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The Spine Toolbox is open-source software for defining, managing, simulating and optimising energy system models. It gives the user the ability to collect, create, organise, and validate model input data, execute a model with selected data and finally archive and visualise results/output data. Spine Toolbox has been designed and developed to support the creation and execution of multivector energy integration models. It conveniently facilitates the linking of models with different scopes, or spatio-temporal resolutions, through the user interface. The models can be organised as a direct acyclic graph and efficiently executed through the embedded workflow management engine. The software helps users to import and manage data, define models and scenarios and orchestrate projects. It supports a self-contained and shareable entity-relationship data structure for storing model parameter values and the associated data. The software is developed using the latest Python environment and supports the execution of plugins. It is shipped in an installation package as a desktop application for different operating systems.

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ ABSTRACT: For a holistic understanding and simulation of the energy markets, many different aspects need to modelled properly. Often no single modelling tool offers the whole picture, but a combination of methods needs to be used. A model linkage platform has been chosen previously and this deliverable describes the integration of the modelling tools used in TradeRES to the linking application, Spine Toolbox. Spine Toolbox was used to build data processing and execution workflows around the energy system modelling tools Backbone, AMIRIS, EMLab, COMPETES, RESTrade and MASCEM. The aim is to integrate selected tools together for answering the research questions in the TradeRES project. The integrations of individual tools and some combinations are described in this document. Mostly, the work is still in progress. Also, a common database to serve the case studies has been created, but populating the database with scenario data is still in progress. A common data model to serve all the modelling tools has been created and the database is implemented using Spine Toolbox. N/A

Spine Toolbox is an application that provides means to define, manage, and execute energy system models. It gives the user the ability to collect, create, organize, and validate model input data, execute a model with selected data and finally archive and visualize the results or output data. Spine Toolbox is designed to support the creation and execution of scenarios in optimization and simulation. In the Spine project, the main use case has been SpineOpt, which is a highly adaptable model generator for multi-energy systems. In addition, Spine Toolbox supports a wide variety of other models and tools if they follow the conventions of Spine Toolbox or there is an interpreter between the application and the external tool. One of the conventions is the Spine data structure, which is an entity-relationship data model for a structured yet flexible storage of data. The interface to the data structure is an integral part of both Spine Toolbox and SpineOpt because it enables them to communicate using a common vocabulary. Spine Toolbox is implemented in Python and SpineOpt in Julia.This deliverable presents a high-level software design for Spine Toolbox and for the various tools it supports. It contains the application use cases, functional and non-functional requirements, system overview, chosen implementation language(s), dependencies, versioning, application validation requirements, testing and security requirements, and general development guidelines. The aforementioned have been collected in co-operation with Spine members and stakeholders, who have been using Spine Toolbox since its inception. The last chapter presents an overview of the Spine data structure. The first version of this deliverable was published in project M02 and this final version presents the updated design of the software. Spine is an open-source project. In the fall of 2018, Spine Toolbox source code and documentation was released to the public. Spine Toolbox as well as the whole Spine software suite is available in a web-based version control repository system called GitHub (https://github.com/Spine-project). In addition, the user guide and other documentation is available on https://readthedocs.org/. Spine Toolbox is licensed under the GNU Lesser General Public License (LGPL). Spine Toolbox documentation, user guide and all original graphics have been released with the Creative Commons BY-SA 4.0 license. We hope to attract a lively and active community around the Spine software suite that will continue development also after the project has ended.

This report is part of the results of Smart Otaniemi WP 4, Local flexibility market. Smart Otaniemi is a smart energy piloting area and ecosystem in Otaniemi, Espoo. This report includes a state-of-the-art analysis of local markets, especially in Germany and the UK, and of the system wide markets in Finland, in which demand response can almost conclusively participate, aggregated or not. The notion of local flexibility markets hits the first snag with the question of who will buy the local flexibility in Finland: analysis shows that it is not other end-users, not the seller per se, nor the local generator unless it is a clearly better solution than selling to the energy market. For example, although a small end-user does not participate in the spot market directly, she can use her flexibility via market price based contracts. The main answer is the DSO. The DSO’s demand for flexibility might have different reasons such as local ancillary service needs or fighting bottlenecks. However, in general, flexibility should be available for multiple uses in order to enable value stacking. Local energy markets could be practical for reasons other than the utilization of flexibility for the management of the local network. Local markets could enable more specialised or granular trading, especially if trading is not possible on existing markets for the interested stakeholders. In addition, the local markets could act as an aggregation platform for existing markets. The more markets and interested parties, including independent aggregators, are involved, the more important and complicated balance settlement will become. One option of simplifying things that this research came up with is to elevate all end-users to the status of balance responsible. To better understand the balance implications for different parties under various conditions, a balance model was created. The report presents a general framework for flexibility markets (Smart Otaniemi logic) consisting of considerations for balance responsibility and product design. The framework could be implemented in Otaniemi and beyond.

Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS). An application of the framework is demonstrated using a power system example, and Backbone is shown to produce results comparable to a commercial tool. However, the adaptability of Backbone further enables the creation and solution of energy systems models relatively easily for many different purposes and thus it improves on the available methodologies.

Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS). An application of the framework is demonstrated using a power system example, and Backbone is shown to produce results comparable to a commercial tool. However, the adaptability of Backbone further enables the creation and solution of energy systems models relatively easily for many different purposes and thus it improves on the available methodologies.

The report summarizes the models and modelling framework (Sceleton) partially developed at VTT for analysing power systems with high share of variable generation. The framework covers regional geographic scale and time domains including investments, unit commitment, dispatch and power flows. Currently three models are included in the Sceleton framework: Balmorel, WILMAR JMM, and PSS@E. The operation of the models can be interlinked. Input data is contained in a single database. Sceleton organizes running multiple scenarios as well as plotting and storing results from the models.

