论文标题
通过在线元预测数据驱动的HVAC系统的响应来减轻对双层决策的需求，以最佳零售定价
Relieving the Need for Bi-Level Decision-Making for Optimal Retail Pricing via Online Meta-Prediction of Data-Driven Demand Response of HVAC Systems
论文作者
论文摘要
基于价格的需求响应（DR）的加热，通风和空调系统（HVAC）系统是一项艰巨的任务，需要全面的模型来代表参与者之间的建筑热动态和游戏理论相互作用。本文考虑了商业建筑中HVAC系统的最佳DR，建议针对分销系统运营商（DSO）确定最佳电力价格的在线学习策略。人工神经网络（ANN）经过建筑能量数据的训练，并使用一组明确的线性和非线性方程表示，没有基于物理的模型参数。然后，使用此方程组制定了基于价格的DR的优化问题，并反复解决了离线的解决，从而为各种电力价格条件和建立热环境的最佳DR计划提供了数据。然后，对另一个ANN进行了在线培训，以直接预测DR时间表的日期电价，这被称为元估计（MP）。通过用支持MP的ANN替换DR优化问题，可以使用单层决策结构来实现最佳的电力定价策略，该结构比BI级更简单，更实用。在仿真案例研究中，拟议的单层策略得到了验证，以成功反映DSO与商业建筑运营商之间的游戏理论关系，以便它们有效利用HVAC系统的运行灵活性使DR应用程序使DR应用程序有利可图，同时确保电网伏特稳定性和乘员热舒适度。
Price-based demand response (DR) of heating, ventilating, and air-conditioning (HVAC) systems is a challenging task, requiring comprehensive models to represent the building thermal dynamics and game theoretic interactions among participants. This paper proposes an online learning-based strategy for a distribution system operator (DSO) to determine optimal electricity prices, considering the optimal DR of HVAC systems in commercial buildings. An artificial neural network (ANN) is trained with building energy data and represented using an explicit set of linear and nonlinear equations, without physics-based model parameters. An optimization problem for price-based DR is then formulated using this equation set and repeatedly solved offline, producing data on optimal DR schedules for various conditions of electricity prices and building thermal environments. Another ANN is then trained online to directly predict DR schedules for day-ahead electricity prices, which is referred to as meta-prediction (MP). By replacing the DR optimization problem with the MP-enabled ANN, an optimal electricity pricing strategy can be implemented using a single-level decision-making structure, which is simpler and more practical than a bi-level one. In simulation case studies, the proposed single-level strategy is verified to successfully reflect the game theoretic relations between the DSO and commercial building operators, so that they effectively exploit the operational flexibility of the HVAC systems to make the DR application profitable, while ensuring the grid voltage stability and occupants thermal comfort.
