Stochastic optimisation-based valuation of smart grid options under firm DG contracts

Under the current EU legislation, Distribution Network Operators (DNOs) are expected to provide firm connections to new DG, whose penetration is set to increase worldwide creating the need for significant investments to enhance network capacity. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to accurately determine in advance where network violations may occur. Hence, conventional network reinforcements run the risk of asset stranding, leading to increased integration costs. A novel stochastic planning model is proposed that includes generalized formulations for investment in conventional and smart grid assets such as Demand-Side Response (DSR), Coordinated Voltage Control (CVC) and Soft Open Point (SOP) allowing the quantification of their option value. We also show that deterministic planning approaches may underestimate or completely ignore smart technologies

Operation of distributed generation under stochastic prices

The ongoing deregulation of electricity industries worldwide is providing incentives for microgrids, entities that use small-scale distributed generation (DG) and combined heat and power (CHP) ap- plications to meet local energy loads, to evolve independently of the traditional centralised grid in order to provide greater flexibility and energy efficiency to end-use consumers. We examine the impact of start-up costs on the option values and operating schedules of on-site DG installed by a microgrid in the presence of stochastic electricity and fuel prices. We proceed by formulating a stochastic dynamic programme (SDP) for the microgrid that minimises its expected discounted cost over a time horizon and solving it using least-squares Monte Carlo (LSMC) simulation. The expected cost saving that the microgrid realises by having gas-fired DG installed relative to meeting its entire electric load via off-site purchases is the implied option value of DG. Numerical examples indicate that although start-up costs do not significantly lower DG value, they, nevertheless, have a profound impact on the optimal DG operating schedule as the microgrid must incorporate not only current, but also future, expected start-up costs into its current decision-making process as an opportunity cost. As a consequence, the microgrid becomes more hesitant to turn DG units on (off), preferring to wait until the electricity price (natural gas generating cost) exceeds the natural gas generating cost (electricity price) by a significant margin before taking action. We demonstrate that ignoring this tradeoff and proceeding myopically as in the case without start-up costs results in drastically higher expected costs and fewer opportunities to use DG

A looming revolution: Implications of self-generation for the risk exposure of retailers. ESRI WP597, September 2018

Managing the risk associated with uncertain load has always been a challenge for retailers in electricity markets. Yet the load variability has been largely predictable in the past, especially when aggregating a large number of consumers. In contrast, the increasing penetration of unpredictable, small-scale electricity generation by consumers, i.e. self-generation, constitutes a new and yet greater volume risk. Using value-at-risk metrics and Monte Carlo simulations based on German historical loads and prices, the contribution of decentralized solar PV self-generation to retailers’ load and revenue risks is assessed. This analysis has implications for the consumers’ welfare and the overall efficiency of electricity markets

Real Option Valuation of a Portfolio of Oil Projects

Various methodologies exist for valuing companies and their projects. We address the problem of valuing a portfolio of projects within companies that have infrequent, large and volatile cash flows. Examples of this type of company exist in oil exploration and development and we will use this example to illustrate our analysis throughout the thesis. The theoretical interest in this problem lies in modeling the sources of risk in the projects and their different interactions within each project. Initially we look at the advantages of real options analysis and compare this approach with more traditional valuation methods, highlighting strengths and weaknesses ofeach approach in the light ofthe thesis problem. We give the background to the stages in an oil exploration and development project and identify the main common sources of risk, for example commodity prices. We discuss the appropriate representation for oil prices; in short, do oil prices behave more like equities or more like interest rates? The appropriate representation is used to model oil price as a source ofrisk. A real option valuation model based on market uncertainty (in the form of oil price risk) and geological uncertainty (reserve volume uncertainty) is presented and tested for two different oil projects. Finally, a methodology to measure the inter-relationship between oil price and other sources of risk such as interest rates is proposed using copula methods.Imperial Users onl

Generation asset planning under uncertainty.

With the introduction of competition in the electric power industry, generation asset planning must change. In this changed environment, energy companies must be able to capture the extrinsic value of their asset operations and long-term managerial flexibility for sound planning decisions. This dissertation presents a new formulation for the generation asset planning problem under market uncertainty, in which short-term operational and long-term coupling constraints associated with investment decisions are simultaneously reflected in the planning process

A Robust Multivariate Long Run Analysis of European Electricity Prices

This paper analyses the interdependencies existing in wholesale European electricity prices. The results of a multivariate long run dynamic analysis of weekly median prices reveal the presence of a strong although not perfect integration among some neighboring markets considered in the sample and the existence of common long-term dynamics of electricity prices and gas prices but not oil prices. The existence of long-term dynamics among gas prices and electricity prices may prove to be important for long-term hedging operations to be conducted even in markets where there are no electricity derivatives.European Electricity Prices, Cointegration, Interdependencies, Equilibrium Correction Model, Oil Prices

Market and Economic Modelling of the Intelligent Grid: End of Year Report 2009

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

The valuation of clean spread options: linking electricity, emissions and fuels

The purpose of the paper is to present a new pricing method for clean spread options, and to illustrate its main features on a set of numerical examples produced by a dedicated computer code. The novelty of the approach is embedded in the use of a structural model as opposed to reduced-form models which fail to capture properly the fundamental dependencies between the economic factors entering the production process

Valuation of defer and relocation options in photovoltaic generation investments by a stochastic simulation-based method

Risk management is crucial when committing investments in electricity markets. Investment projects for the generation of electricity are capital-intensive, in large part irreversible and future performance is subject to high uncertainty. Fortunately, most power generation projects have strategic flexibility for handling uncertainty and for mitigating risks under unfavorable scenarios. Modern corporate finance recognizes Real Option analysis (ROA) as the correct way to value investment projects with these characteristics. Due to both, environmental concerns and escalation of fuel prices, electricity generation from renewable sources has grown dramatically worldwide over the last decade. Renewable investment projects share many of the features mentioned. As such, option valuation methods should be applied to estimate the monetary value of flexibility in renewable energy investments. This work presents an appropriate methodology for assessing the economic value of a photovoltaic power plant under uncertainties. ROA is applied to determine the value of delaying the investment decision while waiting for better market information that would reduce acquisition costs due to progress in solar technology. The flexibility of relocating the solar facility in the future upon the appearance of a more attractive site in terms of cost, network accessibility or regulatory policies is also valued. The problem of option valuation is solved through stochastic simulation combined with recursive approximate dynamic programming techniques. The methodology developed might be used by investors for more efficient decision-making and by regulatory agencies for designing adequate support policies that encourage investment in renewable energy generation.Fil: Pringles, Rolando Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Penizzotto Bacha, Franco Victor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin