Energy Flexometer: Transactive Energy-Based Internet of Things Technology

Babar, Muhammad; Grela, J.; Ożadowicz, A.; Nguyen, Phuong H.; Hanzelka, Zbigniew; Kamphuis, I.G.

doi:10.3390/en11030568

Cited by 19 publications

(26 citation statements)

References 37 publications

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“…In [43,44], the customers' flexibility to price changes in dynamic tariffs was determined empirically, based on real energy consumption measurements, depending on the household appliances used. The interesting method of demand elasticity learning and estimation algorithm is presented in [45] for future smart energy systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles

2019

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The concept of price elasticity of demand has been widely used for the assessment of the consumers’ behavior in the electricity market. As the residential consumers represent a significant percentage of the total load, price elasticity of their demand may be used to design desirable demand side response programs in order to manage peak load in a power system. The method presented in this study proposes an alternative approach towards price elasticity determination for zonal tariff users, based on comparisons of load profiles of consumers settled according to flat and time-of-use electricity tariffs. A detailed explanation of the proposed method is presented, followed by a case-study of price elasticity determination for residential electricity consumers in Poland. The forecasted values of price elasticity of demand for the Polish households using time-of-use (TOU) tariff vary between −1.7 and −2.3, depending on the consumers’ annual electricity consumption. Moreover, an efficiency study of residential zonal tariff is performed to assess the operation of currently applicable electricity tariffs. Presented analysis is based on load profiles published by Distribution System Operators and statistical data, but the method can be applied to the real-life measurements from the smart metering systems as well when such systems are accessible for residential consumers.

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Section: Literature Reviewmentioning

confidence: 99%

Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles

2019

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“…Due to a distributed structure of present and future power systems, the Internet of Things (IoT) environment is needed to fully explore flexibility potential from the endusers and prosumers, to offer a bid to involved actors of the smart energy system. Babar et al (2018) [12] applied a new approach to connecting the market-driven (bottom-up) DR program with the current demand-driven (top-down) energy management system. Three different functional blocks have been composed and performed as an IoT platform logical interface according to the LonWorks technology.…”

Section: B Smart Energy Network and Problems Solving: Challenges And Enablingmentioning

confidence: 99%

Designing Smart Energy Network Ecosystem for Integrated energy services in urban areas

Dai

Tang

Liu

et al. 2020

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)

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Technologies such as electrification, informatization, and intelligence will reshape the energy form of cities in the future. Energy will become a key link in sustainable development and be integrated into sustainable urban development. Starting from the key elements of the energy system for sustainable urban development, considering urban resource endowment and energy demand, combining future urban forms and technological development, from the perspective of P2C (production to consumption) & C2P (consumption to production), a smart energy network ecosystem created by global partners and stakeholders is proposed. The features of the proposed ecosystem are: (1) the wisdom of various participants, the consideration of the renewable energy resources available in cities and towns, and the waste heat and wastewater available in urban are integrated; (2) the integrated integration of smart grid and energy storage applications; considering the integration of big data, artificial intelligence and other technical means are considered; (3) focusing on the energy supply and consumption needs of the prosumers, we will jointly customize the smart energy network ecosystem, realize the complementary use of multiple clean energy sources and the coordinated response of the source, network, and storage to provide fair and diversified comprehensive energy services for urban users. This paper demonstrates the ecosystem's modelling ideas, principles, methods and logical framework. Based on the case of the Smart Otaniemi project in Espoo, Finland, it analyses the implementation plan of the smart energy network ecosystem to provide support for the sustainable development of towns. I. INTRODUCTIONCoping with megatrends such as climate change and accelerating urbanization, and the development of new technologies such as electrification, informatization, and intelligence, which will reshape the energy form of cities in the future. The large-scale, high-proportion of renewable clean and clean energy with significant randomness, intermittent news, and volatility are connected to the power grid at the energy supply side, which brings new challenges to the energy system's balanced regulation and safe and stable operation. At the energy demand side, the access of a large number of distributed power sources, micro-grids, electric vehicles, and new interactive energy-using equipment has turned energy consumers into producers and consumers and changed the traditional one-way flow pattern of source-grid-charge-user.

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“…Equation (18) represents the total reduced load using the LC option, where the binary variable u LC tnk takes the value of 1 if the LR is scheduled by the DRA. Equation (19) represents the cost associated with the LC strategy, and this cost is imposed on the DSO. Equations (20) and (21) represent the maximum and minimum duration of the load reduction, where y LC tnk and z LC tnk are binary variables that assume the value of 1 when contract k starts and finishes, respectively.…”

Section: Demand Response Strategiesmentioning

confidence: 99%

“…In reference [19], an approach to connect market-driven DR programs with current demand-driven energy management systems was proposed. A multi-agent system approach was considered and the concept of an energy flexometer was introduced.…”

Section: Introductionmentioning

confidence: 99%

Demand-Side Management of Smart Distribution Grids Incorporating Renewable Energy Sources

et al. 2019

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The integration of renewable energy resources (RES) (such as wind and photovoltaic (PV)) on large or small scales, in addition to small generation units, and individual producers, has led to a large variation in energy production, adding uncertainty to power systems (PS) due to the inherent stochasticity of natural resources. The implementation of demand-side management (DSM) in distribution grids (DGs), enabled by intelligent electrical devices and advanced communication infrastructures, ensures safer and more economical operation, giving more flexibility to the intelligent smart grid (SG), and consequently reducing pollutant emissions. Consumers play an active and key role in modern SG as small producers, using RES or through participation in demand response (DR) programs. In this work, the proposed DSM model follows a two-stage stochastic approach to deal with uncertainties associated with RES (wind and PV) together with demand response aggregators (DRA). Three types of DR strategies offered to consumers are compared. Nine test cases are modeled, simulated, and compared in order to analyze the effects of the different DR strategies. The purpose of this work is to minimize DG operating costs from the Distribution System Operator (DSO) point-of-view, through the analysis of different levels of DRA presence, DR strategies, and price variations.

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Energy Flexometer: Transactive Energy-Based Internet of Things Technology

Cited by 19 publications

References 37 publications

Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles

Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles

Designing Smart Energy Network Ecosystem for Integrated energy services in urban areas

Demand-Side Management of Smart Distribution Grids Incorporating Renewable Energy Sources

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