Optimization For Family Energy Consumption In Real-time Pricing Environment

The definition of Demand Response (DR) is that the end users directlybased on the market information, such as price signals or encouragementmechanism provided by the electricity supplier, take the initiative tochange their normal electricity consumption behavior which willsignificantly improve the stability and reliability of the electricity market.The Real Time Pricing (RTP) has the capacity to timely show the marginalcost of electricity supply, therefore, it could be theoretically seen as themost ideal and effective implementation strategy for electricity pricing inthe demand side response. RTP can guide the end users to participate inpower system operation and management. Additionally, since it hasencouragement mechanism, users have the motivations to consumeelectricity at the bottom period of demand. Hence, the peak load will bereduced, and the load rate efficiency of the power system will be increased.It also achieves the purpose of load shifting. Meanwhile, the utilizationrate end electricity generation ratio will be largely improved. Furthermore,Real Time Pricing enables the advantages from both supplier side andbuyer side, which means at the time of realizing energy conservation, thecost for end users will be minimized as well.However, due to the end users lack the knowledge of real time pricedemand response, so they cannot fully understand the whole system.Moreover, current market condition cannot provide the sufficient homemanagement system to every family. As a result, both of these factors limitthe spread area of real time electricity price.According to the current researches of smart grid and demand sideresponse, this thesis aims to help the family users to better participate the demand response in real time pricing environment as well as establish thehome energy optimal control model. It mainly includes following aspects:1.Summarize the key structure of Demand Response and Real TimePricing. This paper not only concludes the international and nationalresearch, practice results regarding to demand response, but alsocategorizes research types of demand response. The classification basicallycontains two aspects: encouragement mechanism and pricing mechanism.Research particularly focuses on the analysis of the real time price model,implementation mechanism, users’ responses, system impacts and thetechnical support.2.To solve the problem of real time electricity price forecasting, thispaper based on the research of historical electricity price data, establish theSupport Vector Regression Real Time Pricing forecast method (SVR). TheSVR method is based on the Genetic Algorithm Cross Validation (GACV).This paper also uses simulation comparison to check the validity of thismethod.3.In order to solve the home energy optimization control model problem,this paper also further analyze the user electricity consumption modelbased on the former SVR model. The optimized model is establishedaccording to the Mixed Integer Programming, realizes the aim of helpingfamily to ensure the electricity consumption target. Furthermore, this paperuses the example simulation results to verify the validity and effectivenessof the model.4.Consider the features of smart electricity consumption, and for thepurpose of helping the users to participate the electricity market better, thispaper designed an electricity consumption display interface of smartelectricity interactive terminal in Matlab.