Abstract
Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy resources of households by an energy service provider is developed. We consider houses equipped with technologies that support the actual reduction of energy bills and therefore perform demand response actions. A mathematical formulation is developed to obtain the optimal scheduling of household devices that minimizes energy bill and demand response curtailment actions. In addition to the scheduling model, the innovative approach in this paper includes evolutionary algorithms used to solve the problem under two optimization approaches: (a) the non-parallel approach combine the variables of all households at once; (b) the parallel-based approach takes advantage of the independence of variables between households using a multi-population mechanism and independent optimizations. Results show that the parallel-based approach can improve the performance of the tested evolutionary algorithms for larger instances of the problem. Thus, while increasing the size of the problem, namely increasing the number of households, the proposed methodology will be more advantageous. Overall, vortex search overcomes all other tested algorithms (including the well-known differential evolution and particle swarm optimization) achieving around 30% better fitness value in all the cases, demonstrating its effectiveness in solving the proposed problem.
Highlights
In the current environmental world scenario, countries are adopting a series of counter measures in what regards to the use of energy, renewable sources and DG (Distributed generator) [1]
In order to achieve such ambitious targets, it is expected a systematic and elaborated transformation of the electrical grid, in line with the ambitions of the EU [2]. New technologies such as PV (Photovoltaic) panels and battery systems emerge as a viable solution to promote the penetration of renewables at the local level of the distribution networks
We extend the model proposed in [20], for optimization of households equipped with PV-battery systems and DR capabilities
Summary
In the current environmental world scenario, countries are adopting a series of counter measures in what regards to the use of energy, renewable sources and DG (Distributed generator) [1]. A MILP (Mixed-integer Linear Programming) problem was formulated in [4] for the management of a residential community grid with renewables, batteries, electric vehicles, and DR capabilities. This formulation searched for the minimization of purchased energy cost. In [12], a bi-level formulation for optimal day-ahead price-based DR is proposed and solved by a hybrid approach in which a multi-population genetic algorithm is used for the upper level and distributed individual optimization algorithm for the lower level Another hybrid genetic algorithm is used in [13] to consider the interaction of electricity retailers and DR.
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