ppt_demo

#Introduction to linear optimisation and OSeMOSYS

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<div style=font-size:28.0px>Francesco Gardumi and <b>Youssef </b><b>Almulla</b></div><br>
<div style=font-size:28.0px>almulla@kth.se   </div><br>
<div style=font-size:28.0px>MJ2380/MJ2381 – Introduction to Energy Systems Analysis and Applications</div><br>
<div style=font-size:28.0px>Lecture 2</div><br>
<div style=font-size:28.0px>2020-01-20</div><br>
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#Course overview

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<div style=font-size:14px>Introduction</div><br>
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<div style=font-size:14px>Model development</div><br>
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<div style=font-size:14px>Beyond the energy system</div><br>
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<div style=font-size:16.0px><b>Lec3: </b>Modelling aspects of CLEWs</div><br>
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<div style=font-size:16.0px><b>Lec1: </b>Intro to Energy Systems Analysis and modelling tools</div><br>
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<div style=font-size:16.0px><b>Lab 2</b><i>: </i></div><br>
<div style=font-size:16.0px>RES and temporal representation</div><br>
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<div style=font-size:16.0px><b>Lab 3, 4 and 5</b><i>:</i></div><br>
<div style=font-size:16.0px>Structuring a Simple Model</div><br>
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<div style=font-size:16.0px><b>Lec2: </b>Optimisation models and OSeMOSYS</div><br>
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<div style=font-size:14px>Scenario analysis</div><br>
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<div style=font-size:16.0px><b>Lec4: </b>Normative, descriptive, explorative scenarios</div><br>
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<div style=font-size:14px>Applications</div><br>
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<div style=font-size:16.0px><b>Lec5: </b>Energy systems analysis in real life</div><br>
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<div style=font-size:16.0px><b>Lab 6</b><i>:</i></div><br>
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<div style=font-size:16.0px><b>Lab 1</b><i>: </i></div><br>
<div style=font-size:16.0px>Basics of linear programming</div><br>
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<div style=font-size:14px><b>P/F quiz</b></div><br>
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Structure of the course
Lectures and labs are not mandatory, but you are warmly advised to attend them, because: 1) content of the lectures is important for labs + it is object of the final quiz (you must pass the quiz to pass the course); 2) labs are a space you get to work on the PROs (deliverables) and where you can ask us any questions at any moment
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#Throughout the course

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<div style=font-size:24.0px>Please <b>read carefully</b> the following documents on Canvas</div><br>
<div style=font-size:24.0px><b>MJ2380/MJ2381 - Course description deliverables and grading</b></div><br>
<div style=font-size:24.0px><b>MJ2380/MJ2381 - Course schedule</b></div><br>
<div style=font-size:24.0px><b>MJ2380 - Project description</b></div><br>
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<div style=font-size:24.0px>Please keep the <b>Canvas notifications on</b></div><br>
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<div style=font-size:24.0px>If <b>after having done (1) and (2) </b>you still have questions, please contact: </div><br>
<div style=font-size:24.0px>		Francesco Gardumi – gardumi@kth.se  </div><br>
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#Your feedback is important

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<div style=font-size:24.0px>Right after each lecture, there will be a quick evaluation on Canvas with only two subjects</div><br>
<div style=font-size:24.0px>Rate the lecture</div><br>
<div style=font-size:24.0px>Comment on the content and the lecturer</div><br>
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#Contents

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<div style=font-size:14px></div><br>
<div style=font-size:14px>Optimisation models</div><br>
<div style=font-size:14px>OSeMOSYS: the Open Source energy Modelling System</div><br>
<div style=font-size:14px>Interfaces for OSeMOSYS</div><br>
<div style=font-size:14px>Interpreting modelling results</div><br>
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#Types of modelling tools
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<div style=font-size:16.0px>Draw on macroeconomic relationships and cross-sectoral dependencies.</div><br>
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<div style=font-size:16.0px>Provide a more detailed technological representation of the system.</div><br>
<div style=font-size:16.0px>Resource – to –use representation.</div><br>
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<div style=font-size:16.0px>statistical analysis of historical time-series </div><br>
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<div style=font-size:16.0px>interrelations between various sub-sectors of the economy </div><br>
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<div style=font-size:16.0px>economy wide general equilibrium between sectoral demands and supplies </div><br>
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<div style=font-size:16.0px>Projections based on socio-economic, technology and demographics</div><br>
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<div style=font-size:16.0px>Accounting + rules (for dispatch, investments, …)</div><br>
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<div style=font-size:16.0px>Calculate energy systems attributes driven by an objective function.</div><br>
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<div style=font-size:16.0px>Mix of two or more of the above.</div><br>
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#Optimisation models

