Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Negotiation context analysis in electricity markets

Contextualization is critical in every decision making process. Adequate responses to problems depend not only on the variables with direct influence on the outcomes, but also on a correct contextualization of the problem regarding the surrounding environment. Electricity markets are dynamic environments with increasing complexity, potentiated by the last decades' restructuring process. Dealing with the growing complexity and competitiveness in this sector brought the need for using decision support tools. A solid example is MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), whose players' decisions are supported by another multiagent system – ALBidS (Adaptive Learning strategic Bidding System). ALBidS uses artificial intelligence techniques to endow market players with adaptive learning capabilities that allow them to achieve the best possible results in market negotiations. This paper studies the influence of context awareness in the decision making process of agents acting in electricity markets. A context analysis mechanism is proposed, considering important characteristics of each negotiation period, so that negotiating agents can adapt their acting strategies to different contexts. The main conclusion is that context-dependant responses improve the decision making process. Suiting actions to different contexts allows adapting the behaviour of negotiating entities to different circumstances, resulting in profitable outcomes.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

The Distributional Consequences of Supply-Side Reforms in General Equilibrium

This paper addresses the issue on whether tax reforms consisten with lower public debt-to-GDP in the long-run can lead to a more efficient and equitable economy. To this end we solve a heterogeneous agent model comprised of a government, a representative capitalist and representative skilled and unskilled workers, under both rational expectations and adaptive learning. Our main ndings are that (i) reductions in capital taxation, while bene cial at the aggregate level, lead to increased inequality mainly due to the substitutability of un- skilled labour and capital; (ii) a fall in taxation for skilled labour is Pareto improving, which is largely explained by its complementarity with the other factor inputs; (iii) all agents would prefer increasing the tax rate on capital to increasing the tax rate on skilled and un- skilled labour since it leads to relatively lower welfare losses; and (iv) heterogeneity in initial beliefs under adaptive learning quantitatively matters for welfare.

Educació Física i Internet : un projecte d'integració a l'escola

El projecte explica el procés seguit per a elaborar un projecte telemàtic, destinat a alumnes de cicle superior d'educació primària, que utilitza les TIC per tal d'ajudar a assolir objectius d'educació física.

Structural changes in the US economy: Bad Luck or Bad Policy?

This paper investigates the relationship between time variations in output and inflation dynamics and monetary policy in the US. There are changes in the structural coefficients and in the variance of the structural shocks. The policy rules in the 1970s and 1990s are similar as is the transmission of policy disturbances. Inflation persistence is only partly a monetary phenomena. Variations in the systematic component of policy have limited effects on the dynamics of output and inflation. Results are robust to alterations in the auxiliary assumptions.

Fiscal policy, pricing frictions and monetary accommodation

We investigate the theoretical conditions for effectiveness of government consumptionexpenditure expansions using US, Euro area and UK data. Fiscal expansions taking placewhen monetary policy is accommodative lead to large output multipliers in normal times.The 2009-2010 packages need not produce significant output multipliers, may havemoderate debt effects, and only generate temporary inflation. Expenditure expansionsaccompanied by deficit/debt consolidations schemes may lead to short run output gains buttheir success depends on how monetary policy and expectations behave. Trade opennessand the cyclicality of the labor wedge explain cross-country differences in the magnitude ofthe multipliers.

Structural changes in the US economy: is there a role for monetary policy?

This paper investigates the contribution of monetary policy to the changes in outputgrowth and inflation dynamics in the US. We identify a policy shock and a policy rule ina time-varying coefficients VAR using robust sign restrictions. The transmission of policyshocks has been relatively stable. The variance of the policy shock has decreased over time,but policy shocks account for a small fraction of the level and of the variations in inflationand output growth volatility and persistence. We find little evidence of a significant increasein the long run response of the interest rate to inflation. A more aggressive inflation policyin the 1970s would have produced large output growth costs.

Does money matter in shaping domestic business cycles? An international investigation (with appendices)

We study the contribution of money to business cycle fluctuations in the US,the UK, Japan, and the Euro area using a small scale structural monetary business cycle model. Constrained likelihood-based estimates of the parameters areprovided and time instabilities analyzed. Real balances are statistically importantfor output and inflation fluctuations. Their contribution changes over time. Models giving money no role provide a distorted representation of the sources of cyclicalfluctuations, of the transmission of shocks and of the events of the last 40 years.

