Electricity smart meters have been widely adopted primarily for billing purposes. These meters provide a net-metering of the residential unit without distinguishing between the energy consumption or generation. As the penetration of distributed solar photovoltaic is expected to account for at least one-fourth of the energy mix in 2050, the visibility of the distributed energy resources to distribution network operators is vital. The disaggregation of behind-themeter net metering estimates the energy consumption and generation of residential units which enhances the observability of the low voltage network and provides analytics to support decision making. In this paper, we train a feedforward artificial neural network (ANN) to disaggregate the net metering data into energy consumption and generation. This model can be integrated as part of the distribution network operator tools for supporting decision making. The results show that ANN can disaggregate energy consumption and generation with an average RMSE and MAE performance of 0.1 when applied on real datasets.

The Electric Vehicle (EV) fleet's growth presents opportunities and challenges for the power grid, from reduced emissions and renewable energy integration to unprecedented increases in electricity peak demand. Strategic planning and incentive policies can help ensure a smooth integration of EVs into the power grid, ensuring grid reliability while reaping the associated benefits. Addressing the challenges proactively and leveraging the opportunities presented by the growing EV fleet requires collaboration among various stakeholders, including prosumers, EV Charging Station (EVCS) owners, and utilities. This paper proposes a multi-objective optimization framework considering battery degradation cost, Photovoltaic (PV) build canopy EVCS, and cost reduction programs to reduce the load variance and prosumer cost, minimize power losses, and maximize EVCS benefits. Furthermore, we propose a carbon credit program to compensate EV owners' battery degradation costs and encourage them to participate in the Vehicle-to-Grid (V2G) program. The sensitivity analysis results indicate that the proposed method effectively impacts objective functions and load indices.

With the increasing popularity of domestic solar PV systems there is a need for smart grid network operators to be able to identify solar PV systems attached to their networks. This need is driven by human safety, equipment safety, and regulatory compliance concerns. Given the implementation of smart metering as part of the evolution toward smart grids and the availability of smart metering data, methods that automate the identification of solar PV systems from consumption data are needed to address these concerns. This paper proposes an optimal template approach with genetic algorithm for solar PV detection, which successfully classifies solar PV and non-solar PV customers by utilising genetic algorithm optimisation to find optimal template pairs and matching observations to the closest template. This is done by using domain knowledge to specify a template parameterisation specific to the problem and using genetic algorithm optimisation to find template pairs that are optimised for accuracy.

An ac-regulated single-phase inverter operating in the stationary frame is designed to accurately track a sinusoidal reference signal at a designated frequency. To achieve such a control objective, resonant controllers with a conjugate set of marginally stable poles are required. The explicit implementation of marginally stable dynamics is prone to wind up in the event of the control signal entering a nonlinear saturation condition, resulting in a degraded or even an unstable dynamic response. This article proposes an integrated approach to the design and implementation of a resonant controller for a single-phase current regulator with an intrinsic antiwindup mechanism. The proposed method utilizes a disturbance observer to embed a pair of complex poles into the controller, leading to accurate tracking of a sinusoidal reference signal. Simultaneously, by using the observer architecture with a set of desired closed-loop poles as the performance specification, the implementation form remains completely stable. Furthermore, the closed-loop performance of the proposed resonant control system is analyzed to assess the impact of unmodeled dynamics. Experimental results are presented to demonstrate the efficacy of the proposed approach.

A distributed control law based on Model predictive Control (MPC) scheme is proposed for secondary frequency control in a deregulated market, which utilizes Demand Response (DR) along with Automatic generation Control (AGC). The proposed strategy of combining DR and AGC is termed as Load frequency Control (LFC) in the paper. The main contribution of the paper focuses on developing a model for LFC, which combines DR as well as Governor Response (GR) as manipulated variables. The new model is then used in an embedded integrator based distributed MPC algorithm to optimally choose between the GR and DR for the frequency regulation within system's constraints and cost. The algorithm is tested on a system with two areas interconnected by means of a tie line and shows that by choosing DR the frequency response not only improves but also the cost of frequency regulation reduces.

Model Predictive Control approach is being extensively used by researchers in the power electronics area. Since its early introduction to the field, it has been regarded as a strategy fundamentally different to the classical control approaches, which makes it difficult to compare MPC-based schemes with the existing control schemes. This paper shows, that there exists a direct relationship between classical control approaches and MPC horizon 1 approach. By using a linear load model and a number of practically important disturbance models and applying MPC horizon 1 approach, the paper develops a variety of optimal control structures, including Finite Set MPC, Proportional Integral and Proportional Resonant current controllers. The paper established connections between the resulting optimal controllers and the existing control schemes, and outlines a strategy leading to performance improvement of any linear controller with respect to any disturbance model of interest. The findings of the paper are supported by a practical example of optimization of a Proportional Resonant controller, confirmed via simulation and experiments.

In this paper a control strategy is proposed for Automatic Generation Control (AGC), which focuses on the interconnected system instead of individual areas and minimizes the cost of control while maintaining network constraints. A methodology is developed to maintain the network constraints by limiting the tie line flows within safe thermal limits when the generation and disturbances in the interconnected areas are utilized for AGC. Our contribution comes from extending the Economic AGC approach in [1] so that it is feasible for practical implementation. This is achieved by imposing constraints on a set of non physical auxiliary variables. The optimization function is extended to include the constraints on the auxiliary variables by using a logarithmic barrier function method. It is proved for a two area power system that the tie line flows attain the same values as the auxiliary variables under steady state conditions. A simulation study is presented to show the effectiveness of our approach.

Vulnerability assessment of power system networks is becoming an essential requirement for minimizing the risk of disastrous power outage events. This paper proposes a novel centrality index which treats the power system as two complex networks: real power flow network and reactive power flow network. Two vulnerability indices (real power flow centrality index and reactive power flow centrality index) are proposed which represent the vulnerability level in two different networks. They are combined using fuzzy logic to generate the system composite centrality index. The analysis is carried out on the IEEE 14 bus system.