Accelerated development of eco-friendly technologies such as renewable energy, smart grids, and electric transportation will shape the future of electric power generation and supply. Accordingly, the power consumption characteristics of modern power systems are designed to be more flexible, which impact the system sizing. However, integrating these considerations into the design stage can be complex. Under these terms, this paper presents a novel model based on mixed integer linear programming for the optimization of a hybrid renewable energy system with a battery energy storage system in residential microgrids in which the demand response of available controllable appliances is coherently considered in the proposed optimization problem with reduced calculation burdens. The model takes into account the intrinsic stochastic behavior of renewable energy and the uncertainty involving electric load prediction, and thus proper stochastic models are considered. This paper investigates the effect of load flexibility on the component sizing of the system for a residential microgrid in Okinawa. Also under consideration are different operation scenarios emulating technical limitations and several uncertainty levels.

This paper considers the contribution of independent owners (IOs) operating within microgrids (MGs) toward green power generation in deregulated energy markets. An optimization scheme is introduced for sizing distributed renewable generation (DRG) and a distributed energy storage system (DESS) based on a novel energy management system (EMS) that accounts for demand response (DR), DESS dispatch and performance degradation, dynamic pricing environments, power distribution loss and irregular renewable generation. The proposed EMS utilizes an iterative Newton-Raphson linear programming algorithm that schedules resources in order to minimize the objective function, to deal with the complicated nonlinear nature of the problem and to enable efficient long-term assessments. The EMS is used to evaluate candidate solutions that are generated by a genetic algorithm (GA) to determine the optimal combination of DRG and DESS. A case study for IEEE 34-bus distribution MG in Okinawa, Japan, is used for testing the algorithm and analyzing the potential for IO/MG investments and their strategies.

Sizing the physical components, choosing proper brands, setting the optimal operation parameters, and the computation time are crucial issues when designing a diesel-hybrid renewable energy system. In this paper, a variant of one of the conventional methods used for designing and configuring these systems is proposed. This variant employs a hybrid genetic algorithm resulting in a time-efficient searching method. This method was applied in the design of an autonomous system that supplies two of the different kinds of loads typically encountered in Japan. The predicted costs were feasible and encouraging for more investment in this field of power.

As the needs for distributed renewable generation (DRG) increase driven by the accelerated growth of electric demand in order to deter greenhouse emissions and create a more economic and efficient power supply environment, this papers pays attention to the potential contribution of independent owners (IOs) of DRG operating within the microgrid (MG). In order to assess such role, an optimization scheme is introduced to allocate and size the stochastic DRG with a distributed energy storage system (DESS) based on a novel energy management system (EMS) that accounts for power distribution loss, dynamic pricing environment, demand response, stochastic generation, etc. The proposed EMS utilizes an iterative Newton-Raphson linear programming algorithm that gradually schedule the resources maximizing the objective function and coping with the complicated nonlinear nature of the problem and enabling of efficiently carrying long-term assessments. The EMS is used to evaluate candidate solutions that are generated by a genetic algorithm (GA) working on evolutionary basis to determine the optimal combination of DRG and DESS. A case study for IEEE 34-bus distribution MG in Okinawa, Japan is used for testing the algorithm and analyzing the potential of IO investments and their strategies.