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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Transactive Control Design for Commercial Buildings to Provide Demand Response

Transactive coordination and control is a new type of distributed strategy that is designed to engage various self-interested distributed energy resources such as controllable loads, distributed generators, and energy storage for providing demand response. In this paper, a novel transactive control design is proposed for commercial buildings to coordinate the electricity demand of the package rooftop air conditioning units (RTUs) for demand response. Such a coordination is realized by introducing a local relationship between the temperature setpoint and the market clearing price. With the proposed market-based control, the demand of individual RTUs is adjusted by changing their temperature setpoints as a response to the market clearing prices. Two dierent scenarios of demand response are considered herein. One is to limit the peak demand of commercial buildings, in which a new market clearing strategy based on proportionalintegral- derivative control with respect to the demand limit is proposed. The other one is to perform price responsive control, in which the total demand of commercial buildings will respond to the real-time pricing. The proposed transactive strategy is also tested and demonstrated on a real occupied building located at Pacic Northwest National Laboratory. The eld testing results conrm the eectiveness of the proposed transactive coordination and control for commercial building with RTUs to provide demand response.

Huang, Sen↗
DOE OSTI2018

Transactive System: Part II: Analysis of Two Pilot Transactive Systems using Foundational Theory and Metrics

This document is the second of a two-part report. Part 1 reviewed several demonstrations of transactive control and compared them in terms of their payoff functions, control decisions, information privacy, and mathematical solution concepts. It was suggested in Part 1 that these four listed components should be adopted for meaningful comparison and design of future transactive systems. Part 2 proposes qualitative and quantitative metrics that will be needed to compare alternative transactive systems. It then uses the analysis and design principles from Part 1 while conducting more in-depth analysis of two transactive demonstrations: the American Electric Power (AEP) gridSMART Demonstration, which used a double –auction market mechanism, and a consensus method like that used in the Pacific Northwest Smart Grid Demonstration. Ultimately, metrics must be devised and used to meaningfully compare alternative transactive systems. One significant contribution of this report is an observation that the decision function used for thermostat control in the AEP gridSMART Demonstration has superior performance if its decision function is recast to more accurately reflect the power that will be used under for thermostatic control under alternative market outcomes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Transactive System: Part II: Analysis of Two Pilot Transactive Systems using Foundational Theory and Metrics

This document is the second of a two-part report. Part 1 reviewed several demonstrations of transactive control and compared them in terms of their payoff functions, control decisions, information privacy, and mathematical solution concepts. It was suggested in Part 1 that these four listed components should be adopted for meaningful comparison and design of future transactive systems. Part 2 proposes qualitative and quantitative metrics that will be needed to compare alternative transactive systems. It then uses the analysis and design principles from Part 1 while conducting more in-depth analysis of two transactive demonstrations: the American Electric Power (AEP) gridSMART Demonstration, which used a double-auction market mechanism, and a consensus method like that used in the Pacific Northwest Smart Grid Demonstration. Ultimately, metrics must be devised and used to meaningfully compare alternative transactive systems. One significant contribution of this report is an observation that the decision function used for thermostat control in the AEP gridSMART Demonstration has superior performance if its decision function is recast to more accurately reflect the power that will be used under for thermostatic control under alternative market outcomes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 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 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 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 OSTI2016

Valuation of Transactive Systems

This is a final report from a project funded by the U.S. Department of Energy to formulate and test a methodology for valuation of systems where transaction-based mechanisms coordinate the exchange of value between the system’s actors. Today, the principal commodity being exchanged is electrical energy, and such mechanisms are called transactive energy systems. The authors strove to lay a foundation for meaningful valuations of transactive systems in general, and transactive energy systems as a special case. The word valuation is used in many different ways. This report proposes a valuation methodology that is inclusive of many types of valuations. Many will be familiar with cost-benefit valuations, in which both costs and benefits are assessed to determine whether the assets are worth their cost. Another set of valuation methods attempt to optimize an outcome using available resources, as is the case with integrated resource planning. In the end, this report’s methodology was most influenced by and most resembles the integrated-resource-planning approach. Regardless, we wish to enforce the premise that all valuations are comparative and should clearly specify a baseline scenario. A long, annotated list of prior valuation studies and valuation methodologies that influenced this report has been appended to this report. Much research is being conducted today concerning transactive systems, but only a handful of transactive system mechanisms have been formulated and field tested. They are found to be quite diverse, and the documentation of the various mechanisms is uneven in breadth and quality. It is therefore not adequate to simply assert that a valuation scenario includes a transactive system; certain characteristics and qualities of the chosen transactive system mechanism must be defined and stated. The report lists and discusses most of the known transactive system mechanisms. It offers a set of questions that may be used to help specify important characteristics of the transactive system mechanisms, which should be conveyed along with other valuation results. A valuation methodology is proposed. Some abstraction is necessarily retained so that the methodology may be applied for the many purposes of today’s valuations and across grid, building, societal, and other domains. The report’s methodology advocates separation of operational timescales from long-term growth timescales. Operational models are defined as the models that inform impacts within the relatively short, often yearlong, operational time periods. Growth models define how the scenarios evolve from one operational period to the next (e.g., from year to year). We believe the recommended methodology is a critical step toward collaborative community platforms, where analysts and decision makers alike could contribute and borrow content within their expertise. The report then asks, what is unique about valuations when systems become coordinated by transactive systems? In answer, accurate valuations of transactive systems require careful adherence to the dynamic interaction between a system’s responsive elements and the system’s operational objectives. In every transactive system mechanism, elements respond to incentives that become revealed to them, and certain operational objectives become explicitly incentivized by the transactive system mechanism. The transactive system mechanisms define the important coupling between the responsive elements and the system’s objectives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

