Abstract
With the increasing penetration of wind power in the power systems, the uncertainties in wind power significantly challenge the reliable and economic operation of power systems. Recently, the worst scenario-based robust optimization approaches have been employed to manage the uncertainties in the unit commitment problem. To further improve the robustness and economic efficiency of power system operation, this article proposes a flexible robust risk-constrained unit commitment formulation, in which flexible reserve capacities of conventional generators and energy storage are allocated to cope with the uncertainty of wind power. The proposed model optimizes the unit commitment and dispatch solutions for the base case while guaranteeing that the flexible reserve capacity can be adaptively adjusted after wind generation realization. In contrast to the predefined uncertainty set in the conventional robust unit commitment, the proposed model constructs an adjustable and flexible uncertainty set via balancing the operational costs and the operational risk. The model establishes worst-case constraints to optimally allocate the flexible reserve capacity. The proposed model can be equivalently transformed into a single-level optimization problem using the strong duality theory. Numerical case studies on a modified standard test system demonstrate the effectiveness and the efficiency of the proposed model.
Highlights
Unit commitment (UC) is a critical scheduling decision processes performed by system operators to guide the power system operation in the dispatching day
The uncertainty set in the proposed flexible robust risk-constrained unit commitment (FRRUC) model is variable, which reflects the optimal allocation of flexible reserve capacity for flexible resources as well as the operator's risk preference
The proposed FRRUC is formulated as a two-layer robust optimization problem
Summary
Unit commitment (UC) is a critical scheduling decision processes performed by system operators to guide the power system operation in the dispatching day. The main objective of the UC problem is to determine the on/off schedule and generation plan of generators on the grid to minimize the system comprehensive cost and meet the operation constraints, such as system security and unit-wise constraints. Wind power generation has rapidly developed all over the world due to its clean and renewable characteristics. Wind power is inherently volatile and intermittent. The increasing penetration of wind generation brings significant technical challenges to power system operation. The conventional deterministic optimization method cannot ensure reliable and economic system operation because the power system uncertainty cannot be explicitly captured. In day-ahead scheduling, the UC method should be improved to efficiently account for the uncertainties in wind power generation
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