|Thursday, 25th of September, 2014|
|09:30 - 10:20||(invited) Utz-Uwe Haus: Robust Network Planning Problems
Robust Optimization techniques have turned into a standard toolset for solving Mixed-Integer Optimization problems in real world applications. This includes, in particular, cost-robust and demand-robust network optimization problems. We therefore focus on the more challenging problems arising when structural robustness is required. For problems with a well-defined robustness requirement we will present a reformulation approach preserving much of the combinatorial structure but employing a relaxed notion of feasibility due to Adjiashvili et al. Alternatively, a stochastic view on robustness may be taken, leading to (multistage) stochastic optimization problems. We propose a generic method to reformulate them as mixed-integer linear programs by bundling equivalent scenarios, avoiding scenario sampling. The approach makes use of binary decision diagrams to store a cover of the scenario space, and uses them to derive an inequality system for computing scenario probabilities conditioned on the lower-level decisions. As an application we significantly improve upon the scenario space partitioning approach for the pre-disaster network strengthening problem and discuss applicability to other network reliability improvement problems.
|10:30 - 11:00||Coffee break|
|11:00 - 11:50||(invited) Lars Schewe: Optimization Problems in Gas
|12:00 - 12:25||(contrib) Salvador Pineida Morente: Impact of Forecast
errors on Generation and transmission expansion planning
We present a series of investment models for power systems with high penetration of renewable generation. The first models determine the optimal expansion plan of stochastic generating units to maximize the profit of a power producer participating in electricity markets. The second set of models provide the optimal generation and transmission expansion plan of a central planner aiming at minimizing the sum of operating and investment costs. The main contribution of these models is the consideration of forecast errors of stochastic production through the modeling of a day-ahead and a balancing market. Additionally, we investigate the impact of the market design on the optimal expansion planning of both profit-maximizing power producers and cost-minimizing central planners. The main features of the proposed models are demonstrated through illustrative examples and larger case studies.
|12:30 - 14:00||Lunch break (Some place to eat)|
|14:00 - 14:50||(invited) Claudia D'Ambrosio: On the Mathematical Models and
Methods for the Hydro Unit Commitment Challenges
The Hydro Unit Commitment is a well known and interesting problem that has been studied since decades. Nevertheless, it still represent a challenge for several reasons, for example the wish of better model physical and strategic constraints. In this talk, we'll focus on some of the challenges arising in the deterministic Hydro Unit Commitment context and present mathematical programming methods to tackle them.
|15:00 - 15:25||(contrib) Matteo Salani: Distributed optimization for
demand-side management in smart grids
We discuss the Swiss2Grid project, a pilot and demonstration project for the evaluation
of demand management policies in micro grids. The increasing diffusion of decentralised
energy generation (Photovoltaics) the imminent massive diffusion of plug in Electrice
Vehicles (EVs), can lead to imbalances on the electric grid and the associated huge
capital investments in grid infrastructures to meet the increasing energy flows. In the
Swiss2Grid project we propose a fully decentralized approach to load management in the
local low-voltage grid. Single households, or even single devices, use a local control
algorithm that uses local data to infer the current status of the low-voltage grid. The
algorithm forecasts the status of the grid in the imminent future and exploits the
optionally available level of freedom in shifting loads or using locally stored energy
(e.g., in stationary batteries). We present the overall results of the project. We first
describe the pilot set-up in Mendrisio, a city in Southern Switzerland, we describe the
algorithm principles, we detail the forecast modules and finally we present results
obtained in a comprehensive simulation environment.
