Home energy management system (HEMS) provides an effective solution to assist residential users in dealing with the complexity of dynamic electricity prices. This study proposes a new HEMS in contexts of real-time electricity tariff and high residential photovoltaic penetrations. First, the HEMS accepts user-specified residential energy resource operation restrictions as inputs. Then, based on the forecasted solar power outputs and electricity prices, an optimal scheduling model is proposed to support the decision making of the residential energy resource (RES) operations. For the scheduling of heating, ventilating, and air conditioning system, an advanced adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. For the controllable appliances, the ‘user disturbance value’ metric is proposed to estimate the psychological disturbances of an appliance schedule on the user's preference. The proposed scheduling model aims to minimise the future 1 day energy costs and disturbances to the user. A new biological self-aggregation intelligence inspired metaheuristic algorithm recently proposed by the authors (a natural aggregation algorithm) is applied to solve the model. Extensive simulations are conducted to validate the proposed method.