Derek W. Bunn’s research while affiliated with London Business School and other places

Aggregators are increasingly using portfolios of various end-user resources such as local renewable generation, batteries, EVs, and demand-side management to participate in the electricity markets as virtual power plants. Whilst the smart control of energy within buildings is well established for cost optimization, the prospect of an aggregator collaborating with the energy managers of buildings to trade electricity in the wholesale markets is under researched. This paper uses a state-of-the-art architectural simulator to model the thermal inertia of a typical high-rise building in London and represent the thermodynamics of its hourly occupancy and weather responses. The building is simulated as a virtual battery by manipulating the HVAC settings. Price arbitrage on the electricity wholesale market and contracting with the local distribution network for flexible reserve services are both feasible and profitable. It is concluded that aggregators can apparently use buildings as additional assets within their trading portfolios. JEL Classification: L85 and L94.

The business models for the operation of battery storage systems often depend substantially upon the revenues from arbitrage in the daily electricity wholesale market. Other revenue streams can be attractive, but even with them, wholesale market arbitrage is often used as the benchmark. There can be various trading policies for wholesale market arbitrage and this paper provides a comparison of three main variations. These are: day-ahead spread trading, day-ahead trading with look-ahead and intra-day continuously trading with look-ahead. The example is from the GB electricity market in which wind and solar generation as well as demand forecasts are used to forecast electricity prices. The results of the back-testing indicate the comparative attractiveness of day-ahead spread trading in terms of risk and return performance. JEL Classification: Q28

Problem definition: With the rise of renewables and the decline of fossil fuels, electricity markets are shifting toward a capacity mix in which low-cost generators (LCGs) are dominant. Within this transition, policymakers have been considering whether current market designs are still fundamentally fit for purpose. This research analyses a key aspect: the design of real-time imbalance pricing mechanisms. Currently, markets mostly use either single pricing or dual pricing as their imbalance pricing mechanisms. Single-pricing mechanisms apply identical prices for buying and selling, whereas dual-pricing mechanisms use different prices. The recent harmonization initiative in Europe sets single pricing as the default and dual pricing as the exception. This leaves open the question of when dual pricing is advantageous. We compare the economic efficiency of two dual-pricing mechanisms in current practice with that of a single-pricing design and identify conditions under which dual pricing can be beneficial. We also prove the existence of an optimal pricing mechanism. Methodology/results: We first analytically compare the economic efficiency of single-pricing and dual-pricing mechanisms. Furthermore, we formulate an optimal pricing mechanism that can deter the potential exercise of market power by LCGs. Our analytical results characterize the conditions under which a dual pricing is advantageous over a single pricing. We further compare the economic efficiency of these mechanisms with respect to our proposed optimal mechanism through simulations. We show that the proposed pricing mechanism would be the most efficient in comparison with others and discuss its practicability. Managerial implications: Our analytical comparison reveals market conditions under which each pricing mechanism is a better fit and whether there is a need for a redesign. In particular, our results suggest that existing pricing mechanisms are adequate at low/moderate market shares of LCGs but not for the high levels currently envisaged by policymakers in the transition to decarbonization, where the optimal pricing mechanism will become more attractive. Funding: Rodrigo Belo acknowledges funding by Fundação para a Ciência e a Tecnologia (UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab – PINFRA/22209/2016), POR Lisboa, and POR Norte (Social Sciences DataLab, PINFRA/22209/2016). Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0555 .

An extended specification for estimating the risk premia necessary for the forward pricing of wholesale electricity is developed in order to respond to the increasing need for more precise risk management of hedging positions in practice. Using Taylor expansions, we provide new specifications for the electricity forward premium including its dependency on all four moments of the expected wholesale price density as well as the higher moments of the demand density including skewness and kurtosis. Overall we argue that previous models have been underspecified and that the extended formulation proposed in this analysis is robust and worthwhile.

The design of electricity imbalance pricing mechanisms is internationally controversial. Policies on whether to permit virtual trading for market participants and whether, or how, to impose penalty incentives on the imbalance volumes vary widely. Furthermore, market designs vary depending whether the imbalance prices are obtained directly from real-time trading or based upon the offer and demand functions from the day-ahead energy markets. This paper develops an analytical framework for evaluating designs for imbalance settlement mechanisms and we have selected the Japanese electricity market, which has undergone several revisions in its imbalance mechanism, as a good example to assess such variations. We develop a predictive approach for the imbalance volumes and price densities using two-step quantile regressions and derive a new trading optimization for a virtual traders arbitrage position. We construct supporting models to estimate prediction errors for renewable power and demand as drivers of imbalance volume. The empirical analysis reveals that even in a mechanism with imbalance penalties based upon day-ahead reference prices, virtual trading may still be beneficial to market participants as well as to the system operator. We also find that greater market transparency is crucial for increased benefits. The insights generalize beyond the Japan case study.

Short-term risk management is becoming increasingly significant in power trading as the intermittent renewable generators introduce more weather risk into the price formation dynamics. There is a vacuum in hedging instruments at the day-ahead stage to protect retailers in particular from such volatility and price spikes. Motivated by this requirement, this paper analyses a flexible hedging product, day-ahead cap futures. For pricing this product, we parametrically predict the probability distribution of day-ahead prices using the multifactor Generalized Additive Model for Location, Scale and Shape (GAMLSS) based upon the skew-t distribution with weather forecasts and calendar information as explanatory variables. In particular, we reveal that this higher-order moment model is superior to several lower-order models such as the normal distribution in all the following three aspects: fairness as pricing method, underwriting risk of the risk-taker and the variance reduction effect of the risk hedger.

The risks in daily electricity prices are becoming substantial and it is clear that improvements in price density forecasting can translate into improved risk management. However, the specification of the most appropriate price density function is challenging as the best functional forms differ by time of day evolve over time, dynamically respond to fluctuating exogenous factors such as wind speed and solar irradiance. This research develops and tests a new flexible, functional form based upon the Gamma Mixture of Uniform (GMU) densities which effectively avoids the choice of a particular density function and has conditional moments specified as a function of the dynamic exogenous drivers. Empirical testing shows that it outperforms the multi-factor skewed student-t family of densities, previously advocated in this context. Additionally, using Bayesian estimation the new methodology provides a complete description of the uncertainty in the estimation of the coefficients for those exogenous factors. Empirical testing on day-ahead hourly electricity prices in the German market from 2012 to 2016, where renewable energy sources, such as wind and solar, play a critical role in the formation of electricity price risk, validates the extra accuracy of this formulation.

With the rise of distributed energy resources and the increasing activation of flexibility resources by Distribution Systems Operators (DSOs), the Transmission System Operators (TSOs) need to co-ordinate their actions with those of the DSOs. This research uses a look-ahead multi-interval (LA-MI) framework for analyzing this coordination and explores two formulations. Firstly in the exogenous DSO model, a mixed-integer linear program is developed to reflect the pragmatic approach in many real situations whereby the TSO can only anticipate statistically the actions of the DSO. In the embedded DSO model, as a comparator, we propose a new organizational setup for the TSO-DSO operational coordination mechanism. In the resulting bilevel decomposition, a new method to calculate Benders cuts is developed and tested on a modified IEEE 118-bus test system as a transmission network and two modified IEEE 33-bus test systems as distribution networks. The benefits of the LA-MI coordination framework are substantial in comparison with the current Look-Ahead Single-Interval (LA-SI) coordination framework widely used in Europe.