The Transactive Network Template Metamodel

While transactive energy—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. The transactive network template is a metamodel meant to guide implementers as they configure their own transactive agent within a network of such agents. The transactive network template’s object–oriented design approach provides implementers basic code object types, which may be used and extended to represent each of the assets in the implementer’s circuit region. These objects further facilitate the transactive agent’s necessary computations, which are divided among responsibilities to schedule assets’ power usage, to balance electric supply and demand, and to 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™-based [Python 2018] 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. In the future, the author wishes to make the transactive network template more generally applicable to networks that require more accurate power flow and those employing alternative transactive coordination mechanisms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Using redundant transactions to verify the correctness of program code execution

In the described embodiments, a processor core (e.g., a GPU core) receives a section of program code to be executed in a transaction from another entity in a computing device. The processor core sends the section of program code to one or more compute units in the processor core to be executed in a first transaction and concurrently executed in a second transaction, thereby creating a “redundant transaction pair.” When the first transaction and the second transaction are completed, the processor core compares a read-set of the first transaction to a read-set of the second transaction and compares a write-set of the first transaction to a write-set of the second transaction. When the read-sets and the write-sets match and no transactional error condition has occurred, the processor core allows results from the first transaction to be committed to an architectural state of the computing device.

Gurumurthi, Sudhanva↗
DOE OSTI2018

Transactive System: Part I: Theoretical Underpinnings of Payoff Functions, Control Decisions, Information Privacy, and Solution Concepts

The increased penetration of renewable energy has significantly changed the conditions and the operational timing of the electricity grid. More flexible, faster ramping resources are needed to compensate for the uncertainty and variability introduced by renewable energy. Distributed energy resources (DERs) such as distributed generators, energy storage, and controllable loads could help manage the power grid in terms of both economic efficiency and operational reliability. In order to realize the benefits of DERs, coordination and control approaches must be designed to enable seamless integration of DERs into the power grid. Transactive coordination and control is a new approach for DER integration, where individual resources are automated and engaged through market interaction. Transactive approaches use economic signals—prices or incentives—to engage DERs. These economic signals must reflect the true value of the DER contributions, so that they seamlessly and equitably compete for the opportunities that today are only available to grid-owned assets. Value signals must be communicated to the DERs in near-real time, the assets must be imbued with new forms of distributed intelligence and control to take advantage of the opportunities presented by these signals, and they must be capable of negotiating and transacting a range of market-driven energy services. The concepts of transactive energy systems are not new, but build upon evolutionary economic changes in financial and electric power markets. These concepts also recognize the different regional structures of wholesale power markets, electricity delivery markets, retail markets, and vertically integrated service provider markets. Although transactive energy systems are not revolutionary, they will be transformational in their ability to provide flexibility and operational efficiency. A main goal of this research is to establish useful foundation for analysis of transactive energy systems and to facilitate new transactive energy system design with demonstrable guarantees on stability and performance. Specifically, the goals are to (1) establish a theoretical basis for evaluating the performance of different transactive systems, (2) devise tools to address canonical problems that exemplify challenges and scenarios of transactive systems, and (3) provide guidelines for design of future transactive systems. This report, Part 1 of a two part series, advances the above-listed research objectives by reviewing existing transactive systems and identifying a theoretical foundation that integrates payoff functions, control decisions, information privacy, and mathematical solution concepts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Transactive System Program Communication System Cost Model

