Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates

A market-based transmission planning for HVDC grid—case study of the North Sea

There is significant interest in building HVDC transmission to carry out transnational power exchange and deliver cheaper electricity from renewable energy sources which are located far from the load centers. This paper presents a market-based approach to solve a long-term TEP for meshed VSC-HVDC grids that connect regional markets. This is in general a nonlinear, non-convex large-scale optimization problem with high computational burden, partly due to the many combinations of wind and load that become possible. We developed a two-step iterative algorithm that first selects a subset of operating hours using a clustering technique, and then seeks to maximize the social welfare of all regions and minimize the investment capital of transmission infrastructure subject to technical and economic constraints. The outcome of the optimization is an optimal grid design with a topology and transmission capacities that results in congestion revenue paying off investment by the end the project's economic lifetime. Approximations are made to allow an analytical solution to the problem and demonstrate that an HVDC pricing mechanism can be consistent with an AC counterpart. The model is used to investigate development of the offshore grid in the North Sea. Simulation results are interpreted in economic terms and show the effectiveness of our proposed two-step approach

Entry, Exit, and Structural Change in Pennsylvania's Dairy Sector

Data on the number of Pennsylvania dairy farms by size category are analyzed in a Markov chain setting to determine factors affecting entry, exit, expansion, and contraction within the sector. Milk prices, milk price volatility, land prices, policy, and cow productivity all impact structural change in Pennsylvania's dairy sector. Stochastic simulation analysis suggests that the number of dairy farms in Pennsylvania will likely fall by only 2.0 percent to 2.5 percent annually over the next 20 years, indicating that dairy farming in Pennsylvania is likely to be a significant enterprise for the state in the foreseeable future.dairy, maximum entropy, farm size, Markov chain, simulation, Farm Management, Industrial Organization,

FUNDAMENTAL ECONOMIC STRUCTURE AND STRUCTURAL CHANGE IN REGIONAL ECONOMIES: A METHODOLOGICAL APPROACH

Regional economic structure is defined as the composition and patterns of various components of the regional economy such as: produc-tion, employment, consumption, trade, and gross regional product. Structur-al change is conceptualized as the change in relative importance of the aggregate indicators of the economy. The process of regional development and structural change are intertwined, implying as economic development takes place the strength and direction of intersectoral relationships change leading to shifts in the importance, direction and interaction of economic sectors such as: primary, secondary, tertiary, quaternary and quinary sec-tors. The fundamental economic structure (FES) concept implies that selected characteristics of an economy will vary predictably with region size. The identification of FES leads to an improved understanding of the space-time evolution of regional economic activities at different geograph-ical scales. The FES based economic activities are predictable, stable and important. This paper reviews selected themes in manifesting an improved understanding of the relationship among intersectoral transactions and economic size leading to the identification of FES. The following four ques-tions are addressed in this paper: (1) What are the relationships among sector composition and structural change in the process of economic devel-opment? (2) What are the approaches utilized to study structural change analysis? (3) Can a methodology be developed to identify FES for regional economies? (4) Would the identification of FES manifest an improved con-ception of the taxonomy of economies?STRUCTURAL CHANGE AND FUNDAMENTAL ECONOMIC STRUCTURE

Electric power and the global economy: Advances in database construction and sector representation

The electricity sector plays a crucial role in the global economy. The sector is a major consumer of fossil fuel resources, producer of greenhouse gas emissions, and an important indicator and correlate of economic development. As such, the sector is a primary target for policy-makers seeking to address these issues. The sector is also experiencing rapid technological change in generation (e.g. renewables), primary inputs (e.g. horizontal drilling and hydraulic fracturing), and end-use efficiency. This dissertation seeks to further our understanding of the role of the electricity sector as part of the dynamic global energy-economy, which requires significant research advances in both database construction and modeling techniques. Chapter 2 identifies useful engineering-level data and presents a novel matrix balancing method for integrating these data in global economic databases. Chapter 3 demonstrates the relationship between matrix balancing method and modeling results, and Chapter 4 presents the full construction methodology for GTAP-Power, the foremost, publicly-available global computable general equilibrium database. Chapter 5 presents an electricity-detailed computational equilibrium model that explicitly and endogenously captures capacity utilization, capacity expansion, and their interdependency -- important aspects of technological substitution in the electricity sector. The individual, but interrelated, research contributions to database construction and electricity modeling in computational equilibrium are placed in the context of analyzing the US EPA Clean Power Plan (CPP) CO 2 target of 32 percent reduction of CO2 emissions in the US electricity sector from a 2005 baseline by 2030. Assuming current fuel prices, the model predicts an almost 28 percent CO2 reduction without further policy intervention. Next, a carbon tax and investment subsidies for renewable technologies to meet the CPP full targets are imposed and compared (Chapter 6). The carbon tax achieves the target via both utilization and expansion, while the renewable investment subsidies lead to over-expansion and compromises some of the possibilities via utilization. In doing so, this dissertation furthers our understanding of the role of the electricity sector as part of the dynamic global energy-economy