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<div style=font-size:14px><i>Study of decision problems in which one seeks to minimise or maximise a function by systematically choosing the values of variables within their allowed sets. </i></div><br>
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<div style=font-size:28.0px><b>Basic variables</b></div><br>
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<div style=font-size:22.0px><b>Decision: </b>describe alternative courses of action and are determined by decision makers (e.g. investments in new power plants);</div><br>
<div style=font-size:22.0px><b>Parameters</b>: factors that affect the results. They are determined by the elements of the system and can be called “<b>constraints of the problem</b>” if they limit decision makers (e.g. O&M costs, emission caps, max capacity additions, fuel reserves).</div><br>
<div style=font-size:22.0px><b>Result</b>: represent model outputs; they are frequently described by objective functions, such as profit (max) and cost (min or least-cost).</div><br>
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#Characteristics of optimisation models

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<div style=font-size:14px>Common (but not exclusive!) characteristics:</div><br>
<div style=font-size:14px><b>Perfect competition: </b>all players in the market compete against each other and supply their electricity at their marginal cost (”price takers”);</div><br>
<div style=font-size:14px><b>Perfect information: </b>the market players make decisions with perfect information about parameters influencing them (e.g. all costs of all power plants known);</div><br>
<div style=font-size:14px><b>Perfect foresight</b>: sub-set of the above; market players are assumed to know everything that will happen in the future (e.g. how demand evolves);</div><br>
<div style=font-size:14px><b>Dynamic</b>: it gives solutions for different points in time (i.e. not only a snapshot); </div><br>
<div style=font-size:14px><b>Economically-rational </b><b>consumer behaviour: </b>technologies with the cheapest life-cycle costs are invested in.</div><br>
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#Characteristics of optimisation models

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<div style=font-size:24.0px><b>One category is </b><b>Partial Equilibrium </b><b>models: </b>they calculate the economic equilibrium of supply and demand within the sector being modelled (e.g. energy and power sectors).</div><br>
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#Types of optimisation models

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<div style=font-size:24.0px><b>Linear programming (LP)</b></div><br>
<div style=font-size:24.0px>Type of optimisation in which the objective function and constraints are all linear. It is the base of other optimisation models. Mixed-Integer Linear Programming (MILP) is a subset.</div><br>
<div style=font-size:24.0px><b>Nonlinear programming (NLP)</b></div><br>
<div style=font-size:24.0px>Similar to LP, but where some of the constraints and/or the objective function are nonlinear.</div><br>
<div style=font-size:24.0px><b>Multi-objective programming (MOP)</b></div><br>
<div style=font-size:24.0px>Process in which two or more conflicting objectives are optimised subject to certain constraints. </div><br>
<div style=font-size:24.0px><b>Bi-level programming (BLP) and multi-level programming (MLP)</b></div><br>
<div style=font-size:24.0px>Complex optimisation problems where one problem is embedded in another one (a BLP is a two-level MLP). </div><br>
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#Why using linear optimisation models in energy systems analysis?

