A machine learning-based detection framework is proposed to detect a class of cyber-attacks that redistribute loads by modifying measurements. The detection framework consists of a multi-output support vector regression (SVR) load predictor and a subsequent support vector machine (SVM) attack detector to determine the existence of load redistribution (LR) attacks utilising loads predicted by the SVR predictor. Historical load data for training the SVR are obtained from the publicly available PJM zonal loads and are mapped to the IEEE 30-bus system. The features to predict loads are carefully extracted from the historical load data capturing both temporal and spatial correlations. The SVM attack detector is trained using normal data and randomly created LR attacks so that it can maximally explore the attack space. An algorithm to create random LR attacks is introduced. The results show that the SVM detector trained merely using random attacks can effectively detect not only random attacks but also intelligently designed attacks. Moreover, using the SVR predicted loads to re-dispatch generation when attacks are detected can significantly mitigate the attack consequences.

In smart power systems, information and communication technologies are being broadly installed and two-way communications are being established more effectively and efficiently. However, they are vulnerable to cyber-physical interactive threats and their impact depends on the system infrastructure, degree of interaction, degree of vulnerability, and the intensity and the frequency of the attack. The study addresses this problem and proposes: (i) an innovative Markov-chain based framework of the cyber-intrusion process to implement operating conditions in a smart power system; (ii) an advanced algorithm for power system reliability assessment, incorporating cyber-intrusion process and a detailed model of a heat pump system; and (iii) a new impact assessment framework for different cyber-intrusion detection-time distributions driven by Monte Carlo simulation. The cyber-detection and system recovery process with the presence of cyber-physical interactive operation models suggest that a significant level of impacts could be generated by the interactive operation of the smart power system and a quantitative assessment is needed in order to assess true impacts.

Smart grid (SG) systems are designed to leverage digital automation technologies for monitoring, control and analysis. As SG technology is implemented in increasing number of power systems, SG data becomes increasingly vulnerable to cyber-attacks. Classic analytic physics-model based bad data detection methods may not detect these attacks. Recently, physics-model and data-driven methods have been proposed to use the temporal aspect of the data to learn multivariate statistics of the SG such as mean and covariance matrices of voltages, power flows etc., and then make decisions based on fixed values of these statistics. However, as loads and generation change within a system, these statistics may change rapidly. In this study, an adaptive data-driven anomaly detection framework, Ensemble CorrDet with Adaptive Statistics (ECD-AS), is proposed to detect false data injection cyber-attacks under a constantly changing system state. ECD-AS is tested on the IEEE 118-bus system for 15 different sets of training and test datasets for a variety of current state-of-the-art bad data detection strategies. Experimental results show that the proposed ECD-AS solution outperforms the related strategies due to its unique ability to capture and account for rapidly changing statistics in SG.

Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber-attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs to manipulate the power system state estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the energy management system towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include bad data detection algorithms to eliminate errors from the acquired measurements, e.g. in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. To overcome the limitations of traditional residual-based BDD approaches, data-driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This study provides a comprehensive review of the most up-to-date machine learning methods for detecting FDIAs against power system SE algorithms.

Trade-offs between privacy and cost are studied for a smart grid consumer, whose electricity consumption is monitored in almost real time by the utility provider (UP) through smart meter (SM) readings. It is assumed that an electrical battery is available to the consumer, which can be utilised both to achieve privacy and to reduce the energy cost by demand shaping. Privacy is measured via the mean squared distance between the SM readings and a target load profile, while time-of-use pricing is considered to compute the cost incurred. The consumer can also sell electricity back to the UP to further improve the privacy-cost trade-off. Two privacy-preserving energy management policies (EMPs) are proposed, which differ in the way the target load profile is characterised. A more practical EMP, which optimises the energy management less frequently, is also considered. Numerical results are presented to compare the privacy-cost trade-off of these EMPs, considering various privacy indicators.

