This paper presents a false data injection attack against AC state estimation in distribution system. Such attack is called net local load redistribution attacks (NRLA) which is aimed at misleading the distribution system operator to observe illusory under-voltage issues in the AC state estimation. The attack vector is constructed with only local network information.

In this paper, we propose an AC optimal power flow (ACOPF) model considering distributed flexible AC transmission system (D-FACTS) devices, in which the reactance of D-FACTS equipped lines are introduced as decision variables. This is motivated by increasing interests in using D-FACTS devices to address system operational and cyber-security concerns. First, D-FACTS devices can be incorporated in real-time operations for economic benefits such as managing power congestions and reducing system losses. Second, D-FACTS devices can be utilized by moving target defense (MTD), an emerging concept against cyber-attacks, to prevent attackers from knowing true system configurations. Therefore, system operators can use the proposed ACOPF model to achieve economic benefits and provide the setpoints of D-FACTS devices for MTD at the same time. In addition, we rigorously derive the gradient and Hessian matrices of the objective function and constraints, which are further used to build an interior-point solver of the proposed ACOPF. Numerical results on the IEEE 118-bus transmission system show the validity of the proposed ACOPF model as well as the efficacy of the interior-point solver in minimizing system losses and generation costs.

As home energy management systems (HEMSs) are implemented in homes as ways of reducing customer costs and providing demand response (DR) to the electric utility, homeowner’s privacy can be compromised. As part of the HEMS framework, homeowners are required to send load forecasts to the distribution system operator (DSO) for power balancing purposes. Submitting forecasts allows a platform for attackers to gain knowledge on user patterns based on the load information provided. The attacker could, for example, enter the home to steal valuable possessions when the homeowner is away. In this paper, we propose a framework using a smart contract within a private blockchain to keep customer information private when communicating with the DSO. The results show the HEMS users’ privacy is maintained, while the benefits of data sharing are obtained. Blockchain and its associated smart contracts may be a viable solution to security concerns in DR applications where load forecasts are sent to a DSO.