scenarios. The results also suggest that the minimum regret strategy favors the capital accumulation in the early stage.
基金Project(71001079)supported by the National Natural Science Foundation of China
文摘A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent(STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers' robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.
