Applied Energy, Vol.238, 1179-1191, 2019
Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices
Classifying and categorizing generators according to their financial efficiency is necessary to compare the degree of competitiveness of a generating technology. The levelized cost of energy (LCoE) is arguably the most relevant index for that purpose. It relates the capital and operating expenses to the expected energy output, to encapsulate the financial efficiency into an E/MVVh. It is technology-independent, meaning that it can be used for comparing different generator types in a fair basis. However, the LCoE is defined to provide a cost figure under the assumption of a fixed-tariff. It does not make any difference when comparing the performance of an intermittent generating technology in different spot markets. The LCoE does not inherently account for the variability of prices. This paper details a different approach to classify intermittent renewable generators operating under variable price tariffs. It shows a levelized income loss as a figure of how the generator allocates the available energy. Ideally, a generator with a production allocated in low-price times would have higher LIL than other with the same production responding to high-price times. Generator location, characteristics, and the market to which the generator delivers the energy make a difference. This paper shows a methodology to not only obtain the index under uncertain market prices and energy production, but also details a dynamic program to incorporate energy storage systems (ESS). The idea behind this additional investigation is that the re-allocation of the limited energy of a renewable generator might well improve the economic performance. The dynamic program approaches the ESS switching decisions from a stochastic perspective, offering a rigorous evaluation. Finally, this paper describes the dynamic linear models of several European day-ahead markets and wind datasets to offer a broad valuation of the LIL, and of its possible improvement through the use of ESS.
Keywords:Wind energy;Energy storage systems;Optimization;Dynamic programming;Stochastic processes;Dynamic linear models;Economic valuation