Stable sunspot equilibria in a cash-in-advance economy

We analyze a monetary model with flexible labor supply, cash-inadvance constraints and seigniorage-financed government deficits. If the intertemporal elasticity of substitution of labor is greater than one, there are two steady states, one determinate and the other indeterminate. If the elasticity is less than one, there is a unique steady state, which can be indeterminate. Only in the latter case do there exist sunspot equilibria that are stable under adaptive learning. A sufficient reduction in government purchases can in many cases eliminate the sunspot equilibria while raising consumption/labor taxes even enough to balance the budget may fail to achieve determinacy.

Osaaminen tietämyksen tukena kehitettäessä aikuisopiston työprosesseja

Osaaminen voi muodostua ongelmaksi yritysten kilpailukyvylle, jos siihen ei kiinnitetä huomiota jo strategiasuunnittelusta lähtien. Vaikka asiakkaiden muuttuneet odotukset kyettäisiinkin kohdentamaan ennen kilpailijoita, saattaa olla, että siihen ei pystytä vastaamaan, jos ei ehditä oppimaan uutta tai uudella tavalla. Diplomityön tavoitteena on laatia Etelä-Karjalan aikuisopistollehenkilöstön osaamisen kehittämiskuvaus siitä, kuinka strategian määrittämisestälähtien voidaan henkilöstön osaamista parantaa ja pyrkiä luomaan kilpailuetua markkinoilla. Henkilöstön osaamisen kehittäminen tulee olla suunnitelmallista, tarvittaessa yksilön, ryhmän ja organisaation edut huomioivaa, riittävän yksinkertaista ja konkreettista, jotta suunnitelma voidaan toteuttaa, seurata ja edelleenkehittää. Työn teoriaosassa on kuvattu vision ja strategian merkitystä osaamisen kehittämiseen. Lisäksi on tarkasteltu yksilön oppimista, oppimisen prosessia ja sen kehittymistä organisaation kyvykkyydeksi. Osaamisen infrastruktuuria on lähestytty organisaatiokulttuurin, sitouttamisen ja kehittämisjärjestelmän näkökulmasta. Empiirisessä osuudessa on tuotu esiin aikuisopiston henkilöstön osaamisen kehittämisen tavoitteet, nykyiset käytännöt, kehittämisen vaihtoehdot sekä jatkotoimenpiteet. Osaaminen on aikuisopiston henkilöstön ammattitaidon perusta. Osaamistarpeen määrittämisen tulee keskittyä aikuisopiston ydinosaamisen kehittämiseen. Henkilöstö tulisi nähdä motivoinnin ja sitouttamisen kautta inhimillistä tietopääomaa kasvattavana tekijänä, johon voidaan sujuvasti liittää aineeton pääoma (data, informaatio jne.) sekä strateginen reservi, kuten kilpailuetua tuottava innovointitoiminta.

Design of Personalization of Massive Open Online Courses

Massive Open Online Courses have been in the center of attention in the recent years. However, the main problem of all online learning environments is their lack of personalization according to the learners’ knowledge, learning styles and other learning preferences. This research explores the parameters and features used for personalization in the literature and based on them, evaluates to see how well the current MOOC platforms have been personalized. Then, proposes a design framework for personalization of MOOC platforms that fulfills most of the personalization parameters in the literature including the learning style as well as personalization features. The result of an assessment made for the proposed design framework shows that the framework well supports personalization of MOOCs.

