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Locational-based Coupling of Electricity Markets: Benefits from Coordinating Unit Commitment and Balancing Markets
We formulate a series of stochastic models for committing and dispatching electric generators subject to transmission limits. The models are used to estimate the benefits of electricity locational marginal pricing (LMP) that arise from better coordination of day-ahead commitment decisions and real-time balancing markets in adjacent power markets when there is significant uncertainty in demand and wind forecasts. The unit commitment models optimise schedules under either the full set of network constraints or a simplified net transfer capacity (NTC) constraint, considering the range of possible real-time wind and load scenarios. The NTC-constrained model represents the present approach for limiting day-ahead electricity trade in Europe. A subsequent redispatch model then creates feasible real-time schedules. Benefits of LMP arise from decreases in expected start-up and variable generation costs resulting from consistent consideration of the full set of network constraints both day-ahead and in real-time. Meanwhile, using LMP to coordinate adjacent balancing markets provides benefits because it allows intermarket flow schedules to be adjusted in real-time in response to changing conditions. These models are applied to a stylised four-node network, examining the effects of varying system characteristics on the magnitude of the locational-based unit commitment benefits and the benefits of intermarket balancing. Although previous www.eprg.group.cam.ac.uk EPRG WORKING PAPER studies have examined the benefits of LMP, these usually examine one specific system, often without a discussion of the sources of these benefits, and with simplifying assumptions about unit commitment.
We conclude that both categories of benefits are situation dependent, such that small parameter changes can lead to large changes in expected benefits. Although both can amount to a significant percentage of operating costs, we find that the benefits of balancing market coordination are generally larger than the unit commitment benefits
Flexible resources allocation techniques: characteristics and modelling
At the interface between engineering, economics, social sciences and humanities, industrial engineering aims to provide answers to various sectors of business problems. One of these problems is the adjustment between the workload needed by the work to be realised and the availability of the company resources. The objective of this work is to help to find a methodology for the allocation of flexible human resources in industrial activities planning and scheduling. This model takes into account two levers of flexibility, one related to the working time modulation, and the other to the varieties of tasks that can be performed by a given resource (multiâskilled actor). On the one hand, multiâskilled actors will help to guide the various choices of the allocation to appreciate the impact of these choices on the tasks durations. On the other hand, the working time modulation that allows actors to have a work planning varying according to the workload which the company has to face
Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution
With the increasing share of renewable and distributed generation in
electrical distribution systems, Active Network Management (ANM) becomes a
valuable option for a distribution system operator to operate his system in a
secure and cost-effective way without relying solely on network reinforcement.
ANM strategies are short-term policies that control the power injected by
generators and/or taken off by loads in order to avoid congestion or voltage
issues. Advanced ANM strategies imply that the system operator has to solve
large-scale optimal sequential decision-making problems under uncertainty. For
example, decisions taken at a given moment constrain the future decisions that
can be taken and uncertainty must be explicitly accounted for because neither
demand nor generation can be accurately forecasted. We first formulate the ANM
problem, which in addition to be sequential and uncertain, has a nonlinear
nature stemming from the power flow equations and a discrete nature arising
from the activation of power modulation signals. This ANM problem is then cast
as a stochastic mixed-integer nonlinear program, as well as second-order cone
and linear counterparts, for which we provide quantitative results using state
of the art solvers and perform a sensitivity analysis over the size of the
system, the amount of available flexibility, and the number of scenarios
considered in the deterministic equivalent of the stochastic program. To foster
further research on this problem, we make available at
http://www.montefiore.ulg.ac.be/~anm/ three test beds based on distribution
networks of 5, 33, and 77 buses. These test beds contain a simulator of the
distribution system, with stochastic models for the generation and consumption
devices, and callbacks to implement and test various ANM strategies
The stochastic vehicle routing problem : a literature review, part II : solution methods
Building on the work of Gendreau et al. (Oper Res 44(3):469â477, 1996), and complementing the first part of this survey, we review the solution methods used for the past 20 years in the scientific literature on stochastic vehicle routing problems (SVRP). We describe the methods and indicate how they are used when dealing with stochastic vehicle routing problems. Keywords: vehicle routing (VRP), stochastic programmingm, SVRPpublishedVersio
Decomposition, Reformulation, and Diving in University Course Timetabling
In many real-life optimisation problems, there are multiple interacting
components in a solution. For example, different components might specify
assignments to different kinds of resource. Often, each component is associated
with different sets of soft constraints, and so with different measures of soft
constraint violation. The goal is then to minimise a linear combination of such
measures. This paper studies an approach to such problems, which can be thought
of as multiphase exploitation of multiple objective-/value-restricted
submodels. In this approach, only one computationally difficult component of a
problem and the associated subset of objectives is considered at first. This
produces partial solutions, which define interesting neighbourhoods in the
search space of the complete problem. Often, it is possible to pick the initial
component so that variable aggregation can be performed at the first stage, and
the neighbourhoods to be explored next are guaranteed to contain feasible
solutions. Using integer programming, it is then easy to implement heuristics
producing solutions with bounds on their quality.
