Demand Control Tool for Houses with Thermal Energy Storage Systems
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Journal Title
Journal ISSN
Volume Title
Sähkötekniikan korkeakoulu |
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Authors
Date
2014-10-20
Department
Major/Subject
Power Systems and High Voltage Engineering
Mcode
S3015
Degree programme
EST - Master’s Programme in Electrical Engineering
Language
en
Pages
61+30
Series
Abstract
The goal of this thesis is to develop a load control of houses with Thermal Energy Storage systems in order to avoid the appearance of uncontrolled consumptions during peak hours. In the Nordic countries, the installation of energy storage in households is a common practice: these systems are used to decouple the heating and the electric consumption of the household, enabling the consumer to shift the most electric demand from on-peak to off-peak hours.These kind of electric customers are subscribed to specific electricity tariffs. These tariffs are composed by two levels of electricity price, being cheap to consume at night time and expensive during daytime: they are known as “Time of Use” tariffs. Houses with full storage capacity satisfy their entire heating demand by consuming during “Time of Use” hours; however, houses with partial storage capacity, which own a smaller storage tank, exhibit extra consumptions during daytime out of “Time of Use” hours when their storing capacity is not big enough to supply the heating demand of the house. These extra consumptions are a priori unexpected, and their appearance may cause economic losses to the electricity distributor. Finland buys electricity in the Nord Pool Spot, which runs the largest electrical market in the world. The daily evolution of the electricity price is known a day-ahead in the called Elspot market: this allows distributors to create efficient and economic daily consumption plans for different customers. In this thesis it is presented a tool to create a consumption plan for houses with Thermal Energy Storage by analysing the daily Elspot price evolution. The starting data are electric records obtained by metering at distribution level. These records belong to houses located in four regions of Finland. Among these houses, some of them exhibit similar consumption trends to a Thermal Energy Storage system: they have been clustered and studied independently. Once the most relevant parameters of each house are estimated, such as the boiler power and storage capacity, it is applied a mathematical model in order to simulate the hourly evolution of the storage level. A linear model of heating consumption as a function of the outdoor temperature is presented in this work; in addition, the historical deviations of consumption from the linear model are studied, and play an important role in the demand forecast proposed here. The model of hourly evolution of the storage level is used to develop a day-ahead control. Through the demand forecast, it is computed a feasible consumption level for the next day. A control vector is created every day and given to the customer. This vector offers the most economic loading pattern that satisfies the heating demand, and allows the controller to estimate the actual storage level of the house; the loading pattern is generated by using the Stochastic Genetic Algorithm, an application of the Genetic Algorithm, widely used in optimization processes. Finally, it is also presented a storage level updating tool that allows tracking the behaviour of the customer regarding to the control given.Description
Supervisor
Lehtonen, MattiThesis advisor
Lehtonen, MattiKeywords
stochastic genetic algorithm, genetic algorithm, water tank, energy storage, control tool, heating