In this paper, the relationship between the Gross Domestic Product (GDP), air temperature variations and power consumption is evaluated using the linear regression and Wavelet Coherence (WTC) approach on a 1971-2011 time series for the United Kingdom (UK). The results based on the linear regression approach indicate that some 66% variability of the UK electricity demand can be explained by the quarterly GDP variations, while only 11% of the quarterly changes of the UK electricity demand are caused by seasonal air temperature variations. WTC however, can detect the period of time when GDP and air temperature significantly correlate with electricity demand and the results of the wavelet correlation at different time scales indicate that a significant correlation is to be found on a long-term basis for GDP and on an annual basis for seasonal air-temperature variations. This approach provides an insight into the properties of the impact of the main factors on power consumption on the basis of which the power system development or operation planning and forecasting the power consumption can be improved.

In this paper, the simulation of the disturbance propagation through a large power system is performed on the WSCC 127 bus test system. The signal frequency analysis from several parts of the power system is performed by applying the Wavelet Transf orm (WT). The results show that this approach provides the system operators with some useful information regarding the identification of the power system low-frequency electromechanical oscillations, the identification of the coherent groups of generators and the insight into the speed retardation of some parts of the power system. The ability to localize the disturbance is based on the disturbance propagation through the power system and the time-frequency analysis performed by using the WT is presented along with detailed physical interpretation of the used approach.

In this article, we propose a new paradigm of control, called a maximum hands-off control. A hands-off control is defined as a control that has a much shorter support than the horizon length. The maximum hands-off control is the minimum support (or sparsest) control among all admissible controls. We first prove that a solution to an L1-optimal control problem gives a maximum hands-off control, and vice versa. This result rationalizes the use of L1 optimality in computing a maximum hands-off control. The solution has in general the “bang-off-bang” property, and hence the control may be discontinuous. We then propose an L1/L2-optimal control to obtain a continuous hands-off control. Examples are shown to illustrate the effectiveness of the proposed control method.