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.

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.

In this paper, a coordination method of multiple electric vehicle (EV) aggregators has been devised to flatten the system load profile. The proposed scheme tends to reduce the peak demand by discharging EVs and fills the valley gap through EV charging in the off-peak period. Upper level fair proportional power distribution to the EV aggregators is exercised by the system operator which provides coordination among the aggregators based on their aggregated energy demand or capacity. The lower level min max objective function is implemented at each aggregator to distribute power to the EVs. Each aggregator ensures that the EV customers’ driving requirements are not relinquished in spite of their employment to support the grid. The scheme has been tested on IEEE 13-node distribution system and an actual distribution system situated in Seoul, Republic of Korea whilst utilizing actual EV mobility data. The results show that the system load profile is smoothed by the coordination of aggregators under peak shaving and valley filling goals. Also, the EVs are fully charged before departure while maintaining a minimum energy for emergency travel.

Compelled by environmental and economic reasons and facilitated by modern technological advancements, the share of hybrid energy systems (HES) is increasing at modern smart house (SH) level. This work proposes an intelligent hybrid energy management system (IHEMS) for an SH connected to a power network that allows a bidirectional power flow. The SH has electrical and thermal power loops, and its main components include renewable energy from wind and photovoltaics, electric vehicle (EV), battery energy storage system, a fuel cell which serves as a micro-combined heat and power system, and a boiler. The proposed IHEMS models the components of the SH, defines their constraints, and develops an optimization model based on the real coded genetic algorithm. The key features of the developed IHEMS are highlighted under six simulation cases considering different configurations of the SH components. Moreover, the standard EV charging techniques are compared, and it is observed that the charging method which is flexible in timing and power injection to the EV is best suited for the economic operation of the SH. The simulation results reveal that the proposed IHEMS minimizes the 24-hour operational cost of the SH by optimally scheduling the energy resources and loads.

Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.