|15:30 - 16:00||Coffee break|
|16:00 - 16:50||(invited) Carla Henriques: Input-output MOLP models for Energy-Environment-Economy (E3) planning
The study of the interactions between the economy (at national, global and local levels), the energy sector and the corresponding impacts on the environment inherently involves multiple axes of evaluation of distinct policies. Input-output (IO) analysis offers a consistent framework for developing multiobjective models for assessing the trade-offs associated with those policies. The analytical framework of IO analysis enables to model the interactions between the whole economy and the energy sector, thus identifying the energy required for the provision of goods and services in an economy and also quantifying the corresponding pollutant emissions. This lecture is aimed at reviewing the different modelling approaches available in the scientific literature based on coupling IO analysis with multiobjective models that can be particularly useful for policy decision-makers to assess the trade-offs between the energy-environment-economy (E3) pillars of sustainable development, particularly relevant in the current sluggish economic context.
|17:00 - 18:00||Roundtable on the Wiki-Procedure|
|19:30-22:30||Conference dinner at Rákóczi Grillház|
|Friday, 26th of September, 2014|
|08:30 - 10:30||COST Management Committee|
|10:30 - 11:00||Coffee break|
|11:00 - 11:50||(invited) Jordi Castro: An interior-point solver for convex
separable block-angular problems
Constraints matrices with block-angular structures are pervasive in Optimization. They appear in many fields, for instance, logistics, telecommunications, energy or big-data. Interior-point methods have shown to be competitive for these structured problems by exploiting the linear algebra. One of these approaches solves the normal equations using sparse Cholesky factorizations for the block constraints, and a preconditioned conjugate gradient (PCG) for the linking constraints, which relies on a particular preconditioner. In this talk we will discuss an efficient solver based on this algorithm for linearly constrained convex separable problems (linear, quadratic or nonlinear). The solver has been hooked to SML, a structured conveying modelling language based on the popular AMPL modeling language. Computational results will be reported for some large and difficult instances in the literature. In the largest instances tested, of up to 25 millions of variables and 300000 constraints, this approach was from two to three 3 orders of magnitude faster than state-of-the-art linear and quadratic commercial optimization solvers.
|12:00 - 12:25||(contrib) David Fournier: Metro Energy Optimization through
Rescheduling: Mathematical Models and Heuristic Algorithm Compared to MILP
The use of regenerative braking is a key factor to reduce the energy consumption of a
In the case where no device can store the energy produced during braking, only the metros
that are accelerating at the same time can benefit from it.
Maximizing the power transfers between accelerating and braking metros thus provides a
simple strategy to benefit from regenerative energy without any other hardware device.
|12:30 - 14:00||Lunch break (Some place to eat)|
|14:00 - 14:50||(invited) Laureano Escudero: On modeling the Energy
Generation and Transmission Capacity Expansion Planning problem. An
approximation to the Multicrieria Risk Averse Multistage Stochastic
Mixed 0-1 NonLinear model
One of the great and difficult problems that EU is facing today consists of the estimation of timing for having a clean power generation technologies and electricity free transmission expansion network at a pan-European level in a long term (e.g., 30 years time horizon). EU has established aggressive pollutant emission reduction targets: a 20% reduction in greenhouse gases with respect to 1990 levels by 2020 (most of the member countries are still far away from that target) and endorsing an objective of 80% reductions by 2050. Mathematical optimization models and algorithms for problem solving to address the above challenges are essential computerized tools for helping in the decision making for estimating the appropriate feasible mix of power generation sources (ranging from less coal, nuclear and combined cycle gas turbine to more renewable sources: hydroelectric, wind, solar, photovoltaic and biomass), the power generation plant / farm locations and dimensions , and the location and capacity of new lines in the transmission network, so that the solution satisfies the electricity demand from main focal points in the European region. Additionally, the tools should help to maximize different types of utility criteria at pan-European level, and quantifying the benefits of using cleaner, safer and efficient (cheaper) energy accessible to all the consumption nodes from perhaps far away power generation sites in the network. In this work a stochastic multiperiod mixed integer optimization model is presented as well as some hints on the different