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<div style=font-size:24.0px>to identify <b>least-cost energy systems</b></div><br>
<div style=font-size:24.0px>to identify <b>cost-effective responses </b>to restrictions on emissions</div><br>
<div style=font-size:24.0px>to perform prospective analysis of long-term energy balances under different scenarios</div><br>
<div style=font-size:24.0px>to evaluate new technologies and <b>priorities for R&D</b></div><br>
<div style=font-size:24.0px>to evaluate the effects of <b>regulations, taxes, and subsidies</b></div><br>
<div style=font-size:24.0px>to project inventories of <b>greenhouse gas emissions</b></div><br>
<div style=font-size:24.0px>to estimate the value of <b>regional cooperation</b></div><br>
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<div style=font-size:24.0px>Investigate <b>scenarios</b> (possible futures);</div><br>
<div style=font-size:24.0px>Explore <b>alternatives</b> to business as usual practices;</div><br>
<div style=font-size:24.0px>Inform <b>decisions</b> (policy and strategic planning)</div><br>
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#Examples of energy modelling tools (model generators) using linear optimisation

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<div style=font-size:20.0px><b>Technology explicit, dynamic partial equilibrium models of energy markets.</b></div><br>
<div style=font-size:20.0px>Differ in technical features and  properties  (time length, timeslices definition, etc)</div><br>
<div style=font-size:20.0px>MARKAL and TIMES  are developed by IEA-ETSAP.</div><br>
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<div style=font-size:24.0px><b>MARKAL</b></div><br>
<div style=font-size:24.0px>(MARKet ALlocation)</div><br>
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<div style=font-size:26.0px><b>TIMES</b><b> </b></div><br>
<div style=font-size:26.0px>(The Integrated MARKAL-EFOM System)</div><br>
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<div style=font-size:34.0px><b>MESSAGE</b></div><br>
<div style=font-size:34.0px>(Model for Energy Supply System Alternatives and their General Environmental Impacts)</div><br>
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<div style=font-size:24.0px><b>OSeMOSYS</b></div><br>
<div style=font-size:24.0px>(Open Source Energy Modelling System)</div><br>
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<div style=font-size:20.0px>Flexible framework for the comprehensive assessment of energy challenges</div><br>
<div style=font-size:20.0px>Development of energy scenarios and  identification of socioeconomic and technological response strategies</div><br>
<div style=font-size:20.0px><b>The mathematical formulation ensures that the flows are consistent: demand is met, inflows equal outflows and constraints are not exceeded.</b></div><br>
<div style=font-size:20.0px>MESSAGE is developed by IIASA/IAEA.</div><br>
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<div style=font-size:20.0px>Open source long-term energy model generator based on LP principle</div><br>
<div style=font-size:20.0px>Flexible and organised in blocks; user has access to the code and can implement changes to fit its problem</div><br>
<div style=font-size:20.0px>Developed  by KTH-dESA and partners </div><br>
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#Examples of the use of model generators

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<div style=font-size:36.0px><b>MARKAL: </b>UK MARKAL</div><br>
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<div style=font-size:36.0px><b>TIMES: </b>UKTM-UCL</div><br>
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<div style=font-size:20.0px>TIMES is now preferred as a model generator (more flexible and internationally developed)</div><br>
<div style=font-size:20.0px>Opportunity to undergo a comprehensive review and revision of UK MARKAL’s design, underlying data and assumptions.</div><br>
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<div style=font-size:16.0px>UKTM-UCL introduction video</div><br>
<div style=font-size:16.0px>https://youtu.be/tOV5Q0e_RW0</div><br>
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#Examples of the use of model generators

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<div style=font-size:36.0px><b>MESSAGE</b></div><br>
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<div style=font-size:22.0px>Among others, MESSAGE is used in the assessments and special reports of the IPCC and the Global Energy Assessment (GEA).</div><br>
<div style=font-size:22.0px>Used to generate one of the four <b>Representative Concentration Pathways (RCPs</b><b>)</b>. RCP8.5 is being  used to estimate future climate change in the context of the IPCC 5th Assessment Report (AR5).</div><br>
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<div style=font-size:14px>RCP Database  - Comparison of  RCP world CO2 emissions  (MESSAGE - RCP8.5).</div><br>
<div style=font-size:14px>(source: http://tntcat.iiasa.ac.at/RcpDb/dsd?Action=htmlpage&page=compare)</div><br>
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#OSeMOSYS Applications

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<div style=font-size:36.0px><b>OSeMOSYS</b></div><br>
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<div style=font-size:24.0px>IRENA African Power Pools: Planning and Prospects of Renewable Energy</div><br>
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<div style=font-size:24.0px>IEA WEO 2014 – Africa Energy Outlook</div><br>
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<div style=font-size:24.0px>WB – Enhancing the  Climate Resilience of African Infrastructure</div><br>
</div>

