Mureddu Mario, Caldarelli Guido, Chessa Alessandro, Scala Antonio, Damiano Alfonso
Dipartimento di Fisica, Università di Cagliari, Cagliari, Italy; IMT Institute for Advanced Studies, Lucca, Italy; Department of Physics and Earth Sciences, Jacobs University, Bremen, Germany.
IMT Institute for Advanced Studies, Lucca, Italy; Istituto dei Sistemi Complessi (ISC), Roma, Italy; London Institute for Mathematical Sciences, London, United Kingdom; Linkalab, Cagliari, Italy.
PLoS One. 2015 Sep 3;10(9):e0135312. doi: 10.1371/journal.pone.0135312. eCollection 2015.
The increasing attention to environmental issues is forcing the implementation of novel energy models based on renewable sources. This is fundamentally changing the configuration of energy management and is introducing new problems that are only partly understood. In particular, renewable energies introduce fluctuations which cause an increased request for conventional energy sources to balance energy requests at short notice. In order to develop an effective usage of low-carbon sources, such fluctuations must be understood and tamed. In this paper we present a microscopic model for the description and for the forecast of short time fluctuations related to renewable sources in order to estimate their effects on the electricity market. To account for the inter-dependencies in the energy market and the physical power dispatch network, we use a statistical mechanics approach to sample stochastic perturbations in the power system and an agent based approach for the prediction of the market players' behavior. Our model is data-driven; it builds on one-day-ahead real market transactions in order to train agents' behaviour and allows us to deduce the market share of different energy sources. We benchmarked our approach on the Italian market, finding a good accordance with real data.
对环境问题日益增加的关注正促使基于可再生能源的新型能源模式得以实施。这正从根本上改变能源管理的格局，并带来一些仅得到部分理解的新问题。特别是，可再生能源会引入波动，这导致对传统能源的需求增加，以便在短时间内平衡能源需求。为了有效利用低碳能源，必须理解并控制这种波动。在本文中，我们提出了一个微观模型，用于描述和预测与可再生能源相关的短时间波动，以便估计它们对电力市场的影响。为了考虑能源市场和物理电力调度网络中的相互依存关系，我们使用统计力学方法对电力系统中的随机扰动进行采样，并使用基于智能体的方法来预测市场参与者的行为。我们的模型是数据驱动的；它基于提前一天的实际市场交易来训练智能体的行为，并使我们能够推断不同能源的市场份额。我们在意大利市场对我们的方法进行了基准测试，发现与实际数据有很好的一致性。
