A forward dynamic optimization strategy under contango storage arbitrage with frictions

2017 ◽  
Vol 10 (3) ◽  
pp. 59-85 ◽  
Author(s):  
Behzad Ghafouri ◽  
Matt Davison
2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yiwei Ma ◽  
Ping Yang ◽  
Zhuoli Zhao ◽  
Yuewu Wang

An optimal economic operation method is presented to attain a joint-optimization of cost reduction and operation strategy for islanded microgrid, which includes renewable energy source, the diesel generator, and battery storage system. The optimization objective is to minimize the overall generating cost involving depreciation cost, operation cost, emission cost, and economic subsidy available for renewable energy source, while satisfying various equality and inequality constraints. A novel dynamic optimization process is proposed based on two different operation control modes where diesel generator or battery storage acts as the master unit to maintain the system frequency and voltage stability, and a modified particle swarm optimization algorithm is applied to get faster solution to the practical economic operation problem of islanded microgrid. With the example system of an actual islanded microgrid in Dongao Island, China, the proposed models, dynamic optimization strategy, and solution algorithm are verified and the influences of different operation strategies and optimization algorithms on the economic operation are discussed. The results achieved demonstrate the effectiveness and feasibility of the proposed method.


2011 ◽  
Author(s):  
Kwantip Konakom ◽  
Aritsara Saengchan ◽  
Paisan Kittisupakorn ◽  
Iqbal M. Mujtaba ◽  
Sio-Iong Ao

2019 ◽  
Vol 195 ◽  
pp. 168-179 ◽  
Author(s):  
Hari S. Ganesh ◽  
Hagen E. Fritz ◽  
Thomas F. Edgar ◽  
Atila Novoselac ◽  
Michael Baldea

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bayu Adi Nugroho

PurposeIt is crucial to find a better portfolio optimization strategy, considering the cryptocurrencies' asymmetric volatilities. Hence, this research aimed to present dynamic optimization on minimum variance (MVP), equal risk contribution (ERC) and most diversified portfolio (MDP).Design/methodology/approachThis study applied dynamic covariances from multivariate GARCH(1,1) with Student’s-t-distribution. This research also constructed static optimization from the conventional MVP, ERC and MDP as comparison. Moreover, the optimization involved transaction cost and out-of-sample analysis from the rolling windows method. The sample consisted of ten significant cryptocurrencies.FindingsDynamic optimization enhanced risk-adjusted return. Moreover, dynamic MDP and ERC could win the naïve strategy (1/N) under various estimation windows, and forecast lengths when the transaction cost ranging from 10 bps to 50 bps. The researcher also used another researcher's sample as a robustness test. Findings showed that dynamic optimization (MDP and ERC) outperformed the benchmark.Practical implicationsSophisticated investors may use the dynamic ERC and MDP to optimize cryptocurrencies portfolio.Originality/valueTo the best of the author’s knowledge, this is the first paper that studies the dynamic optimization on MVP, ERC and MDP using DCC and ADCC-GARCH with multivariate-t-distribution and rolling windows method.


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