Abstract
In the smart grid paradigm, residential consumers should participate actively in the energy exchange mechanisms by adjusting their consumption and generation. To this end, a proper home energy management system (HEMS), in addition to achieving a high level of comfort for the consumers, should handle the practical difficulties due to the uncertainty and technical limits. With this aim, in this paper, a new HEMS is proposed to carry out day-ahead management and real-time regulation. While an optimal scheduling solution based on some forecasted values of uncertain parameters is achieved for day ahead management, real-time regulation is accomplished by an adaptive neuro-fuzzy inference system, which can regulate the gaps between the forecasted and real values. Investigated case studies indicate that the proposed HEMS can find an optimal operating scenario with an acceptable success rate for real-time regulation.
Highlights
The key novel contributions of the method proposed in this paper can be summarized as follows: 1. proposing a two-stage model of a home energy management system (HEMS) considering optimal day-ahead management and an adaptive real-time correction mechanism to deal with the forecast errors, 2. incorporating the uncertainties of the distributed renewable resources and loads, the aggregator demand control request, the dissatisfaction cost of inhabitants and the degradation cost of the battery into the scheduling of smart houses, 3. integrating an adaptive neuro-fuzzy inference system combined with an optimization-based training pattern into the HEMS to provide proper real-time operation under sudden changes of working conditions due to the presence of uncertain parameters
The results indicate the high performance of the adaptive neuro-fuzzy inference system (ANFIS)-FCM model and show that the model can be used successfully to estimate the proper interactions
WORKS This paper proposed a two-stage model of HEMS by considering the uncertainties of load and small-scale renewable energy generation
Summary
In [14]–[17], small-scale renewable generations’ management is considered alongside scheduling of home appliances in the HEMS problem. These papers study the energy scheduling of smart houses, which are equipped with a solar panel. In [5], a stochastic model of a HEMS by considering uncertainties of EV availability and small-scale renewable energy generation is proposed. Proposing a two-stage model of a HEMS considering optimal day-ahead management and an adaptive real-time correction mechanism to deal with the forecast errors, 2. Integrating an adaptive neuro-fuzzy inference system combined with an optimization-based training pattern into the HEMS to provide proper real-time operation under sudden changes of working conditions due to the presence of uncertain parameters.
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