Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “transactive energy”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Simulation Methods for Transactive Energy

Transactive energy is a new approach for managing the electric power distribution system based on principles of economic value, with engineering constraints. This combined approach is well established in the bulk power system, but transactive energy incorporates many more participants and new types of participant, including end-use loads and distributed energy resources. The market mechanisms are new and largely untested, which calls for careful design to avoid unwanted results and behaviors. The supporting simulation platforms must be flexible and multi-disciplinary (market, distribution grid, bulk grid, buildings, appliances, distributed energy resources, communication systems). These platforms make it possible for products to be developed and tested on large realistic models, before deployment into the real world.

load modeling↗
DOE OSTI2017

Interaction and Impact Studies for Distributed Energy Resource, Transactive Energy, and Electric Grid, using High Performance Computing ?based Modeling and Simulation

The electric utility industry is undergoing significant transformations in its operation model, including a greater emphasis on automation, monitoring technologies, and distributed energy resource management systems (DERMS). With these changes and new technologies, while driving greater efficiencies and reliability, these new models may introduce new vectors of cyber attack. The appropriate cybersecurity controls to address and mitigate these newly introduced attack vectors and potential vulnerabilities are still widely unknown and performance of the control is difficult to vet. This proposal argues that modeling and simulation (M&S) is a necessary tool to address and better understand these problems introduced by emerging technologies for the grid. M&S will provide electric utilities a platform to model its transmission and distribution systems and run various simulations against the model to better understand the operational impact and performance of cybersecurity controls.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

On Anomaly Detection for Transactive Energy Systems with Competitive Market

Two anomaly-detection criteria are proposed for transactive energy systems with competitive markets. Participants of transactive energy systems seek an optimal power allocation through hybrid economic-control methods to facilitate the integration of various types of distributed energy resources to power distribution systems. In transactive energy systems, every participant is assumed to be a rational entity, and consumers have diminishing marginal utility and suppliers have increasing marginal cost. With the first proposed anomaly-detection criterion, the monotonicity of marginal cost and marginal utility are examined. The impact of line flow constraints is also taken into consideration. Then, the second anomaly-detection criterion is proposed for TESs with marginal cost and marginal utility which change faster than a certain rate. The second criterion is more accurate than the first one for TES with marginal cost and marginal utility which change faster than a certain rate, but it requires the knowledge of that rate. Neither criteria requires more data than those necessary to find the optimal power allocation and the market-clear price in a transactive energy system. Therefore, the proposed criteria do not disclose any more data than necessary. As the monotonicity of marginal cost and marginal utility in a TES with competitive markets results in convex objective functions in an optimization problem and strongly convex ones when marginal cost and marginal utility changes faster than a certain rate, the two detection criteria are also applicable to anomaly detection of general convex optimization problems. Simulations are carried out to show the efficacy of the proposed criteria to detect anomalies caused by cyberattacks.

Wang, Peng↗
DOE OSTI2022

Equity in Transactive Energy Systems

Transactive energy (TE) research primarily focuses on efficient and reliable operation of the electricity grid by using economic or market-based constructs to incorporate significant amounts of responsive, demand-side assets. This research examines the literature to evaluate if the design of TE demonstrations incorporates microeconomic principles of equity and fairness. We also consider the extent to which the design and implementation of TE affects energy inequities, and how these inequities could be addressed in future research with specific equity valuation metrics. The design of TE systems can impact energy equity across several dimensions and we provide recommendations for incorporating microeconomic principles of equity and fairness in TE system architecture as well as metrics for improving equitable outcomes in TE system design, implementation, and performance.

Energy, Transactive Energy, Economics↗
DOE OSTI2018

An Approach to Uncertainty Identification in the Valuation of Transactive Energy Systems

Valuation of transactive energy (TE) systems should be supported by a structured and systematic approach to uncertainty identification, assessment, and treatment in the interest of risk-informed decision making. The proposed approach, a variation of fault tree analysis, is anticipated to support valuation analysts in analyzing conventional and transactive system scenarios. This approach allows for expanding the entire tree up to the level of minute details or collapsing them to a level sufficient enough to get an overview of the problem. Quantification scheme for the described approach lends itself for valuation. The method complements value exchange analysis, simulation, and field demonstration studies. The practicality of the proposed approach is demonstrated through uncertainty assessment of the smart grid interoperability panel peak heat day scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Simulation-Based Valuation of Transactive Energy Systems

