Distributed Generation Investment by a Microgrid under Uncertainty

This paper examines a California-based microgrid?s decision to invest in a distributed generation (DG) unit fuelled by natural gas. While the long-term natural gas generation cost is stochastic, we initially assume that the microgrid may purchase electricity at a fixed retail rate from its utility. Using the real options approach, we find a natural gas generation cost threshold that triggers DG investment. Furthermore, the consideration of operational flexibility by the microgrid increases DG investment, while the option to disconnect from the utility is not attractive. By allowing the electricity price to be stochastic, we next determine an investment threshold boundary and find that high electricity price volatility relative to that of natural gas generation cost delays investment while simultaneously increasing the value of the investment. We conclude by using this result to find the implicit option value of the DG unit when two sources of uncertainty exist

Microgrids and Heterogeneous Power Quality and Reliability

This paper describes two stylized alternative visions of how the power system might evolve to meet future requirements for the high quality electricity service that modern digital economies demand, a supergrids paradigm and a dispersed paradigm. Some of the economics of the dispersed vision are explored, and perspectives are presented on both the choice of homogeneous universal power quality upstream in the electricity supply chain and on the extremely heterogeneous requirements of end-use loads. It is argued that meeting the demanding requirements of sensitive loads by local provision of high quality power may be more cost effective than increasing the quality of universal homogeneous supply upstream in the legacy grid. Finally, the potential role of microgrids in delivering heterogeneous power quality is demonstrated by reference to two ongoing microgrid tests in the U.S. and Japan

Addressing an Uncertain Future Using Scenario Analysis

The Office of Energy Efficiency and Renewable Energy (EERE) has had a longstanding goal of introducing uncertainty into the analysis it routinely conducts in compliance with the Government Performance and Results Act (GPRA) and for strategic management purposes. The need to introduce some treatment of uncertainty arises both because it would be good general management practice, and because intuitively many of the technologies under development by EERE have a considerable advantage in an uncertain world. For example, an expected kWh output from a wind generator in a future year, which is not exposed to volatile and unpredictable fuel prices, should be truly worth more than an equivalent kWh from an alternative fossil fuel fired technology. Indeed, analysts have attempted to measure this value by comparing the prices observed in fixed-price natural gas contracts compared to ones in which buyers are exposed to market prices (see Bolinger, Wiser, and Golove and (2004)). In addition to the routine reasons for exploring uncertainty given above, the history of energy markets appears to have exhibited infrequent, but troubling, regime shifts, i.e., historic turning points at which the center of gravity or fundamental nature of the system appears to have abruptly shifted. Figure 1 below shows an estimate of how the history of natural gas fired generating costs has evolved over the last three decades. The costs shown incorporate both the well-head gas price and an estimate of how improving generation technology has gradually tended to lower costs. The purpose of this paper is to explore scenario analysis as a method for introducing uncertainty into EERE's forecasting in a manner consistent with the preceding observation. The two questions are how could it be done, and what is its academic basis, if any. Despite the interest in uncertainty methods, applying them poses some major hurdles because of the heavy reliance of EERE on forecasting tools that are deterministic in nature, such as the Energy Information Administration's (EIA's) National Energy Modeling System (NEMS). NEMS is the source of the influential Annual Energy Outlook whose business-as-usual (BAU) case, the Reference Case, forms the baseline for most of the U.S. energy policy discussion. NEMS is an optimizing model because: 1. it iterates to an equilibrium among modules representing the supply, demand, and energy conversion subsectors; and 2. several subsectoral models are individually solved using linear programs (LP). Consequently, it is deeply rooted in the recent past and any effort to simulate the consequences of a major regime shift as depicted in Figure 1 must come by applying an exogenously specified scenario. And, more generally, simulating futures that lie outside of our recent historic experience, even if they do not include regime switches suggest some form of scenario approach. At the same time, the statistical validity of scenarios that deviate significantly outside the ranges of historic inputs should be questioned

