Predictive control for residential capacity controlled heat pumps in a smart grid scenario
Predictive control for residential capacity controlled heat pumps in a smart grid scenario
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智能电网场景中住宅容量控制热泵的预测控制
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发表时间：
2015
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通讯作者：
N. Saraf
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作者：
N. Saraf
住宅供暖系统是全球能源的主要消费者之一。最大限度地减少这种能源消耗和成本要求加热系统的节能操作，并增加可再生能源的使用。由于可再生能源发电量的波动，将其纳入电网具有挑战性。利用热储存来分离热需求和电力供应提供了将来自可再生能源的电力与需求侧管理相结合的可能性。容量控制的热泵在与储热器结合时提供高效的加热和高的操作灵活性。这允许使用创新的最佳控制策略，以最大限度地减少能源消耗和电力成本。热泵的模型预测控制（MPC）已被确定为这一挑战的可能解决方案之一。MPC提供诸如约束处理、多变量控制和具有冲突目标的最优性能等属性。它已被证明优于传统的控制策略，并量化的好处在这篇论文中。为了获得最佳性能，优化问题公式必须准确地表示建筑物能源管理系统的控制目标，并且该问题应该是计算上可处理的。供热系统具有运行死区和容量控制热泵效率对控制输入的非线性依赖性等特点。由于这些非线性特征，所得到的优化问题是非线性和非凸的。这个问题的解决方案需要更高的计算能力，使其不适合在嵌入式控制器中实现，简化的配方是可取的。虽然非线性非凸公式表示的控制目标最准确，其性能取决于一个详细的热泵模型，限制了其广泛使用。此外，用于解决该问题的非线性规划算法通常获得次优解（局部最优解）而不是全局最优解。在这篇论文中，提出了简化近似，导致凸优化问题的配方，保证更快地收敛到一个解决方案，不需要一个详细的热泵模型。不同的问题配方进行了研究，通过模拟，以调查理学硕士论文N。Saraf
Residential heating systems are one of the major consumers of energy globally. Minimizing this energy consumption and costs calls for energy efficient operation of heating systems, and increasing use of renewable energy. The integration of renewable energy sources in the electricity network is challenging due to the fluctuation in their generation. The use of thermal storage to decouple heat demand and electricity supply provides the possibility to integrate power from renewable energy sources and demand side management. Capacity controlled heat pumps provide efficient heating and high flexibility in operation when coupled with thermal storage. This allows using innovative optimal control strategies to minimize energy consumption and electricity costs. Model predictive control (MPC) for heat pumps has been identified as one of the possible solutions to this challenge. MPC offers properties such as constraint handling, multivariable control and optimal performance with conflicting objectives. It has been shown to outperform conventional control strategies and the benefits are quantified in this thesis. For the best performance, the optimization problem formulation must accurately represent the control objectives of the building energy management system and the problem should be computationally tractable. The heating system has characteristics such as dead-zone in operation and nonlinear dependency of the efficiency of the capacity controlled heat pump on control inputs. Due to these nonlinear characteristics, the resulting optimization problem is nonlinear and non-convex. The solution of this problem demands higher computational power making it unsuitable for implementation in embedded controllers, for which simplified formulations are desirable. Although the nonlinear non-convex formulation represents the control objectives most accurately, its performance depends on a detailed heat pump model which limits its wide-spread use. Moreover, a nonlinear programming algorithm that is used to solve this problem typically obtains a suboptimal solution (local optimum) instead of the global one. In this thesis, simplifying approximations are proposed that lead to convex optimization problem formulations, which guarantee faster convergence to a solution and do not need a detailed heat pump model. Different problem formulations are studied through simulations in order to investigate Master of Science Thesis N. Saraf