Nonlinear and Randomized Pricing for Distributed Management of Flexible Loads

Papadaskalopoulos, Dimitrios; Štrbac, Goran

doi:10.1109/tsg.2015.2437795

Cited by 39 publications

(17 citation statements)

References 17 publications

(41 reference statements)

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“…Distributed approaches, on the other hand, are robust, and enable fast computations and communication, and they are, thus, more popular. The EMP for microgrids or smart grids has been explored widely in the literature [123]- [125]. The EMP with DSM has been analyzed for minimization of the cost of the system [126], [127] and the electricity bill of the consumer in the residential sector with a HEMS [128]- [131].…”

Section: B Management and Optimization Modelmentioning

confidence: 99%

What is Energy Internet? Concepts, Technologies, and Future Directions

et al. 2020

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The climate change crisis, exacerbated by the global dependency of fossil fuels, has brought significant challenges. In the medium to long term, extensive renewable-energy-based electrification is considered to be one of the most promising development paths to address these challenges. However, this is tangible only if the energy infrastructure can accommodate renewable energy sources and distributed energy resources, such as batteries and heat pumps, without adversely affecting power grid operations. To realize renewable-energy-based electrification goals, a new concept-the Energy Internet (EI)-has been proposed, inspired by the most recent advances in information and telecommunication network technologies. Recently, many measures have also been taken to practically implement the EI. Although these EI models share many ideas, a definitive universal definition of the EI is yet to be agreed. Additionally, some studies have proposed protocols and architectures, but a generalized technological overview is still missing. An understanding of the technologies that underpin and encompass the current and future EI is very important to push toward a standardized version of the EI that will eventually make it easier to implement it across the world. In this paper, we first examine and analyze the typical popular definitions of the EI in scientific literature. Based on definitions, assumptions, scope, and application areas, the scientific literature is then classified into four different groups representing the way in which the papers have approached the EI. Then, we synthesize these definitions and concepts, and keeping in mind the future smart grid, we propose a new universal definition of the EI. We also identify the underlying key technologies for managing, coordinating, and controlling the multiple (distributed or not) subsystems with their own particular challenges. The survey concludes by highlighting the main challenges facing a future EI-based energy system and indicating core requirements in terms of system complexity, security, standardization, energy trading and business models and social acceptance.

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Section: B Management and Optimization Modelmentioning

confidence: 99%

What is Energy Internet? Concepts, Technologies, and Future Directions

et al. 2020

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“…An example of this approach is presented in [11], where the price-responsive loads operate in a pool-based day-ahead market and shift their power consumption to the periods with lowest prices. This fundamental framework has been developed and expanded in different directions, proposing randomized pricing mechanisms to avoid load synchronization and inefficient solutions [12], accounting for network costs through dynamic network tariffs [13], and developing ad-hoc bidding and clearing strategies to maximize the overall social welfare [14].…”

Section: Relevant Workmentioning

confidence: 99%

Distributed Control of Clustered Populations of Thermostatic Loads in Multi-Area Systems: A Mean Field Game Approach

Trovato¹,

Paola²,

Štrbac³

2020

Energies

Self Cite

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Thermostatically controlled loads (TCLs) can effectively support network operation through their intrinsic flexibility and play a pivotal role in delivering cost effective decarbonization. This paper proposes a scalable distributed solution for the operation of large populations of TCLs providing frequency response and performing energy arbitrage. Each TCL is described as a price-responsive rational agent that participates in an integrated energy/frequency response market and schedules its operation in order to minimize its energy costs and maximize the revenues from frequency response provision. A mean field game formulation is used to implement a compact description of the interactions between typical power system characteristics and TCLs flexibility properties. In order to accommodate the heterogeneity of the thermostatic loads into the mean field equations, the whole population of TCLs is clustered into smaller subsets of devices with similar properties, using k-means clustering techniques. This framework is applied to a multi-area power system to study the impact of network congestions and of spatial variation of flexible resources in grids with large penetration of renewable generation sources. Numerical simulations on relevant case studies allow to explicitly quantify the effect of these factors on the value of TCLs flexibility and on the overall efficiency of the power system.

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“…Another interesting case study involving the IoE and its implications for the smart grids is the autonomic power system (APS), that presents a novel concept of "self*" (self-configuring, self-healing, self-optimizing, and self-protecting) system [31]. APS constituted a system-wide approach with the decentralized intelligence making autonomous decisions required for meeting the priorities of the system's stakeholders, employing the integration of a vast number of flexible, diverse, and independently controlled technologies in system operation and planning [4,32,33].…”

Section: Literature Reviewmentioning

confidence: 99%

Internet of Energy (IoE) and High-Renewables Electricity System Market Design

et al. 2019

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The growing importance of the Internet of Energy (IoE) brands the high-renewables electricity system a realistic scenario for the future electricity system market design. In general, the whole gist behind the IoE is developed upon a somewhat broader idea encompassing the so-called “Internet of Things” (IoT), which envisioned a plethora of household appliances, utensils, clothing, smart trackers, smart meters, and vehicles furnished with tiny devices. These devices would record all possible data from all those objects in real time and allow for a two-way exchange of information that makes it possible to optimize their use. IoT employs the Internet Protocol (IP) and the worldwide web (WWW) network for transferring information and data through various types of networks and gateways as well as sensor technologies. This paper presents an outline stemming from the implications of the high-renewables electric system that would employ the Internet of Energy (IoE). In doing so, it focuses on the implications that IoE brings into the high-renewables electricity market inhabited by smart homes, smart meters, electric vehicles, solar panels, and wind turbines, such as the peer-to-peer (P2P) energy exchange between prosumers, optimization of location of charging stations for electric vehicles (EVs), or the information and energy exchange in the smart grids. We show that such issues as compatibility, connection speed, and most notoriously, trust in IoE applications among households and consumers would play a decisive role in the transition to the high-renewables electricity systems of the 21st century. Our findings demonstrate that the decentralized approach to energy system effective control and operation that is offered by IoE is highly likely to become ubiquitous as early as 2030. Since it may be optimal that large-scale rollouts start in the early 2020s, some form of government incentives and funding (e.g. subsidies for installing wind turbines or solar panels or special feed-in-tariffs for buying renewable energy) may be needed for the energy market to make early progress in embracing more renewables and in reducing the costs of later investments. In addition, there might be some other alternative approaches aimed at facilitating this development. We show that the objective is to minimize the overall system cost, which consists of the system investment cost and the system operating cost, subject to CO2 emissions constraints and the operating constraints of generation units, network assets, and novel carbon-free technologies, which is quite cumbersome given the trend in consumption and the planned obsolescence. This can be done through increasing energy efficiency, developing demand side management strategies, and improving matching between supply and demand side, just to name a few possibilities.

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Nonlinear and Randomized Pricing for Distributed Management of Flexible Loads

Cited by 39 publications

References 17 publications

What is Energy Internet? Concepts, Technologies, and Future Directions

What is Energy Internet? Concepts, Technologies, and Future Directions

Distributed Control of Clustered Populations of Thermostatic Loads in Multi-Area Systems: A Mean Field Game Approach

Internet of Energy (IoE) and High-Renewables Electricity System Market Design

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