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Integrated optimization of energy resources in a residential setting – development of an energy management system

Panel: 4. Mobility, transport, and smart and sustainable cities

This is a peer-reviewed paper.

Authors:
Ana Soares, Universidade de Coimbra, Portugal
Carlos Oliveira, INESC Coimbra, Portugal
Alvaro Gomes, INESC Coimbra / University of Coimbra, Portugal
Carlos Henggeler Antunes, INESC Coimbra / University of Coimbra, Portugal

Abstract

The changes in the energy matrix, namely the increasing deployment of distributed generation based on renewable sources, the introduction of new technologies in the residential sector, including electric vehicles and storage systems, the advances in information and communication technologies (ICT) and the adoption of dynamic tariff schemes will foster new challenges regarding the design of strategies for the optimal integrated management of energy resources.

Concerning electricity tariffs, it is widely recognized that flat tariffs do not incentivize response from end-users to reduce peak demand or make adequate use of available local generation. Dynamic tariffs encourage a more conscious use of electricity requiring users to play a more active role.

Energy management systems (EMS) may be used in this context to help residential users making sustainable decisions concerning the use of loads, local generation and storage to minimize their electricity bill without jeopardizing the quality of the energy services provided , namely regarding comfort requirements and operation preferences. These decisions may include scheduling shiftable loads, re-set temperature settings, resolve whether to store, sell or use electricity from storage systems and when to charge the electric vehicle. To assure that these EMS provide usable energy management strategies, several aspects must be considered, namely:

- the technical restrictions associated with each domestic load to be managed;

- the ability to respect end-user’s preferences concerning the acceptable time slots for scheduling certain appliances and the admissible variation in temperature for thermostatically controlled loads;

- the capability to deal with unexpected events and quickly provide new decisions if needed;

- to assure that the quality of the energy services provided is not degraded.

Therefore, a robust methodology should be embedded in the EMS for computing optimal energy management solutions considering all those inputs. For this purpose, a tailored evolutionary algorithm to cope with the combinatorial nature of this problem has been developed and tested through extensive simulations offering effective results.

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