Structural Condition for Controllable Power Flow System Containing Controllable and Fluctuating Power Devices

Abstract

This paper discusses a structural property for a power system to continue a safe operation under power fluctuation caused by fluctuating power sources and loads. Concerns over global climate change and gas emissions have motivated development and integration of renewable energy sources such as wind and solar to fulfill power demand. The energy generated from these sources exhibits fluctuations and uncertainty which is uncontrollable. In addition, the power fluctuations caused by power loads also have the same consequences on power system. To mitigate the effects of uncontrollable power fluctuations, a power flow control is presented which allocates power levels for controllable power sources and loads and connections between power devices. One basic function for the power flow control is to balance the generated power with the power demand. However, due to the structural limitations, i.e., the power level limitations of controllable sources and loads and the limitation of power flow channels, the power balance may not be achieved. This paper proposes two theorems about the structural conditions for a power system to have a feasible solution which achieves the power balance between power sources and power loads. The discussions in this paper will provide a solid theoretical background for designing a power flow system which proves robustness against fluctuations caused by fluctuating power devices.

An Efficient Testing Scheme for Power-Balanceability of Power System Including Controllable and Fluctuating Power Devices

“…When we consider an arbitrary subset T of power loads, the overall power provided to T comes only from the neighbors of T, N(T), while N(T) provides power not only to T but also to power loads in N(N(T))\T, where N(N(T)) ⊇ T. Hence, the overall generated power by N(T) is no smaller than the overall power provided to T. As a result, the following inequality holds. For more detailed explanation and mathematical proof, especially for the sufficiency of the condition, please refer to [27].…”

“…The issue of time complexity is discussed in this subsection in order to show the comparison between new approach presented in this paper and our previous paper [27].…”

“…In our previous research work [27,28], we have discussed two types of solvability conditions, one for a given power flow system with given power generation and demand levels of fluctuating power source and load devices to have a feasible solution Level 1 solvability, and the other for a given power flow system with arbitrary (but within given lower limit and upper limit) power level of fluctuating source and load devices to have a feasible solution Level 2 solvability shown as condition 1-1 and condition 1-2. These conditions are presented as "Theorem-1" in this paper.…”

“…Therefore, because the power levels of real physical power systems alter every time step, the problem of whether the given power system under consideration (i.e., our power flow control problem) has a feasible solution or not in individual time instance is an essential problem to solve. In our previous research work [27,28], we have discussed two types of solvability conditions, one for a given power flow system with given power generation and demand levels of the fluctuating power source and load devices to have a feasible solution Level 1 solvability , and the other for a given power flow system with arbitrary (but within given lower limit and upper limit) power level of fluctuating source and load devices to have a feasible solution Level 2 solvability.…”

Proactive Critical Energy Infrastructure Protection via Deep Feature Learning

“…In our case, abnormal are the wind turbine measurements that do not conform with the the already defined classes, by presenting missing values on several attributes (e.g., Temperatures, Wind Speed), or by depicting extremely high or low feature values within specific time intervals. Consequently, anomaly detection systems should provide high sensitivity in discriminating whether the upcoming SCADA measurements can be considered as normal or not [22]. Generally, anomaly/fault detection techniques may be discriminated into: (i) supervised: in which the anomaly type is available [23], (ii) unsupervised: in which no labelling information is appeared on the input data [24,25], and (iii) semi-supervised: in which partial labelling is available [26][27][28].…”