Water-related modelling in electric power systems: WATERFLEX Exploratory Research Project: version 1

Water is needed for energy. For instance, hydropower is the technology that generates more electricity worldwide after the fossil-fuelled power plants and its production depends on water availability and variability. Additionally, thermal power plants need water for cooling and thus generate electricity. On the other hand, energy is also needed for water. Given the increase of additional hydropower potential worldwide in the coming years, the high dependence of electricity generation with fossil-fuelled power plants, and the implications of the climate change, relevant international organisations have paid attention to the water-energy nexus (or more explicitly within a power system context, the water-power nexus). The Joint Research Centre of the European Commission, the United States Department of Energy, the Institute for Advanced Sustainability Studies, the Midwest Energy Research Consortium and the Water Council, or the Organisation for Economic Co-operation and Development, among others, have raised awareness about this nexus and its analysis as an integrated system. In order to properly analyse such linkages between the power and water sectors, there is a need for appropriate modelling frameworks and mathematical approaches. This report comprises the water-constrained models in electric power systems developed within the WATERFLEX Exploratory Research Project of the European Commission’s Joint Research Centre in order to analyse the water-power interactions. All these models are deemed modules of the Dispa-SET modelling tool. The version 1 of the medium-term hydrothermal coordination module is presented with some modelling extensions, namely the incorporation of transmission network constraints, water demands, and ecological flows. Another salient feature of this version of Dispa-SET is the modelling of the stochastic medium-term hydrothermal coordination problem. The stochastic problem is solved by using an efficient scenario-based decomposition technique, the so-called Progressive Hedging algorithm. This technique is an Augmented-Lagrangian-based decomposition method that decomposes the original problem into smaller subproblems per scenario. The Progressive Hedging algorithm has multiple advantages: — It is easy parallelizable due to its inherent structure. — It provides solution stability and better computational performance compared to Benders-like decomposition techniques (node-based decomposition). — It scales better for large-scale stochastic programming problems. — It has been widely used in the technical literature, thus demonstrating its efficiency. Its implementation has been carried out through the PySP software package which is part of the Coopr open-source Python repository for optimisation. This report also describes the cooling-related constraints included in the unit commitment and dispatch module of Dispa-SET. The cooling-related constraints encompass limitations on allowable maximum water withdrawals of thermal power plants and modelling of the power produced in terms of the river water temperature of the power plant inlet. Limitations on thermal releases or water withdrawals could be imposed due to physical or policy reasons. Finally, an offline and decoupled modelling framework is presented to link such modules with the rainfall-runoff hydrological LISFLOOD model. This modelling framework is able to accurately capture the water-power interactions. Some challenges and barriers to properly address the water-power nexus are also highlighted in the report.JRC.C.7-Knowledge for the Energy Unio

Modeling Storage and Demand Management in Electricity Distribution Grids

Storage devices and demand control may constitute beneficial tools to optimize electricity generation with a large share of intermittent resources through inter-temporal substitution of load. We quantify the related cost reductions in a simulation model of a simplified stylized medium-voltage grid (10kV) under uncertain demand and wind output. Benders Decomposition Method is applied to create a two-stage stochastic program. The model informs an optimal investment sizing decision as regards specific 'smart grid' applications such as storage facilities and meters enabling load control. Model results indicate that central storage facilities are a more promising option for generation cost reductions as compared to demand management. Grid extensions are not appropriate in any of our scenarios. A sensitivity analysis is applied with respect to the market penetration of uncoordinated Plug-In Electric Vehicles which are found to strongly encourage investment into load control equipment for `smart` charging and slightly improve the case for central storage devices.Storage, demand management, stochastic optimization, Benders Decomposition

Investigation and comparison of solution methods of the unit commitment problem for thermal units

This thesis deals with a general investigation and comparison of methods of solution of the unit commitment problem for thermal generating units. An intensive mathematical model for this problem has been developed and presented. The most commonly implemented methods for solving the problem of unit commitment were reviewed and discussed. Every reviewed method had been coded in FORTRAN 77 on a VAX11/785 machine. On the basis of the results obtained, a comprehensive comparison of the different methods ha; been carried out. For the comparison to be realistic and practical, each method was tested on three power systems of different sizes. As a result of the comparison, the unit commitment problem for a case study system has been solved by using the most appropriate method(s) from those discussed in the thesis. A new approach for solving the problem has also been proposed and tested.This thesis deals with a general investigation and comparison of methods of solution of the unit commitment problem for thermal generating units. An intensive mathematical model for this problem has been developed and presented. The most commonly implemented methods for solving the problem of unit commitment were reviewed and discussed. Every reviewed method had been coded in FORTRAN 77 on a VAX11/785 machine. On the basis of the results obtained, a comprehensive comparison of the different methods ha; been carried out. For the comparison to be realistic and practical, each method was tested on three power systems of different sizes. As a result of the comparison, the unit commitment problem for a case study system has been solved by using the most appropriate method(s) from those discussed in the thesis. A new approach for solving the problem has also been proposed and tested

Short-term generation scheduling in a hydrothermal power system.

