5,536 research outputs found
Commitment and Dispatch of Heat and Power Units via Affinely Adjustable Robust Optimization
The joint management of heat and power systems is believed to be key to the
integration of renewables into energy systems with a large penetration of
district heating. Determining the day-ahead unit commitment and production
schedules for these systems is an optimization problem subject to uncertainty
stemming from the unpredictability of demand and prices for heat and
electricity. Furthermore, owing to the dynamic features of production and heat
storage units as well as to the length and granularity of the optimization
horizon (e.g., one whole day with hourly resolution), this problem is in
essence a multi-stage one. We propose a formulation based on robust
optimization where recourse decisions are approximated as linear or
piecewise-linear functions of the uncertain parameters. This approach allows
for a rigorous modeling of the uncertainty in multi-stage decision-making
without compromising computational tractability. We perform an extensive
numerical study based on data from the Copenhagen area in Denmark, which
highlights important features of the proposed model. Firstly, we illustrate
commitment and dispatch choices that increase conservativeness in the robust
optimization approach. Secondly, we appraise the gain obtained by switching
from linear to piecewise-linear decision rules within robust optimization.
Furthermore, we give directions for selecting the parameters defining the
uncertainty set (size, budget) and assess the resulting trade-off between
average profit and conservativeness of the solution. Finally, we perform a
thorough comparison with competing models based on deterministic optimization
and stochastic programming.Comment: 31 page
Unit commitment with valve-point loading effect
Valve-point loading affects the input-output characteristics of generating
units, bringing the fuel costs nonlinear and nonsmooth. This has been
considered in the solution of load dispatch problems, but not in the planning
phase of unit commitment. This paper presents a mathematical optimization model
for the thermal unit commitment problem considering valve-point loading. The
formulation is based on a careful linearization of the fuel cost function,
which is modeled with great detail on power regions being used in the current
solution, and roughly on other regions. A set of benchmark instances for this
problem is used for analyzing the method, with recourse to a general-purpose
mixed-integer optimization solver
On Idle Energy Consumption Minimization in Production: Industrial Example and Mathematical Model
This paper, inspired by a real production process of steel hardening,
investigates a scheduling problem to minimize the idle energy consumption of
machines. The energy minimization is achieved by switching a machine to some
power-saving mode when it is idle. For the steel hardening process, the mode of
the machine (i.e., furnace) can be associated with its inner temperature.
Contrary to the recent methods, which consider only a small number of machine
modes, the temperature in the furnace can be changed continuously, and so an
infinite number of the power-saving modes must be considered to achieve the
highest possible savings. To model the machine modes efficiently, we use the
concept of the energy function, which was originally introduced in the domain
of embedded systems but has yet to take roots in the domain of production
research. The energy function is illustrated with several application examples
from the literature. Afterward, it is integrated into a mathematical model of a
scheduling problem with parallel identical machines and jobs characterized by
release times, deadlines, and processing times. Numerical experiments show that
the proposed model outperforms a reference model adapted from the literature.Comment: Accepted to 9th International Conference on Operations Research and
Enterprise Systems (ICORES 2020
Energy-Efficient Transmission Scheduling with Strict Underflow Constraints
We consider a single source transmitting data to one or more receivers/users
over a shared wireless channel. Due to random fading, the wireless channel
conditions vary with time and from user to user. Each user has a buffer to
store received packets before they are drained. At each time step, the source
determines how much power to use for transmission to each user. The source's
objective is to allocate power in a manner that minimizes an expected cost
measure, while satisfying strict buffer underflow constraints and a total power
constraint in each slot. The expected cost measure is composed of costs
associated with power consumption from transmission and packet holding costs.
The primary application motivating this problem is wireless media streaming.
For this application, the buffer underflow constraints prevent the user buffers
from emptying, so as to maintain playout quality. In the case of a single user
with linear power-rate curves, we show that a modified base-stock policy is
optimal under the finite horizon, infinite horizon discounted, and infinite
horizon average expected cost criteria. For a single user with piecewise-linear
convex power-rate curves, we show that a finite generalized base-stock policy
is optimal under all three expected cost criteria. We also present the
sequences of critical numbers that complete the characterization of the optimal
control laws in each of these cases when some additional technical conditions
are satisfied. We then analyze the structure of the optimal policy for the case
of two users. We conclude with a discussion of methods to identify
implementable near-optimal policies for the most general case of M users.Comment: 109 pages, 11 pdf figures, template.tex is main file. We have
significantly revised the paper from version 1. Additions include the case of
a single receiver with piecewise-linear convex power-rate curves, the case of
two receivers, and the infinite horizon average expected cost proble
Combined hydro-wind generation bids in a pool-based electricity market
Present regulatory trends are promoting the irect participation of wind energy in electricity markets. The final result of these markets sets the production scheduling for the operation time, including a power commitment from the wind generators. However, wind resources are uncertain, and the final power delivered usually differs from the initial power committed. This imbalance produces an overcost in the system, which must be paid by those who produce it, e.g., wind generators among others. As a result, wind farm revenue decreases, but it could increase by allowing wind farms to submit their bids to the markets together with a hydro generating unit, which may easily modify its production according to the expected imbalance. This paper presents a stochastic optimization technique that maximizes the joint profit of hydro and wind generators in a pool-based electricity market, taking into account the uncertainty of wind power prediction.En prens
A bi-objective genetic algorithm approach to risk mitigation in project scheduling
A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. The experiments conducted indicate that GAs provide a fast and effective solution approach to the problem. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement
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