The growth of world energy demand combined with global warming and climate change is one of the most urgent global challenges and induced policy measures to foster the use of renewable energy sources. In order to cope with the intrinsic variability of solar and wind, active management of distribution networks and customers is required, if the creation of the so called Smart Grid is desired.

This paper focuses on the strategies to enable prosumers (i.e. customers able to self-generate all or part of their energy needs) to optimally manage their generation and loads in order to minimize their energy bill and, at the same time, support the distribution grid stability by responding flexibly to its requirements in terms of active load management. In this study an industrial prosumer equipped with solar and wind generation as well as with a co-generation unit with absorption chiller and heat/cold storage was considered. The work presents an optimization algorithm that was developed and applied to this Smart User to manage operations of the CHP in order to optimize the power generation and the usage depending on internal and external inputs as loads, weather forecast and price from the electricity and natural gas market. The proposed algorithm was tested with real experimental inputs of different typical days and its performance was compared with three common scenarios, i.e. traditional supply, electric load following and thermal load following operation of the CHP. Results compare the different control strategies of the CHP (i.e. thermal and electric load following) and shows economic advantages allowed by means of the optimization algorithm, which appears to be an effective instrument to prepare prosumers to the smart grid of the future.

世界能源需求的增长与全球变暖和气候变化相结合，是最紧迫的全球挑战之一，也是旨在鼓励使用可再生能源的政策措施。为了应对太阳能和风的内在变化，如果需要创建所谓的智能电网，就需要对配电网络和客户进行主动管理。

本文着重于使生产者（即能够自行产生全部或部分能量需求的客户）优化管理其发电量和负荷的策略，以最大程度地减少其能源费用，同时为配电网提供支持通过在主动负载管理方面灵活地响应其要求来实现稳定性。在这项研究中，考虑了配备太阳能和风力发电以及带有吸收式制冷机和热/冷存储的热电联产装置的工业生产者。这项工作提出了一种优化算法，该算法已开发并应用于该智能用户，以管理CHP的运行，从而根据内部和外部输入（如负荷，天气预报以及电力和天然气的价格）来优化发电量和使用量市场。该算法在不同典型天数的实际实验输入下进行了测试，并将其性能与传统热电联产的传统供电，电力负荷跟随和热负荷跟随三种常见情况进行了比较。结果比较了CHP的不同控制策略（即跟随热负荷和电力负荷），并显示了优化算法所允许的经济优势，这似乎是为将来的智能电网准备生产者的有效工具。