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ALGORITHMS FOR THE LARGE-SCALE UNIT COMMITMENT PROBLEM IN THE SIMULATION OF POWER SYSTEMS

Lo Unit Commitment Problem (UCP) \ue8 un problema di programmazione matematica dove un insieme di impianti termoelettrici deve essere programmato per soddisfare la domanda di energia e altri vincoli di sistema. Il modello \ue8 impiegato da decenni per supportare la pianificazione operazionale di breve termine dei sistemi elettrici. In questo lavoro affrontiamo il problema di risolvere UCP lineari di larga-scala per realizzare simulazioni accurate di sistemi elettrici, con i requisiti aggiuntivi di impiegare capacit\ue0 di calcolo convenzionali, ad esempio i personal computers, ed un tempo di soluzione di poche ore. Il problema, sotto le medesime condizioni, \ue8 affrontato abitualmente dal nostro partner industriale RSE S.p.A. (Ricerche Sistema Energetico), uno dei principali centri ricerche industriali su sistemi energetici in Italia. L\u2019ottimizzazione diretta di queste formulazioni con solutori generici \ue8 impraticabile. Nonostante sia possibile calcolare buone soluzioni euristiche, ovvero con un gap di ottimalit\ue0 sotto il 10%, in tempi ragionevoli per UCP di larga scala, si richiedono soluzioni pi\uf9 accurate, per esempio con gap sotto l\u20191%, per migliorare l\u2019affidabilit\ue0 delle simulazioni ed aiutare gli esperti di dominio, che potrebbero non essere familiari con i dettagli dei metodi di programmazione matematica, a supportare meglio le loro analisi. Tra le idee che abbiamo esplorato i seguenti metodi risultano i pi\uf9 promettenti: una mateuristica per calcolare efficientemente buone soluzioni e due metodi esatti di bounding: column generation e Benders decomposition. Questi metodi decompongono il problema disaccoppiando il commitment degli impianti termoelettrici, rappresentati da variabili discrete, e il loro livello di produzione, rappresentato da variabili continue. I nostri esperimenti dimostrano che il modello possiede propriet\ue0 intrinseche come degenerazione e forma della funzione obbiettivo piatta che ostacolano o impediscono la convergenza in risolutori allo stato dell\u2019arte. Tuttavia, i metodi che abbiamo sviluppato, sfruttando efficacemente le propriet\ue0 strutturali del modello, permettono di raggiungere soluzioni quasi ottime in poche iterazioni per la maggior parte delle istanze.The Unit Commitment Problem (UCP) is a mathematical programming problem where a set of power plants needs to be scheduled to satisfy energy demand and other system-wide constraints. It has been employed for decades to support short-term operational planning of power plants. In this work we tackle the problem of solving large-scale linear UCPs to perform accurate medium-term power systems simulations, with the additional requirements of employing conventional computing power, such as personal computers, and a solution time of a few hours. The problem, under such conditions, is routinely faced by our industry partner, the Energy Systems Development department at RSE S.p.A. (Ricerche Sistema Energetico), a major industrial research centre on power systems in Italy. The direct optimization of these formulations via general-purpose solvers is impractical. While good heuristic solutions, that is with an optimality gap below 10%, can be found for large-scale UCPs in affordable time, more accurate solutions, for example with a gap below 1%, are sought to improve the reliability of the simulations and help domain experts, who may not be familiar with the details of mathematical programming methods, to better support their analysis. Among the ideas we explored, the following methods are the most promising: a matheuristic to efficiently compute good solutions and two exact bounding methods: column generation and Benders decomposition. These methods decompose the problem by decoupling the commitment of thermal plants, represented by discrete variables, and their level of production, represented by continuous variables. Our experiments proved that the model posses inherent properties as degeneracy and objective flatness which hinder or prevent convergence in state-of-the-art solvers. On the other hand, the methods we devised, by effectively exploiting structural properties of the model, allow to reach quasi-optimal solutions within a few iterations on most instances