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Preprint for a journal paper demonstrating the viability of the Backbone energy system modelling framework for capturing the essence of building-level flexibility for economic model-predictive control.

Driven by the growth of variable renewable electricity generation, the increasing electrification of heating and cooling of buildings holds a lot of demand-side management potential worldwide. However, building-stock-scale models depicting this flexibility for large-scale energy system optimisation models are lacking in literature. Thus, we've been developing an open-source tool called ArchetypeBuildingModel.jlspecifically for this purpose, aggregating building-stock-level statistical and technical data into desired simplified lumped-capacitance thermal models compatible with existing open-source energy system modelling frameworks. This paper aims to demonstrate the feasibility of such simplified thermal models via comparisons against dedicated building simulation software, as well as examine the sensitivities arising from the key modelling and parameter assumptions. In conclusion, parameter and modelling assumptions corroborated by existing literature achieved reasonable performance across all tested buildings and nodal configurations, although caution is advised when modelling buildings with structures heterogeneous in terms of thermal mass. Nevertheless, even at its worst, model performance was deemed plausible.

The purpose of this document is to give an all-encompassing documentation of the Spine Model. During the project, the name “SpineOpt.jl” was adopted to reflect the Julia package naming convention. This document describes SpineOpt and all its capabilities implemented during the project and applied to the case studies. In line with the ambition to provide open-source and easily accessible software, the documentation of SpineOpt is hosted online and open-source on github. The link to the latest version of the documentation is https://spine-project.github.io/SpineOpt.jl/latest/. The link to the latest version of SpineOpt itself is https://github.com/Spine-project/SpineOpt.jl.

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.

Integrating ever-increasing amounts of variable renewable energy (VRE) into the power system could benefit from harnessing widespread residential demand-side management. This paper presents case studies on the potential benefits of power-to-heat (P2H) flexibility and energy efficiency improvements in a hypothetical future Finnish detached housing stock in the year 2030, both as a part of the larger Nordic power system and in an isolated Finnish power system. The housing stock was depicted using two archetype houses modeled using a simple lumped capacitance approach, integrally optimized as a part of a stochastic linear programming unit commitment model of the power system. With sufficient amounts of VRE, residential P2H with thermal storage was found to yield more system cost savings than simple energy efficiency improvements. However, energy efficiency improvements remained more beneficial for house owners, as excessive use of residential P2H for assisting the power system could result in increased heating costs c 2018 by the authors. Licensee MDPI, Basel, Switzerland.

With the increasing penetration of distributed renewable energy generation and dynamic electricity pricing schemes, applications for residential demand side management are becoming more appealing. In this work, we present an optimal control model for studying the economic and grid interaction benefits of smart charging of electric vehicles (EV), vehicle-to-grid, and space heating load control for residential houses with on-site photovoltaics (PV). A case study is conducted on 1–10 net zero energy houses with detailed empirical data, resulting in 8–33% yearly electricity cost savings per household with various electric vehicle and space heating system combinations. The self-consumption of PV is also significantly increased. Additional benefits through increasing the number of cooperating households are minor and saturate already at around 3–5 households. Permitting electricity transfer between the houses and EV charging stations at workplaces increases self-sufficiency significantly, but it provides limited economic benefit. The additional cost savings from vehicle-to-grid compared to smart charging are minor due to increased battery degradation, despite a significant self-sufficiency increase. If the optimization is conducted without taking the battery degradation cost into account, the added monetary value of vehicle-to-grid can even be negative due to the unmanaged degradation. Neglecting battery degradation completely leads to overestimation of the vehicle-to-grid cost benefit.