Under the global trend of renewable energy development, various advanced techniques such as forecasting algorithm, intelligent computation, and optimal control are expected to make the complex and uncertain renewable energy system stable and profitable in the near future. This paper presents a new control strategy for large-scale wind energy conversion systems (WECSs) to achieve a balance between power output maximization and operating cost minimization. First, an intelligent maximum power point tracking (IMPPT) algorithm is proposed such that short-term wind speed prediction, wind turbine dynamics, and MPPT are collectively considered to improve system efficiency. Second, in view of a spatial and temporal distribution of wind speed disturbances, a box uncertain set is embedded in the forecasted wind speed, which is likely more realistic for practicing engineers. Then, the IMPPT and box uncertainties are applied to the WECS control strategy, which is formulated as a min-max optimization problem and efficiently solved with semi-definite programming (SDP). Finally, a comparison with the conventional MPPT control method demonstrates that the proposed approach can obtain a higher efficiency, which validates this research work.Index Terms-Wind energy conversion system (WECS), maximum power point tracking (MPPT), wind speed forecast, wind turbine inertia, semi-definite programming (SDP).
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Synchrophasor systems, providing low-latency, high-precision, and time-synchronized measurements to enhance power grid performances, are deployed globally. However, the synchrophasor system as a physical network, involves communication constraints and data quality issues, which will impact or even disable certain synchrophasor applications. This work investigates the data quality issue for synchrophasor applications. In Part I, the standards of synchrophasor systems and the classifications and data quality requirements of synchrophasor applications are reviewed. Also, the actual events of synchronization signal accuracy, synchrophasor data loss, and latency are counted and analyzed. The review and statistics are expected to provide an overall picture of data accuracy, loss, and latency issues for synchrophasor applications.
To incorporate the superiority of both stochastic and robust approaches, a data-driven stochastic optimization is employed to solve the security-constrained unit commitment model. This approach makes the most use of the historical data to generate a set of possible probability distributions for wind power outputs and then it optimizes the unit commitment under the worst-case probability distribution. However, this model suffers from huge computational burden, as a large number of scenarios are considered. To tackle this issue, a duality-free decomposition method is proposed in this paper. This approach does not require doing duality, which can save a large set of dual variables and constraints, and therefore reduces the computational burden. In addition, the inner max-min problem has a special mathematical structure, where the scenarios have the similar constraint. Thus, the max-min problem can be decomposed into independent sub-problems to be solved in parallel, which further improves the computational efficiency. A numerical study on an IEEE 118-bus system with practical data of a wind power system has demonstrated the effectiveness of the proposal. Index Terms-Data-driven stochastic optimization; duality-free decomposition; security-constrained unit commitment; distributionally robust optimization NOMENCLATURE Hourly periods, running from 1 to =| |. Transmission lines, running from 1 to =| |. Buses, running from 1 to =| |. Wind units, running from 1 to =| |. Thermal units, running from 1 to =| |. Wind power scenarios, running from 1 to =| |. Startup segments, running from 1 (hottest) to =| | (coldest).
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