The increasing penetration of electric vehicles (EVs) in the distribution grid has established them as a prospective resource for ancillary services. These services require adequate control strategies for prompt and efficient operation. In this study, an energy management scheme (EMS) has been proposed to employ an off-board EV smart charger to support the grid during short-term variance of renewables and reactive load onset. The scheme operates by calculating power references for the charger instantaneously. The EMS incorporates a proportional power division methodology, proposed to allocate power references to the individual EVs connected to the charger DC-bus. This methodology considers the state-of-charge and battery sizes of the EVs, and it can aggregate energy from various types of EVs. The proposed scheme is compared with another power allocation method, and the entire EMS is tested under the scenarios of power mismatch and voltage sag/swell events. The results show that the proposed scheme achieves the goal of the aggregation of EVs at the charger level to support the grid. The EMS also fulfills the objectives of voltage regulation and four-quadrant operation of the smart charger.
The lifespan of a battery in battery energy storage systems (BESSs) is affected by various factors such as the operating temperature of the battery, depth of discharge, and magnitudes of the charging/discharging currents supplied to or drawn from the battery. In this study, the optimal location and size of a BESS are found for voltage regulation in a distribution system while increasing the lifespan of the battery. Various factors that affect the lifespan of a battery are considered and modelled. The problem is formulated as a multi-objective optimisation problem with two-objective functions. The first objective function calculates the energy losses in the system, whereas the second objective function represents the total investment cost of the distributed generator (DG) and BESS installations. Wind and solar DGs with uncertainties in their output powers are also considered with the BESSs. An elitist non-dominated sorting genetic algorithm-II with a utopian point method is used to solve the optimisation problem. Furthermore, an IEEE 906 bus European low-voltage test feeder and eight test cases are considered for this study. The results show reduced losses and cost, improvement in the voltage profile, and extended lifespan of the batteries.
Integration of large number of electric vehicles (EVs) with distribution networks is devastating for conventional power system devices such as transformers and power lines etc. This paper proposes a methodology for management of responsive household appliances management and EVs with water-filling algorithm. With the proposed scheme, the load profile of a transformer is retained below its rated capacity while minimally affecting the associated consumers. When the instantaneous demand at transformer increases beyond its capacity, the proposed methodology dynamically allocates demand curtailment limit (DCL) to each home served by transformer. The DCL allocation takes convenience factors, load profile and information of flexible appliances into account to assure the comfort of all the consumers. The proposed scheme is verified by modeling and simulating five houses and a distribution transformer. The smart appliances such as an HVAC, a water heater, a cloth dryer and an EV are also modeled for the study. Results show that the proposed scheme performs to reduce overloading effects of the transformer efficiently and assures comfort of the consumers at the same time.
The provision of ancillary services by electric vehicles (EVs) such as load smoothing and renewable energy (RE) compensation in the form of an aggregated storage is more regulated in the smart grid context. As such, the presence of multiple EV aggregators in the distribution network requires adept supervision by the distribution system operator (DSO). In this paper, a coordination scheme of aggregators is proposed to smoothen the load profile of distribution networks by enacting EV discharging during peak load and off-peak charging, keeping in view the EV driving requirements. A bi-level on-line interaction procedure from the DSO to the aggregators and vice versa is devised to manage the aggregators based upon their energy capacity and requirements. The aggregators employ a water-filling algorithm in a two-step EV power allocation method. The proposed scheme operation is demonstrated on an medium voltage (MV) distribution feeder located in Seoul with its actual traffic density data. The results show the achievement of peak shaving and valley filling objectives under aggregator coordination and that the EVs are completely charged before departure. The effect of various EV penetration levels and adaptivity of the scheme to RE incorporation is also verified. Furthermore, a comparison with an existing peak shaving method shows the superior performance of the proposed scheme.
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