Simple analysis of energy consumption patterns recorded by smart meters can be used to deduce household occupancy. With access to higher-resolution smart-meter readings, we can infer more detailed information about the household including the use of individual electric appliances through non-intrusive load monitoring techniques. The extent of privacy concerns caused by smart meters has proved to an obstacle in the roll-out of smart meters in some countries. This highlights the need for investigating smart-meter privacy. Mechanisms for ensuring smart-meter privacy fall in broad categories of data manipulation, demand shaping, and load scheduling. In smart-meter data manipulation, the smart meter collects real, potentially high-resolution data about the energy consumption within the house. This data is then manipulated before communication with to utility providers and retailers. The manipulation could be non-stochastic, such as aggregation, binning, and down-sampling, or stochastic, such as additive noise. In demand shaping and load scheduling, smart-meter readings are communicated without any interference but the consumption is manipulated by renewable energy sources, batteries, or shifting loads to render non-intrusive load monitoring ineffective. In this study, the author reviews these approaches and presents several methods relying on homomorphic encryption, differential privacy, information theory, and statistics for ensuring privacy.

Energy storage and virtual power plant technologies have been developed and become important technical means to enhance power system stability and reduce real-time dispatching costs. In this study, the dispatching capability and dispatching cost characteristics of the virtual power plants are analysed firstly in detail, as well as the dispatching difficulties under the traditional market modes. Hence, virtual power plant real-time bidding package model and virtual auction-based real-time power market mechanism are proposed. Data-driven virtual power plant real-time packing method and bidding package model integrated virtual Vickrey–Clarke–Groves auction model are put forward. Finally, the feasibility and validity of the proposed mechanism and method are verified by case studies and result in analyses of the IEEE-30 bus test system with multiple virtual power plants, providing a scientific foundation and a practical solution to the real-time power market.

In this study, a topology defragmentation method is developed for co-placements of phasor measurement units (PMUs) and their communication infrastructure. Electric power networks are defragmented into sets of branches and a realistic cost model, based on reports from industry, is developed. Instead of considering channel limitations, more practical consideration of the presence of dual-use line relays and PMUs with different channel capacities is used to obtain a least-cost solution for specified levels of observability. Formulations are proposed to address budget limitations, to maximise benefits from add-on application, and to enhance application-sensitive deployments. The approach is demonstrated on a number of IEEE test networks and results reflect practical situations where optimal solutions (especially PMU buses and the capacity of deployed PMUs) depend on the availability of equipment, existing devices, and the level of observability specified. Solutions obtained to specify the bus, branch, PMU type, and phasor data concentrator to connect to and are optimal without the need for algorithmic parameter tuning. It is established, among other conclusions, that placement results should transcend beyond mere statements of the number of installations, but on the specifics of the deployments at the buses, branches, and storage locations.

This paper presents a new data-driven voltage control approach for distribution networks based on kernel methods. Voltage control becomes more and more challenging due to the increased penetration of Distributed Generation (DG), bidirectional power flow and faster voltage dynamics. State-of-art strategies for voltage control rely on physics model-based Optimal Power Flow (OPF) solutions, which can be implemented in a centralized or distributed manner. Nevertheless, such strategies require a detailed model of the network, and often lack scalability due to the large number of nodes and the limited communication infrastructure in distribution networks. In order to achieve real-time voltage control in distribution networks of meshed and radial topology, this paper presents a data-driven approach, which relies on local or regional measurements and does not require accurate models of the grid or an advanced communication infrastructure. Specifically, the proposed data-driven approach uses functional stochastic gradient descent in Reproducing Kernel Hilbert Spaces (RKHSs), to learn the control strategies for Distributed Generation (DG) units in real-time that lead to near-optimal operation costs, while maintaining adequate voltage profiles in the network and alleviating congestions for time-varying load and generation conditions.

Transactive energy systems (TES) are modern mechanisms in electric power systems that allow disparate control agents to utilise distributed generation units to engage in energy transactions and provide ancillary services to the grid. Although voltage regulation is a crucial ancillary grid service within active distribution networks (ADNs), previous work has not adequately explored how this service can be offered in terms of its incentivisation, contract auditability, and enforcement. Blockchain technology shows promise in being a key enabler of TES, allowing agents to engage in trustless, persistent transactions that are both enforceable and auditable. To that end, this study proposes a blockchain based TES that enables agents to receive incentives for providing voltage regulation services by (i) maintaining an auditable reputation rating for each agent that is increased proportionately with each mitigation of a voltage violation, (ii) utilising smart contracts to enforce the validity of each transaction and penalise reputation ratings in case of a mitigation failure, and (iii) automating the negotiation and bidding of agent services by implementing the contract net protocol as a smart contract. Experimental results on both simulated and real-world ADNs are executed to demonstrate the efficacy of the proposed system.