Our study is performed on a university course timetabling problem used in the
2007 International Timetabling Competition, also known as the Udine Course
Timetabling Problem. In the proposed heuristic, an objective-restricted
neighbourhood generator produces assignments of periods to events, with
decreasing numbers of violations of two period-related soft constraints. Those
are relaxed into assignments of events to days, which define neighbourhoods
that are easier to search with respect to all four soft constraints. Integer
programming formulations for all subproblems are given and evaluated using ILOG
CPLEX 11. The wider applicability of this approach is analysed and discussed.Comment: 45 pages, 7 figures. Improved typesetting of figures and table
Genetic algorithms in timetabling and scheduling
Thio thesis investigates the use of genetic algorithms (GAs) for solving a range of
timetabling and scheduling problems. Such problems arc very hard in general, and
GAs offer a useful and successful alternative to existing techniques.A framework is presented for GAs to solve modular timetabling problems in eduÂŹ
cational institutions. The approach involves three components: declaring problemspecific
constraints, constructing a problem specific evaluation function and using a
problem-independent GA to attempt to solve the problem. Successful results are
demonstrated and a general analysis of the reliability and robustness of the approach is
conducted. The basic approach can readily handle a wide variety of general timetabling
problem constraints, and is therefore likely to be of great practical usefulness (indeed,
an earlier version is already in use). The approach rclicG for its success on the use of
specially designed mutation operators which greatly improve upon the performance of
a GA with standard operators.A framework for GAs in job shop and open shop scheduling is also presented. One
of the key aspects of this approach is the use of specially designed representations
for such scheduling problems. The representations implicitly encode a schedule by
encoding instructions for a schedule builder. The general robustness of this approach
is demonstrated with respect to experiments on a range of widely-used benchmark
problems involving many different schedule quality criteria. When compared against
a variety of common heuristic search approaches, the GA approach is clearly the most
successful method overall. An extension to the representation, in which choices of
heuristic for the schedule builder arc also incorporated in the chromosome, iG found to
lead to new best results on the makespan for some well known benchmark open shop
scheduling problems. The general approach is also shown to be readily extendable to
rescheduling and dynamic scheduling
Application of PSO for optimization of power systems under uncertainty
The primary objective of this dissertation is to develop a black box optimization
tool. The algorithm should be able to solve complex nonlinear, multimodal, discontinuous
and mixed-integer power system optimization problems without any
model reduction. Although there are many computational intelligence (CI) based
algorithms which can handle these problems, they require intense human intervention
in the form of parameter tuning, selection of a suitable algorithm for a given
problem etc. The idea here is to develop an algorithm that works relatively well on
a variety of problems with minimum human effort. An adaptive particle swarm
optimization algorithm (PSO) is presented in this thesis. The algorithm has special
features like adaptive swarm size, parameter free update strategies, progressive
neighbourhood topologies, self learning parameter free penalty approach etc.
The most significant optimization task in the power system operation is the
scheduling of various generation resources (Unit Commitment, UC). The current
practice used in UC modelling is the binary approach. This modelling results in a
high dimension problem. This in turn leads to increased computational effort and
decreased efficiency of the algorithm. A duty cycle based modelling proposed in
this thesis results in 80 percent reduction in the problem dimension. The stern uptime
and downtime requirements are also included in the modelling. Therefore,
the search process mostly starts in a feasible solution space. From the investigations
on a benchmark problem, it was found that the new modelling results in high
quality solutions along with improved convergence.
The final focus of this thesis is to investigate the impact of unpredictable nature
of demand and renewable generation on the power system operation. These quantities
should be treated as a stochastic processes evolving over time. A new PSO
based uncertainty modelling technique is used to abolish the restrictions imposed
by the conventional modelling algorithms. The stochastic models are able to incorporate
the information regarding the uncertainties and generate day ahead UC
schedule that are optimal to not just the forecasted scenario for the demand and
renewable generation in feed but also to all possible set of scenarios. These models
will assist the operator to plan the operation of the power system considering
the stochastic nature of the uncertainties. The power system can therefore optimally
handle huge penetration of renewable generation to provide economic operation
maintaining the same reliability as it was before the introduction of uncertainty
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