The design of renewable energy systems such as wind turbines or solar panels conventionally employs Levelized Cost of Energy (LCOE), but this metric fails to account for the time-varying value of energy. This is true both for a single turbine or an entire wind farm. To remedy this, two novel, relatively simple metrics are developed herein to value energy based on the time of generation and the grid demand: Levelized Avoided Cost of Energy simplified (LACEs) and Cost of Valued Energy (COVE). These two metrics can be obtained with: 1) a linear price-demand relationship, 2) an estimate of hourly demand, and 3) an estimate of predicted hourly generation data. The results show that value trends for both wind and solar energy were reasonably predicted with these simplified models for the PJM region (a mid-Atlantic region in the USA) with less than 6% error on average, despite significant stochastic variations in actual price and demand throughout the year. A case study with wind turbine machine design showed that increasing Capacity Factor can significantly reduce COVE and thus increase Return on Investment. As such, COVE and LACEs can be valuable tools (compared to LCOE) when designing and optimizing renewable energy systems. (C) 2020 Elsevier Ltd. All rights reserved.