The optimal use of reservoir hydropower is a complex problem. To describe the marginal value of a reservoir connected to a hydropower plant the term water value is used. In an electricity system with both hydropower and thermal capacity, water value is equal to the absolute change of total production costs when there is one unit more water available. Water value is a function of time and reservoir filling level. The less water in the reservoirs, the more valuable it is. Annual water inflow is unequally spread over the year and therefore affects the available amount of water and the water values. Increasing the share of wind power in electricity generation causes integration costs in the system. These costs are due to e.g. increased need for regulation. Reservoir hydropower is efficient in balancing the system with wind, but the use of reservoirs has to be re-optimised. Wilmar Joint Market Model (JMM) is a stochastic electricity market model created for the study of the effects of large scale wind integration. In this study a new method is presented to calculate water values for the JMM using VTT’s Markkinahintamalli (MH model). MH model is a tool for predicting electricity production costs in the Nordic countries. Wilmar Long Term Model, which is the original water value component of the JMM, and a method following historical reservoir levels were used as a comparison. The advantage of the MH model is the fast optimisation of water reservoirs using stochastic dynamic programming. It is also possible to calibrate the interplay with the JMM, unlike with the other two methods. Water values are calculated from the total cost function of the MH model. Water value is the derivative of the total cost function with respect to reservoir level. JMM was used to simulate electricity production in the Nordic countries during the year 2001. Three methods to calculate water values were compared. The most important results are marginal production costs, water reservoir levels and electricity production by generation type. These were compared with the historical data. The results with the new method are promising. The simulated reservoir levels throughout the year are very close to the historical values. Production shares are not accurate with any of the methods, but the production time series appears realistic and stable with the new MH model method. Additional work will be needed to calibrate and enhance the JMM and the MH model. The most important areas of development are the modelling of different kinds of reservoirs within both the JMM and the MH model, the defining of realistic plant outages as well as the share of run-of-river power and the estimation of the variable costs of hydropower production in the MH model. The long-term variation of wind power generation should also be included in the MH model.

Säännösteltävän vesivoiman optimaaliseen käyttöön liittyy kysymys tuotannon ajoittamisesta taloudellisesti edullisimpaan hetkeen. Vesiarvo kuvaa säännösteltävän vesialtaan marginaaliarvoa, ja sähköjärjestelmässä, jossa on sekä vesivoimaa että lämpövoimalaitoksia, vastaa vesiarvo sähköntuotannon kokonaiskustannusten muutosta kun käytettävissä on yksi yksikkö enemmän vettä. Vesiarvo on ajan ja altaan täyttöasteen funktio; mitä vähemmän altaissa on vettä, sitä arvokkaampaa se on. Epätasaisesti vuoden aikana jakautunut tulovirtaama vaikuttaa saatavilla olevaan vesimäärään ja siten myös vesiarvoihin. Tuulivoiman osuuden lisääminen sähköntuotannossa aiheuttaa integraatiokustannuksia sähköjärjestelmässä. Kustannukset aiheutuvat muun muassa lisääntyneestä säädön tarpeesta. Säännösteltävää vesivoimaa voidaan käyttää tehokkaasti sähköntuotannon säätelyyn. Vesivoiman käyttö tulee kuitenkin optimoida uudella tavalla. Wilmar Joint Market Model -sähkömarkkinamalli (JMM) on työkalu sähköntuotannon kustannusten arviointiin, kun järjestelmään lisätään paljon tuulivoimaa. Tässä työssä toteutettiin uudenlainen vesiarvolaskenta JMM:iin käyttäen VTT:n Markkinahintamallia (MH-malli), joka on sähköntuotannon kustannusten mallinnukseen Pohjoismaissa kehitetty ohjelmisto. Vertailukohteina uudelle menetelmälle käytettiin Wilmar Long Term Model -mallia, joka on JMM:n alkuperäinen vesiarvokomponenttti, ja historiallisia vesitasoja seuraavaa menetelmää. MH-mallin etuja ovat stokastisella dynaamisella ohjelmoinnilla toteutettu nopea vesialtaiden käytön optimointi sekä sen säätömahdollisuudet verrattuna aikaisempiin toteutuksiin. Vesiarvot saadaan MH-mallin tuottamasta kokonaiskustannusten odotusarvosta derivoimalla allastilan suhteen. JMM:lla simuloitiin vuoden 2001 sähköntuotantoa Pohjoismaissa käyttäen yllä mainittuja kolmea eri vesiarvomenetelmää. Tärkeimmät tulokset ovat sähkön mar ginaalikustannus, vesialtaiden tila sekä sähkön tuotantomäärät tuotantomuodoittain, joita verrattiin myös toteutuneisiin arvoihin. Tulokset uudella menetelmällä ovat lupaavia. MH-mallin kanssa simuloitu allastila vuoden aikana on hyvin lähellä toteutunutta. Kustannustaso on myös tällä toteutuksella lähimpänä toteutunutta markkinahintaa. Tuotantomäärät eivät millään vesiarvototeutuksella ole toteutuneiden tasolla, mutta tuntituotannon aikasarja on MH-mallin kanssa stabiili. Kehitystyötä tarvitaan vielä MH-mallin ja JMM:n yhteensovittamiseksi. Tärkeimmät kohteet ovat erilaisten allastyyppien kuvaaminen sekä JMM:ssa että MH-mallissa, jokivesivoiman osuuden määrittely MH-mallissa, voimalaitosten huoltoseisokkien määrittely ja vesivoiman muuttuvien kustannusten parempi määrittely. Lisäksi MH-mallin tulisi ottaa huomioon tuulivoiman tehovaihtelut. /Kir11