Essais en microéconomie appliquée

La thèse comporte trois essais en microéconomie appliquée. En utilisant des modèles d’apprentissage (learning) et d’externalité de réseau, elle étudie le comportement des agents économiques dans différentes situations. Le premier essai de la thèse se penche sur la question de l’utilisation des ressources naturelles en situation d’incertitude et d’apprentissage (learning). Plusieurs auteurs ont abordé le sujet, mais ici, nous étudions un modèle d’apprentissage dans lequel les agents qui consomment la ressource ne formulent pas les mêmes croyances a priori. Le deuxième essai aborde le problème générique auquel fait face, par exemple, un fonds de recherche désirant choisir les meilleurs parmi plusieurs chercheurs de différentes générations et de différentes expériences. Le troisième essai étudie un modèle particulier d’organisation d’entreprise dénommé le marketing multiniveau (multi-level marketing). Le premier chapitre est intitulé "Renewable Resource Consumption in a Learning Environment with Heterogeneous beliefs". Nous y avons utilisé un modèle d’apprentissage avec croyances hétérogènes pour étudier l’exploitation d’une ressource naturelle en situation d’incertitude. Il faut distinguer ici deux types d’apprentissage : le adaptive learning et le learning proprement dit. Ces deux termes ont été empruntés à Koulovatianos et al (2009). Nous avons montré que, en comparaison avec le adaptive learning, le learning a un impact négatif sur la consommation totale par tous les exploitants de la ressource. Mais individuellement certains exploitants peuvent consommer plus la ressource en learning qu’en adaptive learning. En effet, en learning, les consommateurs font face à deux types d’incitations à ne pas consommer la ressource (et donc à investir) : l’incitation propre qui a toujours un effet négatif sur la consommation de la ressource et l’incitation hétérogène dont l’effet peut être positif ou négatif. L’effet global du learning sur la consommation individuelle dépend donc du signe et de l’ampleur de l’incitation hétérogène. Par ailleurs, en utilisant les variations absolues et relatives de la consommation suite à un changement des croyances, il ressort que les exploitants ont tendance à converger vers une décision commune. Le second chapitre est intitulé "A Perpetual Search for Talent across Overlapping Generations". Avec un modèle dynamique à générations imbriquées, nous avons étudié iv comment un Fonds de recherche devra procéder pour sélectionner les meilleurs chercheurs à financer. Les chercheurs n’ont pas la même "ancienneté" dans l’activité de recherche. Pour une décision optimale, le Fonds de recherche doit se baser à la fois sur l’ancienneté et les travaux passés des chercheurs ayant soumis une demande de subvention de recherche. Il doit être plus favorable aux jeunes chercheurs quant aux exigences à satisfaire pour être financé. Ce travail est également une contribution à l’analyse des Bandit Problems. Ici, au lieu de tenter de calculer un indice, nous proposons de classer et d’éliminer progressivement les chercheurs en les comparant deux à deux. Le troisième chapitre est intitulé "Paradox about the Multi-Level Marketing (MLM)". Depuis quelques décennies, on rencontre de plus en plus une forme particulière d’entreprises dans lesquelles le produit est commercialisé par le biais de distributeurs. Chaque distributeur peut vendre le produit et/ou recruter d’autres distributeurs pour l’entreprise. Il réalise des profits sur ses propres ventes et reçoit aussi des commissions sur la vente des distributeurs qu’il aura recrutés. Il s’agit du marketing multi-niveau (multi-level marketing, MLM). La structure de ces types d’entreprise est souvent qualifiée par certaines critiques de système pyramidal, d’escroquerie et donc insoutenable. Mais les promoteurs des marketing multi-niveau rejettent ces allégations en avançant que le but des MLMs est de vendre et non de recruter. Les gains et les règles de jeu sont tels que les distributeurs ont plus incitation à vendre le produit qu’à recruter. Toutefois, si cette argumentation des promoteurs de MLMs est valide, un paradoxe apparaît. Pourquoi un distributeur qui désire vraiment vendre le produit et réaliser un gain recruterait-il d’autres individus qui viendront opérer sur le même marché que lui? Comment comprendre le fait qu’un agent puisse recruter des personnes qui pourraient devenir ses concurrents, alors qu’il est déjà établi que tout entrepreneur évite et même combat la concurrence. C’est à ce type de question que s’intéresse ce chapitre. Pour expliquer ce paradoxe, nous avons utilisé la structure intrinsèque des organisations MLM. En réalité, pour être capable de bien vendre, le distributeur devra recruter. Les commissions perçues avec le recrutement donnent un pouvoir de vente en ce sens qu’elles permettent au recruteur d’être capable de proposer un prix compétitif pour le produit qu’il désire vendre. Par ailleurs, les MLMs ont une structure semblable à celle des multi-sided markets au sens de Rochet et Tirole (2003, 2006) et Weyl (2010). Le recrutement a un effet externe sur la vente et la vente a un effet externe sur le recrutement, et tout cela est géré par le promoteur de l’organisation. Ainsi, si le promoteur ne tient pas compte de ces externalités dans la fixation des différentes commissions, les agents peuvent se tourner plus ou moins vers le recrutement.