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This paper aims to integrate decentralized energy systems in a village in Switzerland, which has the goal to phase out fossil fuels and rely on local renewable energy sources. To reach this ambitious target, a revision of the current energy system is required together with retrofitting of the building stock. The method applied is based on the energy hub concept, which can be used to optimize the energy consumption during planning and operation. To apply the energy hub concept at neighbourhood level, a three step approach is required, including modelling the energy demand of the buildings, the evaluation of the local renewable potential, and the management and optimization of demand and supply. Centralized and decentralized local renewable sources are investigated, namely photovoltaics, biomass, or small hydro power. Scenarios are evaluated based on their environmental performance and savings of CO 2 emissions. Results show that an energy sustainability (ratio of energy demand covered by renewables) of 83% and a CO 2 emissions reduction of 86% can be achieved. The results further suggest that up to 50% of the available energy potential from renewables cannot be utilized in this community when insufficient storage is provided in the energy system.

We present new formulations of the ‘energy hub’ model and evaluate their performance. The energy hub model consists of a mixed-integer linear programming problem that balances energy demand and supply between multiple energy carriers by determining the optimal conversion and storage schedule within certain constraints. The new formulations extend the model to account for performance constraints concerning system efficiencies, storage losses and operating limits. Each formulation allows a more accurate representation of real plant performance to be included in the optimisation, giving more accurate optimised schedules and carbon emissions totals. The first major innovation is a means of limiting the number of state changes (startups or shutdowns). This is achieved by specifying a minimum time for which the plant must operate once it is running. The second innovation is the use of stepwise approximations of efficiency curves, thus allowing part-load behaviour to be accurately simulated using a linear model. The third innovation adds a storage loss term that is a percentage of the current amount stored, rather than a fixed value. The new formulations are demonstrated in an example case, where the impact on the optimal schedule is observed. They are also analysed for each week of the heating season, and their impact on the time taken to find the optimal solution is also discussed. Overall changes in the predicted carbon emissions of up to 22% were found, highlighting the importance of accurate plant representation in energy hub models.

Longwave (infrared) radiation is exchanged between exterior building surfaces, the ground and the sky. This process affects exterior surface temperatures and hence building energy consumption. Current approaches to modelling this exchange in building and microclimate analysis tools are discussed. There are various shortcomings, the most significant concerning radiation exchange between building surfaces at different temperatures. This work implements improvements to the longwave exchange processes in the EnergyPlus simulation engine, allowing improved calculation of surface temperatures and thus of heating and cooling loads. The improvements cover ground temperature assumptions and the coupling of other building surfaces. The coupling of radiative transfer was achieved by means of a timelag; it is shown that for small timesteps this approach is adequate. The new approaches are compared to the existing implementation in EnergyPlus for arrangements including a street canyon and shading devices. The combined impact gave an increase in surface temperature of up to 6 °C, with an average change of 2 °C. This caused a decrease in annual heating load of 18% and an increase in cooling load of 19%. This confirms the importance of accurate longwave radiation modelling both for buildings in dense urban areas and for buildings with external shading devices.

The built environment represents a major share of global energy consumption. To effectively reduce the energy consumption of urban conglomerations, concepts to sufficiently integrate and manage energy from renewables are necessary. In this paper the energy-hub concept will be applied, which describes the relation between input and output energy flows and can be used to optimize the energy consumption during planning and operation. The concept will be used to evaluate a number of future energy scenarios for a village in Switzerland which has the goal of eliminating the consumption of fossil fuels. As a starting point the existing situation concerning the energy demand of the village with respect to different uses, the different energy carriers, their origin, their distribution and networks is captured and analyzed. In the next step the potential for different means of decentralized energy production is evaluated. Decentralized energy production includes building integrated or local renewable energy production by photovoltaics, biomass, or small hydro power. In the third step, different future energy scenarios for an energy sustainable community are defined. These different scenarios are distinguished by their scale of implementation. Finally an energy hub model of the village is developed and used to evaluate the different energy scenarios., Energy Procedia, 57, ISSN:1876-6102, 2013 ISES Solar World Congress