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<div style=font-size:24.0px>UNECE transboundary nexus assessments</div><br>
<div style=font-size:24.0px>        (Sava and Drina)</div><br>
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#OSeMOSYS Applications

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<div style=font-size:24.0px>TEMBA – The Energy Model Base for Africa</div><br>
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<div style=font-size:24.0px>SAMBA – South America Model Base</div><br>
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<div style=font-size:24.0px>CLEWS applications (GLUCOSE, Uganda, Nicaragua, Zimbabwe, Mauritius, Kenya, Bolivia, …)</div><br>
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<div style=font-size:24.0px>Among UNDESA modelling tools for sustainable development policies.  </div><br>
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#OSeMOSYSthe Open Source energy MOdelling SYStem
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#Imagine yourselves in the shoes of...

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#OSeMOSYS Applications

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#OSeMOSYS in the modelling suite

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#OSeMOSYS

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<div style=font-size:28.0px><b>www.osemosys.org</b></div><br>
<div style=font-size:28.0px><b>Howells, M., et al. (2011). </b><b>OSeMOSYS</b><b>: The Open Source Energy Modeling System: An introduction to its ethos, structure and development</b><b>, </b><b>Energy Policy</b><b>, 39(10), p.5850-5870.</b></div><br>
<div style=font-size:28.0px><b>Model generator converting the energy system structure represented by equations into a matrix to be solved by specific solvers</b></div><br>
<div style=font-size:28.0px></div><br>
<div style=font-size:28.0px><b>Linear optimisation</b></div><br>
<div style=font-size:28.0px>Freely available and Open source</div><br>
<div style=font-size:28.0px>Deterministic</div><br>
<div style=font-size:28.0px>Dynamic</div><br>
<div style=font-size:28.0px>Perfect foresight</div><br>
<div style=font-size:28.0px>Paradigm comparable to MESSAGE and TIMES</div><br>
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#What does OSeMOSYS do?

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<div style=font-size:28.0px>It determines the energy system configuration with the <b>minimum total discounted cost</b> for a time domain of decades, <b>constrained by</b>:</div><br>
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<div style=font-size:28.0px><b>Demand for energy </b>(e.g. electricity, heating, cooling, km-passengers, etc.) that needs to be met</div><br>
<div style=font-size:28.0px><b>Available technologies </b>and their techno-economic characteristics</div><br>
<div style=font-size:28.0px><b>Emission taxations </b>and generation targets (e.g. renewables)</div><br>
<div style=font-size:28.0px><b>Other constraints </b>(e.g. ramping capability, availability of resources, investment decisions, etc.)</div><br>
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#What does OSeMOSYS do?

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<div style=font-size:26.0px>Technologies compete to <b>gain a share in the energy supply</b>, based on:</div><br>
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<div style=font-size:26.0px>a)their <b>characteristics </b>(e.g. cost, life time, efficiency etc);</div><br>
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<div style=font-size:26.0px>b) other <b>constraints</b>  (e.g. <b>demand</b>, min renewable generation, emissions, use of resources, etc).</div><br>
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#OSeMOSYS Structure – Blocks of functionality

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<div style=font-size:20.0px><b>Three levels of abstraction: Plain English description, Mathematical formulation and Code formulation</b></div><br>
<div style=font-size:20.0px><b>Ref: </b><b>OSeMOSYS</b><b> Manual - </b><b>https://osemosys.readthedocs.io/en/latest/</b><b> </b></div><br>
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<div style=font-size:20.0px><b>Objective</b></div><br>
<div style=font-size:20.0px>To estimate the lowest Net Present Value (NPV) of an energy system to meet given demand(s) for energy</div><br>
<div style=font-size:20.0px><b>Costs</b></div><br>
<div style=font-size:20.0px>Account for the costs incurred by each <b>technology</b> in each <b>year</b> and in each <b>region</b></div><br>
<div style=font-size:20.0px><b>Capacity adequacy</b></div><br>
<div style=font-size:20.0px>There must be enough capacity for each technology in order to meet its energy use or production requirements:</div><br>
<div style=font-size:20.0px>Capacity adequacy A: Each time slice / Capacity adequacy B: Each year</div><br>
<div style=font-size:20.0px><b>Storage</b></div><br>
<div style=font-size:20.0px>It adds independent energy balances and constraints for storage options.</div><br>
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#OSeMOSYS Structure – Blocks of functionality

