An MPC-based Energy Management System for multiple residential microgrids
An MPC-based Energy Management System for multiple residential microgrids
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DOI：
10.1109/coase.2015.7294033
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
A. Parisio;C. Wiezorek;Timo Kyntäjä;Joonas Elo;K. Johansson
中科院分区：
文献类型：
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作者：
A. Parisio;C. Wiezorek;Timo Kyntäjä;Joonas Elo;K. Johansson
在这项研究中，我们提出了一个模型预测控制（MPC）的方法，能源管理系统（EMS）的多个住宅微电网。EMS负责根据最终用户的偏好、天气依赖的发电和需求预测、电价、技术和运营限制，优化调度住宅层面的最终用户智能家电、供暖系统和本地发电设备。所提出的框架的核心是一个混合整数线性规划（MILP）模型，旨在最大限度地减少每个住宅微电网的总成本。在每一个时间步，计算的最优决策是根据天气依赖的本地发电和供热需求的实际值进行调整，然后，纠正措施及其相应的成本占，以科普不平衡。在下一个时间步，基于更新的预测和初始条件重新计算优化问题。所提出的方法在虚拟测试环境中进行评估，该环境集成了形成住宅微电网的能源系统的精确模拟器，包括发电和热电机组，储能设备和灵活负载。测试环境还可以在标准网络接口上模拟真实的网络介质条件。数值结果表明了该方法的可行性和有效性。
In this study we present a Model Predictive Control (MPC) approach to Energy Management Systems (EMSs) for multiple residential microgrids. The EMS is responsible for optimally scheduling end-user smart appliances, heating systems and local generation devices at the residential level, based on end-user preferences, weather-dependent generation and demand forecasts, electric pricing, technical and operative constraints. The core of the proposed framework is a mixed integer linear programming (MILP) model aiming at minimizing the overall costs of each residential microgrid. At each time step, the computed optimal decision is adjusted according to the actual values of weather-dependent local generation and heating requirements; then, corrective actions and their corresponding costs are accounted for in order to cope with imbalances. At the next time step, the optimization problem is re-computed based on updated forecasts and initial conditions. The proposed method is evaluated in a virtual testing environment that integrates accurate simulators of the energy systems forming the residential microgrids, including electric and thermal generation units, energy storage devices and flexible loads. The testing environment also emulates real-word network medium conditions on standard network interfaces. Numerical results show the feasibility and the effectiveness of the proposed approach.