Biblio

In the restructured electricity industry, Generation Expansion Planning (GEP) is an oligopoly of strategic Generation Companies (GenCos) with private information investing in a highly uncertain environment. Strategic planning and uncertainties can result in market manipulation and underinvestment (short-term planning). We present a forward moving approach to the problem of investment expansion planning in the restructured electricity industry. This approach accounts for technological, political and environmental uncertainties in the problem’s environment and leads to long-term planning. At each step of the approach we present a block investment market mechanism that has the following features. (F1) It is individually rational. (F2) It is budget balanced. (F3) The expansion and production allocations corresponding to the unique Nash Equilibrium (NE) of the game induced by the mechanism are the same as those that maximize the sum of utilities of the producers and the demand. (F4) It is price efficient that is, the price for electricity at equilibrium is equal to the marginal utility of the demand and to the marginal cost of production by producers with free capacity.

In the restructured electricity industry, Generation Expansion Planning (GEP) is an oligopoly of strategic Generation Companies (GenCos) with private information investing in a highly uncertain environment. Strategic planning and uncertainties can result in market manipulation and underinvestment (short-term planning). We present a forward moving approach to the problem of investment expansion planning in the restructured electricity industry. This approach accounts for technological, political and environmental uncertainties in the problem's environment and leads to long-term planning. At each step of the approach we present a block investment market mechanism that has the following features. (F1) It is individually rational. (F2) It is budget balanced. (F3) The expansion and production allocations corresponding to the unique Nash Equilibrium (NE) of the game induced by the mechanism are the same as those that maximize the sum of utilities of the producers and the demand. (F4) It is price efficient that is, the price for electricity at equilibrium is equal to the marginal utility of the demand and to the marginal cost of production by producers with free capacity.

The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker’s capabilities at any given time. This leads to the formulation of the defender’s problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeaSure action to deploy given the current information.

In the restructured electricity industry, electricity pooling markets are an oligopoly with strategic producers possessing private information (private production cost function). We focus on pooling markets where aggregate demand is represented by a non-strategic agent. We consider demand to be elastic. We propose a market mechanism that has the following features. (F1) It is individually rational. (F2) It is budget balanced. (F3) It is price efficient, that is, at equilibrium the price of electricity is equal to the marginal cost of production. (F4) The energy production profile corresponding to every nonzero Nash equilibrium of the game induced by the mechanism is a solution of the corresponding centralized problem where the objective is the maximization of the sum of the producers' and consumers' utilities. We identify some open problems associated with our approach to electricity pooling markets.

An analytical approach for a dynamic cyber-security problem that captures progressive attacks to a computer network is presented. We formulate the dynamic security problem from the defender’s point of view as a supervisory control problem with imperfect information, modeling the computer network’s operation by a discrete event system. We consider a min-max performance criterion and use dynamic programming to determine, within a restricted set of policies, an optimal policy for the defender. We study and interpret the behavior of this optimal policy as we vary certain parameters of the supervisory control problem.