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<div style=font-size:20.0px><b>Energy balance</b></div><br>
<div style=font-size:20.0px>Operation levels are calculated for each time slice and each year. The production, use and demand must be feasible at each timeslice and annually</div><br>
<div style=font-size:20.0px>Energy balance A: Each time slice / Energy balance B: Each year</div><br>
<div style=font-size:20.0px><b>Constraints</b></div><br>
<div style=font-size:20.0px>Maximum/minimum limit on capacity of a technology allowed for a year or total period and a region</div><br>
<div style=font-size:20.0px>Maximum/minimum limit on new capacities of a technology for a year and a region</div><br>
<div style=font-size:20.0px>Maximum/minimum limit on activity of a technology for a year or total period and a region</div><br>
<div style=font-size:20.0px>There must be enough capacity to provide a reserve margin (for specified technologies)</div><br>
<div style=font-size:20.0px><b>Emissions</b></div><br>
<div style=font-size:20.0px>The extent to which pollutants are emitted is determined by multiplying “emissions per unit of activity” and the annual activity of a technology</div><br>
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#Mathematical Formulation of OSeMOSYS

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#Example of Mathematical Formulation of OSeMOSYS

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#Example of Code of OSeMOSYS

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#OSeMOSYS – Key terms

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<div style=font-size:14px>Five key terms in OSeMOSYS: </div><br>
<div style=font-size:14px><b>Sets:</b> Input, constant for all scenarios</div><br>
<div style=font-size:14px><b>Parameters</b>: Input, can differ between scenarios</div><br>
<div style=font-size:14px><b>Variables</b>: Modelling outputs</div><br>
<div style=font-size:14px><b>Constraints</b>:  can be equalities or inequalities the limit the solution space and should be satisfied all the time. </div><br>
<div style=font-size:14px><b>Objective Function: </b>of the linear programing problem (to minimize cost)</div><br>
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<div style=font-size:14px>Two versions of the code: </div><br>
<div style=font-size:14px><b>Long code</b>: easy to read but requires larger matrix size and computational time.</div><br>
<div style=font-size:14px><b>Short code</b>: combines some of the intermediate equations to reduce the matrix size and computational time.</div><br>
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#Interfaces for OSeMOSYS
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#Interfaces for OSeMOSYS

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<div style=font-size:28.0px>MoManI – Model Management Infrastructure</div><br>
<div style=font-size:28.0px>LEAP – the Long-range Energy Alternatives Planning system</div><br>
<div style=font-size:28.0px>Run an input file directly on the command line, e.g, using Notepad++ to write input file and a compatible solver</div><br>
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#Interfaces for OSeMOSYS

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<div style=font-size:28.0px><b>MoManI – Model Management Infrastructure</b></div><br>
<div style=font-size:28.0px>LEAP – the Long-range Energy Alternatives Planning system</div><br>
<div style=font-size:28.0px>Run an input file directly on the command line, e.g, using Notepad++ to write input file and a compatible solver</div><br>
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#Interfaces for OSeMOSYS: MoManI

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<div style=font-size:28.0px><b>http://courses.momaniweb.com/#/models</b><b> </b></div><br>
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#Input parameters

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<div style=font-size:14px>Data collection</div><br>
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<div style=font-size:14px>Data pre-processing</div><br>
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<div style=font-size:14px>Model calibration</div><br>
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<div style=font-size:18.0px>Electricity demand projections</div><br>
<div style=font-size:18.0px>Primary resources potentials</div><br>
<div style=font-size:18.0px>Existing capacity</div><br>
<div style=font-size:18.0px>Technology costs and characteristics</div><br>
<div style=font-size:18.0px>Country/region specific constraints</div><br>
<div style=font-size:18.0px>Fuel prices</div><br>
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<div style=font-size:18.0px>Discretization of demand curves</div><br>
<div style=font-size:18.0px>Regression analyses where projections are not available</div><br>
<div style=font-size:18.0px>Etc…</div><br>
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<div style=font-size:18.0px>I.e. set the starting year of the model as a past year for which actual data is available. From this starting point all that follows depends!</div><br>
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#Interpreting modelling results
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#Interpreting modelling results

