Competition and the Electric Utility Industry: An Evaluation

Electric utilities have historically been granted monopoly franchises to take advantage of the cost benefits of centralized production. In return for the monopoly franchise, the utility gave the state the right to regulate price and quality of service. In recent years, many have begun to question whe-ther cost advantages of centralized production continue to exist in the electric utility industry. Legislation has been proposed that would deregulate the industry and allow greater competition

Approximating Generalized Network Design under (Dis)economies of Scale with Applications to Energy Efficiency

In a generalized network design (GND) problem, a set of resources are assigned to multiple communication requests. Each request contributes its weight to the resources it uses and the total load on a resource is then translated to the cost it incurs via a resource specific cost function. For example, a request may be to establish a virtual circuit, thus contributing to the load on each edge in the circuit. Motivated by energy efficiency applications, recently, there is a growing interest in GND using cost functions that exhibit (dis)economies of scale ((D)oS), namely, cost functions that appear subadditive for small loads and superadditive for larger loads. The current paper advances the existing literature on approximation algorithms for GND problems with (D)oS cost functions in various aspects: (1) we present a generic approximation framework that yields approximation results for a much wider family of requests in both directed and undirected graphs; (2) our framework allows for unrelated weights, thus providing the first non-trivial approximation for the problem of scheduling unrelated parallel machines with (D)oS cost functions; (3) our framework is fully combinatorial and runs in strongly polynomial time; (4) the family of (D)oS cost functions considered in the current paper is more general than the one considered in the existing literature, providing a more accurate abstraction for practical energy conservation scenarios; and (5) we obtain the first approximation ratio for GND with (D)oS cost functions that depends only on the parameters of the resources' technology and does not grow with the number of resources, the number of requests, or their weights. The design of our framework relies heavily on Roughgarden's smoothness toolbox (JACM 2015), thus demonstrating the possible usefulness of this toolbox in the area of approximation algorithms.Comment: 39 pages, 1 figure. An extended abstract of this paper is to appear in the 50th Annual ACM Symposium on the Theory of Computing (STOC 2018

Distributed Stochastic Market Clearing with High-Penetration Wind Power

Integrating renewable energy into the modern power grid requires risk-cognizant dispatch of resources to account for the stochastic availability of renewables. Toward this goal, day-ahead stochastic market clearing with high-penetration wind energy is pursued in this paper based on the DC optimal power flow (OPF). The objective is to minimize the social cost which consists of conventional generation costs, end-user disutility, as well as a risk measure of the system re-dispatching cost. Capitalizing on the conditional value-at-risk (CVaR), the novel model is able to mitigate the potentially high risk of the recourse actions to compensate wind forecast errors. The resulting convex optimization task is tackled via a distribution-free sample average based approximation to bypass the prohibitively complex high-dimensional integration. Furthermore, to cope with possibly large-scale dispatchable loads, a fast distributed solver is developed with guaranteed convergence using the alternating direction method of multipliers (ADMM). Numerical results tested on a modified benchmark system are reported to corroborate the merits of the novel framework and proposed approaches.Comment: To appear in IEEE Transactions on Power Systems; 12 pages and 9 figure

Notes on Graph Cuts with Submodular Edge Weights

Generalizing the cost in the standard min-cut problem to a submodular cost function immediately makes the problem harder. Not only do we prove NP hardness even for nonnegative submodular costs, but also show a lower bound of (|V |1/3) on the approximation factor for the (s, t) cut version of the problem. On the positive side, we propose and compare three approximation algorithms with an overall approximation factor of O(min|V |,p|E| log |V |) that appear to do well in practice

