Peer-to-peer trading is a next-generation energy management technique that economically benefits proactive consumers (prosumers) transacting their energy as goods and services. At the same time, peer-to-peer energy trading is also expected to help the grid by reducing peak demand, lowering reserve requirements, and curtailing network loss. However, large-scale deployment of peer-to-peer trading in electricity networks poses a number of challenges in modeling transactions in both the virtual and physical layers of the network. As such, this article provides a comprehensive review of the state-of-the-art in research on peer-to-peer energy trading techniques. By doing so, we provide an overview of the key features of peer-to-peer trading and its benefits of relevance to the grid and prosumers. Then, we systematically classify the existing research in terms of the challenges that the studies address in the virtual and the physical layers. We then further identify and discuss those technical approaches that have been extensively used to address the challenges in peer-to-peer transactions. Finally, the paper is concluded with potential future research directions.

Peer-to-Peer Trading in Electricity Networks: An Overview

Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks.

A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids

Among different sources of alternate energy, wind and solar are two prominent and promising alternatives to meet the future electricity needs for mankind. Generally, these sources are integrated at the distribution utilities to supply the local distribution customers. If the power generated by these sources is bulk, then they are either integrated at the distribution/transmission level or may be operated in an island mode if feasible. The integration of these renewables in the power network will change the fault level and network topologies. These fault levels are intermittent in nature and existing protection schemes may fail to operate because of their pre-set condition. Therefore, the design and selection of a proper protection scheme is very much essential for reliable control and operation of renewable integrated power systems. Depending upon the level of infeed and location of the renewable integration, the protection requirements are different. For low renewable infeed at the distribution level, the existing relay settings are immune from any small change in the network fault current from new incoming renewables. However, bulk renewable infeed requires modification in the existing protection schemes to accommodate the fault current variation from the incoming renewables. For bulk penetration of the renewable, the requirement of modified/additional protection schemes is unavoidable. Adaptive relaying and non-adaptive relaying schemes are discussed in the literature for protection of power networks, which are experiencing dynamic fault currents and frequent changing network topologies. This article presents a detailed review of protection schemes for renewable integrated power networks which includes distribution, transmission and microgrid systems. The merits and demerits of these protection schemes are also identified in this article for the added interest of the readers. The visible scope of advance protection schemes which may be suitable for providing reliable protection for dynamic fault current networks is also explored.

Protection challenges under bulk penetration of renewable energy resources in power systems: A review

Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges

This article proposes a distributed multi-period multi-energy operational model for the multi-carrier energy system. In this model, energy hubs function as distributed decision-makers and feature the synergistic interactions of generation, delivery, and consumption of coupled electrical, heating, and natural gas energy networks. The multi-period multi-energy scheduling is a challenging optimization problem due to its strong couplings and inherent nonconvexities within the multi-energy networks. The original problem is thus reformulated as a mixed integer second-order cone programming (MISOCP) and subsequently solved with a sequential second-order cone programming (SOCP) approach to guarantee a satisfactory convergence performance. Furthermore, a fully-distributed consensus-based alternating direction method of multipliers (ADMM) approach with only neighboring information exchange required is developed to optimize the multi-energy flows while considering the local energy-autonomy of heterogeneous energy hubs. The proposed methodology is performed and benchmarked on a four-hub urban multi-energy system over a 24 hourly scheduling periods. Simulation results demonstrated the superiority of the proposed scheme in system operational economy and renewable energy utilization, and also verify the effectiveness of the proposed distributed approach.

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Distributed Multi-Energy Operation of Coupled Electricity, Heating, and Natural Gas Networks

A tri-level optimization model to mitigate coordinated attacks on electric power systems in a cyber-physical environment

A distributed energy management strategy for resilient shipboard power system

Optimal coordinated operation scheduling for electric vehicle aggregator and charging stations in an integrated electricity-transportation system

Modern shipboard power systems (SPSs) with advanced cyber infrastructure need urgent attention because they have higher risk of cyber attacks In particular, false data injection (FDI) attacks can interfere with state estimation by tampering with measurement devices, or they may also directly target the central control system This paper proposes a twofold strategy to mitigate the effects of such an unconventional FDI attack, using battery to actively reduce load curtailment To detect signs of malicious data, a multiagent system (MAS) that checks commands from the central energy management system is employed A novel bilevel optimization problem is formulated to model the interaction between the battery and the compromised SPS A heuristic defense parameter is developed to improve the detection of corrupted commands The merits of the proposed scheme are evaluated using a risk analysis model The results of the case studies prove that a combination of autonomous battery with MAS-based heuristic method is effective in mitigating the effects of the cyber attack

Risk-Based Mitigation of Load Curtailment Cyber Attack Using Intelligent Agents in a Shipboard Power System

The power distribution grid is typically unobservable due to a lack of measurements. While deploying more sensors can alleviate this issue, it also presents new challenges related to data aggregation and the underlying communication infrastructure. Therefore, developing state estimation methods that enhance situational awareness at the grid edge with compressed measurements is critical. For this purpose, a suite of sparsity-based approaches that exploit the correlation among states/measurements in spatial as well as temporal domains have been proposed recently. This article presents a systematic comparison and evaluation of these approaches. Specifically, the performance and complexity of spatial methods (1-D compressive sensing and matrix completion) and spatio-temporal methods (2-D compressive sensing and tensor completion) are compared using the IEEE 37 and IEEE 123 bus test systems. Additionally, new robust formulations of these sparsity-based methods are derived and shown to be robust to bad data and network parameter uncertainties. Among the sparsity-based approaches, compressive sensing methods tend to outperform matrix completion and tensor completion methods in terms of error performance.

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Kexing Lai

Papers

A tri-level optimization model to mitigate coordinated attacks on electric power systems in a cyber-physical environment

A distributed energy management strategy for resilient shipboard power system

Optimal coordinated operation scheduling for electric vehicle aggregator and charging stations in an integrated electricity-transportation system

Risk-Based Mitigation of Load Curtailment Cyber Attack Using Intelligent Agents in a Shipboard Power System

Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study