Blockchain is an immutable type of distributed ledger that is capable of storing data without relying on a third party. The blockchain technology has attracted significant interest in research areas, including its application in smart grid for cyber security. Although great efforts have been devoted in utilizing blockchain in smart grid for cyber security, there is no comprehensive survey on blockchain in smart grid for cyber resiliency in both application and technological perspectives. To fill this gap, we conducted a comprehensive survey on blockchain for smart gird cyber security. This conducted survey presents the latest insights of ideas, architectures, and techniques of implementation that are relevant to blockchain's application in smart grid for cyber security. This paper aims at providing helpful guidance and reference for future research efforts specific to blockchain for cyber security in smart grid.

The existing research on false data injection (FDI) attacks against state estimation in transmission systems cannot be trivially extended to distribution feeders. The main reason is that a strong condition that requires the attacker to know the estimated state of distribution systems is needed, which makes the traditional FDI attacks difficult to be implemented in practice. In this paper, we propose a practical FDI attack model against state estimation in distribution systems, without paying expensive cost for obtaining the system state. We show that the attacker can approximate the system state based on power flow or injection measurements without too much effort. For local FDI attacks, the strong condition can be further relaxed to the knowledge of local state, which can be approximated based on a small number of power flow or injection measurements. Simulation results based on the IEEE test feeder demonstrate that the proposed practical FDI attack, even with the approximated system state, is more likely to compromise the state estimation without being detected, in comparison with the traditional attacks. This paper provides a basis to study the attack behaviors in distribution systems and a theoretical guide to develop protective countermeasures.

In power transmission systems, the false data injection (FDI) attacks against state estimation (SE) have been well studied. However, due to the unique features of power distribution systems including the low x/r ratio, existence of one-and/or two-phase branches, unbalanced load distributions, and unsymmetrical line parameters, the research on FDI attacks against distribution system state estimation (DSSE) is still open. In this paper, we investigate the vulnerability of DSSE to FDI attacks. In particular, we firstly propose a local state-based linear DSSE for multiphase and unbalanced smart distribution systems, which can facilitate the construction of FDI attacks numerically with the least information of system states. Then, the construction of three-phase coupled FDI attacks is introduced. The consideration of the coupling among phases by the three-phase coupled FDI attacks may require the modification of a large number of measurements by the attackers. To reduce the number of required measurements, the perfect three-phase decoupled FDI attacks, which consider the weak couplings among phases, is investigated. The probabilities of successful three-phase decoupled FDI attacks in strongly three-phase coupled systems are also derived numerically. The performance of the proposed FDI attacks against DSSE is evaluated based on IEEE test feeders. The case study results indicate the feasibility of the FDI attacks against DSSE in practical multiphase and unbalanced smart distribution systems. Future research directions including potential countermeasures are also highlighted.

Advancement of battery energy storage systems (BESSs) has made BESSs typical cyber-physical systems (CPSs), which exposes BESSs, especially for the state of charge (SoC) estimation function, to severe cyber attacks. This paper investigates the vulnerability of SoC estimation of BESSs in smart distribution networks (SDNs) to false data injection attacks (FDIAs) to provide a basis to study the attack mechanisms against BESSs and a theoretical guide to develop countermeasures. The mechanism of FDIAs against SoC estimation is investigated by theoretically analyzing the SoC estimation errors caused by compromised measurements, and a static FDIA model targeting one snapshot of SoC estimation is formulated. Considering temporal correlation among measurements, a detection method using the innovation test is proposed for static FDIAs, where the innovations are derived statistically. Considering the error accumulation effect, a novel sequential FDIA is proposed, which consists of a sequence of static FDIAs with small magnitudes. They can bypass most of the existing bad data detection algorithms, including the innovation test, with significant attack effects. An online approach is proposed for the practical construction of sequential FDIAs, which is formulated as a linear programming problem. Case studies based on modified IEEE 13 bus test feeder demonstrate the vulnerability of SoC estimation to FDIAs.

In this paper, the stochastic energy management of electric bus charging stations (EBCSs) is investigated, where the photovoltaic (PV) with integrated battery energy storage systems (BESS) and bus-to-grid (B2G) capabilities of electric buses (EBs) are included for cost-effective charging of EBs. Also, the dayahead dynamic prices are derived to mitigate charging impacts on power distribution systems. This problem is formulated as a distributionally robust Markov decision process (DRMDP) with uncertain transition probabilities and costs to address the impacts of random bus loads with inaccurate probability density function estimation. An event-based ambiguity set with combined statistical distance and moment information is developed to achieve minimax-regret criteria for less-conservative and robust solutions. To facilitate practical applications with reduced computational complexity, a heuristic regret function is proposed, based on which the dynamic prices are derived. Case studies based on EB data from St. Albert Transit and IEEE test feeders indicate that the proposed method can minimize EB charging cost with mitigated impacts on power distribution systems.

In this paper, we propose a stochastic multi-timescale energy management scheme of greenhouses with renewable energy sources (RES), including a PV system, combined heat and power (CHP) unit, and energy storage systems. The optimal energy management problem is formulated as a multi-timescale Markov decision process (MMDP) to address the randomness of RES and the outside weather conditions. In particular, a fast-timescale (FTS) process is used to model the rapidly changing electrical process with fine granularity, while a slow-timescale (STS) process is used to model the gradually varying thermal process to reduce the computational complexity. Exact solution of the optimal energy management problem is derived to minimize the cost of greenhouse operation. To further reduce the computational complexity of finding the optimal energy management policy, an approximation solution is also derived. The proposed energy management scheme is evaluated based on a commercial greenhouse structure from Bonnyville Forest Nursery Inc. for spruce, as well as real data of weather conditions, PV generation, CHP unit, and energy storage systems.