This work presents a framework for eciently managing the energy needs of a set of houses connected in a micro-grid conguration. The micro-grid consists of houses and local renewable plants, each seen as independent agents with their specic goals. In particular houses have the option to buy energy from the national grid or from the local renewable plants. We discuss a practical heuristic that leads to energy allocation schedules that are cost-eective for the individual houses and protable for the local plants. We present experiments describing the benets of our proposal. The results illustrate that houses and micro plants can make considerable saving when they work in micro grid compared with working alone.
This paper describes how a variant of the knapsack optimization problem can be applied to the solution of an allocation problem arising in the management of the renewable energy generated by a micro-generation plant. Theoretical and empirical analysis shows that the proposal is viable, it results in significant energy savings, and can be adapted to a number of different usage patterns.
This framework proposes a heuristic algorithm based on LP (linear programming) for optimizing the electricity cost in large residential buildings, in a smart grid environment. Our heuristic tackles large multi-objective energy allocation problem (large number of appliances and high time resolution). The primary goal is to reduce the electricity bills, and discomfort factor. Also, increase the utilization of domestic renewable energy, and reduce the running time of the optimization algorithm. Our heuristic algorithm uses linear programming relaxation, and two rounding strategies. The first technique, called CR (cumulative rounding), is designed for thermostatic appliances such as air conditioners and electric heaters, and the second approach, called MCR (minimum cost rounding), is designed for other interruptible appliances. The results show that the proposed heuristic algorithm can be used to solve large MILP (mixed integer linear programming) problems and gives a decent suboptimal solution in polynomial time.
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