在线机器学习方法对电力市场长期投资决策和发电机利用率的影响
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets
摘要：
电力供应必须始终与需求相匹配。这有助于减少诸如负载频率控制和停电等问题的机会。为了更好地了解未来24小时可能需要的负荷，需要进行不确定性下的估计。这在分散的电力市场中尤其困难，因为许多微型生产者不受中央控制。在本文中，我们研究了11种离线学习和5种在线学习算法对预测未来24小时电力需求曲线的影响。我们通过在长期的基于代理的模型ElecSim中进行集成来实现这一目标。通过预测未来24小时的电力需求曲线，我们可以模拟对未来一天市场的预测。一旦我们做出了这些预测，我们从剩余分布中采样，并使用模拟ElecSim扰动电力市场需求。这使我们能够了解错误对分散式电力市场长期动态的影响。我们表明，我们可以减少30%的平均绝对误差使用在线算法相比，最好的离线算法，同时减少所需的投标国家电网储备。国家电网储备的减少导致成本和排放的节省。我们还表明，预测准确性的大误差对17年时间框架内的投资以及电力组合有不成比例的误差。
英文摘要：
Electricity supply must be matched with demand at all times. This helps reduce the chances of issues such as load frequency control and the chances of electricity blackouts. To gain a better understanding of the load that is likely to be required over the next 24h, estimations under uncertainty are needed. This is especially difficult in a decentralized electricity market with many micro-producers which are not under central control. In this paper, we investigate the impact of eleven offline learning and five online learning algorithms to predict the electricity demand profile over the next 24h. We achieve this through integration within the long-term agent-based model, ElecSim. Through the prediction of electricity demand profile over the next 24h, we can simulate the predictions made for a day-ahead market. Once we have made these predictions, we sample from the residual distributions and perturb the electricity market demand using the simulation, ElecSim. This enables us to understand the impact of errors on the long-term dynamics of a decentralized electricity market. We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm, whilst reducing the required tendered national grid reserve required. This reduction in national grid reserves leads to savings in costs and emissions. We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame, as well as electricity mix.
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