Influence of demand response actions on thermal comfort and electricity cost for residential houses

Demand response control algorithms assist in shifting a part of the electricity demand of building from periods of high demand and price towards periods of lower price. Without demand response, also the grid operators must rely on and use expensive and fossil fuel power plants during peak periods of electricity usage. The objective of this doctoral thesis was to study the influence of demand response actions on thermal comfort and electricity cost in residential buildings with different heating systems in Finland. This simulation-based thesis used building energy simulation, hourly electricity price and weather data to study the influence of demand response actions. The first step of this thesis was to find out the acceptable range of indoor air and operative temperatures complying with the recommended thermal comfort categories in accordance with the EN 15251 standard. To conduct this, the Fanger approach was used to predict the thermal comfort of occupants. The second step was to reduce the electricity cost by means of demand response control algorithms, without sacrificing the thermal comfort. These aims were applied for electrically heated and heat-pump heated residential buildings. Several rule-based demand response control algorithms and a model-based one were presented, developed and studied. The rule-based control algorithms were based on the trend of previous hourly electricity prices, present electricity price, and trend of future hourly electricity prices. Also, these control algorithms were based on median, maximum subarray and moving average methods. The model-based control algorithm was based on the trend of future hourly electricity prices, and method of linear programming with a hybrid wavelet transform and a dynamic neural network. All presented control algorithms maintain the thermal comfort of the occupants at the desired levels. The obtained results from the case studies showed that the rule- and model-based demand response control algorithms are able to reduce the electricity consumption and cost of different heating systems, and in the best case the electricity consumption and cost can be decreased by around 10 and 15%, respectively