Final Report: Market and Economic Modelling of the Intelligent Grid

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy

An Empirical Study on Purchase Decision Process of Shaving Foam in Bangladesh

Now-a-days men are more aware of their body. The demand for beautifulness, aesthetics, youthfulness, healthiness, and thinness drag them away from old opinion of “only sanitation”. Shaving foam is an essential skin care product for men and it is a daily use of modern male. To be presentable men needs shaving. Like other products, purchasing shaving foam involves a decision making process. This paper basically aims to identify the stages of males’ decision process of purchasing shaving foam in details; all the activities that men perform in each of the stages. Both primary and secondary data were used in preparing this report. Percentage and descriptive statistics were used to reveal the results. The study found the major reasons for using shaving foam is hygiene purpose. Hopefully the survey findings will be helpful to existing and upcoming shaving foam companies in making better decisions. Keywords: Buying process, Men’s skin care product, Consumer behavior, Shaving foam

Minimizing energy costs for geographically distributed heterogeneous data centers

2018 Summer.Includes bibliographical references.The recent proliferation and associated high electricity costs of distributed data centers have motivated researchers to study energy-cost minimization at the geo-distributed level. The development of time-of-use (TOU) electricity pricing models and renewable energy source models has provided the means for researchers to reduce these high energy costs through intelligent geographical workload distribution. However, neglecting important considerations such as data center cooling power, interference effects from task co-location in servers, net-metering, and peak demand pricing of electricity has led to sub-optimal results in prior work because these factors have a significant impact on energy costs and performance. In this thesis, we propose a set of workload management techniques that take a holistic approach to the energy minimization problem for geo-distributed data centers. Our approach considers detailed data center cooling power, co-location interference, TOU electricity pricing, renewable energy, net metering, and peak demand pricing distribution models. We demonstrate the value of utilizing such information by comparing against geo-distributed workload management techniques that possess varying amounts of system information. Our simulation results indicate that our best proposed technique is able to achieve a 61% (on average) cost reduction compared to state-of-the-art prior work

Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning

Demand response (DR) becomes critical to manage the charging load of a growing electric vehicle (EV) deployment. Initial DR studies mainly adopt model predictive control, but models are largely uncertain for the EV scenario (e.g., customer behavior). Model-free approaches, based on reinforcement learning (RL), are an attractive alternative. We propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of charging stations. State-of-the-art algorithms either focus on a single EV, or control an aggregate of EVs in multiple steps (e.g., 1) make aggregate load decisions and 2) translate the aggregate decision to individual EVs). In contrast, our RL approach jointly controls the whole set of EVs at once. We contribute a new MDP formulation with a scalable state representation independent of the number of charging stations. Using a batch RL algorithm, fitted $Q$ -iteration, we learn an optimal charging policy. With simulations using real-world data, we: 1) differentiate settings in training the RL policy (e.g., the time span covered by training data); 2) compare its performance to an oracle all-knowing benchmark (providing an upper performance bound); 3) analyze performance fluctuations throughout a full year; and 4) demonstrate generalization capacity to larger sets of charging stations

A Framework for Decision-based Consistencies

International audienceConsistencies are properties of constraint networks that can be enforced by appropriate algorithms to reduce the size of the search space to be explored. Recently, many consistencies built upon taking decisions (most often, variable assignments) and stronger than (general- ized) arc consistency have been introduced. In this paper, our ambition is to present a clear picture of decision-based consistencies. We identify four general classes (or levels) of decision-based consistencies, denoted by S∆φ, E∆φ, B∆φ and D∆φ, study their relationships, and show that known consistencies are particular cases of these classes. Interestingly, this gen- eral framework provides us with a better insight into decision-based con- sistencies, and allows us to derive many new consistencies that can be directly integrated and compared with other ones

Market and Economic Modelling of the Intelligent Grid: Interim Report 2011

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy

Improving data center efficiency through smart grid integration and intelligent analytics