algorithmic approaches for that gigantic problem solving. The main parameters are uncertain, so, a set of scenarios should be generated for: fuels prices, electricity demand and prices at the network nodes of the energy system, operating hours per period of power generation technologies, CO2 emission permits and Green Certificates prices and allowed bounds, power generation cost of different technologies, electricity loss of new transmission technologies, characteristics (i.e., maximum energy flow and reactance) of cable types on new energy transmission lines, user-driven investment allocation bounds on cost of total power generation and energy transmission technologies, etc. There is not a unique function-criterion to consider. Rather it is a multicriteria problem, since the model must consider the maximization of the NPV of expected investment and consumer stakeholders goals over the scenarios along the time horizon subject to some risk reduction of the negative impact of non- wanted scenarios on different types of utility objectives and stakeholders at European level, such as maximizing power share of cleaner, safer and efficient energy accessible to all consumption nodes, cost investment from private and public institutions, generation and transmission network reliability, EC directives on environmental issues and others, EU governments, etc. Additionally, the maximization of the NPV of the expected global profit in the proposed model is subject to time stochastic dominance constraints for a set of profiles for each function (including the objective one) , such that each profile is given by the 4-tupla: threshold on the objective function value and maximum shortfall allowed for each scenario group at selected time periods as well as related target bounds on the probability of failure on reaching the threshold and expected shortfall. The gigantic problem cannot be solved up to optimality such that a realistic approach consists of a combination of sample scenario schemes, inexact scenario (scenario groups) decomposition algorithms and high performance computing.
|15:00 - 15:25||(contrib) Mehdi Madani: A new primal-dual framework for
European day-ahead electricity auctions, with algorithmic and
economic modelling applications
We propose a new « primal-dual framework » to deal with European day-ahead electricity
auctions, which has applications in both the algorithmic and economic modelling fields.
Those auctions are at the core of the Price Coupling of Regions project and the
corresponding coupling algorithm EUPHEMIA (see [e]), aimed at building a fully integrated
Pan-European exchange platform for electricity traders in such day-ahead markets
(coupling most of European power exchanges). They include products rendering the auction
clearing problem a non-linear non-convex optimization problem with integer constraints.
In such contexts where classical strong duality results fail, most of the time no market
equilibrium with uniform prices exists. The relaxation from a perfect equilibrium with
uniform prices adopted in European markets is to allow for paradoxically rejected orders,
which are orders earning money at the computed market clearing prices, but nonetheless
rejected, incurring opportunity costs to some bidders. The objective function in practice
is to optimize welfare, an optimization problem considered in [a,c]. However,
practitioners and stakeholders have in the past been interested in knowing what could be
‘in theory’ the minimum opportunity costs possible, or the minimum number of those
paradoxically rejected orders, while still satisfying European market clearing rules [b].
It should be noted that computed prices depend in particular on the combination of block
and complex orders selected according to the chosen objective, the admissible selections
themselves depending on prices. This is why [d] proposes the term of “price-based
decisions” for this kind of problems.
|15:30 - 15:55||(contrib) Andrea Taverna: Optimization algorithms for
Large-Scale Unit Commitment Problems in Medium-Term Energy System Simulations
We consider a Large-Scale Unit Commitment Problem (LSUCP) arising in the strategic
planning of energy networks, as a joint project with a major industrial research centre
|16:00 - 16:30||Coffee break|
|16:30 - 17:20||(invited) Martin Mevissen: Sparse Polynomial
Optimization for Urban Distribution Networks
In many optimization problems over urban distribution networks, the decision maker faces the combined challenge of nonlinear constraints, system parameters affected by uncertainty, and the scale of the underlying network. However, such problems also exhibit structure, notably sparsity, which can be exploited in order to improve the scalability of polynomial optimization solvers. On challenging problems including AC optimal power flow and pressure management in water networks, we demonstrate an approach, which exploits sparsity and strengthened low-order instances of SDP hierarchies.
|17:30 - 18:00||Roundtable on Future Actions and Closing Session|