Methods for supporting the bidding processes of hybrid wind-photovoltaic (W-PV) farms are scarce, especially when numerous goals are included in the optimization problem. Therefore, the primary objective of this study is to develop a novel model that can help bidding of W-PV farms considering a range of objectives that maximize the environmental and welfare benefits. This new approach contributes to energy planning for any type of hybrid farm through multi-objective programming, even in cases where the optimization of several correlated outputs is desired. Using the proposed approach the optimal system configuration can be obtained in these cases with low computational costs. A non-linear multi-objective optimization (NL-MO) is proposed to optimize the area occupied by the W-PV farm, minimum feasibility price, electricity production expected, and standard-deviation of the electricity produced. The model has been elaborated from non-linear optimization using the normal-boundary intersection (NBI) method, exploratory factor analysis (EFA), and Taguchi signal-to-noise ratio (SNR). The optimal values for the response variables are an area of 132.92 km², minimum price of 182.95 R$/MWh, annual electricity production of 72.17 GWh, with a standard deviation of 1.74 GWh and the ideal share is 41% wind power and 59% PV power.

支持混合风力光伏发电场（W-PV）的招标过程的方法很少，尤其是在优化问题中包含众多目标时。因此，本研究的主要目的是开发一个新颖的模型，考虑到一系列最大化环境和福利的目标，该模型可以帮助W-PV农场竞标。即使需要优化多个相关输出，这种新方法也可以通过多目标编程为任何类型的混合农场进行能源规划。使用所提出的方法，可以在这些情况下以较低的计算成本获得最佳的系统配置。提出了非线性多目标优化（NL-MO），以优化W-PV农场的占地面积，最低可行价格，预期的发电量，和所产生电力的标准偏差。该模型是使用正态边界交集（NBI）方法，探索性因子分析（EFA）和田口信噪比（SNR）通过非线性优化进行详细阐述的。响应变量的最佳值是132.92 km²最低价格为182.95 R $ / MWh，年发电量为72.17 GWh，标准偏差为1.74 GWh，理想的份额是41％的风力发电和59％的PV发电。