An Evolutionary Analysis of Investment in Electricity Markets

Electricity markets are being liberalised and open to private competition in several countries. These liberalized electricity markets are very complex as the interactions between demand and supply are subject to several technicalities arising from the commodity being traded: electricity. One of these technicalities is that generators cannot store electricity: this fact implies that it needs to generate its production real-time. A second problem with this market are the different generation technologies used at different levels of demand, which implies that at different times of the day different generation costs are supported to meet demand: due to ramp-rate constraints, capacity available, and fixed and start-up costs. In this paper we analyze the issue of investment and the electricity system’s long-term security in an industry where a regulator controls the short-term prices, imposing a perfect competition outcome for “low†demand hours and a price cap at times where load is shed. We look at the following research questions: a) How does the oligopolistic structure of the market interact with the value of the different technologies? b) How do players define their investment strategies? c) How do the regulatory policies affect the investment in generation? Do they work similarly under perfect competition and oligopoly? d) Can markets invest enough capacity to ensure the long run security of the market? The main results of our analysis are following: 1. The impact of a given investment on the market price is independent of the player investing. 2. The impact of an investment on price is a function of the technology in which the investment takes place and of the cycle to which the price refers to. 3. The impact of price caps on the evolution of the market structure is non-linear, it cannot be too low or too high. 4. An oligopolistic electricity market fails to deliver the needed investment unless the regulators intervene. 5. The higher the reserve margin the higher the total investment. However, this instrument by itself was not able to provide the incentive needed to ensure the long-term security of the system, as in any of the experiments analyzed the peak demand is not completely satisfied. 6. Even a slight increase in demand, due to the reserve margin, leads to important changes on the relative value of the different technologies. 7. The main task of the regulatory authorities is to define a level of capacity payments that give the necessary incentive to investment, at the minimum cost: Capacity Payments are very important in shaping the generation structure. 8. Uncertainty reduces the value of Peak plants: this result clearly contradicts any common sense in these matters, as one would expect the presence of price uncertainty to be beneficial to Peak plants. The proportion invested in baseload plants increases with uncertainty of the energy price, decreasing the investment in shoulder plant.agent-based, electricity markets, evolution, investment, regulation, simulation

Procurement risk management in a petroleum refinery.

We analyze a petroleum refinery's procurement strategy, explaining how risk management affects optimal sourcing from long-term, spot, and swap contracts. We use time series analysis to model the interaction between petroleum prices, transportation costs, and gross product worth. These models are then used to generate the scenarios incorporated in the stochastic program applied to compute the conditional value-at-risk. We prove the necessary and sufficient conditions for the optimal procurement and risk management strategies, and show that risk aversion can be better represented by the weighted average between expected profit and conditional value-at-risk, deriving the respective ISO curves. We estimate that an increase in the degree of risk aversion decreases the use of swap contracts. Our model is applied to the analysis of a refinery based in Singapore. Using regression analysis, we show we cannot reject the hypothesis of a statistically significant relationship between the way Saudi Arabia prices the long-term contracts and the shape of the forward curve. We then study how risk aversion influences the procurement strategies, profitability, and risk exposure of the refinery. Finally, we analyze the pricing of long-term (forward) contracts by Saudi Arabia, and study how the country could benefit from a different pricing policy

The emergence of social inequality: a co-evolutionary analysis.

Social inequality is an important issue at the core of economic thought. Based on a stylized pie-sharing game, this article proposes a co-evolutionary computational model to study the interaction between social classes, aiming to answer the following questions: Why do social classes exist, and how do they affect social efficiency and individual effectiveness? Why are wealth inequality and social exclusion persistent? From a methodological perspective, this article extends the pie-sharing game to include a network of interactions and classes, social mobility, evolving wealth, and learning agents. The results show that social inequality is self-emergent. Surprisingly, in the simulations, the existence of social classes increases individual effectiveness, mainly benefiting the poor. Nonetheless, flatter societies (where social classes exist) have a higher average individual effectiveness. As expected, it was observed that wealth inequality is persistent in hierarchical societies, and the upper classes keep a higher proportion of wealth. Furthermore, the analysis extends the bargaining game to include social mobility, showing that, surprisingly, it increases the robustness of the class system and wealth inequality. Finally, the simulation of dual societies shows that these are an evolutionary equilibrium: social exclusion is persistent and accepted by wealthy and poor individuals, and resources are efficiently used

Limitations of learning in automata-based systems.