Transactive Energy (TE) has been recognized as a promising technique for integrating responsive loads and distributed energy resources as well as advancing grid modernization. To help the industry better understand the value of TE and compare different TE schemes in a systematic and transparent manner, a comprehensive simulation-based TE valuation method is developed. The method has the following salient features: 1) it formally defines the valuation scenarios, use cases, baseline and valuation metrics; 2) an open-source simulation platform for transactive energy systems has been developed by integrating transmission, distribution and building simulators, and plugin TE and non-TE agents through the Framework for Network Co-Simulation (FNCS); 3) transparency and flexibility of the valuation is enhanced through separation of simulation and valuation, base valuation metrics and final valuation metrics. In conclusion, a valuation example based on the Smart Grid Interoperability Panel (SGIP) Use Case 1 is provided to demonstrate the developed TE simulation program and the valuation method.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Valuation Diagramming and Accounting of Transactive Energy Systems

Transactive energy (TE) systems support both economic and technical objectives of a power system including efficiency and reliability. TE systems utilize value-driven mechanisms to coordinate and balance responsive supply and demand in the power system. Economic performance of TE systems cannot be assessed without estimating their value. Estimating the potential value of transactive energy systems requires a systematic valuation methodology that can capture value exchanges among different stakeholders (i.e., actors) and ultimately estimate impact of one TE design and compare it against another one. Such a methodology can help decision makers choose the alternative that results in preferred outcomes. This paper presents a valuation methodology developed to assess value of TE systems. A TE use-case example is discussed, and metrics identified in the valuation process are quantified using a TE simulation program.

Makhmalbaf, Atefe↗
DOE OSTI2019

Transactive Energy: Stabilizing Oscillations in Integrated Wholesale-Retail Energy Markets

Transactive energy is a control technique that uses market mechanisms to achieve desired control objectives. A number of studies have identified a well-known economic theory effect called a ``cobweb'' that can occur when the transactive control system is not properly designed. This work proposes a control framework to mitigate this effect by adding an extra control layer. This proposed control layer expresses the flexibility of load on the distribution side into the transmission system. By using FNCS, a co-simulation platform, new control framework is evaluated on a power system model composed of a single transmission system with the potential of thousands of flexible distribution systems. The co-simulation is able to replicate the cobweb effect and demonstrate that improper transactive control design can create oscillations at high penetrations of price-responsive loads. Furthermore, the proposed transactive control system is shown to be able to eliminate the cobweb effect while also effectively diminishing price and load spikes.

power distribution control, power distribution eco↗
DOE OSTI2016

The View from the Top of the Mountain: Building a Community of Practice with the GridWise Transactive Energy Framework

The topic of “transactive energy” has received growing attention over the past eighteen months. It has been a part, for example, of the NY Reforming the Energy Vision discussions and the topic of activities such as the National Institute of Standards Transactive Energy Challenge. The growing discussion stems from the realization that new approaches are needed to efficiently and reliably integrate growing numbers of distributed energy resources. In anticipation of the applicability of transactive energy systems to emerging challenges in Grid integration, the GridWise® Architecture Council (GWAC) began to build a community of practice in the area of transactive energy systems. Starting with a workshop on the topic of “transactive energy” in May 2011, the Council brought together about a dozen interested researchers and practitioners from utilities, vendors, labs and academia to compare their approaches and experience in order to create common definitions and understanding within this topical area. This was followed in March 2012 with a second workshop during which about twice as many attendees continued the discussion. At this workshop the need for both a roadmap and a document documenting the foundations of transactive energy, common vocabulary and other definitional aspects was recognized. These two workshops led to the Council organizing the First International Conference and Workshop on Transactive Energy which took place May 23 – 24, 2013 in Portland, Oregon. The Council has continued this work with additional topical workshops, the Second International Conference and Workshop on Transactive Energy held in December 2014, and is currently organizing the Third International Conference and Workshop on Transactive Energy Systems to be held in May 2016. This article provides a summary of the Council’s work to build the community of practice through creation of a Transactive Energy Framework document and related activities. In addition to seeing transactive energy discussions on the agenda for many conferences there are also group activities relating to transactive energy being coordinated by both NIST and SGIP with which GWAC is also involved. The NIST work aims to develop and enhance modeling and simulation tools and integration into modeling and simulation platforms for Transactive Energy evaluation, as well as demonstrate how different transactive approaches may be used to improve reliability and efficiency of the electric grid. This will be accomplished through development of a set of scenarios that can serve as ongoing reference points for modeling and simulation. It is also an example of helping to develop a Transactive Energy community. The ongoing Transactive Energy Coordination Group formed by SGIP reviews the progress and directions of transactive energy activities in related parts of the SGIP and collaborating organizations such as GWAC. One of its activities is assembling a core set of transactive energy use cases as representative of the transactive energy interface requirements. This will enable assessment of interoperability requirements for transactive energy applications and an analysis of standards coverage, gaps, and future needs.

