Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to
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The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery
Equation reveals that the remaining electricity of the energy storage at the period t is mainly related to the remaining power at the period t−1, the self-discharge rate, charging and discharging power and efficiency.
At present, renewable energy sources (RESs) and electric vehicles (EVs) are presented as viable solutions to reduce operation costs and lessen the negative environmental effects of microgrids (μGs). Thus, the rising
This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, focusing on enhancing profitability while factoring in battery
The recent worldwide uptake of EVs has led to an increasing interest for the EV charging situation. A proper understanding of the charging situation and the ability to answer
t,cs ⊆N units of energy available for EV charging. On top of this, the charging station is equipped with a battery bresponsible for energy storage, which has a maximum emax b, and a current e
Charging infrastructure is one of the critical factors in the growth of Electric vehicles (EVs). This paper provides a detailed model of charging stations. The modeling
Energy time-shift works by charging an energy storage system when electricity is cheap—typically during off-peak hours when demand is low and renewable energy sources
Proposed formulation reflects nonlinear characteristic of battery degradation and cycle life calculation. Formulation aids optimal scheduling of various type of grid-connected battery energy storage systems. Developed method is compatible with off-the-shelf optimization solvers.
Novel battery degradation cost formulation based on the RCA is proposed for optimal scheduling. Proposed formulation reflects nonlinear characteristic of battery degradation and cycle life calculation. Formulation aids optimal scheduling of various type of grid-connected battery energy storage systems.
With a physics-based battery model, a multi-objective optimal control problem is proposed to investigate the charging strategies that optimally trade off the temperature rise, charging time, and loss. First, a fast-charging strategy (minimum time) with the sole purpose of reducing charging time is presented and experimentally validated.
Nizami et al. targeted EV battery coordinated charging (G2V) and discharging (V2G) resource optimization to minimize the cost of EV owners using a mixed-integer programming (MIP)-based optimization model.
Other methods, such as neural network-based approach , gray-predicted charging , and ant colony system algorithm , have also been used for charging batteries. Most of these methods are heuristic in some way. That is, they use basic knowledge, empirical observations, and battery electrical characteristics to design charging strategies.
Charging loss is another important factor during charging. Reducing the charging loss increases the charge efficiency. Here, we extend the cost function in the fast-charging strategy to include the penalty for charge energy loss. Dynamic programming is used to trade off between charging time and charging loss.
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