In this article, we aim to analyze the limitations of learning in automata-based systems by introducing the L+ algorithm to replicate quasi-perfect learning, i.e., a situation in which the learner can get the correct answer to any of his queries. This extreme assumption allows the generalization of any limitations of the learning algorithm to less sophisticated learning systems. We analyze the conditions under which the L+ infers the correct automaton and when it fails to do so. In the context of the repeated prisoners' dilemma, we exemplify how the L+ may fail to learn the correct automaton. We prove that a sufficient condition for the L+ algorithm to learn the correct automaton is to use a large number of look-ahead steps. Finally, we show empirically, in the product differentiation problem, that the computational time of the L+ algorithm is polynomial on the number of states but exponential on the number of agents

Strategic procurement in spot and forward markets considering regulation and capacity constraints.

With the generalization of business-to-business electronic exchanges, online spot markets have become an important component of suppliers' procurement strategies in their aim to increase flexibility and reduce transaction costs. In this article we analyze, both analytically and computationally, how these online spot markets interact with forward contracts as strategic procurement tools. We consider non-storable commodity markets in which the suppliers have market power. We derive the equations describing the equilibrium of this game considering capacity constraints and regulation. We show that price caps increase forward trading and we analyze the conditions under which, in the capacitated model, some suppliers can buy forward to sell spot. Furthermore, we prove that inefficient producers continue to operate in the market as arbitrageurs, selling forward and buying spot. We model the game with asymmetric suppliers, identifying the situations in which it is well defined, and describing how these asymmetries are important for market equilibrium. Finally, we analyze a game with multiple sequential forward contracts: we prove that, when suppliers readjust their forward positions until the start of the spot market, the number of time periods (i.e., market liquidity) has neither effect on the suppliers' strategic procurement nor on market efficiency

A causal map analysis of supply chain decentralization.

We study the inclusion of loops in automated theory development based on causal logic. As an area of application, we formalize a model of learning, adaptation, and selection in supply chain management. Our methodological contribution is to analyze a causal network with propositional logic, explaining the difference between material and intentional causality and considering cumulative causality. In the application domain, we prove that the ability of a supply chain to attract resources in turbulent environments depends on its governance structures, the degree of decentralization, and learning incentives, while in stable environments, a supply chain fails to attract resources if a dominant firm appropriates the rents created by others or if it lacks the ability to replicate its own structure. Furthermore, in turbulent times, adequate resources and dynamic routines allow the supply chain to survive

Modeling emotions and reason in agent-based systems.

We analyze how to develop an agent-based system in which agents evolve co-evolutionary endogenous rules of behavior by using best response and emotions. We show that best response is not sufficient to define complete and consistent rules of behavior and we prove that the use of emotions, which complement reason, is necessary to learn rules of behavior. We model four different emotions (apathy, patience, anger and confidence) which enable the agent to deal with the rewards and with others. We propose an algorithm to model automata-based systems incorporating rationality and emotions

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)

A creativity support system based on causal mapping.

Theory development is a very complex process that requires creativity and highly specialized analytical skills. This article presents a new algorithm, based on causal mapping, for assisting in the creation of qualitative theories. This algorithm is able to conjecture and prove new theorems, to test for consistency and completeness of the theory, and to derive meta-theorems comparing the different concepts in it. The use of the algorithm is exemplified in developing a theory to explain structural inertia in organizations