Optimal sizing of grid-connected hybrid renewable energy systems without storage: a generalized optimization model

Abstract

In this study, a weighted multi-objective mixed-integer linear programming (WMO-MILP) model considering both economic and environmental factors is proposed for the optimal sizing of the grid-connected hybrid renewable energy systems without storage (HRES-WS). The proposed model is capable of designing the system including several different types of renewable energy generation units to meet the demands of various consumption points. One of the significant values of the model is that it holistically combines the operational, technical, physical and/or capacity constraints which are rarely considered in an integrated way in the literature. Another contribution of the model is its ability to evaluate the tradeoff between the cost-related and CO2 related conflicting objectives by allocating them various weights resembling the decision-maker’s cost-based, environmental-based, or partially cost- and environmental-based priorities. A case study is utilized to demonstrate the value of the model. In order to take into consideration the stochastic nature of the modeling environment, the Monte Carlo simulation is used to predict weather data and load demand based on the historical data. The findings indicate that the combined effect of environmental and cost-related objectives influences the demand to be met by RES at acceptable cost and CO2 emission level. For example, focusing only on the environmental objective, the annual amount of CO2 emission decreases by 14% and the total installed capacity increases by 41%, and therefore the system cost increases by 205% as compared to the base case in which the weight of each objective function is assumed to be equal. The proposed model has the potential to significantly support decision-making process when evaluating a grid-connected HRES-WS both economically and environmentally. © 2020 Taylor & Francis Group, LLC.