Maximizing photovoltaic array energy usage within a house using model predictive controla

Ahmad, Aziz; Anderson, Timothy; Lie, Tek Tjing; Swain, Akshya

doi:10.1109/acept.2017.8168591

Cited by 1 publication

(1 citation statement)

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“…A method for sizing a photovoltaic installation for a single-family house in a way that reduces the annual cost of purchasing energy from the power grid was presented in [2]. The paper in [3] uses model predictive control (MPC) to control the components of the renewable installation. Artificial neural networks (ANNs) were used to predict the energy yield of the photovoltaic installation.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria Optimisation of the Structure of a Hybrid Power Supply System for a Single-Family Housing Estate in Poland, Taking into Account Different Electromobility Development Scenarios

Tomczewski

Mikulski

Piotrowski³

et al. 2023

Energies

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This article focuses on determining the optimum structure for a hybrid generation and storage system designed to power a single-family housing estate, taking into account the different number of electric vehicles in use and an assumed level of self-consumption of the generated energy. In terms of generation, two generation sections—wind and solar—and a lithium-ion container storage system will be taken into account. With regards to energy consumption, household load curves, determined on the basis of the tariff for residential consumers and modified by a random disturbance, will be taken into account, as well as the processes for charging electric cars with AC chargers, with power outputs ranging between 3.6 and 22 kW. Analyses were carried out for three locations in Poland—the Baltic Sea coast (good wind conditions), the Lublin Uplands (the best insolation in Poland) and the Carpathian foothills (poor wind and insolation conditions). The mathematical and numerical model of the system and the MOPSO (multiobjective particle swarm optimisation) algorithm were implemented in the Matlab environment. The results include Pareto fronts (three optimisation criteria: minimisation of energy storage capacity, minimisation of energy exchanged with the power grid and maximisation of the self-consumption rate) for the indicated locations and three electromobility development scenarios with determined NPVs (net present values) for a 20-year lifetime. The detailed results relate to the inclusion of an additional expert criterion in the form of a coupled payback period of no more than 10 years, a maximum NPV in the last year of operation and a self-consumption rate of at least 80%. The economic calculations take into account the decrease in PV installation capacity as a function of the year of operation, as well as changes in electricity and petrol prices and variations in energy prices at purchase and sale.

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Section: Introductionmentioning

confidence: 99%