Integrated Energy System Dispatch Optimization

On-site cogeneration of heat and electricity, thermal and electrical storage, and curtailing/rescheduling demand options are often cost-effective to commercial and industrial sites. This collection of equipment and responsive consumption can be viewed as an integrated energy system(IES). The IES can best meet the sites cost or environmental objectives when controlled in a coordinated manner. However, continuously determining this optimal IES dispatch is beyond the expectations for operators of smaller systems. A new algorithm is proposed in this paper to approximately solve the real-time dispatch optimization problem for a generic IES containing an on-site cogeneration system subject to random outages, limited curtailment opportunities, an intermittent renewable electricity source, and thermal storage. An example demonstrates how this algorithm can be used in simulation to estimate the value of IES components

Competitive ancillary service procurement in California

California has undertaken a major restructuring of its electricity utility sector. Most electricity is now sold in open markets operated by the PX and other entities. Bilateral contracting among some market participants is also permitted. A group of independent generating companies bids into these markets together with out of state resources. In addition to these markets, CAISO operates markets for both imbalance energy and AS, a quite unusual feature of the California system. These markets were initially quite chaotic and were rife with market power problems. However, various reforms have now created a system that functions well. During the restructuring process, special provisions were made to protect public purpose programs, including renewable generation

Assessment of Distributed Energy Adoption in Commercial Buildings:Part 1: An Analysis of Policy, Building Loads, Tariff Design, andTechnology Development

Rapidly growing electricity demand brings into question theability of traditional grids to expand correspondingly while providingreliable service. An alternative path is the wider application ofdistributed energy resource (DER) that apply combined heat and power(CHP). It can potentially shave peak loads and satiate its growing thirstfor electricity demand, improve overall energy efficiency, and lowercarbon and other pollutant emissions. This research investigates a methodof choosing economically optimal DER, expanding on prior studies at theBerkeley Lab using the DER design optimization program, the DistributedEnergy Resources Customer Adoption Model (DER-CAM). DER-CAM finds theoptimal combination of installed equipment from available DERtechnologies, given prevailing utility tariffs, site electrical andthermal loads, and a menu of available equipment. It provides a globaloptimization, albeit idealized, that shows how the site energy loads canbe served at minimum cost by selection and operation of on-sitegeneration, heat recovery, and cooling. Utility electricity and gastariffs are key factors determining the economic benefit of a CHPinstallation, however often be neglected. This paper describespreliminary analysis on CHP investment climate in the U.S. and Japan. DERtechnologies, energy prices, and incentive measures has beeninvestigated

Introduction to the Buildings Sector Module of SEDS

SEDS is a stochastic engineering-economics model that forecasts economy-wide energy consumption in the U.S. to 2050. It is the product of multi-laboratory collaboration among the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Argonne National Laboratory (ANL), Lawrence Berkeley National Laboratory (LBNL), and Lumina Decision Systems. Among national energy models, SEDS is unique, as it is the only model written to explicitly incorporate uncertainty in its inputs and outputs. The primary purpose of SEDS is to estimate the impact of various US Department of Energy (DOE)R&D and policy programs on the performance and subsequent adoption rates of technologies relating to every energy consuming sector of the economy (shown below). It has previously been used to assist DOE in complying with the Government Performance and Results Act of 1993 (GPRA). The focus of LBNL research has been exclusively on develop the buildings model (SBEAM), which is capable of running as a stand-alone forecasting model, or as a part of SEDS as a whole. The full version of SEDS, containing all sectors and interaction is also called the 'integrated' version and is managed by NREL. Forecasts from SEDS are often compared to those coming from National Energy Modeling System (NEMS). The intention of this document is to present new users and developers with a general description of the purpose, functionality and structure of the buildings module within the Stochastic Energy Deployment System (SEDS). The Buildings module, which is capable of running as a standalone model, is also called the Stochastic Buildings Energy and Adoption Model (SBEAM). This document will focus exclusively on SBEAM and its interaction with other major sector modules present within SEDS. The methodologies and major assumptions employed in SBEAM will also be discussed. The organization of this report will parallel the organization of the model itself, being divided into major submodules. As the description progresses, the nature of modules will change from broad, easily understood concepts to lower-level data manipulation. Because SBEAM contains dozens of submodules and hundreds of variables, it would not be relevant or useful to describe each and every one. Rather, the investigation will focus more generally on the operations performed throughout the model. This manual is by no means a complete description of SBEAM; however it should provide the foundation for an introductory understanding of the model. The manual assumes a basic level of understating of Analytica{reg_sign}, the platform on which SEDS and SBEAM have been developed