Demand planning of a nearly zero energy building in a PV/grid-connected system.

• Metaheuristic Multi-Start algorithm for the demand consumption planning. • Determining the optimum PV installation sizing. • Nearly Zero Energy Building using photovoltaic energy. The energy consumption planning of smart homes connected to a grid with flexible power pricing, and complemented with a local PV installation, is studied. Advanced energy management is necessary to implement the Nearly Zero Energy Building (nZEB) concept in these smart homes. To do so, in the Energy Management System, it is essential to consider the flexibility provided by power pricing schemes, the distributed generation from renewable energies and the use of smart household appliances. This paper studies a novel formulation that depends on the comfort index required by the user, the PV power production, and the price variation of the grid energy. The study is performed on two levels: in the first, a metaheuristic multi-start, consisting of two steps, constructive heuristics and refinement heuristics (local search), is proposed to regulate the power demand of household appliances. The second level consists of determining the optimum PV installation that ensures the economic viability of the plant. The novelty of the paper is that the user defines a desired comfort level as input data, and the algorithm defines a demand plan that meets this comfort at the lowest possible cost. Six real case studies were carried out, considering the insertion of photovoltaic generation, to evaluate the performance of the proposed model from a technical and economic point of view. The results show that the proposed approach allows user requirements to be fulfilled and the energy production cost to be minimized. Without needing to reduce user comfort, the proposed demand planning algorithm can reduce the energy cost by 48.07 %. In this same scenario, combining the proposed method and the insertion of a photovoltaic system composed of 15 modules (Scenario 6), the total energy cost is reduced by 73.90 %. [ABSTRACT FROM AUTHOR]