Grid-edge technology can unlock flexibility from consumers to contribute to meeting the growing need for flexibility in European energy systems. Furthermore, power-to-heat technology such as heat pumps and thermal energy storage has been shown to both decarbonise heat and enable the cost-effective integration of more renewable electricity into the grid. The consumer's reaction to price signals in this context presents the opportunity to simultaneously unlock operational cost reductions for consumers and localised implicit demand-side flexibility to benefit grid operators.

In this paper, the prediction accuracy, run-time, and reliability of several (metaheuristic) optimisation algorithms to derive optimal operation schedules for heat pump-based grid-edge technology are investigated. To compare effectiveness, an optimisation effectiveness indicator OEI is defined. Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) were found to be most effective and robust in yielding quasi-optimal minima for the non-linear, multi-modal, and discontinuous cost function. GA optimisation with binary variables is 5–15 times more effective than with continuous variables. Using continuous variables, PSO is more effective than GA due to smaller optimisation error, shorter run-time, and higher reliability (smaller standard deviation). Simulated Annealing and Direct (Pattern) Search were found to be not very effective.

网格边缘技术可以释放消费者的灵活性，从而有助于满足欧洲能源系统对灵活性不断增长的需求。此外，热电技术（例如热泵和热能存储）已显示出既可以将热量脱碳，又可以将更多可再生能源以成本效益的方式集成到电网中。在这种情况下，消费者对价格信号的反应提供了同时解锁消费者的运营成本降低和局部隐含的需求方灵活性的机会，从而使电网运营商受益。

本文研究了基于热泵的网格边缘技术的几种（元优化）优化算法的预测精度，运行时间和可靠性，以得出最佳运行计划。为了比较有效性，定义了优化有效性指标OEI。发现粒子群优化（PSO）和遗传算法（GA）在产生非线性，多模态和不连续成本函数的拟最佳最小值时最有效，最鲁棒。使用二元变量进行GA优化的效率比连续变量高5至15倍。使用连续变量，由于优化误差较小，运行时间较短和可靠性较高（标准偏差较小），因此PSO比GA更有效。发现模拟退火和直接（模式）搜索不是很有效。