NERC Control Performance Standards Research Articles

CPS1 Compliance-Constrained AGC Gain Determination for a Single-Balancing Authority

The integration of non-dispatchable generation has increased the need for ancillary services to maintain frequency control performance, and the determination of adequacy to ensure reliability has become an important operating concern. This work determines the AGC gain of a single balancing authority interconnection in order for system frequency to comply with NERC control performance standards, ensuring secondary control adequacy. The particular case of wind expansion is considered, and a simulation of ERCOT is presented to evaluate the performance of the formulation.

Stochastic Optimal Relaxed Automatic Generation Control in Non-Markov Environment Based on Multi-Step $Q(\lambda)$ Learning

This paper proposes a stochastic optimal relaxed control methodology based on reinforcement learning (RL) for solving the automatic generation control (AGC) under NERC's control performance standards (CPS). The multi-step Q(λ) learning algorithm is introduced to effectively tackle the long time-delay control loop for AGC thermal plants in non-Markov environment. The moving averages of CPS1/ACE are adopted as the state feedback input, and the CPS control and relaxed control objectives are formulated as multi-criteria reward function via linear weighted aggregate method. This optimal AGC strategy provides a customized platform for interactive self-learning rules to maximize the long-run discounted reward. Statistical experiments show that the RL theory based Q(λ) controllers can effectively enhance the robustness and dynamic performance of AGC systems, and reduce the number of pulses and pulse reversals while the CPS compliances are ensured. The novel AGC scheme also provides a convenient way of controlling the degree of CPS compliance and relaxation by online tuning relaxation factors to implement the desirable relaxed control.

Feasibility of decomposing A~C~E~/sub 1/ to identify the impact of selected loads on CPS1 and CPS2

Control areas are held to the NERC's control performance standards (CPS), CPS1 and CPS2. A control area serving rapid and widely varying loads would like to know how load variations in certain subareas or zones affect its area's CPS1 and CPS2 measures. This paper addresses the question of decomposability of the 1-min average value of area control error (ACE) A~C~E~/sub 1/, the primary variable in the computation of CPS1 and CPS2, into components attributable to load fluctuations within defined zones. It includes description of dynamic models of ACE and A~C~E~/sub 1/, and simulated results confirming the decomposability of A~C~E~/sub 1/.

Feasibility of Decomposing ACE to Identify the Impact of Selected Loads on CPS1 and CPS2

Control areas are held to the NERC's control performance standards, CPS1 and CPS2. A control area serving rapid and widely varying loads would like to know how load variations in certain subareas or zones affect its area's CPS1 and CPS2 measures. This paper addresses the question of decomposability of the 1-minute average value of ACE, ACE1, the primary variable in the computation of CPS1 and CPS2, into components attributable to load fluctuations within defined zones. It includes descriptions of dynamic models of ACE and ACE1, and simulated results confirming the decomposability of ACE1.