Free Trade in Electric Power

This Article develops the core legal framework of a new electricity trading ecosystem in which anyone, anytime, anywhere, can trade electricity in any amount with anyone else. The proliferation of solar and other distributed energy resources, business model innovation in the sharing economy, and climate change present enormous challenges—and opportunities—for America’s energy economy. But the electricity industry is ill-equipped to adapt to and benefit from these transformative forces, with much of its physical infrastructure, regulatory institutions, and business models relics of the early days of electrification. This Article suggests a systematic rethinking to usher in a new trading paradigm and propel the electric utility industry into the twenty-first century. This model has the potential to revolutionize the way electricity is generated, delivered, and used without requiring dramatic legal reform or radically new technologies. Instead, this Article draws on recent Supreme Court precedent and readily available technologies to democratize the electric grid and unlock free trade in electric power. It refines and expands pilot initiatives currently under way in California and New York to combine existing wholesale markets with new trading platforms similar to Airbnb and Uber. Enhanced market access will empower previously captive consumers to emancipate themselves from their local utilities while also ensuring the proper valuation and integration of a diverse portfolio of energy resources. Transformative change, however necessary and beneficial in the long run, will not come easily in an industry famous for its resistance to reform efforts of any kind. Accordingly, this proposal does not start with a clean slate, but, rather, envisions a hybrid system where competitive markets coexist with traditional utility governance structures while regulators and stakeholders adjust to the new trading paradig

Free Trade in Electric Power

This Article develops the core legal framework of a new electricity-trading ecosystem in which anyone, anytime, anywhere, can trade electricity in any amount with anyone else. The proliferation of solar and other distributed energy resources, business model innovation in the sharing economy, and climate change present enormous challenges — and opportunities — for America’s energy economy. But the electricity industry is ill equipped to adapt to and benefit from these transformative forces, with much of its physical infrastructure, regulatory institutions, and business models a relic of the early days of electrification. We suggest a systematic rethinking to usher in a new trading paradigm and propel the electric utility industry into the 21st century. Our model has the potential to revolutionize the way electricity is generated, delivered, and used without requiring dramatic legal reform or radically new technologies. Instead, this Article draws on recent Supreme Court precedent and readily available technologies to democratize the electric grid and unlock free trade in electric power. We refine and expand pilot initiatives currently under way in California and New York to combine existing wholesale markets with new trading platforms similar to Airbnb and Uber. Enhanced market access will empower previously captive consumers to emancipate themselves from their local utilities while also ensuring the proper valuation and integration of a diverse portfolio of energy resources. Transformative change, however necessary and beneficial in the long run, will not come easy in an industry famous for its resistance to reform efforts of any kind. Accordingly, our proposal does not start with a clean slate but, rather, envisions a hybrid system where competitive markets coexist with traditional utility governance structures while regulators and stakeholders adjust to the new trading paradigm

Free Trade in Electric Power

This Article develops the core legal framework of a new electricity-trading ecosystem in which anyone, anytime, anywhere, can trade electricity in any amount with anyone else. The proliferation of solar and other distributed energy resources, business model innovation in the sharing economy, and climate change present enormous challenges — and opportunities — for America’s energy economy. But the electricity industry is ill equipped to adapt to and benefit from these transformative forces, with much of its physical infrastructure, regulatory institutions, and business models a relic of the early days of electrification. We suggest a systematic rethinking to usher in a new trading paradigm and propel the electric utility industry into the 21st century.Our model has the potential to revolutionize the way electricity is generated, delivered, and used without requiring dramatic legal reform or radically new technologies. Instead, this Article draws on recent Supreme Court precedent and readily available technologies to democratize the electric grid and unlock free trade in electric power. We refine and expand pilot initiatives currently under way in California and New York to combine existing wholesale markets with new trading platforms similar to Airbnb and Uber. Enhanced market access will empower previously captive consumers to emancipate themselves from their local utilities while also ensuring the proper valuation and integration of a diverse portfolio of energy resources. Transformative change, however necessary and beneficial in the long run, will not come easy in an industry famous for its resistance to reform efforts of any kind. Accordingly, our proposal does not start with a clean slate but, rather, envisions a hybrid system where competitive markets coexist with traditional utility governance structures while regulators and stakeholders adjust to the new trading paradigm

Grid governance; what new roles for the community energy movement?

In the Netherlands, energy cooperatives are increasingly active in the production of renewable energy. Many cooperatives have concrete plans to invest in energy projects, such as solar fields and wind turbines. Unfortunately, in the coming years there will hardly be any room for such projects in the electricity grid. In their quest to help solve this predicament, energy cooperatives develop new and innovative energy services, for example delivering grid services to distribution system operators (DSOs). However, in this endeavor they encounter legal as well as economic obstacles

Annulment proceedings and multilevel judicial conflict

This open access book provides an exhaustive picture of the role that annulment conflicts play in the EU multilevel system. Based on a rich dataset of annulment actions since the 1960s and a number of in-depth case studies, it explores the political dimension of annulment litigation, which has become an increasingly relevant judicial tool in the struggle over policy content and decision-making competences. The book covers the motivations of actors to turn policy conflicts into annulment actions, the emergence of multilevel actors’ litigant configurations, the impact of actors’ constellations on success in court, as well as the impact of annulment actions on the multilevel policy conflicts they originate from

