• Energy Research

Abstract This article deals with feasibility issues of the hydro-unit commitment relative to units along a valley in the price-taker revenue-maximizing setting. The problem is formulated as a mixed-integer linear programming model. Besides physical constraints, we consider two additional specifications that apply to a subset of units and reservoirs within a valley, namely the power-flow curves of each unit feature discrete operational points and each reservoir level should meet target volumes. These specifications, together with the standard issues affecting real-world data, make our problem harder to solve, often infeasible. We follow a step-by-step approach to identify and repair one source of infeasibility at a time, namely numerical errors and model infeasibilities. The former is analyzed and fixed through tools like an exact solver and a model and data preprocessing. The remaining infeasibilities are eliminated with a 2-stage method. In the first stage, a minimal deviation from target volumes, i.e., strategic, thus relaxable, constraints, is computed to make the problem feasible. In the second stage, the original problem is solved with a possible deviation from the target volumes as defined in the first stage. Computational results confirm the effectiveness of the proposed method to recover feasibility on a challenging real-world test set.

Abstract In this article we survey mathematical programming approaches to problems in the field of drinking water distribution network optimization. Among the predominant topics treated in the literature, we focus on two different, but related problem classes. One can be described by the notion of network design, while the other is more aptly termed by network operation. The basic underlying model in both cases is a nonlinear network flow model, and we give an overview on the more specific modeling aspects in each case. The overall mathematical model is a Mixed Integer Nonlinear Program having a common structure with respect to how water dynamics in pipes are described. Finally, we survey the algorithmic approaches to solve the proposed problems and we discuss computation on various types of water networks.

The paper deals with a unit commitment problem of a generation company whose aim is to find the optimal scheduling of a multiunit pump-storage hydro power station, for a short term period in which the electricity prices are forecasted. The problem has a mixed-integer nonlinear structure, which makes very hard to handle the corresponding mathematical models. However, modern mixed-integer linear programming (MILP) software tools have reached a high efficiency, both in terms of solution accuracy and computing time. Hence we introduce MILP models of increasing complexity, which allow to accurately represent most of the hydroelectric system characteristics, and turn out to be computationally solvable. In particular we present a model that takes into account the head effects on power production through an enhanced linearization technique, and turns out to be more general and efficient than those available in the literature. The practical behavior of the models is analyzed through computational experiments on real-world data.

The automated real time control of an electrical network is achieved through the estimation of its state using phasor measurement units. Given an undirected graph representing the network, we study the problem of finding the minimum number of phasor measurement units to place on the edges such that the graph is fully observed. This problem is also known as the Power Edge Set problem, a variant of the Power Dominating Set problem. It is naturally modeled using an iteration‐indexed binary linear program, whose size turns out to be too large for practical purposes. We use a fixed‐point argument to remove the iteration indices and obtain a more compact bilevel formulation. We then reformulate the latter to a single‐level mixed‐integer linear program, which performs better than the natural formulation. Lastly, we provide an algorithm that solves the bilevel program directly and much faster than a commercial solver can solve the previous models. We also discuss robust variants and extensions of the problem. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 68(2), 104–120 2016

We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with higher shares of VRE, assessing the impact of new technologies and of climate variability and conducting sensitivity studies. Specifically, to limit the algorithm’s complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production–demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time series and fitted to available observations. We illustrate e4clim’s potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate data sources and strategies and assessing the impact of climate variability and the robustness of the results.

Abstract In this paper, we tackle the hydro unit commitment problem and scheduling in a hydro valley. We first decompose the problem into several simpler subproblems, one for each reservoir/plant. Then, we model each of them as an optimization problem on graphs with or without resource constraints. We compare our method with a commercial solver for mixed integer linear programming, run on a formulation of the problem and show promising results.

We consider three easy-to-implement methods for the piecewise linear approximation of functions of two variables. We experimentally evaluate their approximation quality, and give a detailed description of how the methods can be embedded in a MILP model. The advantages and drawbacks of the three methods are discussed on numerical examples.