Tranasactive Energy↗
DOE OSTI2024

The Transactive Energy Network Template Metamodel

While transactive energy, which is defined as an allocation of electricity based on dynamically discovered values or prices, has been extensively studied, its uptake and use has been slow. This report describes a tool, the transactive network template, which should hasten the creation and uptake of transactive energy networks. Some basic principles of transactive energy are familiar from existing wholesale electricity markets. Locational prices are calculated today for zones within bulk electric transmission systems. Locational prices differ while accounting for the locational costs of electricity generation and the losses and constraints incurred when electricity is transmitted from generators and distributed to consumers. A transactive energy network might include these transmission zones. However, current research strives to apply transactive energy also in electricity distribution circuits, buildings, and even for individual generating and consuming devices. At the same time, researchers explore how to apply transactive energy in real time during increasingly shorter time intervals. Automated computational agents become necessary as transactive energy becomes applied to smaller circuit zones and at faster dynamic timescales. A transactive energy network is an example of a multi-agent system. Each zone in the network is represented by its transactive agent, which makes decisions for and acts on behalf of a business entity that is responsible for and manages one of the circuit regions. A transactive energy network is also an example of a decentralized, distributed control system. Control decisions and responsibilities are distributed among the network’s transactive agents. The transactive agents are independent; that is, there typically is no centralized authority or oversight function. Instead, transactive agents exchange transactive signals and thereby negotiate the prices and quantities of electricity that they will exchange. Initially, the circuit regions and responsibilities of transactive agents appear to be very dissimilar. Each circuit region may comprise transmission, distribution, or building-level circuits. Each has a unique position and electrical connectivity within the transactive energy network. Each possesses unique assets that either generate or consume electricity, and these (e.g., renewable energy generator, diesel generator, aggregate utility load, building load, space conditioning, refrigerator, etc.) may further differ in their price flexibility and in their strategies for responding to dynamic electricity prices. Given such diversity, an implementer’s first inclination might be to start from scratch to define all these devices and to engineer their seemingly unique interactions. Given that each implementer’s perspective may be narrow within a transactive energy network, it is unlikely that uniquely engineered systems would interact well. This is where the transactive network template is applicable. The transactive network template is a metamodel that has been developed to guide implementers as they configure their own transactive agent within a network of such agents. The object-oriented design of the transactive network template provides basic code object types that may be used and extended by implementers to represent each of the assets in their circuit region. These objects further facilitate the transactive agent’s necessary computations, which are divided among responsibilities to schedule power usage, balance electric supply and demand, and coordinate the exchange of electricity with the other transactive agents. This report addresses the conceptual transactive network template design. Implementers are directed to more formal design documents and reference implementations. A Python™-based1 reference implementation of the transactive network template has been coded, and three implementations have been configured to represent a national laboratory and two university campuses. Version 2 of the transactive node template generalizes the market class and its methods to facilitate multiple, and more diverse market coordination mechanisms than were facilitated by and demonstrated using Version 1. Version 3 includes new Appendix B, which addresses the designs of methods that would make dynamic prices track approved electricity rates. In the future, the author wishes to make the transactive network template more generally applicable to networks that require more accurate power flow. Development of the transactive network template is jointly funded by the U.S. Department of Energy (DOE) Energy Efficiency and Renewable Energy and the DOE Office of Electricity. In late 2015, one of the first projects to be funded by the DOE Grid Laboratory Modernization Laboratory Consortium was the Clean Energy and Transactive Campus project, led by Pacific Northwest National Laboratory. DOE funds were matched by an investment by the Washington Department of Commerce through its Clean Energy Fund. The transactive network template was developed to guide the implementation of transactive energy networks within this project’s scope.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Swing Contract-Based Valuation for Distributed Energy Resources in Transactive Energy Systems: A Reinforcement Learning Approach

With the proliferation of distributed energy resources (DERs) and power grids with high fractions of renewable energy, market constructs are evolving to allow DERs to participate in multiple possible markets, at different levels of grid hierarchy. The effective participation of DERs in market environments is aided by swing contract-based pricing mechanisms, whereby DERs have a two-part compensation structure – one for their reservation/commitment and another for performancedriven ex-post payment for their actual mobilization during dispatch. In this paper, we propose a reinforcement learningbased (Q-learning) approach that allows a rational DER agent to select the market it wants to participate in within a composite market environment where individual markets are coordinated by possibly different actors. The proposed Q-learning framework aids DERs in their self-valuation by implicitly maximizing their own payoff through market participation, assuming a swing contract-based compensation structure. We complement our work through simulation-based investigations where factors affecting the DER decision making process, such as parametric uncertainties in market (and grid) environments, are studied.

