Optimal Abandonment of Coal-Fired Stations in the EU

36 p.

Valuation of wind energy projects: A real options approach

27 p.

Sustainable IT and IT for sustainability

Energy and sustainability have become one of the most critical issues of our generation. While the abundant potential of renewable energy such as solar and wind provides a real opportunity for sustainability, their intermittency and uncertainty present a daunting operating challenge. This thesis aims to develop analytical models, deployable algorithms, and real systems to enable efficient integration of renewable energy into complex distributed systems with limited information.

The first thrust of the thesis is to make IT systems more sustainable by facilitating the integration of renewable energy into these systems. IT represents the fastest growing sectors in energy usage and greenhouse gas pollution. Over the last decade there are dramatic improvements in the energy efficiency of IT systems, but the efficiency improvements do not necessarily lead to reduction in energy consumption because more servers are demanded. Further, little effort has been put in making IT more sustainable, and most of the improvements are from improved "engineering" rather than improved "algorithms". In contrast, my work focuses on developing algorithms with rigorous theoretical analysis that improve the sustainability of IT. In particular, this thesis seeks to exploit the flexibilities of cloud workloads both (i) in time by scheduling delay-tolerant workloads and (ii) in space by routing requests to geographically diverse data centers. These opportunities allow data centers to adaptively respond to renewable availability, varying cooling efficiency, and fluctuating energy prices, while still meeting performance requirements. The design of the enabling algorithms is however very challenging because of limited information, non-smooth objective functions and the need for distributed control. Novel distributed algorithms are developed with theoretically provable guarantees to enable the "follow the renewables" routing. Moving from theory to practice, I helped HP design and implement industry's first Net-zero Energy Data Center.

The second thrust of this thesis is to use IT systems to improve the sustainability and efficiency of our energy infrastructure through data center demand response. The main challenges as we integrate more renewable sources to the existing power grid come from the fluctuation and unpredictability of renewable generation. Although energy storage and reserves can potentially solve the issues, they are very costly. One promising alternative is to make the cloud data centers demand responsive. The potential of such an approach is huge.

To realize this potential, we need adaptive and distributed control of cloud data centers and new electricity market designs for distributed electricity resources. My work is progressing in both directions. In particular, I have designed online algorithms with theoretically guaranteed performance for data center operators to deal with uncertainties under popular demand response programs. Based on local control rules of customers, I have further designed new pricing schemes for demand response to align the interests of customers, utility companies, and the society to improve social welfare.

Análisis de precios del mercado diario de electricidad español

El trabajo analiza los precios de la electricidad del mercado diario español. En el mismo se realiza un estudio del mercado eléctrico español y sus componentes junto con un análisis de los cambios regulatorios más significativos durante el periodo muestral. A partir de allí, se analiza la muestra a través de los estadísticos descriptivos principales. Luego se presenta un modelo general que es analizado a partir de los resultados empíricos principales obtenidos con su estimación. Finalmente, se realizan ajustes al mismo para obtener un modelo simplificado que se ajuste mejor a lo que se quiere conseguir, que es analizar la evolución de los precios de la electricidad. Los resultados del ajuste arrojan que los precios horarios dependen en su mayor parte de los precios de las horas anteriores. También que el modelo recoge muy bien la estacionalidad mensual y horaria que presenta la muestra. Por otro lado, características de la serie de precios como son la volatilidad y los saltos no quedan bien recogidos por el modelo, lo que lleva a plantearse la búsqueda de modelos alternativos.

Análisis de precios del mercado diario de electricidad español

El trabajo analiza los precios de la electricidad del mercado diario español. En el mismo se realiza un estudio del mercado eléctrico español y sus componentes junto con un análisis de los cambios regulatorios más significativos durante el periodo muestral. A partir de allí, se analiza la muestra a través de los estadísticos descriptivos principales. Luego se presenta un modelo general que es analizado a partir de los resultados empíricos principales obtenidos con su estimación. Finalmente, se realizan ajustes al mismo para obtener un modelo simplificado que se ajuste mejor a lo que se quiere conseguir, que es analizar la evolución de los precios de la electricidad. Los resultados del ajuste arrojan que los precios horarios dependen en su mayor parte de los precios de las horas anteriores. También que el modelo recoge muy bien la estacionalidad mensual y horaria que presenta la muestra. Por otro lado, características de la serie de precios como son la volatilidad y los saltos no quedan bien recogidos por el modelo, lo que lleva a plantearse la búsqueda de modelos alternativos.

Prices need no preferences: social trends determine decisions in experimental markets for pain relief.

OBJECTIVE: A standard view in health economics is that, although there is no market that determines the "prices" for health states, people can nonetheless associate health states with monetary values (or other scales, such as quality adjusted life year [QALYs] and disability adjusted life year [DALYs]). Such valuations can be used to shape health policy, and a major research challenge is to elicit such values from people; creating experimental "markets" for health states is a theoretically attractive way to address this. We explore the possibility that this framework may be fundamentally flawed-because there may not be any stable values to be revealed. Instead, perhaps people construct ad hoc values, influenced by contextual factors, such as the observed decisions of others. METHOD: The participants bid to buy relief from equally painful electrical shocks to the leg and arm in an experimental health market based on an interactive second-price auction. Thirty subjects were randomly assigned to two experimental conditions where the bids by "others" were manipulated to follow increasing or decreasing price trends for one, but not the other, pain. After the auction, a preference test asked the participants to choose which pain they prefer to experience for a longer duration. RESULTS: Players remained indifferent between the two pain-types throughout the auction. However, their bids were differentially attracted toward what others bid for each pain, with overbidding during decreasing prices and underbidding during increasing prices. CONCLUSION: Health preferences are dissociated from market prices, which are strongly referenced to others' choices. This suggests that the price of health care in a free-market has the capacity to become critically detached from people's underlying preferences.

