Agent-based community coordination of local energy distribution

Yasir, Muhammad; Purvis, Martin; Purvis, Maryam; Savarimuthu, Bastin Tony Roy

doi:10.1007/s00146-013-0528-1

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…ABM has also been used to simulate the dynamics of competitive wholesale electricity markets [33][34][35] and has been identified as a key approach in modeling electricity markets [36]. A few studies have applied ABM to simulate decentralized market structures for various natural resources [26,27,37,38]. Gnansounou et al [26] developed a multiagent approach for planning activities in decentralized electricity markets, leading to a conceptual market model that included consumers, transmission system operators, distributors, wholesalers, and retailers.…”

Section: Agent-based Modeling For P2p Energy Marketsmentioning

confidence: 99%

“…Yasir et al [27] simulated community coordination of local energy distribution using an ABM approach; they modeled decentralized trades between a utility and multiple community micro-grids with wind generation and battery energy storage present. Another set of studies simulated a market that allows households to trade rainwater via a non-potable water infrastructure network [38] and a shared aquifer [37]. Other ABMs have focused specifically on P2P energy markets within a residential sector.…”

Section: Agent-based Modeling For P2p Energy Marketsmentioning

confidence: 99%

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Evaluating Peer-to-Peer Electricity Markets across the U.S. Using an Agent-Based Modeling Approach

Monroe¹,

Ramsey²,

Berglund³

2023

Adv Environ Eng Res

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The diffusion of distributed energy resources can overcome some challenges associated with the historical centralized model of electric power distribution. Decentralized generation by residential solar photovoltaic cells creates the potential for peer-to-peer (P2P) electricity trading, where households can act as consumers and prosumers to buy and sell renewable electricity. P2P energy markets are emerging at locations across the globe, and market performance is affected by various social, economic, and environmental factors. This research applies an agent-based modeling (ABM) framework to simulate electricity trades between heterogeneous households in a decentralized market. The P2P system is tested for 15 locations in the United States that vary in climate parameters and local economic factors. The results from these simulations are compared to assess how differences in climate, demand pattern, retail rate, and irradiance affect market performance. Simulations demonstrate that market outcomes rely on the ratio of prosumers to consumers, environmental factors, and geographic conditions. Battery energy storage overcomes limitations associated with faulty forecasting and improves the flexibility of household-generated solar resources to increase the proportion of production that is sold in the P2P market. The application of the agent-based modeling framework demonstrates how P2P markets can be expected to perform for various locations and can be applied to assess alternative locations for market performance.

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Section: Agent-based Modeling For P2p Energy Marketsmentioning

confidence: 99%

Section: Agent-based Modeling For P2p Energy Marketsmentioning

confidence: 99%

Evaluating Peer-to-Peer Electricity Markets across the U.S. Using an Agent-Based Modeling Approach

Monroe¹,

Ramsey²,

Berglund³

2023

Adv Environ Eng Res

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show abstract

“…A machine learning approach and its integration with a widely used energy simulation platform was proposed. Yasir et al (2015) present an ABM architecture for coordinating locally-connected microgrids, thereby supporting more cost-effective integration into the main power grid. The interconnected microgrids, with renewable energy sources and energy storage devices, employ agents so that each microgrid can choose to save or resell its stored energy in an open market in order to optimize its utility and revenues.…”

Section: Smart Gridsmentioning

confidence: 99%

Agent Based Models in Power Systems

Santos

Saraiva

2021

UPjeng

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In the last two decades, power systems have experienced several changes, mainly related to organizational and operational restructuring. The appearance of new actors contributes to developing new business models and modifies its traditional operation activities. As a direct result, there is a need for new control solutions and strategies to integrate these different players. Agent-Based Models (ABM) have been increasingly used to model complex systems since they are especially suited to model systems influenced by social interactions between flexible, autonomous, and proactive agents. This paper provides a review of the literature regarding ABM in power systems followed by an analysis in more detail regarding specific applications that are becoming relevant in this new paradigm.

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“…One of the approaches to address this issue is the interconnection of nearby MGs which, by trading among the communities, can reduce the impact of irregularity with respect to renewable energy sources . An agent‐based architecture for local energy distribution among MGs has been presented by Yasir et al In their work, each MG represents a community, which has its own power generation based on renewable energy sources and also its own electric energy demand that varies hourly. Every community has a coordinator agent, which is responsible for power trading with other interconnected communities or with the utility grid when the community has a power surplus or deficit.…”

Section: Introductionmentioning

confidence: 99%

Complementary‐based coalition formation for energy microgrids

Yasir

Purvis

et al. 2018

Computational Intelligence

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In recent years, the notion of electrical energy microgrids (MGs), in which communities share their locally generated power, has gained increasing interest. Typically, the energy generated comes from renewable resources, which means that its availability is variable, ie, sometimes there may be energy surpluses and at other times energy deficits. This energy variability can be ameliorated by trading energy with a connected electricity grid. However, since main electricity grids are subject to faults or other outages, it can be advantageous for energy MGs to form coalitions and share their energy among themselves. In this work, we present our model for the dynamic formation of such MG coalitions. In our model, MGs form coalitions on the basis of complementary weather patterns. Our agent‐based model, which is scalable and affords autonomy among the MGs participating in the coalition (agents can join and depart from coalitions at any time), features methods to reduce overall “discomfort” so that, even when all participating MGs in a coalition experience deficits, they can share energy so that their overall discomfort is reduced. We demonstrate the efficacy of our model by showing empirical studies conducted with real energy production and consumption data.

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Agent-based community coordination of local energy distribution

Cited by 10 publications

References 8 publications

Evaluating Peer-to-Peer Electricity Markets across the U.S. Using an Agent-Based Modeling Approach

Evaluating Peer-to-Peer Electricity Markets across the U.S. Using an Agent-Based Modeling Approach

Agent Based Models in Power Systems

Complementary‐based coalition formation for energy microgrids

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