Capacity expansion under uncertainty in an oligopoly using indirect reinforcement-learning.

We model capacity expansion in oligopolistic markets, with endogenous prices, under uncertainty, considering multiple production technologies. As this environment is complex, capacity expansion is the outcome of a learning process by individual firms. We propose indirect reinforcement-learning to model the interaction between price determination and capacity decisions, in the context of an oligopoly game. We apply our model to the analysis of the Iberian electricity market, considering multiple technologies, focusing on how subsidies, the prices of CO2 emissions and gas affect the capacity expansion policies

Capacity expansion in liberalized electricity markets with locational pricing and renewable energy investments.

We study the long-term incentives for expanding production capacity in liberalized electricity markets. How does electricity market design affect the prices of energy, capacity and social welfare? And how is this capacity market affected by the geographical features of the electricity market? Should the system operator design the capacity market to provide incentives for investment in renewable technologies? We analyze the conditions under which capacity payments and markets enable higher investment relative to an energy-only market in which generators sell electricity but not capacity. We show that capacity markets benefit consumers and investors by increasing investment and reliability and capping peak prices. We prove that generators benefit from owning a portfolio of peak and baseload plants and show that investment strategies must consider regional capacity auctions. We demonstrate that a capacity payment per technology increases investment in renewable technologies and leads to the early retirement of older, carbon-emitting technologies. Regional capacity investment targets effectively decrease energy prices and significantly increase investment in renewable technologies

Learning and evolution in games and oligopoly models

Dynamic models of adjustment, as well as static models of equilibrium, are important to understand economic reality. This thesis considers such dynamic models applied to economic games. The models can broadly be divided into two categories: learning and evolution. This thesis analyzes reinforcement learning and imitation dynamic on the learning side and the indirect evolution approach on the evolution side. It demonstrates the relation between the concept of Nash equilibrium and the long run outcome of the namic processes. The applications of the dynamic models to economic games include, among others, Cournot oligopoly and merger games.

SUSTAINABLE AND UNCHALLENGED ALGORITHMIC TACIT COLLUSION

Algorithmic collusion has the potential to transform future markets, leading to higher prices and consumer harm. And yet, algorithmic collusion may remain undetected and unchallenged, in particular, when it is used to facilitate conscious parallelism. The risks posed by such undetected collusion have been debated within antitrust circles in Europe, the US, and beyond. Some economists, however, downplay algorithmic tacit collusion as unlikely, if not impossible. “Keep calm and carry on,” they argue, as future prices will remain competitive. This paper explores the rise of algorithmic tacit collusion and responds to those who downplay it, by pointing to new emerging evidence and the gap between law and this particular economic theory. We explain why algorithmic tacit collusion is not only possible but warrants the increasing concerns of many enforcers

Tourism in an Open System: What do Theories of International Trade and Competition Teach Us?

Tourism is a critical determinant of international trade and GDP. Openness affects tourism by allowing (especially small) countries to achieve sufficient economies of scale and scope. Thus, clarifying the relationship between tourism and factors of international demand and supply is essential to improved understanding of international trade and economic growth. This chapter reviews the main theories of international trade from tourism's perspective, focusing in particular on supply-side (comparative costs, factors endowments, new trade theory, endogenous comparative advantage) and demand-side (demand-driven trade) theories. The chapter also stresses their theoretical predictions and empirical validations in static and dynamic settings, discussing the determinants of international tourism and its implications on economic growth, and the relationship between the tourism-growth nexus and natural amenities

The Dynamics of Innovation and Investment, with application to Australia 1984 - 1998

