This paper develops a high time-resolution optimal power generation mix model in its time resolution of 10 minutes on 365 days by linear programming technique. The model allows us to analyse the massive deployment of ...This paper develops a high time-resolution optimal power generation mix model in its time resolution of 10 minutes on 365 days by linear programming technique. The model allows us to analyse the massive deployment of photovoltaic system and wind power generation in power system explicitly considering those short-term output variation. PV （photovoltaic） and wind output are estimated, employing meteorological database. Simulation results reveal that variable fluctuation derived from a high penetration level of those renewables is controlled by quick load following operation of natural gas combined cycle power plant, pumped-storage hydro power, stationary NAS （sodium and sulfur） battery and the output suppression control of PV and wind. It additionally turns out that the operational configuration of those technologies for the renewable variability differs significantly depending on those renewable output variations in each season and solving the seasonal electricity imbalance as well as the daily imbalance is important if variable renewables are massively deployed.展开更多
This paper studies multi-period risk management problems by presenting a dynamic risk measure. This risk measure is the sum of conditional value-at-risk of each period. The authors model it by Markov decision processe...This paper studies multi-period risk management problems by presenting a dynamic risk measure. This risk measure is the sum of conditional value-at-risk of each period. The authors model it by Markov decision processes and derive its optimality equation. This equation is further transformed equivalently to an analytically tractable one. The authors then use the model and its results to a multi-period portfolio optimization when the return rate vectors at each period form a Markov chain.展开更多
文摘This paper develops a high time-resolution optimal power generation mix model in its time resolution of 10 minutes on 365 days by linear programming technique. The model allows us to analyse the massive deployment of photovoltaic system and wind power generation in power system explicitly considering those short-term output variation. PV （photovoltaic） and wind output are estimated, employing meteorological database. Simulation results reveal that variable fluctuation derived from a high penetration level of those renewables is controlled by quick load following operation of natural gas combined cycle power plant, pumped-storage hydro power, stationary NAS （sodium and sulfur） battery and the output suppression control of PV and wind. It additionally turns out that the operational configuration of those technologies for the renewable variability differs significantly depending on those renewable output variations in each season and solving the seasonal electricity imbalance as well as the daily imbalance is important if variable renewables are massively deployed.
基金This research was supported in part by the National Natural Science Foundation of China under Grant Nos. 70971023 and 71001089 and in part by the Natural Science Foundation of Zhejiang Province under Grant No. Y60860040.
文摘This paper studies multi-period risk management problems by presenting a dynamic risk measure. This risk measure is the sum of conditional value-at-risk of each period. The authors model it by Markov decision processes and derive its optimality equation. This equation is further transformed equivalently to an analytically tractable one. The authors then use the model and its results to a multi-period portfolio optimization when the return rate vectors at each period form a Markov chain.
