Optimization, Renewable, Energy, Storage, Life cycle assessment
In an optimally designed grid-connected system with distributed energy resources where the grid plays the role of an energy buffer, it is interesting to analyze the economic feasibility to employ energy storage systems. A grid-connected system synchronizes with the power fluctuations, lowering the costs of energy compared to the cost of using conventional energy storage systems. An adaptive code is developed using computer programming that is used for lifetime simulation of the energy dispatch system with a specified time step and for optimization algorithm with comprehensive reliability/cost assessment. The results can be extended to a long period considering various economic factors. The programming code can be integrated with any system model, which can be flexibly implemented to any number of applications. In the present work, a strategic framework is developed for determining the optimal energy technology allocation to a typically selected commercial building located in the United States. The optimum design and management strategy of grid connected renewable generating systems composed of energy conversion units is considered. The provision of a hybrid system of energy storage is investigated. A genetic algorithm optimization-based approach is adopted for carrying out the optimization. The optimization of the set problem consisted of the minimization of the total lifecycle costs considered as the objective function, whereas the fulfillment of the users demand for energy was considered as the key constraint. The most suitable systems with an operation on hourly basis and the best strategy for the storage of energy were considered to generate the optimization results providing the optimal size and total cost of the system components. Furthermore, the possibility of using alternative energy dispatch systems was explored that might reduce the total lifetime costs below the cost of a benchmark case in which the entire demand for electricity is fulfilled from the grid. Four scenarios were analyzed to measure the impact of planning and operating the distributed energy resources: typical, off grid, on grid, feed-in-tariffs.