Naqvi, Syed Ahsan Raza↗
DOE OSTI2013

GridWise Transactive Energy Framework (DRAFT Version)

Over the past decade, the use of demand response and other flexible distributed resources for market efficiency and grid reliability has grown dramatically. Federal and state policy objectives point to an important role for customers’ loads, generation and storage in the management of an increasingly unpredictable power system. As we consider the need to substantially scale the use of flexible distributed energy resources, there has been growing attention to the need to address not only the economics, but also the control system implications to ensure grid reliability. This has led to a focus on an area of activity called “Transactive Energy.” Transactive Energy refers to the combination of economic and control techniques to improve grid reliability and efficiency. These techniques may also be used to optimize operations within a customer’s facility. The Department of Energy has supported the GridWise® Architecture Council (“the Council”) in developing a conceptual framework that can be used in developing architectures, and designing solutions related to transactive energy. The goal of this effort is to encourage and facilitate collaboration among the many stakeholders involved in the transformation of the power system and thereby advance the practical implementation of transactive energy.

GridWise↗
DOE OSTI2019

Considerations for Commercial Building Participation in a Transactive Energy System

With transactive energy (TE), commercial customers have the potential to provide flexibility to the electricity grid by shifting electricity demand to avoid high-priced periods. The characteristics of commercial buildings and end uses are explored to determine factors supporting the feasibility of participation in a TE system. The key factors of commercial buildings that affect the potential benefits of participating in a TE system are the ownership and lease arrangement, the perceived impact of occupant comfort on worker productivity and customer sales, the state of buildings and equipment or the readiness of the buildings, and in some cases the additional standards in place that are unique to commercial settings. These characteristics will affect the commercial customer’s ability to adjust the timing of electricity consumption and benefit from a TE system.

transactive energy, commercial lease, occupant com↗
DOE OSTI2017

Transactive Energy Simulation Platform

Transactive Energy Simulation Platform (TESP) combines existing domain simulators in the electric power grid, buildings, and communication systems, with new transactive agents, growth models and evaluation scripts. The existing domain simulators include GridLAB-D for the distribution grid and single-family residential buildings, MATPOWER for transmission and bulk generation, EnergyPlus for large buildings and ns-3 for communication systems. More are planned for subsequent versions of TESP. The new elements are: TEAgents - simulate market participants and transactive systems for market clearing. Some of this functionality was extracted from GridLAB-D and implemented in Python for customization by PNNL and others. Growth Model - a means for simulating system changes over a multiyear period, including both normal load growth and specific investment decisions. Customizable in Python code. Evaluation Script - a means of evaluating different transactive systems through customizable post-processing in Python code. TESP will run on Linux, Windows and Mac OS X.

McDermott, Tom↗
DOE OSTI2013

Creating a Transactive Energy Framework: Improving Reliability and Efficiency

The use of demand response and other flexible distributed resources over the past decade for market efficiency and grid reliability has grown dramatically. Federal and state policy objectives point to an important role for customer loads, generation and storage in the management of an increasingly unpredictable power system. As we consider the need to substantially scale the use of flexible distributed energy resources, there has been growing attention to the need to address not only the economics, but also the control system implications to ensure grid reliability. This has led to a focus on an area of activity called “Transactive Energy.” Transactive Energy refers to the combination of economic and control techniques that improve grid reliability and efficiency. These techniques may also be used to optimize operations within a customer’s facility. The Department of Energy has supported the GridWise® Architecture Council (“the Council”) to develop a framework to provide opportunity for collaboration among the many stakeholders involved in the transformation of the power system.

GWAC↗
DOE OSTI2018

A Novel Transactive Energy Control Mechanism for Collaborative Networked Microgrids

Collaboration of networked microgrids (NMGs) with diverse generation sources is a promising solution to smooth volatile generation output and enhance the utilization efficiency of renewable energies. In addition to centralized and decentralized collaboration mechanisms, transactive energy control (TEC) is an emerging and effective market-based control to enable energy transactions among distributed entities such as NMGs. However, existing studies on TEC suffer from several major weaknesses such as unconstrained/simplified model formulations and slow convergence rates. This paper proposes here a novel TEC mechanism to tackle these weaknesses. First, the centralized mechanism, de-centralized mechanism, and subgradient-based TEC mechanism to coordinate the operation of NMGs are briefly reviewed and modeled by a scenario-based stochastic optimization method. A new TEC mechanism is then proposed, consisting of a TEC framework, mathematical model, pricing rule, and algorithm. The optimality of the proposed TEC mathematical model and pricing rule is demonstrated. The effectiveness of the proposed TEC mechanism is verified in case studies where the NMGs operate in grid-connected, islanded, and congested modes. The advantages of the proposed TEC mechanism are also illustrated through comparisons with the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism.

24 POWER TRANSMISSION AND DISTRIBUTION↗