Ever since the start of the Industrial Revolution in Britain in the 1760s, innovation and investment have been crucial elements in economic explanations of the dynamics of capitalism. Classical economics recognises that innovation embodied in the form of new machines through fixed capital investment is the essential process for realising economic development. This study sets up a theoretical linkage between innovation and investment in historical time, without reference to any static equilibrium model. In this way, the relationship between instability of cycles and trend growth can be clearly identified. A theoretical framework and specific model of innovation and investment are developed. This is followed by an empirical investigation in support of this analysis to show plausibility in the important linkages between innovation and investment that have been missed when examined through static analysis of these relations. The statistical analysis is based on recent Australian industry sector data (1984-98) on R&D and capital expenditure in panel data form and in evolutionary industry life-cycle form. Conclusions from this work indicate the need to re-examine the way strategies are formed and developed in both the private and public sectors for more effective appropriation of innovation into the investment planning process.economics of technology ;

Information requirements for strategic decision making: energy market

Over the last two decades, the electricity sector has been involved in a challenging restructuring process in which the vertical integrated structure (monopoly) is being replaced by a horizontal set of companies. The growing supply of electricity, flowing in response to free market pricing at the wellhead, led to increased competition. In the new framework of deregulation, what characterizes the electric industry is a commodity wholesale electricity marketplace. This new environment has drastically changed the objective of electricity producing companies. In the vertical integrated industry, utilities were forced to meet all the demand from customers living in a certain region at fixed rates. Then, the operation of the Generation Companies (GENCOs) was centralized and a single decision maker allocated the energy services by minimizing total production costs. Nowadays, GENCOs are involved not only in the electricity market but also in additional markets such as fuel markets or environmental markets. A gas or coal producer may have fuel contracts that define the production limit over a time horizon. Therefore, producers must observe this price levels in these other markets. This is a lesson we learned from the Electricity Crisis in California. The Californian market\u27s collapse was not the result of market decentralization but it was triggered by other decisions, such as high natural gas prices, with a direct impact in the supply-demand chain. This dissertation supports generation asset business decisions -from fuel supply concerns to wholesale trading in energy and ancillary services. The forces influencing the value chain are changing rapidly, and can become highly controversial. Through this report, the author brings an integrated and objective perspective, providing a forum to identify and address common planning and operational needs. The purpose of this dissertation is to present theories and ideas that can be applied directly in algorithms to make GENCOs decisions more efficient. This will decompose the problem into independent subproblems for each time interval. This is preferred because building a complete model in one time is practically impossible. The diverse scope of this report is unified by the importance of each topic to understanding or enhancing the profitability of generation assets. Studies of top strategic issues will assess directly the promise and limits to profitability of energy trading. Studies of ancillary services will permit companies to realistically gauge the profitability of different services, and develop bidding strategies tuned to competitive markets

Low-carbon Energy Transition and Planning for Smart Grids

With the growing concerns of climate change and energy crisis, the energy transition from fossil-based systems to a low-carbon society is an inevitable trend. Power system planning plays an essential role in the energy transition of the power sector to accommodate the integration of renewable energy and meet the goal of decreasing carbon emissions while maintaining the economical, secure, and reliable operations of power systems. In this thesis, a low-carbon energy transition framework and strategies are proposed for the future smart grid, which comprehensively consider the planning and operation of the electricity networks, the emission control strategies with the carbon response of the end-users, and carbon-related trading mechanisms. The planning approach considers the collaborative planning of different types of networks under the smart grid context. Transportation electrification is considered as a key segment in the energy transition of power systems, so the planning of charging infrastructure for electric vehicles (EVs) and hydrogen refueling infrastructure for fuel cell electric vehicles is jointly solved with the electricity network expansion. The vulnerability assessment tools are proposed to evaluate the coupled networks towards extreme events. Based on the carbon footprint tracking technologies, emission control can be realized from both the generation side and the demand side. The operation of the low-carbon oriented power system is modeled in a combined energy and carbon market, which fully considers the carbon emission right trading and renewable energy certificates trading of the market participants. Several benchmark systems have been used to demonstrate the effectiveness of the proposed planning approach. Comparative studies to existing approaches in the literature, where applicable, have also been conducted. The simulation results verify the practical applicability of this method