The potential of energy flexibility of space heating and cooling in Portugal

Abstract This study assesses the potential of energy flexibility of space heating and cooling for a typical household under different geographical conditions in Portugal. The proposed approach modifies the demand through the optimization of the thermostat settings using a genetic algorithm to reduce either operational costs or interaction with the grid. The results show that the used energy flexibility indicator expresses the available potential and that flexibility depends on several factors, namely: i) thermal inertia of the archetypical household; ii) the time of use electricity tariffs; iii) users’ comfort boundaries; and iv) the geographical location of the houses.

[1]  B. Fox,et al.  Demand side management of an urban water supply using wholesale electricity price , 2017 .

[2]  T. J. Hammons.  Integrating renewable energy sources into European grids , 2008 .

[3]  E.A.M. Klaassen,et al.  A methodology to assess demand response benefits from a system perspective: A Dutch case study , 2017 .

[4]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[5]  Ali F. Alajmi,et al.  Selecting the most efficient genetic algorithm sets in solving unconstrained building optimization problem , 2014 .

[6]  Álvaro Gomes,et al.  Optimizing the management of smart home energy resources under different power cost scenarios , 2019, Applied Energy.

[7]  Ali F. Alajmi,et al.  Efficient Genetic Algorithm sets for optimizing constrained building design problem , 2016 .

[8]  J. Torriti.  The significance of occupancy steadiness in residential consumer response to Time of Use pricing: Evidence from a stochastic adjustment model , 2013 .

[9]  T. Simões,et al.  Mapping Flexibility of Urban Energy Systems (FIRST) project: Rationale and study design of an exploratory project , 2019, IOP Conference Series: Earth and Environmental Science.

[10]  Mark O'Malley,et al.  Challenges and barriers to demand response deployment and evaluation , 2015 .

[11]  Dirk Saelens,et al.  Energy flexible buildings: an evaluation of definitions and quantification methodologies applied to thermal storage , 2018 .

[12]  Johan Carlsson,et al.  Estimation of European Union residential sector space cooling potential , 2017 .

[13]  Henrik Madsen,et al.  Characterizing the energy flexibility of buildings and districts , 2018, Applied Energy.

[14]  A. Picciariello,et al.  Quantifying distribution-system operators' economic incentives to promote residential demand response , 2015 .

[15]  Tobias Boßmann,et al.  Model-based assessment of demand-response measures—A comprehensive literature review , 2016 .

[16]  J. Torriti,et al.  Demand response experience in Europe: Policies, programmes and implementation , 2010 .

[17]  Laura Aelenei,et al.  Smart City: A Systematic Approach towards a Sustainable Urban Transformation , 2016 .

[18]  Mohammad Ali Fotouhi Ghazvini,et al.  Liberalization and customer behavior in the Portuguese residential retail electricity market , 2019, Utilities Policy.