The ever-increasing growth of the demand in IT computing, storage and large-scale cloud services leads to the proliferation of data centers that consist of (tens of) thousands of servers. As a result, data centers are now among the largest electricity consumers worldwide. Data center energy and resource efficiency has started to receive significant attention due to its economical, environmental, and performance impacts. In tandem, facing increasing challenges in stabilizing the power grids due to growing needs of intermittent renewable energy integration, power market operators have started to offer a number of demand response (DR) opportunities for energy consumers (such as data centers) to receive credits by modulating their power consumption dynamically following specific requirements. This dissertation claims that data centers have strong capabilities to emerge as major enablers of substantial electricity integration from renewables. The participation of data centers into emerging DR, such as regulation service reserves (RSRs), enables the growth of the data center in a sustainable, environmentally neutral, or even beneficial way, while also significantly reducing data center electricity costs. In this dissertation, we first model data center participation in DR, and then propose runtime policies to dynamically modulate data center power in response to independent system operator (ISO) requests, leveraging advanced server power and workload management techniques. We also propose energy and reserve bidding strategies to minimize the data center energy cost. Our results demonstrate that a typical data center can achieve up to 44% monetary savings in its electricity cost with RSR provision, dramatically surpassing savings achieved by traditional energy management strategies. In addition, we investigate the capabilities and benefits of various types of energy storage devices (ESDs) in DR. Finally, we demonstrate RSR provision in practice on a real server. In addition to its contributions on improving data center energy efficiency, this dissertation also proposes a novel method to address data center management efficiency. We propose an intelligent system analytics approach, "discovery by example", which leverages fingerprinting and machine learning methods to automatically discover software and system changes. Our approach eases runtime data center introspection and reduces the cost of system management.2018-11-04T00:00:00

Contractor Programming

WOSInternational audienceThis paper describes a solver programming method, called "contractor programming", that copes with two issues related to constraint processing over the reals. First, continuous constraints involve an inevitable step of solver design. Existing softwares provide an insufficient answer by restricting users to choose among a list of fixed strategies. Our first contribution is to give more freedom in solver design by introducing programming concepts where only configuration parameters were previously available. Programming consists in applying operators (intersection, composition, etc.) on algorithms called "contractors" that are somehow similar to propagators. Second, many problems with real variables cannot be cast as the search for vectors simultaneously satisfying the set of constraints, but a large variety of different outputs may be demanded from a set of constraints (e.g., a paving with boxes inside and outside of the solution set). These outputs can actually be viewed as the result of different "contractors" working concurrently on the same search space, with a bisection procedure intervening in case of deadlock. Such algorithms (which are not strictly speaking solvers) will be made easy to build thanks to a new branch & prune system, called "paver". Thus, this paper gives a way to deal harmoniously with a larger set of problems while giving a fine control on the solving mechanisms. The contractor formalism and the paver system are the two contributions. The approach is motivated and justified through different cases of study. An implementation of this framework named Quimper is also presented

Interpretable and fast dimension reduction of multivariate data

The main objective of this thesis is to propose new techniques to simplify the interpretation of newly formed 'variables' or components, while reducing the dimensionality of multivariate data. Most attention is given to the interpretation of principal components, although one chapter is devoted to that of factors in factor analysis. Sparse principal components are proposed, in which some of the component loadings are made exactly zero. One approach is to make use of the idea of correlation biplots, where orthogonal matrix of sparse loadings is obtained from computing the biplot factors of the product of principal component loading matrix and functions of their variances. Other approaches in volve clustering of variables as a pre-processings tep, so that sparse components are computed from the data or correlation matrix of each cluster. New clustering techniques are proposed for this purpose. In addition, a penalized varimax approach is proposed for simplifying the interpretation of factors in factor analysis, especially for factor solutions with considerably different sum of squares. This is done by adding a penalty term to the ordinary varimax criterion. Data sets of varying sizes, both synthetic and real, are used to illustrate the proposed methods, and the results are compared with those of existing ones. In the case of principal component analysis, the resulting sparse components are found to be more interpretable (sparser) and explain higher cumulative percentage of adjusted variance compared to their counterparts from other techniques. The penalized varimax approach contributes in finding a factor solution with simple structures which are not revealed by the standard varimax solution. The proposed methods are very simple to understand and involve fast algorithms compared to some of the existing methods. They contribute much to the interpretation of components in a reduced dimension while dealing with dimensionality reduction of multivariate data

Power peak shaving : how to schedule charging of electric vehicles and organize mutually beneficial vehicle to grid (V2G)