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<div style=font-size:14px><b>The results provide insights on questions such as:</b></div><br>
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<div style=font-size:14px>Which technologies are phasing out? By when? </div><br>
<div style=font-size:14px>What are the optimal investments in new technologies to meet the demand in the future? When is it best to invest?</div><br>
<div style=font-size:14px>What are the key generation technologies in the total energy mix? </div><br>
<div style=font-size:14px>Which capacities are NOT being utilized? Why?</div><br>
<div style=font-size:14px>What costs will the energy system incur?</div><br>
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#Interpreting modelling results

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<div style=font-size:24.0px>What needs to be done and what will be the costs to supply modern energy sources to remote areas?  </div><br>
<div style=font-size:24.0px>What if environmental regulations are made more stringent?  </div><br>
<div style=font-size:24.0px>What needs to be done to increase the share of renewable technologies? </div><br>
<div style=font-size:24.0px>Should the electricity import be allowed? </div><br>
<div style=font-size:24.0px>Should the existing nuclear facilities be closed down?</div><br>
<div style=font-size:24.0px>Can energy conservation program help in reducing cost of energy supply?</div><br>
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#Representative OSeMOSYS results

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<div style=font-size:14px>Electricity Generation (PJ)</div><br>
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<div style=font-size:14px><b>Hydro and CCGT most competitive</b></div><br>
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<div style=font-size:14px><b>Initial capacity of COAL PP phased out at end of life</b></div><br>
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#Representative OSeMOSYS results

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<div style=font-size:14px>Electricity Generation (PJ)</div><br>
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<div style=font-size:14px><b>More generation from COAL PP, less reliance on HYDRO</b></div><br>
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<div style=font-size:24.0px><b>What happens in a climate ‘water scarcity’ scenario?</b></div><br>
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#Key take away messages
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#Key take away messages

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<div style=font-size:14px><b>Keywords</b></div><br>
<div style=font-size:14px>Optimisation, Model generator, Linear Programming, OSeMOSYS, MoManI</div><br>
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<div style=font-size:14px><b>Messages</b></div><br>
<div style=font-size:14px>Different optimisation techniques, e.g. LP, MILP, NLP</div><br>
<div style=font-size:14px>Common characteristics of optimisation model generators: perfect competition, information and foresight; dynamic; rational behaviour</div><br>
<div style=font-size:14px>Sub-set: partial equilibrium models</div><br>
<div style=font-size:14px>OSeMOSYS is a long-term, perfect foresight, dynamic <b>cost-</b><b>optimisation</b> (not simulation!) <b>model </b><b>generator</b></div><br>
<div style=font-size:14px><b>Inputs</b> to OSeMOSYS are: Sets and Parameters while <b>outputs</b> are called variables. </div><br>
<div style=font-size:14px>MoManI is an <b>interface</b> used for OSeMOSYS. Do not confuse the two!</div><br>
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#Reading material

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<div style=font-size:14px>OSeMOSYS structure (Howells et al., 2011): https://www.sciencedirect.com/science/article/pii/S0301421511004897 </div><br>
<div style=font-size:14px>OSeMOSYS code: https://github.com/KTH-dESA/OSeMOSYS/tree/master/OSeMOSYS_GNU_MathProg </div><br>
<div style=font-size:14px>OSeMOSYS user manual: https://osemosys.readthedocs.io/en/latest/ </div><br>
<div style=font-size:14px>MoManI user manual: http://www.osemosys.org/uploads/1/8/5/0/18504136/momani_training_manual-_rev180601.pdf </div><br>
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#Thank you

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<div style=font-size:28.0px>Francesco Gardumi and<b> Youssef </b><b>Almulla</b></div><br>
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