Optimization heuristics for residential energy load management
(2012) In LUTFMA32382012 FMA820 20122Mathematics (Faculty of Engineering)
 Abstract
 The MS thesis is concerned with the problem of scheduling the
daily energy loads in a multihouse environment from the point of
view of an energy retailer.
We assume that the residential users own a set of home appliances
(washing machines, dishwashers, ovens, microwave ovens, vacuum
cleaners, boilers, fridges, water purifiers, irons, TVs, personal
computers and lights) that are supposed to be used during the day.
Houses can also be equipped with Photo Voltaic (PV) panels, which
produce energy in a discontinuous way, and batteries that allow the
system to store and release energy when required. The day is subdivided
into 96 timeslots of 15 minutes each. For each appliance, we
suppose to know the load profile, that is, a set of... (More)  The MS thesis is concerned with the problem of scheduling the
daily energy loads in a multihouse environment from the point of
view of an energy retailer.
We assume that the residential users own a set of home appliances
(washing machines, dishwashers, ovens, microwave ovens, vacuum
cleaners, boilers, fridges, water purifiers, irons, TVs, personal
computers and lights) that are supposed to be used during the day.
Houses can also be equipped with Photo Voltaic (PV) panels, which
produce energy in a discontinuous way, and batteries that allow the
system to store and release energy when required. The day is subdivided
into 96 timeslots of 15 minutes each. For each appliance, we
suppose to know the load profile, that is, a set of successive timeslots
with the corresponding amount of energy required.
Given the load profile of each appliance, the time windows in
which the appliances must be executed, the physical characteristics
of the batteries, the energy amount produced by the PV systems, the
problem is that of scheduling the various appliances (assigning their
starting timeslots) so as to minimize an appropriate objective function
while respecting the maximum capacity of the meters (usually 3 kW).
We consider minimizing the total maximum peak.
This Residential Energy Load Management Problem is a challenging
extension of the classical Generalized Assignment Problem (GAP).
Since the Mixed Integer Linear Programming (MILP) formulation can
be solved within reasonable computing time only for small instances,
we developed various methods to tackle mediumtolarge size instances:
a Greedy Randomized Adaptive Search Procedure (GRASP) to
generate initial feasible solutions, a metaheuristic à la Tabu Search
(TS) to improve initial solutions, and other techniques based on the
solution of reduced MILP problems. In the TS algorithm we proposed
different types of moves (appliances shift, batteries charge or discharge…) to explore the neighbourhood.
We have tested our methods on a data set of 180 realistic instances
with different number of houses (20, 200 and 400), PV panels and
batteries. The solutions provided by the heuristics are compared with
those obtained by solving the MILP model by using a stateoftheart
solver.
For instances without batteries all our heuristics yield high quality
solutions – within 3% from the reference solution – in a short computing
time for the largest instances. Heuristics that solve reduced MILPs
achieved the same results even for instances with batteries. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/3242728
 author
 Mattera, Claudio Giovanni ^{LU}
 supervisor

 Karl Åström ^{LU}
 Edoardo Amaldi
 organization
 course
 FMA820 20122
 year
 2012
 type
 H2  Master's Degree (Two Years)
 subject
 keywords
 Operation research, Residential energy management, Energy optimization, Heuristic, Mixed Integer Linear Programming
 publication/series
 LUTFMA32382012
 report number
 LUTFMA32382012
 ISSN
 14046342
 other publication id
 2012: E45
 language
 English
 id
 3242728
 date added to LUP
 20130425 17:29:19
 date last changed
 20130425 17:29:19
@misc{3242728, abstract = {The MS thesis is concerned with the problem of scheduling the daily energy loads in a multihouse environment from the point of view of an energy retailer. We assume that the residential users own a set of home appliances (washing machines, dishwashers, ovens, microwave ovens, vacuum cleaners, boilers, fridges, water purifiers, irons, TVs, personal computers and lights) that are supposed to be used during the day. Houses can also be equipped with Photo Voltaic (PV) panels, which produce energy in a discontinuous way, and batteries that allow the system to store and release energy when required. The day is subdivided into 96 timeslots of 15 minutes each. For each appliance, we suppose to know the load profile, that is, a set of successive timeslots with the corresponding amount of energy required. Given the load profile of each appliance, the time windows in which the appliances must be executed, the physical characteristics of the batteries, the energy amount produced by the PV systems, the problem is that of scheduling the various appliances (assigning their starting timeslots) so as to minimize an appropriate objective function while respecting the maximum capacity of the meters (usually 3 kW). We consider minimizing the total maximum peak. This Residential Energy Load Management Problem is a challenging extension of the classical Generalized Assignment Problem (GAP). Since the Mixed Integer Linear Programming (MILP) formulation can be solved within reasonable computing time only for small instances, we developed various methods to tackle mediumtolarge size instances: a Greedy Randomized Adaptive Search Procedure (GRASP) to generate initial feasible solutions, a metaheuristic à la Tabu Search (TS) to improve initial solutions, and other techniques based on the solution of reduced MILP problems. In the TS algorithm we proposed different types of moves (appliances shift, batteries charge or discharge…) to explore the neighbourhood. We have tested our methods on a data set of 180 realistic instances with different number of houses (20, 200 and 400), PV panels and batteries. The solutions provided by the heuristics are compared with those obtained by solving the MILP model by using a stateoftheart solver. For instances without batteries all our heuristics yield high quality solutions – within 3% from the reference solution – in a short computing time for the largest instances. Heuristics that solve reduced MILPs achieved the same results even for instances with batteries.}, author = {Mattera, Claudio Giovanni}, issn = {14046342}, keyword = {Operation research,Residential energy management,Energy optimization,Heuristic,Mixed Integer Linear Programming}, language = {eng}, note = {Student Paper}, series = {LUTFMA32382012}, title = {Optimization heuristics for residential energy load management}, year = {2012}, }