Household energy control method considering user comfort risk preferences

Abstract

The invention discloses a household energy control method considering user comfort risk preferences. The household energy control method comprises the following steps: classifying equipment accordingto characteristics of electric equipment; predicting and obtaining prediction data of the next day according to the historical data before the day, wherein the prediction data comprises user electricity consumption behaviors, photovoltaic output, outdoor temperature and electricity price fluctuation; updating a probability distribution function of the historical data according to statistics of thehistorical data; considering comfort risk preference setting of the user, and setting a risk coefficient for each equipment; considering the risk preference setting of the electricity purchasing costof the user, and taking the economic risk coefficient into account as one of the objective functions;, adopting an improved genetic algorithm to solve the target function, and an optimization resultrepresenting a user electricity purchasing cost minimum scheme considering a risk coefficient; carrying out second-stage optimization by considering the electricity purchasing out-of-limit requirementof a power grid company, and selecting a result with the minimum peak-valley difference from the multiple results to serve as an electricity purchasing plan; and sending the obtained power purchase plan to a household energy control terminal to carry out power utilization control.

Description

technical field [0001] The invention relates to the field of smart home, in particular to a home energy control method considering user comfort risk preference. Background technique [0002] With the development of smart grids and the popularization of smart meters and smart homes, the ability of users to actively participate in demand-side response has been improved. Through the supply side and the demand side, users can use the home energy management system (HEMS) to achieve the goals of optimizing the user's electricity consumption and improving the comfort of electricity consumption. [0003] Renewable energy sources, such as photovoltaic (PV) power and wind generators, and flexible loads such as electric vehicles (EVs) have been widely used in HEMS. In [1], photovoltaic power generation forecasting is combined with HEMS under a dynamic pricing system to reduce expenditure and improve end-user comfort. The solar auxiliary heat load in literature [2] was incorporated in...

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Problems solved by technology

[0004] The research on the home energy management system in the above literature focuses on the contribution of electrical equipment to HEMS. Some articles consider the prediction risk caused by the day-ahead prediction, and use stochastic programming, stochastic dynamic programming, robust optimization, and chance-constrained programming to deal with uncertainties. method, but most of the articles did not take into account the risks of various uncertainties, and also lacked the division of risks

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