Accurate demand response participation in regulating power system frequency by Modified Active Disturbance Rejection Control

Abstract

One of the significant problems in demand response (DR) participation in the smart power system is calculating how much electrical power is required to extract from DR ( ∆PDR) to keep the power system frequency in the allowable range. Calculating the precise amount of this value is impossible due to the random nature of load disturbance (LoD). In this paper, a new method is presented to control the DR participation in the load frequency control considering communication time delay. Extended State Observer is used to estimate the magnitude of the LoD, considering the structural uncertainty of the power system parameters. Then, the DR program is implemented in load frequency control to compensate the whole or part of LoD considering the available electrical power at the aggregators (PAV‐AGG) and communication time delay. Afterward, Active Disturbance Rejection Control is modified and adopted to control the uncompensated LoD. The salp swarm algorithm is employed to design the parameters of the proposed method, which is verified in comparison with the previous methods. The results demonstrate that the presented approach has potentially significant performance, assisting the power system frequency to be damped immediately with the small overshoot and undershoot.

Feasibility Study of a Zero Energy Building Based on Photovoltaic-Low Speed Wind-Battery Hybrid System: A Case Study in Golestan-North of Iran

“…The proposed method uses a Markov prediction chain and a dynamic decision algorithm to minimize the cost of electricity while ensuring that the power demand of all microgrids is met. Rouhani et al (3) proposed a new method for using demand response to regulate power system frequency. The proposed method uses a modified active disturbance rejection control (M-ADRC) algorithm to ensure that the demand response signal is accurate and responsive to changes in the power grid.…”

“…Ah ÷ 0.8 = 665.62 Ah[3] Battery output is dependent on battery and environment temperature. If the average temperature is 10 degrees in winter, then the temperature dependence coefficient is 1.19: 665.62 Ah ×1.19 = 792.09 Ah[4] …”