This thesis contributes to a project by the Norwegian University of Life Sciences (NMBU) featuring a pilot Vehicle to Grid (V2G) park at Oslo Gardermoen Airport. The goal of the project is two-fold. On one hand, the goal is to provide the airport, a large power consumer who pays power tariffs, with viable measures to shave power peaks and thereby reduce costs. On the other hand, the example of an airport is used to illustrate how V2G can be implemented in a feasible way for EV owners. If successful, this would be advantageous both to power grid operators, to EV owners, and to large power consumers who facilitate EV charging. The thesis approaches power peak shaving by utilizing electric vehicles (EVs) from two angles: Load shifting by scheduling EV charging, and EVs as alternative power supply through vehicle to grid (V2G). EVs with one-way charging capability can be utilized for the first approach, while EVs with two-way charging capability (currently not many) can be utilized for both. In a setting where a large power consumer facilitates long term parking and charging of EVs on its property, both approaches in combination can contribute to reducing power tariffs for the large power consumer. Before V2G is ready for full scale implementation, scheduling the charging is a step in the right direction, and can be seen as ground work for V2G. This thesis presents a Python program demonstrating a method based on scheduling theory, adjusted to minimize simultaneous power demand from EVs, and schedule it outside of expected power peaks. To this author's knowledge, the theory has not been used for this purpose before. While V2G is most commonly regarded from a grid operation perspective, the focus of this thesis is to organize V2G as a mutually beneficial cooperation between representatives of grid interests and the owners of EVs. The technical process that occurs during V2G can be described in very different business terms, depending on perspective. While control based V2G contracts are most commonly considered, stemming from the perspective that the grid operator takes control over (rents) the EV battery to use for V2G, this thesis explores contract designs that regard EV owners as electricity traders, who own the electricity in their battery until they decide to sell it. This leaves more control in the hands of EV owners. Different demand response mechanisms are explored to trigger electricity sale under different circumstances. The thesis concludes with a volume based V2G contract design for the case at Oslo Gardermoen Airport, where EV owners agree to sell a certain electricity volume during a predefined time frame, that the airport may extract when it suits their purposes. Elements from a price based contract, where EV owners define a sales price that triggers a sale when matched by the market price, is also included for certain circumstances. An approach to design V2G contracts for different circumstances can be derived from the discussion.Denne oppgaven bidrar til et prosjekt i regi av Norges Miljø- og Biovitenskapelige Universitet (NMBU), som omhandler et V2G-pilotprosjekt ved Oslo Lufthavn, Gardermoen. Prosjektets mål er todelt. På den ene siden er målet å tilby flyplassen, en stor strømkunde som betaler effekttariffer, virkemidler for å jevne ut effekttopper og dermed redusere kostnader. På den annen side brukes flyplassen som et eksempel på hvordan V2G kan innføres på en gangbar måte for elbileiere. Hvis dette lykkes, vil det komme både nettoperatører, elbileiere og store strømkunder som fasiliterer elbillading, til gode. Oppgaven tilnærmer seg effekttopputjevning ved hjelp av elbiler fra to ulike vinkler: Lastforflytning gjennom tidsplanlegging av elbillading, og elbiler som alternativ kraftforsyning gjennom vehicle to grid (V2G). Elbiler med batterier som kan lades én vei kan brukes til den første tilnærmingen, og elbiler som kan lade to veier (foreløpig ikke mange) kan brukes til begge deler. I tilfeller der en stor strømkunde fasiliterer langtidsparkering og lading av elbiler på eiendommen sin, kan en kombinasjon av begge tilnærmingene bidra til å redusere effekttariffer for strømkunden. Før V2G er modent for innføring i full skala, er tidsplanlegging av ladingen et steg i riktig retning, og kan ses på som forarbeid for V2G. Denne oppgaven presenterer et Python-program som demonstrerer en metode bygget på scheduling-teori, tilpasset til å sikre at minst mulig effekt trekkes samtidig til lading av elbiler, i tillegg til å planlegge det utenfor forventede effekttopper. Såvidt denne forfatteren vet er ikke teorien blitt brukt til dette formålet tidligere. V2G er oftest diskutert sett fra en nettoperatørs perspektiv. Denne oppgaven fokuserer på å organisere V2G som et samarbeid mellom representater for kraftnettets interesser og elbileiere, til gjensidig nytte for begge. Den tekniske prosessen som skjer ved V2G kan beskrives på flere måter i forretningsøyemed, avhengig av perspektiv. V2G er vanligvis diskutert som en kontrollbasert kontrakt, sprunget ut av et perspektiv der nettoperatøren tar kontroll over (leier) elbilbatteriet til V2G-bruk. Oppgaven utforsker kontraktsutforminger som springer ut av et perspektiv der elbileieren anses som en krafthandler, som eier elektrisiteten i sitt eget batteri, og kan velge å selge den. Dette gir elbileieren mer kontroll. Forskjellige etterspørselsrespons-mekanismer utforskes for å utløse salg av elektrisitet under ulike omstendigheter. Oppgaven konkluderer med en volumbasert kontraktsutforming til case-studien ved Oslo Lufthavn, Gardermoen, der elbileiere forplikter seg til å selge et visst elektrisitetsvolum ila. en forhåndsdefinert tilkoblingsperiode. Flyplassen kan kan kjøpe dette volumet på tidspunkt som passer deres formål innenfor den avtalte perioden. Elementer fra en prisbasert kontrakt, der en forhåndsdefinert salgspris utløser et elektrisitetssalg idet markedsprisen matcher den, er også inkludert for visse tilfeller. En tilnærming til V2G-kontraktsutforming til forskjellige sammenhenger kan utledes fra diskusjonen.M-M