Optimal Design of a Residential Photovoltaic Renewable System in South Korea

Abstract

An optimal design model for residential photovoltaic (PV) systems in South Korea was proposed. In the optimization formulation, the objective function is composed of three costs, including the monthly electricity bill, the PV system construction cost (including the government’s subsidy), and the PV system maintenance cost. Here, because the monthly electricity bill is not differentiable (it is a stepped piecewise linear function), it cannot be solved by using traditional gradient-based approaches. For details considering the residential electric consumption in a typical Korean household, consumption was broken down into four types (year-round electric appliances, seasonal electric appliances, lighting appliances, and stand-by power). For details considering the degree of PV generation, a monthly generation dataset with different PV tilt angles was analyzed. The optimal design model was able to obtain a global design solution (PV tilt angle and PV size) without being trapped in local optima. We hope that this kind of practical approach will be more frequently applied to real-world designs in residential PV systems in South Korea and other countries.

Meteorological Variables’ Influence on Electric Power Generation for Photovoltaic Systems Located at Different Geographical Zones in Mexico

“…According to this, the rate is changed regarding the full range of the measured signal (max-min) instead in order to obtain a reliable value. Equation (13) represents the modified MAPE.…”

“…• 100 (13) With respect to the data from HS and MCS, Table 9 shows the values of the errors mentioned in Equations (11) and (13). Table 9 shows the errors for both locations, where the MAE result values for HS have a range from 138.1913 W to 299.7928 W and an overall of 200.6071 W. Considering MCS, its MAE goes from 590.6460 W to 1192.3 W, with an overall of 1130.4 W. From these results we can highlight that the values are low according to the total value range for each location, being up to 2500 W and 45 kW for HS and MCS, respectively.…”

“…Nowadays, the PV systems are widely used to generate electricity due its accessible cost [5,6,11,12]. Some studies aimed at reducing this cost even more, e.g., in Reference [13], a design optimization model for the residential PV systems in South Korea was proposed, where the objective function to be minimized consisted of three costs, such as the monthly electric bill, the PV-related construction costs, and the PV-related maintenance cost.…”

“…According to the above and considering References [1,13,30], the aim of this work is to present a statistical analysis based on the gradient descent method that is easy to implement and has a low computational load to estimate the electric power generation from the meteorological data such as the solar radiation, outdoor temperature, wind speed, and daylight time collected from PV systems located in Mexico City and Sonora [33]. This is important because most of the current photovoltaic system deployments do not monitor these factors or employ them in adaptation and prediction tasks [34][35][36].…”

Solar Collectors Modeling and Controller Design for Solar Thermal Power Plant

“…• The Photovoltaic (PV) method uses semi-conductor technology [9] to directly convert sunlight into electricity. This technology generates power when solar radiation is present [10] and the battery stores it as electric energy [11], [12].…”