Power Load Management as a Computational Market

Power load management enables energy utilities to reduce peak loads and thereby save money. Due to the large number of different loads, power load management is a complicated optimization problem. We present a new decentralized approach to this problem by modeling direct load management as a computational market. Our simulation results demonstrate that our approach is very efficient with a superlinear rate of convergence to equilibrium and an excellent scalability, requiring few iterations even when the number of agents is in the order of one thousand. Aframework for analysis of this and similar problems is given which shows how nonlinear optimization and numerical mathematics can be exploited to characterize, compare, and tailor problem-solving strategies in market-oriented programming

Constructing Speculative Demand Functions in Equilibrium Markets

In computational markets utilizing algorithms that establish a general equilibrium, competitive behavior is usually assumed: each agent makes its demand (supply) decisions so as to maximize its utility (profit) assuming that it has no impact on market prices. However, there is a potential gain from strategic behavior via speculating about others because an agent does affect the market prices, which affect the supply/demand decisions of others, which again affect the market prices that the agent faces. Determining the optimal strategy when the speculator has perfect knowledge about the other agents is a well known problem which has been studied in oligopoly theory in economics. We describe the computation of such a strategy, and focus on an issue that has received little attention in economics, but which is of fundamental importance in computational markets: the revelation of demand strategies that drive the market to the desired equilibrium. The more realistic setting where the speculator has imperfect information about the other agents is more delicate. We demonstrate how speculation under biased beliefs about the other agents can result in considerable losses if traditional oligopoly strategies for perfect information are used. Furthermore, we show how the optimal demand is computed from probability distributions on the other agents\u27 supply/demand functions. We also theoretically show when an optimal revealed demand function can be constructed independently of the probability distributions. Some pragmatics of choosing a demand function in the case of imperfect information (particularly useful for construction of computational agents for equilibrium markets) are given, and we show - with some empirical support - that it can be relatively easy to construct demand functions that results in a gain from speculation, even when estimation errors are large. Finally, game theoretic issues related to multiple agents counterspeculating simultaneously are discussed

Laststyrning i Energisystem som en artificiell marknad med flera varor

The current report investigates the application of market-oriented programming to power load management. Power load management (or load management for short) is the management of loads at the customer side in order to manage energy systems more efficiently. As energy systems are very large and heavily distributed (typically including millions of loads in an area covering counties or countries), efficient and conceptually attractive methods are required for making load management successful. This report demonstrates how market-oriented programming can be utilized to meet the above demands.I rapporten beskrivs hur applikationen direkt laststyrning kan modelleras som en artificiell marknad. Den nya metodens fördelar jämfört med existerande metoder beskrivs

Market-Oriented Programming and its Application to Power Load Management

Market-oriented programming is a new approach to design and implementation of resource allocation mechanisms in computer systems. It has its roots in different disciplines, such as economics and computer science (in particular the area of multi-agent systems. This thesis is divided into two different parts, focusing on: 1) central foundations and mechanisms of market-oriented programming, and 2) the use of market-oriented programming in practical applications. Market-oriented programming is seen as a programming paradigm based on abstractions such as prices and demands. Concepts, terminology and theory from micro-economics form the foundations of the paradigm. Central aspects of these foundations are investigated and some new insights are presented. Furthermore, some relations between standard optimization/resource allocation approaches and markets are described, and novel theorems are introduced. A plethora of algorithms (some stemming from mathematical optimization and numerical analysis, and some new) for the main computational problem of market-oriented programming -- the computation of general equilibrium -- are described, analyzed and compared. Some issues of self-interested agents in market-oriented programming are also investigated. A published, and generally recognized, market-oriented approach to the application building climate control is analyzed in some detail. A new approach to this application, based on market-oriented programming, is introduced and shown to be superior to the analyzed approach in many ways. The case study pinpoints a number of potential pitfalls as well as advantages of market-oriented approaches to this and other applications. A second investigated application is power load management, i.e. the management of loads at the customers' side for obtaining more efficient energy systems management. The basis of the application is described and a new market-oriented approach is introduced and analyzed. The approach is shown to have a number of advantages compared to existing approaches to this problem. The main conclusion of the thesis is that there are some potential pitfalls of market-oriented programming, but when used with care it provides a highly natural and efficient means for resource allocation in computer systems

Market-Oriented Programming and its Application to Power Load Management

Market-oriented programming is a new approach to design and implementation of resource allocation mechanisms in computer systems. It has its roots in different disciplines, such as economics and computer science (in particular the area of multi-agent systems. This thesis is divided into two different parts, focusing on: 1) central foundations and mechanisms of market-oriented programming, and 2) the use of market-oriented programming in practical applications. Market-oriented programming is seen as a programming paradigm based on abstractions such as prices and demands. Concepts, terminology and theory from micro-economics form the foundations of the paradigm. Central aspects of these foundations are investigated and some new insights are presented. Furthermore, some relations between standard optimization/resource allocation approaches and markets are described, and novel theorems are introduced. A plethora of algorithms (some stemming from mathematical optimization and numerical analysis, and some new) for the main computational problem of market-oriented programming -- the computation of general equilibrium -- are described, analyzed and compared. Some issues of self-interested agents in market-oriented programming are also investigated. A published, and generally recognized, market-oriented approach to the application building climate control is analyzed in some detail. A new approach to this application, based on market-oriented programming, is introduced and shown to be superior to the analyzed approach in many ways. The case study pinpoints a number of potential pitfalls as well as advantages of market-oriented approaches to this and other applications. A second investigated application is power load management, i.e. the management of loads at the customers' side for obtaining more efficient energy systems management. The basis of the application is described and a new market-oriented approach is introduced and analyzed. The approach is shown to have a number of advantages compared to existing approaches to this problem. The main conclusion of the thesis is that there are some potential pitfalls of market-oriented programming, but when used with care it provides a highly natural and efficient means for resource allocation in computer systems