• Energy Research

An energy-trading system is essential for the successful integration of Electric vehicles (EVs) into the smart grid. Existing systems merely focus on making optimal decisions while others depend on anonymization to achieve EVs drivers' privacy which is not enough because they can be identified from visited locations. In this paper, leveraging blockchain technology, we propose a privacy-preserving charging-station-to-vehicle (CS2V) energy trading scheme. To preserve privacy, EVs are anonymous, however, a malicious EV may abuse the anonymity to launch Sybil attacks by pretending as multiple non-exiting EVs to launch powerful attacks such as Denial of Service (DoS) by submitting multiple reservations/offers without committing to them, to prevent other EVs from charging and make the trading system unreliable. To thwart the Sybil attacks, we use a common prefix linkable anonymous authentication scheme, so that if an EV submits multiple reservations/offers at the same timeslot, the blockchain can identify such submissions. To further protect the privacy of EV drivers, we introduce an anonymous and efficient blockchain-based payment system that cannot link individual drivers to specific charging locations. Our experimental results indicate that our schemes are secure and privacy-preserving with low communication and computation overheads.

An energy trading system is essential for the successful integration of Electric Vehicles (EVs) into the smart grid. In this paper, leveraging blockchain technology, we first propose a privacy-preserving charging-station-to-vehicle (CS2V) energy trading scheme. The CS2V scheme is useful in crowded cities where there is a need for a charging infrastructure that can charge many EVs daily. We also propose a privacy-preserving vehicle-to-vehicle (V2V) energy trading scheme. The V2V scheme is useful when charging stations are not available or far and cheaper prices can be offered from EVs, e.g., if they charge from renewable energy sources. In the V2V scheme, the privacy of both charging and discharging EVs including location, time, and amount of power are preserved. To preserve privacy in both schemes, EVs are anonymous, however, a malicious EV may abuse the anonymity to launch Sybil attacks by pretending as multiple non-exiting EVs to launch powerful attacks such as Denial of Service (DoS) by submitting multiple reservations/offers without committing to them, to prevent other EVs from charging and make the trading system unreliable. To thwart the Sybil attacks, we use a common prefix linkable anonymous authentication scheme, so that if an EV submits multiple reservations/offers at the same timeslot, the blockchain can identify such submissions. To further protect the privacy of EV drivers, we introduce an anonymous and efficient blockchain-based payment system that cannot link individual drivers to specific charging locations. Our experimental results indicate that our schemes are secure and privacy-preserving with low communication and computation overheads.

In this paper, we propose centralized and decentralized privacy-preserving and collusion-resistant charging coordination schemes for Energy Storage Units (ESUs). The centralized charging coordination (CCC) scheme is useful where robust communication infrastructure is available that connects ESUs to a charging coordinator (CC), whereas the decentralized charging coordination (DCC) scheme is useful in remote areas and isolated microgrids. In CCC scheme, ESUs acquire tokens to send charging request anonymously to the CC via local aggregators. So, if the CC and the aggregator collude, they cannot identify requests' senders. To prevent linkability attacks, ESUs sends multiple requests with random Time to complete charge (TCC) and state of charge (SoC). Then, the CC compute schedules to maximize the power delivered to ESUs. In DCC scheme, charging is coordinated using a privacy-preserving data aggregation technique. Each ESU selects some ESUs as proxies, and shares a secret mask with each proxy. Then, each ESU adds a mask to its request and encrypts it so by aggregating requests, all masks are nullified and the total demand is known. DCC scheme is secure against collusion attacks due to the masking technique. The results of extensive experiments confirm that our schemes are efficient and secure, and preserve ESUs privacy.

With the rapid emergence of smart grids, charging coordination is considered the intrinsic actor that merges energy storage units (ESUs) into the grid in addition to its substantial role in boosting the resiliency and efficiency of the grid. However, it suffers from several challenges beginning with dependency on the energy service provider (ESP) as a single entity to manage the charging process, which makes the grid susceptible to several types of attacks such as a single point of failure or a denial-of-service attack (DoS). In addition, to schedule charging, the ESUs should submit charging requests including time to complete charging (TCC) and battery state of charge (SoC), which may disclose serious information relevant to the consumers. The analysis of this data could reveal the daily activities of those consumers. In this paper, we propose a privacy-preservation charging coordination scheme using a blockchain. The blockchain achieves decentralization and transparency to defeat the security issues related to centralized architectures. The privacy preservation will be fulfilled using a verifiable aggregation mechanism integrated with an aggregated signing technique to identify the untrusted aggregator and assure the data source and the identity of the sender. Security and performance evaluations are performed, including off-chain and on-chain experiments and simulations, to assess the security and efficiency of the scheme.

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers’ privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors’ parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers’ privacy.