The rise of distributed energy resources (DERs) can enhance the efficiency of system operations by providing flexibility services to the different agents involved, but they also pose a major resource allocation problem. This study considers three different agents procuring DER services: distribution system operators (DSOs) for local congestion management, transmission system operators (TSOs) for system-wide reserve deployment, and retailers for hedging against network usage tariffs based upon peak-load pricing. A variety of market mechanisms are identified to co-ordinate these needs, and three schemes are developed in detail. These are separate markets for each agent, co-ordinated Shapley value allocations for TSO and DSO, and a co-ordinated mechanism including retailers. These designs are evaluated on a realistic distribution network in Britain for two operational days. The results show a more efficient dispatch from the TSO-DSO co-ordinated procurement over independent sequential procurements. However, the inclusion of retailers in the joint dispatch is surprisingly less attractive due to the lack of improvement in social welfare and the undesirable impacts on the DSO.

Increasing variable renewable power generation (e.g., wind) is expected to reduce wholesale electricity prices by virtue of its low marginal production cost. This merit-order effect of renewables displacing incumbent conventional (e.g., gas) generation forms the theoretical underpinning for investment decisions and policy in the power industry. This paper uses a game-theoretic market model to investigate how intermittently available wind generation affects electricity prices in the presence of forward markets, which are widely used by power companies to hedge against revenue variability ahead of near-real-time spot trading. We find that in addition to the established merit-order effect, renewable generation affects power prices through forward-market hedging. This forward effect reinforces the merit-order effect in reducing prices for moderate amounts of wind generation capacity but mitigates or even reverses it for higher capacities. For moderate wind capacity, uncertainty over its output increases hedging, and these higher forward sales lead to lower prices. For higher capacities, however, wind variability conversely causes power producers to behave less aggressively in forward trading for fear of unfavorable spot-market positions. The lower sales counteract the merit-order effect, and prices may then paradoxically increase with wind capacity despite its lower production cost. We confirm the potential for such reversals in a numerical study, suggesting new empirical questions while providing potential explanations for previously contradictory observed effects of market fundamentals. We conclude that considering the conventional merit-order effect alone is insufficient for evaluating the price impacts of variable renewable generation in the presence of forward markets. This paper was accepted by Vishal Gaur, operations management.

The wide range of models needed to support the various short-term operations for electricity generation demonstrates the importance of accurate specifications for the uncertainty in market prices. This is becoming increasingly challenging, since hourly price densities for electricity exhibit a variety of shapes, with their characteristic features changing substantially within the day and evolving over time. Furthermore, the influx of renewable power, wind, and solar, in particular, has made these density shapes very weather dependent. We develop a general four-parameter stochastic model for hourly prices, in which the four moments of the density function are dynamically estimated as latent state variables and, furthermore, modelled as functions of several plausible exogenous drivers. This provides a transparent and credible model that is sufficiently flexible to capture the shape-shifting effects, particularly with respect to the wind and solar output variations causing dynamic switches in the upside and downside risks. Extensive testing on German wholesale price data, benchmarked against quantile regression and other models in out-of-sample backtesting, validated the approach and its analytical appeal. The e-companion is available at https://doi.org/10.1287/opre.2018.1733 .

This paper provides an in-depth analysis of investment decisions and shifts related to onshore and offshore wind power supply chains. Insights about cost reductions are generated from analyzing project financing data, using a 'European learning curve' derived from a European learning system. In doing so, we take into account the limited number of leading technology suppliers in the global market. Experience curve analysis and insights from qualitative research are used to discuss the market entry barriers to the offshore wind power market. We also use data generated from a questionnaire survey circulated among the European wind power industry. From the analysis we conclude that the adoption of offshore technology has been evolving over time when companies from closely related industries, and not start-up companies, started to enter the offshore market. For policy-makers, it is essential to acknowledge that sophisticated industry structures need to be established, and that barriers to adoption among the players in the onshore supply industry, which seem to differ, can be effectively removed.