The increasing levels of renewable generation and the electrification of transport and heating as parts of the movement towards low-carbon energy systems to cope with climate change will place significant challenges on the electricity system to facilitate the way towards future low carbon energy systems in a cost effective way and ensure secure power delivery. New solutions and higher levels of flexibility are required than currently exist in order to reduce the integration costs of low carbon generation and demand technologies. Price-based demand response in residential sector is considered as one of these potential solutions. However, a certain level of automation is required to reduce both the uncertainty in the consumer response and the complexity for consumers to react to the price signal. This thesis presents a comprehensive and general residential optimization-based Automated Demand Response (ADR). The modelling of home appliances has been extensively developed to include all the classifications proposed in the literature, namely, deferrable and thermal in addition to new groups of critical and fully curtailable loads. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer’s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user’s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer’s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different case studies show the effectiveness of the proposed controller to minimize the household’s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks. This thesis also proposes a two-stage distribution-planning framework to assess the benefits of the proposed ADR models in response to a location-specific time of use Distribution Use of Systems Charge (DUoSC) on the required investments to connect future low-carbon technologies. The network investments and the satisfaction of consumers in terms of energy curtailment are both quantified. The first stage aims to generate location-specific time of use price signals for all users in the network, which represents their contributions in future network investments due to congestion and security constraints. The second stage relates to a group of ADR controllers at residential premises that aims to minimise the daily energy payment whilst maximising consumer comfort in response to the corresponding price signal produced from the first stage.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:677824 |
| Date | January 2015 |
| Creators | Althaher, Sereen |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/optimal-demand-response-from-home-energy-management-system-modeling-and-benefits-for-distribution-networks(cc2221c7-9b1b-49c5-aa32-bf71a241eb0e).html |
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