Competition, risk and learning in electricity markets: An agent-based simulation study

Aliabadi, Danial Esmaeili; Kaya, Murat; Şahin, Güvenç

doi:10.1016/j.apenergy.2017.03.121

Cited by 52 publications

(10 citation statements)

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“…Focused on the description of financial markets, Fabretti and Herzel (2017) discuss the effects of convex incentives on trading behavior of agents in a naive rational-investors-vs-noise-traders model, by showing that the risk aversion (through the incentives) may affect market dynamics. A broader approach is used by Aliabadi et al (2017), who show how agents' behavior varies according to the combined effect of individual risk attributes and to learning abilities to account for errors done in past predictions. Other recent contributions show the impact of self-correcting behavior on long-run expectations, as in Colasante et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Learning to forecast, risk aversion, and microstructural aspects of financial stability

Biondo

2018

Economics

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This paper presents a simulative model of a financial market, based on a fully operating order book with limit and market orders. The heterogeneity of traders is characterized not only with regards to their trading rules, but also by introducing a behavioral individual risk aversion and a learning ability influencing the process of expectations formation. Results show that individual learning may play a role in stabilizing the aggregate market dynamics, whereas the risk aversion has, counterintuitively, the opposite effect.(Published in Special Issue Agent-based modelling and complexity economics ) JEL E44 E47 C63

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Section: Introductionmentioning

confidence: 99%

Learning to forecast, risk aversion, and microstructural aspects of financial stability

Biondo

2018

Economics

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“…However, this method is actively used in energy applications to study national climate mitigation strategies (Gerst et al 2013;Gotts and Polhill 2017), energy producer behavior (Aliabadi et al 2017), renewable energy auctions (Anatolitis and Welisch 2017), consumer adoption of energy-efficient technology (Chappin and Afman 2013;Jackson 2010;Palmer et al 2015;Rai and Robinson 2015), shifts in consumption patterns (Bravo et al 2013), changes in energy policy processes (Iychettira et al 2017), and diffusion of energy-related actions and technology (Ernst and Briegel 2017;Kangur et al 2017). Many cases of ABM still either lack a theoretical framework (Groeneveld et al 2017) or relevance to empirical data, especially when studying energy behavior of households (Amouroux et al 2013).…”

Section: Methodsmentioning

confidence: 99%

Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions

et al. 2019

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In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions.

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“…Most publications falling into the second use case category identified by Prasanna et al (2019) apply relatively simple reinforcement learning approaches like Q-learning (e.g., Esmaeili Aliabadi et al, 2017) or Erev-Roth learning (e.g., Mengelkamp et al, 2018;Zhou et al, 2011). Still, some noteworthy exceptions using supervised learning exist, which are addressed next.…”

Section: Introductionmentioning

confidence: 99%

Advanced price forecasting in agent-based electricity market simulation

et al. 2021

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Competition, risk and learning in electricity markets: An agent-based simulation study

Cited by 52 publications

References 51 publications

Learning to forecast, risk aversion, and microstructural aspects of financial stability

Learning to forecast, risk aversion, and microstructural aspects of financial stability

Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions

Advanced price forecasting in agent-based electricity market simulation

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