Risks and Prospects of Smart Electric Grids Systems measured with Real Options

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Strategic Behaviour under Regulation Benchmarking

Liberalisation of generation and supply activities in the electricity sectors is often followed by regulatory reform of distribution networks. In order to improve the efficiency of distribution utilities, some regulators have adopted incentive regulation schemes that rely on performance benchmarking. Although regulation benchmarking can influence the �regulation game�, the subject has received limited attention. This paper discusses how strategic behaviour can result in inefficient behaviour by firms. We also present a survey of issues encountered by electricity regulators. We then use the Data Envelopment Analysis (DEA) method with US utility data to examine implications of selected cases of strategic behaviour. The results show that gaming can have significant effects on the measured performance and profitability of firms

New Forecasts of the Equity Premium

If investors are myopic mean-variance optimizers, a stock's expected return is linearly related to its beta in the cross section. The slope of the relation is the cross-sectional price of risk, which should equal the expected equity premium. We use this simple observation to forecast the equity-premium time series with the cross-sectional price of risk. We also introduce novel statistical methods for testing stock-return predictability based on endogenous variables whose shocks are potentially correlated with return shocks. Our empirical tests show that the cross-sectional price of risk (1) is strongly correlated with the market's yield measures and (2) predicts equity-premium realizations especially in the first half of our 1927-2002 sample.

pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems

pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems. It provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. pandapower includes a Newton-Raphson power flow solver formerly based on PYPOWER, which has been accelerated with just-in-time compilation. Additional enhancements to the solver include the capability to model constant current loads, grids with multiple reference nodes and a connectivity check. The pandapower network model is based on electric elements, such as lines, two and three-winding transformers or ideal switches. All elements can be defined with nameplate parameters and are internally processed with equivalent circuit models, which have been validated against industry standard software tools. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies as well as for educational purposes. A comprehensive, publicly available case-study demonstrates a possible application of pandapower in an automated time series calculation

Removing Cross-Border Capacity Bottlenecks in the European Natural Gas Market: A Proposed Merchant-Regulatory Mechanism

We propose a merchant-regulatory framework to promote investment in the European natural gas network infrastructure based on a price cap over two-part tariffs. As suggested by Vogelsang (2001) and Hogan et al. (2010), a profit maximizing network operator facing this regulatory constraint will intertemporally rebalance the variable and fixed part of its two-part tariff so as to expand the congested pipelines, and converge to the Ramsey-Boiteaux equilibrium. We confirm this with actual data from the European natural gas market by comparing the bi-level price-cap model with a base case, a no-regulation case, and a welfare benchmark case, and by performing sensitivity analyses. In all cases, the incentive model is the best decentralized regulatory alternative that efficiently develops the European pipeline system.regulation, transportation network, investment

A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.

Massively parallel approximate Gaussian process regression

We explore how the big-three computing paradigms -- symmetric multi-processor (SMC), graphical processing units (GPUs), and cluster computing -- can together be brought to bare on large-data Gaussian processes (GP) regression problems via a careful implementation of a newly developed local approximation scheme. Our methodological contribution focuses primarily on GPU computation, as this requires the most care and also provides the largest performance boost. However, in our empirical work we study the relative merits of all three paradigms to determine how best to combine them. The paper concludes with two case studies. One is a real data fluid-dynamics computer experiment which benefits from the local nature of our approximation; the second is a synthetic data example designed to find the largest design for which (accurate) GP emulation can performed on a commensurate predictive set under an hour.Comment: 24 pages, 6 figures, 1 tabl

Floating solar panel park

Treball desenvolupat dins el marc del programa 'European Project Semester'.This Final Report is the culmination of a four month long design study on floating solar panel park feasibility in Vaasa, Finland. The Floating Ideas Team was tasked with coming up with a design that would not only work, but also make a profit. The team focused a lot of time on initial research, an iterative design process, and experiments to gather information that could not be found during the research phase. In this report, one can expect to find the major findings from research in many different areas such as location, panel design, flotation design, cooling techniques, and efficiency adding techniques. The first takeaway is that implementing floating solar parks in Finland would require adding efficiency techniques such as mirrors or concentrators. Second, how the panels are placed means a lot in a location so far north. Placing the panels far away from each other and horizontally will reduce the negative impact of shadows. And third, the rotation of the structure is important in increasing efficiency. Multiple axis tracking is not necessary, but tracking in the vertical axis can add a 50% increase in power generated. This research then lead into the defining of four initial designs which were eventually paired down into one. The largest factors leading to the change in design were the combination of rotation and anchoring methods, the flotation structure, and the structure required hold the panel modules together. In the end, the final design is a modular circular design with panels and mirrors to help add efficiency, approximately 37%. From there, an economic and environmental feasibility study was done and for both, this design was deemed feasible for Finland. With the design, detailed in this report, it would be possible to implement this and make a profit off of it, leading the team to believe that this should be implemented in places looking for alternatives for renewable energy production

Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies

Deep learning techniques have been widely applied in the field of stock market prediction particularly with respect to the implementation of active trading strategies. However, the area of portfolio management and passive portfolio management in particular has been much less well served by research to date. This research project conducts an investigation into the science underlying the implementation of portfolio management strategies in practice focusing on enhanced indexing strategies. Enhanced indexing is a passive management approach which introduces an element of active management with the aim of achieving a level of active return through small adjustments to the portfolio weights. It then proceeds to investigate current applications of deep learning techniques in the field of financial market predictions and also in the specific area of portfolio management. A series of successively deeper neural network models were then developed and assessed in terms of their ability to accurately predict whether a sample of stocks would either outperform or underperform the selected benchmark index. The predictions generated by these models were then used to guide the adjustment of portfolio weightings to implement and forward test an enhanced indexing strategy on a hypothetical stock portfolio