Soft open points (SOPs) have the ability to regulate the power flow among their terminals in a continuous manner, which can solve the problems brought by the synergy of distribution networks and distributed generation, and improve the system reliability and power supply capability. To address the current lack of quantitative measurement on the effects of SOPs, the power supply capability evaluation method for active distribution networks (ADNs) with four-terminal SOPs considering reliability is proposed. Firstly, the topology and configuration modes of four-terminal SOPs are studied, and their control modes are investigated in normal operation and supply restoration conditions. Then, using the feeder partition method, the effects of four-terminal SOPs on the power outage duration of different load areas after a fault are studied, and the reliability evaluation process for ADNs with four-terminal SOPs is developed based on the quasi-Monte Carlo method. Later, with reliability as the main constraint, the power supply capability evaluation model for ADNs with four-terminal SOPs is established, and the solution algorithm is proposed. Finally, the effectiveness and applicability of the method proposed are verified through a case study.

The development of smart grid technologies has resulted in increased interdependence between power and communication systems. Many of the operations in the existing power system rely on a stable and secured communication system. For electrically weak systems and time-critical applications, this reliance can be even greater, where a small degradation in communication performance can degrade system stability. However, despite inter-dependencies between power and communication systems, only a few studies have investigated the impacts of communication system performance on power system dynamics. This study investigates the dependencies of power system dynamics operations on a communication system performance. First, a detailed, dynamic networked microgrid model is developed in the GridLAB-D simulation environment, along with a representative multi-traffic, multi-channel, multi-protocol communication system model, developed in the network simulator (ns-3). Second, a hierarchical engine for large-scale infrastructure co-simulation framework is developed to co-simulate microgrid dynamics, its communication system, and a microgrid control system. The impact of communication system delays on the dynamic stability of networked microgrids is evaluated for the loss of generation using three use-cases. While the example use-cases examine microgrid applications and the impact to resiliency, the framework can be applied to all levels of power system operations.

This study proposes a method of coordinating electric vehicle charging to reduce losses in a distribution system, using only knowledge of the phase that each charger is connected to. Reducing network losses cuts costs and can be achieved through demand response mechanisms. However, directly minimising losses requires accurate values of the line impedances, which can be difficult to obtain. Flattening load over time and balancing load across phases have both been proposed as alternate solutions which indirectly reduce losses. Here, the practical differences between load flattening and explicitly minimising losses are quantified using simulations of residential charging in European style, three-phase distribution networks. Then, a new smart charging strategy, which incorporates phase balancing as a secondary objective to load flattening, is proposed. This requires only the knowledge of the phase that each load is on and achieves 30–70% of the potential reduction in losses.

Quick and accurate detection of cyber-attacks is key to the normal operation of the smart grid system. In this study, joint state estimation and sequential attack detection method for a given bus with grid frequency drift is proposed that utilises the commonly monitored output voltage. In particular, based on a non-linear state-space model derived from the three-phase sinusoidal voltage equations, the authors employ the sequential Monte Carlo (SMC) filtering to estimate the system state. The output of the SMC filter is fed into a cumulative sum control chart test to detect the attack in the fastest way. Moreover, an adaptive sampling strategy is proposed to reduce the rate of taking measurements and communicating with the controller. Extensive simulation results demonstrate that the proposed method achieves high adaptivity and efficient detection of various types of attacks in power systems.

