.png){kind=icon}
返回
Dispatchability Maximization for Co-Optimized Energy and Reserve Dispatch With Explicit Reliability Guarantee
.png){kind=icon}
PRE
AI
DOI：10.1109/TPWRS.2015.2477348.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we consider dispatchability as the set of all admissible nodal wind power injections that will not cause infeasibility in real-time dispatch (RTD). Our work reveals that the dispatchability of the affine policy based RTD (AF-RTD) is a polytope whose coefficients are linear functions of the generation schedule and the gain matrix of affine policy. Two mathematical formulations of the dispatchability maximized energy and reserve dispatch (DM-ERD) are proposed. The first one maximizes the distance from the forecast to the boundaries of the dispatchability polytope subject to the available production cost or reserve cost. Provided the forecast value and variance of wind power, the generalized Gauss inequality (GGI) is adopted to evaluate the probability of infeasible RTD without the exact probability distribution of wind power. Combining the first formulation and the GGI approach, the second one minimizes the total cost subject to a desired reliability level through dispatchability maximization. Efficient convex optimization based algorithms are developed to solve these two models. Different from the conventional robust optimization method, our model does not rely on the specific uncertainty set of wind generation and directly optimizes the uncertainty accommodation capability of the power system. The proposed method is also compared with the affine policy based robust energy and reserve dispatch (AR-ERD). Case studies on the PJM 5-bus system illustrate the proposed concept and method. Experiments on the IEEE 118-bus system demonstrate the applicability of our method on moderate sized systems and its scalability to large dimensional uncertainty.
Keyword:
Affine policy
convex optimization
dispatchability
energy and reserve dispatch
uncertainty
wind generation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
分享收藏Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem安全约束机组组合问题的自适应鲁棒优化
OAAI
OAAI分享收藏Distributionally Robust Solution to the Reserve Scheduling Problem With Partial Information of Wind Power含风电部分信息的备用调度问题的分布式鲁棒解
PREAI
分享收藏Multi-Area Energy and Reserve Dispatch Under Wind Uncertainty and Equipment Failures风力不确定性和设备故障下的多区域能量和储备调度OAAI
OAAI分享收藏
查看更多内容