Trade & Cap: A Customer-Managed, Market-Based System for Trading Bandwidth Allowances at a Shared Link

We propose Trade & Cap (T&C), an economics-inspired mechanism that incentivizes users to voluntarily coordinate their consumption of the bandwidth of a shared resource (e.g., a DSLAM link) so as to converge on what they perceive to be an equitable allocation, while ensuring efficient resource utilization. Under T&C, rather than acting as an arbiter, an Internet Service Provider (ISP) acts as an enforcer of what the community of rational users sharing the resource decides is a fair allocation of that resource. Our T&C mechanism proceeds in two phases. In the first, software agents acting on behalf of users engage in a strategic trading game in which each user agent selfishly chooses bandwidth slots to reserve in support of primary, interactive network usage activities. In the second phase, each user is allowed to acquire additional bandwidth slots in support of presumed open-ended need for fluid bandwidth, catering to secondary applications. The acquisition of this fluid bandwidth is subject to the remaining "buying power" of each user and by prevalent "market prices" – both of which are determined by the results of the trading phase and a desirable aggregate cap on link utilization. We present analytical results that establish the underpinnings of our T&C mechanism, including game-theoretic results pertaining to the trading phase, and pricing of fluid bandwidth allocation pertaining to the capping phase. Using real network traces, we present extensive experimental results that demonstrate the benefits of our scheme, which we also show to be practical by highlighting the salient features of an efficient implementation architecture.

A methodology to analyse the impact of offshore wind forecasting error on electricity markets

Currently wind power is dominated by onshore wind farms in the British Isles, but both the United Kingdom and the Republic of Ireland have high renewable energy targets, expected to come mostly from wind power. However, as the demand for wind power grows to ensure security of energy supply, as a potentially cheaper alternative to fossil fuels and to meet greenhouse gas emissions reduction targets offshore wind power will grow rapidly as the availability of suitable onshore sites decrease. However, wind is variable and stochastic by nature and thus difficult to schedule. In order to plan for these uncertainties market operators use wind forecasting tools, reserve plant and ancillary service agreements. Onshore wind power forecasting techniques have improved dramatically and continue to advance, but offshore wind power forecasting is more difficult due to limited datasets and knowledge. So as the amount of offshore wind power increases in the British Isles robust forecasting and planning techniques are even more critical. This paper presents a methodology to investigate the impacts of better offshore wind forecasting on the operation and management of the single wholesale electricity market in the Republic of Ireland and Northern Ireland using PLEXOS for Power Systems. © 2013 IEEE.

Market Economies with an Endogenous Social Division of Labour

This paper considers a general equilibrium theory of a competitive market economy with an endogenous social division of labour. The theory is founded on the notion of a “consumer- producer”, who consumes as well as produces commodities. In this approach, the emergence of a meaningful social division of labour is guided by the property of increasing returns to specialisation and the process of trade among fully specialised individuals. All decisions of individual consumer-producers are based on a set of perfectly competitive market prices of the commodities in the economy.
We show that a perfectly competitive price mechanism supports a dichotomy of production and consumption at the level of the individual consumer-producer. In this context we show existence of competitive equilibria and characterise these equilibria under increasing returns to specialisation: Under certain well-described conditions, markets are equilibrated through adjustment of the social division of labour; therefore prices are fully determined by the supply side of the economy.

A risk-averse optimization model for trading wind energy in a market environment under uncertainty

In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. (C) 2011 Elsevier Ltd. All rights reserved.

Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsampling

With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.

A congestion management and transmission price simulator for competitive electricity markets

In a liberalized electricity market, the Transmission System Operator (TSO) plays a crucial role in power system operation. Among many other tasks, TSO detects congestion situations and allocates the payments of electricity transmission. This paper presents a software tool for congestion management and transmission price determination in electricity markets. The congestion management is based on a reformulated Optimal Power Flow (OPF), whose main goal is to obtain a feasible solution for the re-dispatch minimizing the changes in the dispatch proposed by the market operator. The transmission price computation considers the physical impact caused by the market agents in the transmission network. The final tariff includes existing system costs and also costs due to the initial congestion situation and losses costs. The paper includes a case study for the IEEE 30 bus power system.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Study of distribution network demand response events in the Portuguese system

In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. Grid operators and utilities are taking new initiatives, recognizing the value of demand response for grid reliability and for the enhancement of organized spot markets’ efficiency. This paper proposes a methodology for the selection of the consumers that participate in an event, which is the responsibility of the Portuguese transmission network operator. The proposed method is intended to be applied in the interruptibility service implemented in Portugal, in convergence with Spain, in the context of the Iberian electricity market. This method is based on the calculation of locational marginal prices (LMP) which are used to support the decision concerning the consumers to be schedule for participation. The proposed method has been computationally implemented and its application is illustrated in this paper using a 937 bus distribution network with more than 20,000 consumers.

Multi-agent simulation of continental, regional, and micro electricity markets

Renewable based power generation has significantly increased over the last years. However, this process has evolved separately from electricity markets, leading to an inadequacy of the present market models to cope with huge quantities of renewable energy resources, and to take full advantage of the presently existing and the increasing envisaged renewable based and distributed energy resources. This paper proposes the modelling of electricity markets at several levels (continental, regional and micro), taking into account the specific characteristics of the players and resources involved in each level and ensuring that the proposed models accommodate adequate business models able to support the contribution of all the resources in the system, from the largest to the smaller ones. The proposed market models are integrated in MASCEM (Multi- Agent Simulator of Competitive Electricity Markets), using the multi agent approach advantages for overcoming the current inadequacy and significant limitations of the presently existing electricity market simulators to deal with the complex electricity market models that must be adopted.