Transactive energy is an economics and controls technique that aims to dynamically manage the generation and consumption of electricity within the power system. Much research has been done in the area of demonstrating the capability and performance of transactive systems, as well as valuing the benefits a transactive system can provide. Significantly less research has been done in the area of estimating the costs of a transactive system. This work aimed to build a model that would estimate the costs of the communications system that would be necessary to support a transactive system and be realized by a local utility. Dynamic pricing, and a customer’s response to this pricing, is a key aspect of a transactive system; this would require a two-way communication system between utilities and customers. Since the system will vary based on many factors, the cost model was consequently designed for use in a wide variety of applications. The cost model requires some basic user inputs regarding the system being analyzed, such as location and size. The user also identifies one or more generic circuit models that best represent the system. Additionally, a user can, but is not required to, change assumed equipment and installation costs and equipment lifetime within the model. These inputs will result in 20 year cost estimates for three different communication technologies that are capable of supporting the bandwidth needs of a transactive energy system. The three communication systems included in the model are cellular, fiber optic and wireless mesh. Each vary significantly from one another and offer unique advantages and barriers. A cellular network avoids high upfront costs, but is limited to areas where there is coverage by the service provider and subject to the ongoing rate established with the service provider. A fiber optic system requires a large up-front cost, but it will provide a reliable system to areas where cellular and mesh may be unable to do so. Additionally, fiber provides more bandwidth than necessary for transactive systems and can offer additional business opportunities. A mesh system can be installed for less upfront costs than fiber and offers enough bandwidth to support a transactive system, but the ability to model this technology is done with less certainty than the other two options. The estimated costs include the communication system that connects the customers to their nearest substation, in addition to the smart meter and radio that would be required for each customer. The communications from the substation to the utility are not included in estimates, as the need for upgrading that system would be assessed on a case-by-case basis. Although the cost of the smart meter and radio is included, no other appliance specific cost is included in the estimate provided by the model. The communication system is a key aspect of the development of a transactive system. This work resulted in easily being able to estimate the costs of a communication system specific to the system being studied. This cost model will aid ongoing valuation and simulation work in the field of transactive energy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Transactive System: Part I: Theoretical Underpinnings of Payoff Functions, Control Decisions, Information Privacy, and Solution Concepts

The increased penetration of renewable energy has significantly changed the conditions and the operational timing of the electricity grid. More flexible, faster ramping resources are needed to compensate for the uncertainty and variability introduced by renewable energy. Distributed energy resources (DERs) such as distributed generators, energy storage, and controllable loads could help manage the power grid in terms of both economic efficiency and operational reliability. In order to realize the benefits of DERs, coordination and control approaches must be designed to enable seamless integration of DERs into the power grid. Transactive coordination and control is a new approach for DER integration, where individual resources are automated and engaged through market interaction. Transactive approaches use economic signals—prices or incentives—to engage DERs. These economic signals must reflect the true value of the DER contributions, so that they seamlessly and equitably compete for the opportunities that today are only available to grid-owned assets. Value signals must be communicated to the DERs in near-real time, the assets must be imbued with new forms of distributed intelligence and control to take advantage of the opportunities presented by these signals, and they must be capable of negotiating and transacting a range of market-driven energy services. The concepts of transactive energy systems are not new, but build upon evolutionary economic changes in financial and electric power markets. These concepts also recognize the different regional structures of wholesale power markets, electricity delivery markets, retail markets, and vertically integrated service provider markets. Although transactive energy systems are not revolutionary, they will be transformational in their ability to provide flexibility and operational efficiency. A main goal of this research is to establish useful foundation for analysis of transactive energy systems and to facilitate new transactive energy system design with demonstrable guarantees on stability and performance. Specifically, the goals are to (1) establish a theoretical basis for evaluating the performance of different transactive systems, (2) devise tools to address canonical problems that exemplify challenges and scenarios of transactive systems, and (3) provide guidelines for design of future transactive systems. This report, Part 1 of a two part series, advances the above-listed research objectives by reviewing existing transactive systems and identifying a theoretical foundation that integrates payoff functions, control decisions, information privacy, and mathematical solution concepts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Improved Particle Swarm Algorithm Using Rubik’s Cube Topology for Bilevel Building Energy Transaction

Following the rapid growth of distributed energy resources (e.g., renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as reducing power loss and stabilizing the main power grid. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. Additionally, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush–Kuhn–Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence-based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubik’s Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubik’s Cube topology-based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology-based PSO and its scalability on larger scale applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Rubik’s Cube Topology Based Particle Swarm Algorithm for Bilevel Building Energy Transaction

Following the rapid growth of distributed energy resources (e.g. renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as, reducing power loss, stabilizing the main power grid, etc. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. In addition, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush-Kuhn-Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubiks Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubiks Cube topology based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology based PSO and its scalability on larger scale applications.

Feng, Xiaochun↗
DOE OSTI2019

Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters

To maximize potential energy and cost savings, networked clusters of local buildings are formed for energy transactions. Both centralized and distributed decision approaches were explored in past decades as a way to enable efficient transactive operations. However, online distributed stochastic transactive operation has been overlooked in the literature. To bridge gaps in the research, a bi-level distributed stochastic model predictive control framework was proposed to study the transactive operations of building clusters where a system-level agent is employed to coordinate multiple building agents at the subsystem level. The energy transaction is optimized by a marginal price-based particle swarm optimizer at the system level. Given the energy transaction decisions, each building can independently solve a scenario-based two-stage stochastic model to optimally dispatch the electricity and ancillary services for optimal energy performance. The effectiveness of the proposed framework and coordination algorithm are demonstrated in deterministic, stochastic, and online operations and compared to centralized decisions using several sets of experiments. Additionally, the proposed approach can realize autonomous transactive operation and be extended to community-level building clusters in a plug-and-play way.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