This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices.

A Proportional-Integral-Derivative (PID)-controlled synchronous buck converter (SBC)-based battery charging system was designed to charge a lead-acid cell battery using commercially available Photovoltaic (PV) panel. The proposed system was installed aboard a fishing trawler to power its electrical system replacing the conventional system, which uses a diesel generator and a few kerosene lamps for lighting purposes. A PID algorithm instead of traditional Maximum power point tracker (MPPT) is used in the proposed system since the charging process of the battery requires a maximum current instead of maximum power. The proposed control algorithm is compared with the popular MPPT technique Perturb and Observation (P&O) to validate its dynamic performance at different solar irradiance levels using MATLAB/Simulink^®. The simulation and the experimental results have demonstrated that the dynamic response of the proposed algorithm is significantly improved by considering higher charging current, the capability to charge the battery at low irradiance, high stability, and lower cost. Finally, a successful 15-day field trial was conducted at sea using the proposed system, and a maximum charging current output of 6.5 A was achieved by the SBC during noon time; it was sufficient to charge a 12 V, 100 Ah battery, with a state of charge (SoC) of 33%, at a voltage charging rate of +0.3 V/h.

Nowadays the expansion of technology is leading to the development of a new electrical power system, called smart grid. It combines traditional electrical grid, emerging communication technologies, new electric devices, such as smart devices, and renewable energy sources to satisfy the ever increasing energy demand. The power flow is seen as an energy plan defining the way power is transmitted from producers to end users. The power grid is making its way to a smart electrical power system by the combination of power and communication networks. In the future, smart grid will see an increased integration of applications and information technologies that contribute to the operation, the planning and the control of the power system. These applications should extensively be tested and examined before their implementation in real power grid to avoid costly and risky operations. Therefore, models and tools can be used to conduct studies and evaluation of the behaviors and the performances of such applications. In this paper, we propose a model of smart grid, using classes, entities and relations, to define the interactions between the different objects involved in energy management and those contributing to the transmission of energy. We used this model to highlight the role of the storage units in a microgrid. Indeed, the study of energy plan involves the introduction of energy storage units (e.g. batteries); the objective is to avoid fluctuations caused by renewable energies (e.g. wind/solar energy) during generation and supply. Simulations show the effect of energy storage units and their role in providing grid stabilization and power quality.

This paper addresses the issue of selecting architecture of smart grid that can be used so as to test applications and uses cases related to services. Most existing architectures are based on the NIST conceptual model that defines seven high-level domains (Bulk Generation, Transmission, Distribution, Customers, Operations, Markets and Service Providers), which is not suitable with recent progress in grid modernization. The National Institute of Standards and Technology (NIST) model does not underline the importance of advanced energy storage and peak-shaving technology. However an efficient architecture should be close to reality defining all components that can increase customer participation in the energy management and improve flexibility, consumption reduction, demand-side management development and incorporation. As a solution, we propose a layered smart grid architecture where each layer provides a set of services. To validate our architecture we consider a use case that highlights the integration of energy storage units in smart grids using batteries that can either produce or store energy. The batteries are implemented with different values of capacity and simulation result shows the impact of energy storage systems and their role in providing grid stabilization, improving power quality and reducing peak demand.

Not far into the future, the Smart Grid is regarded as the next generation power grid. In fact, this system uses information, two-way cyber-secure communication technologies, and computational intelligence. It is an integrated fashion across electricity generation, transmission, distribution, storage and consumption. This will achieve a system that is clean, safe, secure, reliable, resilient, efficient, and sustainable. Therefore, the power grid needs to integrate reliable components, smart devices, distributed energy resources (DER), energy storage system (ESS) and advanced ICT. To do so, a virtualization of the system is primordial in order to test the implementation of such components and avoid costly operations. In this paper, we propose an object-oriented model to determine actors and highlight their roles and interactions. The effectiveness of the proposed modeling concept is shown through an entity/relation model. To validate our model, we focus on demand response and dynamic pricing programs. The objective is to explore the effect of a residential double-auction market, using transactive controllers and billing mode. Simulation results show that the association of automated bidding and demand response leads to utility demand reduction. Moreover, it helps customers to control their bill and use the electricity wisely.