Factor Demand and Market Power

This thesis consists of five self-contained papers on factor demand and market power. The main objective of Paper [I] is to analyze potential effects on the Swedish forest sector of a continuing rise in the use of forest resources as a fuel in energy generation. The background to the problem can be found in the commitments Sweden has made concerning energy policy. Two such commitments are the phase-out of nuclear power, and a decision that the Swedish energy system should be sustainable. However, an increasing use of forest resources as an energy input may have effects outside the energy sector. In this paper we attempt to consider this by estimating a system of demand and supply equations for the four main actors on the Swedish roundwood market; forestry, sawmills, pulpmills, and the energy sector. In Paper [II], we specify and estimate a dynamic factor demand model for the Swedish pulp industry. The model is estimated using firm specific Translog cost functions, and panel data from 1972 to 1990. We find weak evidence of adjustment costs for capital. Short- and long-term elasticities are calculated and the variances are estimated using the bootstrap technique. The results suggest that the user cost of capital is a significant determinant of pulp industry investments, while output level is not. We also find that pulp industry investments are insensitive to variations in the price of electricity. Paper [III] proposes a flexible form of adjustment cost function, which allows for constant, linear, concave, or convex costs of adjustment. An empirical illustration shows that the flexible form can detect both convex and non-convex adjustment costs. Furthermore, the flexible form permits testing for the experience effect on adjustment cost. The objective of paper [IV] is to analyze the price development and price formation for wood fuel used by the Swedish district heating sector. According to previous research there is a significant potential for increasing the use of wood fuel in Sweden, at a fairly moderate cost. The basic question raised in this paper is then why this potential is not realized. Specifically we propose a methodology for testing whether the reason is that market imperfections are present. According to our results we cannot reject the efficient market hypothesis for all years. The objective of Paper [V] is to test for market power on the market for biofuels. To achieve our objective we employ a statistical model and make use of the idea of Granger causality. We use a panel data set of plant specific input prices and quantities of wood chip covering a sample of Swedish district heating plants. If past values of quantity contribute significantly to the determination of price, quantity is said to Granger cause price, which we will treat as a sign of market power. According to our findings this effect is present and we conclude that the investigated plants to some degree has market power in the market for wood chips.demand and supply; dynamic factor demand; djustment costs; bootstrap; panel data; market power

Load Hiding of Household's Power Demand

With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy

Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations

Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEVs' arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. The present paper develops a model predictive control (MPC)- based approach to address the joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, the known PEVs' future demand, or the unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this approach

Optimizing Energy Storage Participation in Emerging Power Markets

The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving.Comment: The full (longer and extended) version of the paper accepted in IGSC 201