Abstract
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.
Highlights
Meeting electricity demand, with its accelerated growth, is one of the biggest economic and environmental challenges facing developing nations
An integrated and comprehensive model that accounts for demand response (DR), distributed energy storage system (DESS) dispatch and performance degradation, dynamic pricing environments, power distribution loss and irregular renewable generation
We propose an aggregated model for integrating the controllable load of appliances with the dispatch problem, resulting in a reduced computational burden, as follows: For each appliance type k P A, the total electric load during its shifting time windowHk should be larger than the minimum electricity consumption because of the mandatory occurrence of some tasks and non-controllable loads within Hk
Summary
With its accelerated growth, is one of the biggest economic and environmental challenges facing developing nations. The energy management in MGs can be complicated and challenging, and many studies proposed suitable solutions that utilize various optimization methods, such as mathematical optimization [26,27], dynamic programming [28] and heuristic-based algorithms [29,30] These methods proved to be accurate and efficient for short-term scheduling and real-time applications, applying them to our problem for long-term assessments in which hundreds or thousands of different combinations of DRG and DESS must be evaluated is not computationally efficient. A case study for IEEE 34-bus distribution MG in Okinawa, Japan, is used for testing the algorithm and analyzing the potential investments of IOs/MG and their strategies
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