Evidence-Based Analysis of Cyber Attacks to Security Monitored Distributed Energy Resources

This work proposes an approach based on dynamic Bayesian networks to support the cybersecurity analysis of network-based controllers in distributed energy plants. We built a system model that exploits real world context information from both information and operational technology environments in the energy infrastructure, and we use it to demonstrate the value of security evidence for time-driven predictive and diagnostic analyses. The innovative contribution of this work is in the methodology capability of capturing the causal and temporal dependencies involved in the assessment of security threats, and in the introduction of security analytics supporting the configuration of anomaly detection platforms for digital energy infrastructures

Essays on Quantitative Methods for Consequences of Political Institutions

In this dissertation, I develop and apply sophisticated Bayesian models to the analysis of institutional effects on electoral and legislative behavior in the policy making process. Leveraging the flexibility of Bayesian methods for statistical modeling, I deal with several methodological problems encountered by political scientists, and social scientists in general, in some established research agenda. This dissertation shows the improvement of the ability to evaluate the success of conflicting theories when these methodological issues are properly dealt with. The consequences of political institutions are investigated at three different levels in this dissertation: countries, political parties, and individual legislators. First of all, at the country level, I investigate whether there is a difference between the performances of democratic and nondemocratic regimes in social provision policy in 18 Latin American countries by focusing on the rarely changing property of political regimes. An appropriate model for the dynamic nature of rarely changing variables is built to thoroughly explore how democratic institutions improve social welfare. Second, at the party level, I develop a Bayesian structural equation model to examine the interdependence between party policy strategies and party support in multiparty systems, in an effort to illustrate the endogenous dynamics of multiparty systems. The results show that party manifestos do not provide clear-cut division of party policy positions. Instead, party labels are more important information than changes in party manifestos to the electorate. Finally, at the level of legislators, I focus on the role of the president and political parties in Brazilian legislative process, in which political exchanges between the government and legislature is an essential feature. By recognizing the existence of the non-ideological effect on voting behavior, I develop a random item-difficulty ideal-point model implied by the spatial voting model to analyze the relationship between coalition dynamics and party-based voting behavior of legislators

Model based forecasting for demand response strategies

The incremental deployment of decentralized renewable energy sources in the distribution grid is triggering a paradigm change for the power sector. This shift from a centralized structure with big power plants to a decentralized scenario of distributed energy resources, such as solar and wind, calls for a more active management of the distribution grid. Conventional distribution grids were passive systems, in which the power was flowing unidirectionally from upstream to downstream. Nowadays, and increasingly in the future, the penetration of distributed generation (DG), with its stochastic nature and lack of controllability, represents a major challenge for the stability of the network, especially at the distribution level. In particular, the power flow reversals produced by DG cause voltage excursions, which must be compensated. This poses an obstacle to the energy transition towards a more sustainable energy mix, which can however be mitigated by using a more active approach towards the control of the distribution networks. Demand side management (DSM) offers a possible solution to the problem, allowing to actively control the balance between generation, consumption and storage, close to the point of generation. An active energy management implies not only the capability to react promptly in case of disturbances, but also to ability to anticipate future events and take control actions accordingly. This is usually achieved through model predictive control (MPC), which requires a prediction of the future disturbances acting on the system. This thesis treat challenges of distributed DSM, with a particular focus on the case of a high penetration of PV power plants. The first subject of the thesis is the evaluation of the performance of models for forecasting and control with low computational requirements, of distributed electrical batteries. The proposed methods are compared by means of closed loop deterministic and stochastic MPC performance. The second subject of the thesis is the development of model based forecasting for PV power plants, and methods to estimate these models without the use of dedicated sensors. The third subject of the thesis concerns strategies for increasing forecasting accuracy when dealing with multiple signals linked by hierarchical relations. Hierarchical forecasting methods are introduced and a distributed algorithm for reconciling base forecasters is presented. At the same time, a new methodology for generating aggregate consistent probabilistic forecasts is proposed. This method can be applied to distributed stochastic DSM, in the presence of high penetration of rooftop installed PV systems. In this case, the forecasts' errors become mutually dependent, raising difficulties in the control problem due to the nontrivial summation of dependent random variables. The benefits of considering dependent forecasting errors over considering them as independent and uncorrelated, are investigated. The last part of the thesis concerns models for distributed energy markets, relying on hierarchical aggregators. To be effective, DSM requires a considerable amount of flexible load and storage to be controllable. This generates the need to be able to pool and coordinate several units, in order to reach a critical mass. In a real case scenario, flexible units will have different owners, who will have different and possibly conflicting interests. In order to recruit as much flexibility as possible, it is therefore importan

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

On Model-Selection and Applications of Multilevel Models in Survey and Causal Inference

This thesis includes three parts. The overarching theme is how to analyze multilevel structured datasets, particularly in the areas of survey and causal inference. The first part discusses model selection of hierarchical models, in the context of a national political survey. I found that the commonly used model selection criteria based on predictive accuracy, such as cross validation, don't perform very well in the case of political survey and explore the possible causes. The second part centers around a unique data set on the presidential election collected through an online platform. I show that with adequate modeling, meaningful and highly accurate information could be extracted from this highly-biased data set. The third part builds on a formal causal inference framework for group-structured data, such as meta-analysis and multi-site trials. In particular, I develop a Gaussian Process model under this framework and demonstrate additional insights that can be gained compared with traditional parametric models