Solving Large-Scale AC Optimal Power Flow Problems Including Energy Storage, Renewable Generation, and Forecast Uncertainty

AC Optimal Power Flow;Integration of Renewable Energy;Stochastic Optimization;Electrical Engineering;Engineering;Electrical Engineering: Systems

Marley, JenniferWollenberg, Bruce F. ;

University of Michigan

关键词: AC Optimal Power Flow;    Integration of Renewable Energy;    Stochastic Optimization;    Electrical Engineering;    Engineering;    Electrical Engineering: Systems;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/138664/jkfelder_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Renewable generation and energy storage are playing an ever increasingrole in power systems. Hence, there is a growing need for integratingthese resources into the optimal power flow (OPF) problem. Whilestorage devices are important for mitigating renewable variability,they introduce temporal coupling in the OPF constraints, resulting ina multiperiod OPF formulation. This work explores a solution methodfor multiperiod AC OPF problems that combines a successive quadraticprogramming approach (AC-QP) with a second-order cone programming(SOCP) relaxation of the OPF problem. The solution of the SOCP relaxationis used to initialize the AC-QP OPF algorithm. Additionally, the lowerbound on the objective value obtained from the SOCP relaxationprovides a measure of solution quality. Compared to other initialization schemes, the SOCP-based approach offers improved convergencerate, execution time and solution quality.A reformulation of the the AC-QP OPF method that includes wind generation uncertainty is then presented. Theresulting stochastic optimization problem is solved using a scenario basedalgorithm that is based on randomized methods that provideprobabilistic guarantees of the solution.This approach producesan AC-feasible solution while satisfying reasonable reliabilitycriteria.The proposed algorithm improves on techniques in prior work, as it does not rely upon model approximations and maintains scalability with respect to the number of scenarios considered in the OPF problem.The optimality of the proposed method is assessed using the lower bound from the solution of an SOCP relaxation and is shown to be sufficiently close to the globally optimal solution. Moreover, the reliability of the OPF solution is validated via Monte Carlo simulation and is demonstrated to fall within acceptable violation levels.Timing results are provided to emphasize the scalability of the method with respect to the number of scenarios considered and demonstrate its utility for real-time applications.Several extensions of this stochastic OPF are then developed for both operational and planning purposes.The first is to include the cost ofgenerator reserve capacity in the objective of the stochastic OPF problem.The need for the increased accuracy provided by the AC OPFis highlighted by a case study that compares the reliability levels achieved by the AC-QP algorithm to those from the solution ofa stochastic DC OPF.Next, the problem is extended to aplanning context, determining the maximum wind penetration that can be added in a network while maintaining acceptablereliability criteria.The scalability of this planning method with respect not only to large numbers of wind scenarios but also to moderate network size isdemonstrated.Finally, a formulation that minimizes both the cost of generation and the cost of reserve capacity while maximizing the wind generation added in the network is investigated. The proposed framework is then used to explore the inherent tradeoff between these competing objectives.A sensitivity study is then conducted to explore how the cost placed on generator reserve capacity can significantly impact the maximum wind penetration that can be reliably added in a network.