Distributed Energy Neural Network Integration System: Year One Final Report

Regan, Timothy F.; Sinnock, H.; Davis, Andrew F.

doi:10.2172/15003911

Cited by 7 publications

(3 citation statements)

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“…However, accuracy is sacrificed by misrepresenting the tariff. Regan, Sinnock, and Davis (2003) describe the initial stage of work done in developing a distributed generation dispatch controller suitable for a neighborhood.…”

Section: Dg Dispatch Optimizationmentioning

confidence: 99%

The Effect of Magnetic Field on HTS Leads What Happens when thePower Fails at RAL?

Green¹

2007

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This report describes the development and application of a dispatch optimization algorithm for integrated energy systems (IES) comprised of on-site cogeneration of heat and electricity, energy storage devices, and demand response opportunities. This work is intended to aid commercial and industrial sites in making use of modern computing power and optimization algorithms to make informed, nearoptimal decisions under significant uncertainty and complex objective functions. The optimization algorithm uses a finite set of randomly generated future scenarios to approximate the true, stochastic future; constraints are included that prevent solutions to this approximate problem from deviating from solutions to the actual problem.The algorithm is then expressed as a mixed integer linear program, to which a powerful commercial solver is applied. A case study of United States Postal Service Processing and DistributionCenters (P&DC) in four cities and under three different electricity tariff structures is conducted to 1) determine the added value of optimal control to a cogeneration system over current, heuristic control strategies; 2) determine the value of limited 2 electric load curtailment opportunities, with and without cogeneration; and 3) determine the trade-off between least-cost and least-carbon operations of a cogeneration system.Key results for the P&DC sites studied include 1) in locations where the average electricity and natural gas prices suggest a marginally profitable cogeneration system, optimal control can add up to 67% to the value of the cogeneration system; optimal control adds less value in locations where cogeneration is more clearly profitable; 2) optimal control under real-time pricing is a) more complicated than under typical time-of-use tariffs and b) at times necessary to make cogeneration economic at all; 3) limited electric load curtailment opportunities can be more valuable as a compliment to the cogeneration system than alone; and 4) most of the trade-off between least-cost and least-carbon IES is determined during the system design stage; for the IES system considered, there is little difference between leastcost control and least-carbon control.i Preface This report describes the development and application of a dispatch optimization algorithm for integrated energy systems comprised of on-site cogeneration of heat and electricity, energy storage devices, and demand response opportunities. This work is intended to aid commercial and industrial sites in making use of modern computing power and optimization algorithms to make informed, near-optimal decisions under significant uncertainty and complex objective functions.This work makes headway in the more general and daunting field of multi-stage operational optimization, where multi-stage is in the hundreds of time-steps and optimization is over many mixed-integer decision variables. The context of the work, however, is an application to commercial and industrial energy consumption, a topic rising in popularity on the coat-tails of economic...

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Section: Dg Dispatch Optimizationmentioning

confidence: 99%

The Effect of Magnetic Field on HTS Leads What Happens when thePower Fails at RAL?

Green¹

2007

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“…While prior research has considered dispatch optimization for IES systems comprised of DG, storage, and/or curtailment (e.g. [4]), a literature review revealed none that considered demand charges or stochastic DG outages. These, however, are exactly details of the IES problem that cause real-world results to deviate from design estimates.…”

Section: The Optimization Problem For Integrated Energy Systemsmentioning

confidence: 99%

Integrated Energy System Dispatch Optimization

Firestone

Städler

Marnay

2006

2006 IEEE International Conference on Industrial Informatics

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-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 site's 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.

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“…This simplifies the dispatch problem considerably: each time-step can be examined independently, and total annual or lifetime costs are just the sum of costs at each time-step. However, accuracy is sacrificed by misrepresenting the tariff Regan, Sinnock, and Davis (2003). describe the initial stage of work done in developing a distributed generation dispatch controller suitable for a neighborhood.…”

mentioning

confidence: 99%

Optimal Real-time Dispatch for Integrated Energy Systems

Firestone

2007

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This report describes the development and application of a dispatch optimization algorithm for integrated energy systems (IES) comprised of on-site cogeneration of heat and electricity, energy storage devices, and demand response opportunities. This work is intended to aid commercial and industrial sites in making use of modern computing power and optimization algorithms to make informed, nearoptimal decisions under significant uncertainty and complex objective functions. The optimization algorithm uses a finite set of randomly generated future scenarios to approximate the true, stochastic future; constraints are included that prevent solutions to this approximate problem from deviating from solutions to the actual problem. The algorithm is then expressed as a mixed integer linear program, to which a powerful commercial solver is applied. A case study of United States Postal Service Processing and Distribution Centers (P&DC) in four cities and under three different electricity tariff structures is conducted to 1) determine the added value of optimal control to a cogeneration system over current, heuristic control strategies; 2) determine the value of limited My doctoral experience was made meaningful, enjoyable, and possible with the help and support of many, many people. It is with great pleasure that I have an opportunity to acknowledge some of them. I would like to start by thanking my parents, Marc and Carole Firestone, who raised me to take education seriously, be curious, and pursue what interests me. I would further like to thank my father for keeping tabs on my research and offering his empathy during my times of frustration. Many other relatives have also provided encouragement throughout. Thanks to my grandparents Sam and Bertha Farber and Leon and Nancy Firestone-I wish I could have finished a little sooner so that you could enjoy this more. Thanks also to my sister Lisa, my cousins, and my aunts and uncles who have encouraged me and challenged me to explain my work in simple terms. Many faculty members at U.C. Berkeley have given generously of their time and energy. I would like to thank the following professors for there assistance: • Alice Agogino, Mechanical Engineering-qualifying exam committee member. Alice and her research group were very kind in critiquing a trial run of my exam presentation. • Dave Auslander, Mechanical Engineering-dissertation committee chair. • Van Carey, Mechanical Engineering-qualifying exam committee member.

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Distributed Energy Neural Network Integration System: Year One Final Report

Cited by 7 publications

References 0 publications

The Effect of Magnetic Field on HTS Leads What Happens when thePower Fails at RAL?

The Effect of Magnetic Field on HTS Leads What Happens when thePower Fails at RAL?

Integrated Energy System Dispatch Optimization

Optimal Real-time Dispatch for Integrated Energy Systems

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