No abstract

Volt-VAR optimization (VVO) has been investigated extensively in power systems. However, under the era of integrated energy systems (IES), the growing interdependencies between different energy systems complicate traditional VVO. This is further hardened by incurred gas quality problems due to the hydrogen injection in IES, produced by widely applied power-to-gas (P2G) facilitates that couple between power and gas systems. This paper develops a two-stage volt-VAR-pressure optimization (VVPO) model for PV-penetrated IES to manage the variation of system voltages while managing gas quality indices. In addition to the traditional voltage regulating devices, P2G facilities, which can mitigate fluctuating PV output via converting the surplus generation into hydrogen, are also used for voltage management. A two-stage distributionally robust optimization (DRO) based on moment information is utilized to model PV uncertainty. A semidefinite programming model is formulated and finally solved by the constraint generation algorithm. A 33-bus-20-node IES is used to verify the effectiveness of the proposed VVPO on voltage management, ensured gas quality with high economic efficiency. The proposed VVPO is applicable to injecting other green gases into gas systems while ensuring power quality and enable system operators to provide low-cost but high-quality multi-energy to customers.

No abstract

False data injection (FDI), could cause severe uneconomic system operation and even large blackout, which is further compounded by the increasingly integrated fluctuating renewable generation. As a commonly conducted type of FDI, load redistribution (LR) attack is judiciously manipulated by attackers to alter the load measurement on power buses and affect the normal operation of power systems. In particular, LR attacks have been proved to easily bypass the detection of state estimation. This paper presents a novel distributionally robust optimization (DRO) for operating transmission systems against cyber-attacks while considering the uncertainty of renewable generation. The FDI imposed by an adversary aims to maximally alter system parameters and mislead system operations while the proposed optimization method is used to reduce the risks caused by FDI. Unlike the worst-case-oriented robust optimization, DRO neglects the extremely low-probability case and thus weakens the conservatism, resulting in more economical operation schemes. To obtain computational tractability, a semidefinite programming problem is reformulated and a constraint generation algorithm is utilized to efficiently solve the original problem in a hierarchical master and sub-problem framework. The proposed method can produce more secure and economic operation for the system of rich renewable under LR attacks, reducing load shedding and operation cost to benefit end customers, network operators, and renewable generation.

No abstract