This paper presents a comprehensive review of all significant research applying computational optimisation to sustainable building design problems. A summary of common heuristic optimisation algorithms is given, covering direct search, evolutionary methods and other bio-inspired algorithms. The main summary table covers 74 works that focus on the application of these methods to different fields of sustainable building design. Key fields are reviewed in detail: envelope design, including constructions and form; configuration and control of building systems; renewable energy generation; and holistic optimisations of several areas simultaneously, with particular focus on residential and retrofit. Improvements to the way optimisation is applied are also covered, including platforms and frameworks, algorithmic comparisons and developments, use of meta-models and incorporation of uncertainty. Trends, including the rise of multi-objective optimisation, are analysed graphically. Likely future developments are discussed.

An integrated coordination of multi-carrier energy networks including gas, heating, cooling and electricity can increase the flexibility, efficiency and sustainability of energy systems. The optimal dispatch of such systems is complicated by the non-convex nature of their energy conversion processes. Although these processes can be represented in mixed-integer linear programmes, real-time constraints of an online dispatcher may not be satisfied. In this paper, two approaches for alleviating this problem are developed and compared: one is based on a relaxed mixed-integer linear formulation and the other on mathematical optimization with complementarity constraints. Simulation results on realistic systems demonstrate that both approaches solve large multi-carrier dispatch problems efficiently. The mathematical optimization with complementarity constraints is computationally less intensive but the relaxed mixed-integer linear formulation is numerically more robust., Energy Procedia, 122, ISSN:1876-6102, CISBAT 2017 International Conference Future Buildings & Districts – Energy Efficiency from Nano to Urban Scale

The aim of this paper is to analyse how electrical storage and grid upgrades influence the optimal design and operation of a microgrid. A multi-objective (cost versus carbon emissions) mixed integer linear programming model is developed and applied to a microgrid case study. The optimisation model is based on the coupling of an energy hub modelling approach for system design and operation, and a linearized electrical grid model for steady-state power flow calculation in order to optimize the overall design and operation of a microgrid. The trade-off between electrical storage (ES) and grid upgrade options is examined and performance is compared to a reference case. Additionally, the optimal locations, capacities and charging power of an electrical storage are determined. The results showed that the grid upgrade is only viable in the lowest carbon emissions after the installation of the electrical storage.

The newly-released Standard Assessment Procedure (SAP) 2009 [1] underpins all energy calculations for Building Regulations compliance and Code for Sustainable Homes ratings for domestic buildings in the UK. A newly-developed three-stage optimisation framework is applied to the outputs of SAP for a case study concerning a 2-bed mid-level flat. Firstly a comprehensive full-factorial Design-of-Experiments analysis is performed to determine the significance of each input to the outputs of SAP (carbon emissions, running costs and overheating risk). This allows many of the inputs to be disregarded as non-significant. Next a multi-objective optimisation algorithm is applied to all significant variables to simultaneously optimise regulated carbon emissions versus capital and running costs, constrained by limits on overheating and roof area. Finally a more detailed multi-objective optimisation using greater precision is conducted on all variables that exhibit complex behaviour, i.e. which do not take a single value for all optimum solutions. Information is obtained concerning parameter significance and optimal parameter settings, which is presented as graphical design guidance using the process of ‘innovisation’. This will assist engineers in achieving high-performing, cost-effective designs.

Multi-objective optimisation can help civil engineers achieve higher performance for lower costs in their designs. This is true whether ‘performance’ applies to structural strength or energy use, or whether ‘cost’ measures financial outlay or occupant satisfaction: if it can be quantified it can be optimised in some form. By exploring trade-offs between conflicting objectives and constraints, multi-objective optimisation enables informed decision-making. This paper outlines the principles and benefits of multi-objective optimisation and the means of implementation. The complementary aspects of parametric modelling and optimisation are discussed as an aid to the flexible design of buildings and structures. A range of real design problems are considered, including structural and environmental examples.