Fernando S. Oliveira

Agent-based simulation-an application to the new electricity trading arrangements of England and Wales

This paper presents a large-scale application of multiagent evolutionary modeling to the proposed new electricity trading arrangements (NETA) in the UK. This is a detailed plant-by-plant model with an active specification of the demand side of the market. NETA involves a bilateral forward market followed by a balancing mechanism and then an imbalance settlement process. This agent-based simulation model was able to provide pricing and strategic insights, ahead of NETAs actual introduction.

Evaluating Individual Market Power in Electricity Markets via Agent-Based Simulation

We use agent-based simulation in a coordination game to analyse the possibility of market power abuse in a competitive electricity market. The context of this was a real application to the England and Wales electricity market as part of a Competition Commission Inquiry into whether two particular generators could profitably influence wholesale prices. The research contributions of this paper are both in the areas of market power and market design policy issues for electricity markets, and in the methodological use of large industry-wide evolutionary simulation models.

Contract design and supply chain coordination in the electricity industry

In this article we propose a model of the supply chain in electricity markets with multiple generators and retailers and considering several market structures. We analyze how market design interacts with the different types of contract and market structure to affect the coordination between the different firms and the performance of the supply chain as a whole. We compare the implications on supply chain coordination and on the players’ profitability of two different market structures: a pool based market vs. bilateral contracts, taking into consideration the relationship between futures and spot markets. Furthermore, we analyze the use of contracts for differences and two-part-tariffs as tools for supply chain coordination. We have concluded that there are multiple equilibria in the supply chain contracts and structure and that the two-part tariff is the best contract to reduce double marginalization and increase efficiency in the management of the supply chain.

Modeling the Impact of Market Interventions on the Strategic Evolution of Electricity Markets

This paper presents a large-scale computationally intensive model for understanding the dynamic strategic evolution of electricity-generating asset portfolios in response to various market interventions, and the consequent longer-term effects of such changes on market structure and prices. We formulate a multistage model involving a Cournot representation of the wholesale electricity market, the performance of which then determines plant trading between players and the coevolution of market structure. An algorithm to model this game is presented. We apply this model to the full England and Wales system, as it was in 2000, and simulate the strategic responses to divestiture, capacity targets, and the two market mechanism variants of pool and bilateral market clearing.

Developing a market-based approach to managing the US strategic petroleum reserve

The Strategic Petroleum Reserve has not been used effectively to manage the consequences of oil shocks in the United States. The main reason is that political decision makers tend to hoard the reserves during crises and bureaucratic processes delay the sale of the reserves. Also, the enabling legislation focused on ameliorating shortages whereas disruptions result price spikes rather than shortages. We develop a Markov game of the buildup and drawdown of the reserve in which a public player aims to maximize consumer welfare at the same time private holders of inventory maximize their profit. The methodological contribution in this paper is the development of financial options to implement the public players optimal policy. We use the solution of this game to calculate the number and value of options necessary for the private marketplace to trigger the optimal buildup and drawdown of the reserve.

A risk management system for sustainable fleet replacement

This article analyzes the fleet management problem faced by a firm when deciding which vehicles to add to its fleet. Such a decision depends not only on the expected mileage and tasks to be assigned to the vehicle but also on the evolution of fuel and CO2 emission prices and on fuel efficiency. This article contributes to the literature on fleet replacement and sustainable operations by proposing a general decision support system for the fleet replacement problem using stochastic programming and conditional value at risk (CVaR) to account for uncertainty in the decision process. The article analyzes how the CVaR associated with different types of vehicle is affected by the parameters in the model by reporting on the results of a real-world case study.

Dynamic pricing policies for interdependent perishable products or services using reinforcement learning

Dynamic prices maximize the expected revenue of interdependent products.Reinforcement learning optimizes the pricing of interdependent products.Interdependent pricing enhances learning. Many businesses offer multiple products or services that are interdependent, in which the demand for one is often affected by the prices of others. This article considers a revenue management problem of multiple interdependent products, in which dynamically adjusted over a finite sales horizon to maximize expected revenue, given an initial inventory for each product. The main contribution of this article is to use reinforcement learning to model the optimal pricing of perishable interdependent products when demand is stochastic and its functional form unknown. We show that reinforcement learning can be used to price interdependent products. Moreover, we analyze the performance of the Q-learning with eligibility traces algorithm under different conditions. We illustrate our analysis with the pricing of services.

Analysis of relationship between forward and spot markets in oligopolies under demand and cost uncertainties

In this paper, we consider interaction between spot and forward trading under demand and cost uncertainties, deriving the equilibrium of the multi-player dynamic games. The stochastic programming and worst-case analysis models based on discrete scenarios are developed to analyze the impact of demand uncertainty and risk aversion on oligopoly (forward and spot) markets’ structure in terms of the forwards and spot pricing, traded quantities and production. A real case of the Iberian electricity market is studied to illustrate performance of the models. The numerical experiments show that cost uncertainty impacts on the strategic decisions more than demand uncertainty.

Dynamic capacity planning using strategic slack valuation

In this paper we analyze a particular aspect of capacity planning that is concerned with the active trading of production facilities. For a homogenous product market we provide a theoretical rationale for the valuation and trading of these assets based on a metric of strategic slack. We show that trading production assets with non-additive portfolio profitability involves complex coordination with multiple equilibria and that these equilibria depend on the foresight in the planning horizon. Using the concept of strategic slack we have analyzed the dynamics of market structure, the impact of asset trading on the level of production of the industry, and to derive boundaries on the value of the traded assets. Moreover, through computational learning, the formulation is applied to a large oligopolistic electricity market, showing that plant trading tends to lead to increased market concentration, high prices, lower production and a decrease in consumer surplus.

Bottom-up design of strategic options as finite automata

In this paper we look at the problem of strategic decision making. We start by presenting a new formalisation of strategic options as finite automata. Then, we show that these finite automata can be used to develop complex models of interacting options, such as option combinations and product options. Finally, we analyse real option games, presenting an algorithm to generate option games (based on automata).