An Antitrust Rule for Software Integration

What is the proper legal standard for product integration involving software? Because software is subject to low marginal costs, network effects, and rapid technological innovation, the Supreme Court\u27s existing antitrust rules on tying arrangements, which evolved from industries not possessing such characteristics, are inappropriate. In this Article, I ask why firms integrate software products. Next, I review the Supreme Court\u27s tying decisions in Jefferson Parish and Eastman Kodak. I propose an approach to judging the lawfulness of product integration in technologically dynamic markets that supplements the Supreme Court\u27s current standard with four additional steps in cases of tying of computer software. Thereafter, I examine the D.C. Circuit\u27s approach to software integration, which arose from that court\u27s 1998 interpretation, in Microsoft II, of an antitrust consent decree between the US. Department of Justice and Microsoft Corporation. I argue that the D.C. Circuit\u27s rule has general applicability and should be recognized as the appropriate standard for software integration under antitrust law. I show how my approach imparts greater clarity to the D.C. Circuit\u27s rule. I examine the competing product integration rule proposed in 2000 by Professor Lawrence Lessig as amicus curiae in the government\u27s subsequent antitrust case against Microsoft, concerning the integration of Internet Explorer and Windows 98. My approach enables Professor Lessig\u27s analysis to be reconciled with the D.C. Circuit\u27s rule, but Professor Lessig\u27s rule, on its own, would contain serious shortcomings. Thereafter, I evaluate Judge Thomas Penfield Jackson\u27s April 2000 findings of law on the integration of Internet Explorer and Windows 98. I conclude that Judge Jackson\u27s approach, in contrast to the D.C. Circuit\u27s rule as refined by my approach, would harm consumers in the technologically dynamic market for computer software

A General Framework for Learning-Augmented Online Allocation

Online allocation is a broad class of problems where items arriving online have to be allocated to agents who have a fixed utility/cost for each assigned item so to maximize/minimize some objective. This framework captures a broad range of fundamental problems such as the Santa Claus problem (maximizing minimum utility), Nash welfare maximization (maximizing geometric mean of utilities), makespan minimization (minimizing maximum cost), minimization of ?_p-norms, and so on. We focus on divisible items (i.e., fractional allocations) in this paper. Even for divisible items, these problems are characterized by strong super-constant lower bounds in the classical worst-case online model. In this paper, we study online allocations in the learning-augmented setting, i.e., where the algorithm has access to some additional (machine-learned) information about the problem instance. We introduce a general algorithmic framework for learning-augmented online allocation that produces nearly optimal solutions for this broad range of maximization and minimization objectives using only a single learned parameter for every agent. As corollaries of our general framework, we improve prior results of Lattanzi et al. (SODA 2020) and Li and Xian (ICML 2021) for learning-augmented makespan minimization, and obtain the first learning-augmented nearly-optimal algorithms for the other objectives such as Santa Claus, Nash welfare, ?_p-minimization, etc. We also give tight bounds on the resilience of our algorithms to errors in the learned parameters, and study the learnability of these parameters

Optimal Policies for the Management of a Plug-In Hybrid Electric Vehicle Swap Station

Optimizing operations at plug-in hybrid electric vehicle (PHEV) battery swap stations is internally motivated by the movement to make transportation cleaner and more efficient. A PHEV swap station allows PHEV owners to quickly exchange their depleted PHEV battery for a fully charged battery. The PHEV-Swap Station Management Problem (PHEV-SSMP) is introduced, which models battery charging and discharging operations at a PHEV swap station facing nonstationary, stochastic demand for battery swaps, nonstationary prices for charging depleted batteries, and nonstationary prices for discharging fully charged batteries. Discharging through vehicle-to-grid is beneficial for aiding power load balancing. The objective of the PHEV-SSMP is to determine the optimal policy for charging and discharging batteries that maximizes expected total profit over a fixed time horizon. The PHEV-SSMP is formulated as a finite-horizon, discrete-time Markov decision problem and an optimal policy is found using dynamic programming. Structural properties are derived, to include sufficiency conditions that ensure the existence of a monotone optimal policy. A computational experiment is developed using realistic demand and electricity pricing data. The optimal policy is compared to two benchmark policies which are easily implementable by PHEV swap station managers. Two designed experiments are conducted to obtain policy insights regarding the management of PHEV swap stations. These insights include the minimum battery level in relationship to PHEVs in a local area, the incentive necessary to discharge, and the viability of PHEV swap stations under many conditions