Prolific integration of renewable energy sources (RESs) such as solar photovoltaic systems into the distribution network will result in various issues associated with their intermittent nature. Energy storage is a vital component for overcoming issues associated with the intermittent nature of such RES. Though stationary battery systems are used as energy storage for such applications, smart energy storage (SES) systems are also becoming popular owing to various advantages and advent of smart grid systems. SES can be achieved by aggregating electric vehicles (EVs) or by using demand response management for loads with large time constants. Aggregated residential refrigerators are potential candidates for creating SES which has virtual storage capacity, unlike EVs. In this study, residential refrigerators are modelled analogously to energy capacity and self-discharge of electro-chemical batteries using the artificial neural network based kWh modelling. The model is further extended to estimate the virtual energy storage (VES) capacity with aggregated residential refrigerators; particularly in high-rise residential buildings. Simulation results are presented for scenarios covering the complete range of thermal capacity of typical refrigerators applicable in Singapore's climatic condition. Furthermore, a brief description of the possible applications for the estimated VES, pertaining to smart grid architecture and cyber-attack is also presented.

Phasor Measurement Units (PMUs) have enabled real-time power grid monitoring and control applications realizing an integrated power grid and communication system. The communication network formed by PMUs has strict latency requirements. If PMU measurements cannot reach the control centre within the latency bound, they will be invalid for calculation and may compromise the observability of the whole power grid as well as related applications. To address this issue, this study proposes a model to account for the power grid observability under communication constraints, where effective capacity is adopted to perform a cross-layer statistical analysis in the communication system. Based on this model, three algorithms are proposed for improving power grid observability, which are an observability redundancy algorithm, an observability sensitivity algorithm and an observability probability algorithm. These three algorithms aim at enhancing the power system observability via the optimal communication resource allocation for a given grid infrastructure. Case studies show that the proposed algorithms can improve the power system performance under constrained wireless communication resources.

This study presents a fully distributed control paradigm for secondary control of islanded AC microgrid (MG). The proposed method addresses both voltage and frequency restoration for inverter-based distributed generators (DGs). The MG system has droop controlled DG units with predominantly inductive transmission lines and different communication topologies. The restoration scheme is fully distributed in nature, and the DGs need to communicate with their neighbours using a sparse communication network. The proposed control scheme is efficient to provide quick restoration of the voltage and frequency whilst accurate power-sharing is achieved despite disturbances. Further, convergence and stability analysis of the proposed control scheme is presented. The proposed algorithm avoids the need for a central controller and complex communication structure thereby reducing the computational burden and the risk of single-point-failure. The performance of the proposed control scheme has been verified considering variations in load and communication topologies and link delay by pursuing an extensive simulation study in MATLAB/SimPowerSystem toolbox. The proposed control scheme supports plug-and-play demand and scalability of MG network. The proposed control scheme is also compared with the neighbourhood tracking error based distributed control scheme and observed that the former exhibit faster convergence and accurate performance despite disturbances in MG network.

In this study, a robust and dynamic transactive energy system in smart grid (SG) environment is proposed based on the Tsypkin–Polyak theorem. A key factor of the proposed system is to consider the uncertainties in electrical grid parameters. Moreover, oligopolistic behaviours of agents, i.e. demands and generating units, are considered in the modelling. It is proved that selfish agents offer their true cost parameters in the proposed transactive energy system. Therefore, optimal power flow (OPF) of the electrical grid is obtained in the proposed transactive energy system. In addition, real-time locational marginal prices (LMPs) are also presented for demand-side management (DSM). Hence, all levels of demands side can interact and communicate with generation side through real-time LMPs. This characteristic is known as interoperability of transactive energy systems. However, in addition to all benefits of the proposed system, it has adverse impacts on the stability of the electrical grid. Here, to solve this challenge, a hierarchical and robust control system is proposed by using the Tsypkin–Polyak theorem to control stability and OPF simultaneously. Finally, the effectiveness of the proposed system is validated by implementing it on a test electrical grid.