To dynamically reduce voltage unbalance (VU) along low voltage distribution feeders, a distributed intelligent residential load transfer scheme is proposed. In this scheme, residential loads are transferred from one phase to another to minimize VU along the feeder. The central controller, installed at the distribution transformer, observes the power consumption in each house and determines the house(s) to be transferred from an initially connected phase to another. The transfer is carried out by the help of a static transfer switch, with a three-phase input and a single-phase output connection, through which each house is supplied. The steady-state and dynamic performances of the proposed load transfer scheme are investigated by MATLAB analyses and PSCAD/EMTDC simulations.

The conventional volt-watt control method used in PV inverters to overcome the overvoltage problems can result in significant unfairness in the curtailed active power in PV-rich distribution networks. In this paper, a novel decentralized control strategy is presented to improve the fairness in the curtailed active power of inverters. In this strategy, first, each PV inverter calculates the ratio of its node voltage magnitude change to its injected active power. Then, the calculated value by the farthest PV inverter of each phase from the transformer is shared with other PV inverters connected to the same phase. Using this value, PV inverters adjust their control parameters of an enhanced volt-var-watt control method. The performance of this method is evaluated using various simulation studies by making reference to the IEEE European low-voltage (LV) test feeder and considering different levels of PV system integration. It is shown that the proposed control strategy is able to improve the fairness of the active power curtailment among PV inverters significantly alongside solving the overvoltage problem. Finally, the result of a comparative analysis between the proposed strategy and a state-of-the-art communication-based method is reported.

To minimise the number of load sheddings in a microgrid (MG) during autonomous operation, islanded neighbour MGs can be interconnected if they are on a self‐healing network and an extra generation capacity is available in the distributed energy resources (DER) of one of the MGs. In this way, the total load in the system of interconnected MGs can be shared by all the DERs within those MGs. However, for this purpose, carefully designed self‐healing and supply restoration control algorithm, protection systems and communication infrastructure are required at the network and MG levels. In this study, first, a hierarchical control structure is discussed for interconnecting the neighbour autonomous MGs where the introduced primary control level is the main focus of this study. Through the developed primary control level, this study demonstrates how the parallel DERs in the system of multiple interconnected autonomous MGs can properly share the load of the system. This controller is designed such that the converter‐interfaced DERs operate in a voltage‐controlled mode following a decentralised power sharing algorithm based on droop control. DER converters are controlled based on a per‐phase technique instead of a conventional direct‐quadratic transformation technique. In addition, linear quadratic regulator‐based state feedback controllers, which are more stable than conventional proportional integrator controllers, are utilised to prevent instability and weak dynamic performances of the DERs when autonomous MGs are interconnected. The efficacy of the primary control level of the DERs in the system of multiple interconnected autonomous MGs is validated through the PSCAD/EMTDC simulations considering detailed dynamic models of DERs and converters.

The global market share of electric vehicles (EVs) is on the rise, resulting in a rapid increase in their charging demand in both spatial and temporal domains. A remedy to shift the extra charging loads at peak hours to off-peak hours, caused by charging EVs at public charging stations, is an online pricing strategy. This paper presents a novel combinatorial online pricing strategy that has been established upon a reward-based model to prevent network instability and power outages. In the proposed solution, the utility provides incentives to the charging stations for their contributions in the EVs charging load shifting. Then, a constraint optimization problem is developed to minimize the total charging demand of the EVs during peak hours. To control the EVs charging demands in supporting utility’s stability and increasing the total revenue of the charging stations, treated as a multi-agent framework, an online reinforcement learning model is developed which is based on the combination of an adaptive heuristic critic and recursive least square algorithm. The effective performance of the proposed model is validated through extensive simulation studies such as qualitative, numerical, and robustness performance assessment tests. The simulation results indicate significant improvement in the robustness and effectiveness of the proposed solution in terms of utility’s power saving and charging stations’ profit.