Impact of Long-Term Water Inflow Uncertainty on Wholesale Electricity Prices in Markets with High Shares of Renewable Energies and Storages

Abstract

Renewable energy shares in electricity markets are increasing and therefore also require an increase in flexibility options. Conventional electricity price modelling with optimisation models in thermally dominated markets is not appropriate in markets with high shares of renewable energies and storages because price structures are not adequately represented. Previous research has already identified the impact of uncertainty in renewable energy feed-in on investment and dispatch decisions. However, we are not aware of any work that investigates the influence of uncertainties on price structures by means of optimisation models. Appropriate modelling of electricity price structures is important for investment and policy decisions. We have investigated the influence of uncertainty concerning water inflow by applying a second stage stochastic dual dynamic programming approach in a linear optimisation model using Norway as an example. We found that the influence of uncertainty concerning water inflow combined with high shares of storages has a strong impact on the electricity price structures. The identified structures are highly influenced by seasonal water inflow, electricity demand, wind, and export profiles. Additionally, they are reinforced by seasonal primary energy source prices and import prices. Incorporating uncertainties in linear optimisation models improves the price modelling and provides, to a large extent, an explanation for the seasonal patterns of Norwegian electricity market prices. The paper explains the basic pricing mechanisms in markets with high shares of storages and renewable energies which are subject to uncertainty. To identify these fundamental mechanisms, we focused on uncertainty regarding water inflow, but the basic results hold true for uncertainties regarding other renewable energies as well.

Identification of the Efficiency Gap by Coupling a Fundamental Electricity Market Model and an Agent-Based Simulation Model

“…As a result, the efficiency gap increased in this scenario approximately to 0.11% of the normative system costs. Thus, we were able to show that, considering uncertainties due to the lack of information, it influences the unit commitment, the resulting system costs, and the efficiency gap as other authors did for some of the aspects by different approaches as well [11]. As a first representation of the complex implementation of multi-agent strategies, the results of the robust bidding strategy (RB) may represent a more conservative storage operation.…”

“…The results of fundamental optimization models represent a normative approach characterizing the extreme case, e.g., minimum costs for reaching certain targets. This is still valid, in case stochastic approaches are applied, e.g., in order to assess the impact of uncertainties such as wind volatility [9,10] or water inflow seasonality [11].…”