Agent-based micro-storage management for the Smart Grid

By Perukrishnen Vytelingum, T. Voice, Sarvapali D. Ramchurn, Alex Rogers, Nicholas R. Jennings. Adaptive Agents and Multi-Agent Systems, 2010

358 citations recorded by the source corpus.

The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, requires more generation capacity, results in more carbon emissions, and, in the worst case, breaks down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).

View Paper: https://www.semanticscholar.org/paper/c60e00141eeb5af319e6a75813a0c23448ee5bea

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Citations in the corpus

Demand-Side Management via Distributed Energy Generation and Storage Optimization (2013, background): “Given defined as in (17), Algorithm 1 converges if the following two conditions are satisfied: i) the Jacobian of is positive semidefinite [14, Th. 4.3]; ii) the matrix , with (27) is a P-matrix [14, Cor. 4.1], where we have introduced (28) (29) with denoting the smallest eigenvalue of the matrix ar”
A blockchain-based smart grid: towards sustainable local energy markets (2017, background): “Zero-intelligence agents serve as a lower bound of the market efficiency [38].”
A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches (2015, background): “To alleviate such issues, the work in [59] strategically guides users when to charge and discharge their batteries or PHEVs.”
Putting the 'smarts' into the smart grid (2012, background): “Potential solutions point to the application of regulatory measures to reduce speculation and more importantly, to congestion pricing mechanisms [39] within the distribution network, similar to the locational-based pricing that is used within the transmission network in many parts of the US [35].”
Trading on local energy markets: A comparison of market designs and bidding strategies (2017, background): “All simulations with zero-intelligence agents can serve as a lower bound of the market efficiency [22] for the local electricity market.”
A Distributed Demand Response Algorithm and Its Application to PHEV Charging in Smart Grids (2012, background): “More recently, researchers have applied intelligent agents and game theory to micro-storage management for the smart grid [28], in which Nash equilibrium is reached when the agents are able to optimize the energy usage and storage profile of the dwelling and learn the best storage profile given mark”
Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications (2012, unknown): “In this setting, as di scussed in [33], the objective is to enable the users to choose their storage profile si at every hourh so as to minimize the energy cost incurred over a whole period H, i.”
Vehicle-to-Aggregator Interaction Game (2012, background): “introduced an agent based approach for micro-storage management in [26].”
Robust Energy Management for Microgrids With High-Penetration Renewables (2012, background): “(1) VariablesP t Bj are constrained in the following ways: i) The amount of (dis)charging is bounded, that is P Bj ≤P t Bj ≤ P Bj (2) −ηjB t−1 j ≤P t Bj (3) with boundsP Bj < 0 andP max Bj > 0, while ηj ∈ (0, 1] is the efficiency of DS unitj [14], [15].”