Networked and interconnected microgrids can improve resilience of critical end-use loads during extreme events. However, the frequency deviations in microgrids during transient events are significantly larger than those typically seen in bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. Grid Friendly Appliance^TM (GFA) controllers can mitigate the transient event effects by engaging end-use loads. This paper presents a method to select set-points for end-use loads equipped with GFA controllers, while minimizing the interruptions to end-use customers. An online (i.e. real-time), device-level algorithm adjusts individual GFA controller frequency setpoints based on the operational characteristics of each end-use load and on the changing grid dynamic characteristics to selectively engage the load for mitigating the switching transients. The adaptive gradient-descent-based algorithm does not require control or coordination amongst end-use devices for adapting frequency set-points. The method is validated using dynamic simulations on a modified version of the IEEE 123-node test system with three microgrids using the GridLAB-D^TM simulation environment. The improved dynamic stability achieved through the engagement of GFAs support the switching operations necessary for networked microgrid operations.

This paper develops a novel approach for distribution system monitoring and state estimation, where time synchronisation of smart-meter measurements is carried out via the Precision Time Protocol (PTP). The approach is based on the concept of a Modified Smart Meter (MSM), a distribution system monitoring instrument that enables accurate time synchronisation of smart meter data. The design, application, communication technique and protocols of the MSM are described in detail. The proposed MSM device features PTP-based time synchronisation of smart meter measurements, and the concept of unbundling is applied to collect measurements utilising the existing smart meter sensors. This is expected to reduce the overall implementation cost of an MSM-based distribution network monitoring system compared to a system based on Phasor Measurement Units (PMUs). The problem of requiring open sky access for GPS links can potentially be solved by means of PTP synchronisation. Three-phase state estimation simulations using the IEEE-13 and 123 bus unbalanced test networks are employed to demonstrate the applicability of the MSM, and its performance is compared to standard PMU devices. The results indicate that the MSM may represent a workable monitoring solution for MV and LV distribution networks, with an acceptable trade-off between cost and performance.

A new decentralised demand response (DR) model relying on bi-directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q-learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation-based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.

This study quantifies the benefits of implementing model predictive control on residential solar PV and energy storage systems considering a time-of-use demand tariff, feed-in tariff and varying PV system sizes and battery life-cycle costs. The control system analysed makes use of economic model predictive control (EMPC) whereby the objective function is directly tied to the economics of the system. Using residential load and PV data from an Australian distribution network service provider, the EMPC controller is compared to a rule-based controller, highlighting the benefits of EMPC in regards to annual economic performance and battery energy throughput. The EMPC algorithm is then tested using 10 residential customers at the low voltage feeder level showing the capacity for the EMPC controller to shift peak demand and flatten the aggregated load profile of 30 residential customers.

Due to the high cost of peak hour power generation and a push towards sustainability, the need for demand response (DR) is increasing. Compared to commercial-level DR, residential-level DR is more challenging. Residents are reluctant to participate, and DR controllers lack sufficient real-time activity information to balance energy savings with residents' need for comfort and convenience. To address the above challenges, we propose a sensor data-driven activity-based controller for heating, ventilation, and air conditioning devices. Using our proposed novel strategy, resident activities are recognized in real-time through a random forest machine learning approach. Integrating activity information and forecasted electricity pricing, the proposed controller can simultaneously reduce energy consumption for sustainability and maintain resident constraints for comfort based on recognized activities. Results demonstrate the superiority of the proposed approach.

This study provides a solution for the feeder reconfiguration of autonomous microgrids (MGs). The objective is to minimise power loss, switching costs, and enhance voltage stability index, considering time-variations of loads. The daily load profiles for different seasons (spring, summer, fall, and winter) of different customers (i.e. residential, industrial, and commercial) are considered. In order to reduce the dimensions of the optimisation problem, k-means algorithm is implemented that clusters seasonal/yearly load profile into a few groups. The daily load profile is obtained based on the average of the group which has maximum members. This ensures the selection of a subset of load profiles that effectively represent the entire year's profile to reduce the complexity and execution time of the model. Subsequently, a new method is developed to break the daily load profile into intervals that guarantee less switching frequency for dynamic network reconfiguration. A controlled mutation differential evolution algorithm (CMDEA) compatible with long-term reconfiguration problem is developed with superior performance compared to conventional DEA and invasive weed optimisation algorithm. The CMDEA is employed to solve the reconfiguration problem on 33-bus and 69-bus autonomous MGs. Simulation results validate the effectiveness of the proposed method to reduce operational costs and computational burden in a smart grid environment.