Optimization heuristics for residential energy load management
(2012) In LUTFMA-3238-2012 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 medium-to-large size instances:
a Greedy Randomized Adaptive Search Procedure (GRASP) to
generate initial feasible solutions, a meta-heuristic 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 state-of-the-art
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/student-papers/record/3242728
- author
- Mattera, Claudio Giovanni LU
- supervisor
- 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
- LUTFMA-3238-2012
- report number
- LUTFMA-3238-2012
- ISSN
- 1404-6342
- other publication id
- 2012: E45
- language
- English
- id
- 3242728
- date added to LUP
- 2013-04-25 17:29:19
- date last changed
- 2013-04-25 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 medium-to-large size instances:
a Greedy Randomized Adaptive Search Procedure (GRASP) to
generate initial feasible solutions, a meta-heuristic 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 state-of-the-art
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 = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{LUTFMA-3238-2012}},
title = {{Optimization heuristics for residential energy load management}},
year = {{2012}},
}