Ralph Evins

Variability between domestic buildings: the impact on energy use

Variations in operational use (in the time domain) and in design and use (between buildings) are critical for district systems. The effects on energy use of behavioural (stochastic profiles of occupancy and end uses) and physical variations (size, orientation, insulation and air tightness) amongst many buildings is examined. Rather than investigating just the variability of these factors, the aim is to identify subsequent impacts on building energy use. To achieve this, dynamic building energy simulations in EnergyPlus are performed. Results include total demands and their distributions, and temporal and probabilistic profiles. Very large variations in total heating demand are noted. Temporal profiles show changes in peak loads, load durations and periods of zero load. Probabilistic profiles and cumulative distributions show that a few buildings are responsible for the majority of total loads. Full detailed simulations are identified as critical when assessing temporal effects such as peak loads and storage sizing.

Reducing Computation Time with a Rolling Horizon Approach Applied to a MILP Formulation of Multiple Urban Energy Hub System

Abstract Energy hub model is a powerful concept allowing the interactions of many energy conversion and storage systems to be optimized. Solving the optimal configuration and operating strategy of an energy hub combining multiple energy sources for a whole year can become computationally demanding. Indeed the effort to solve a mixed-integer linear programming (MILP) problem grows dramatically with the number of integer variables. This paper presents a rolling horizon approach applied to the optimisation of the operating strategy of an energy hub. The focus is on the computational time saving realized by applying a rolling horizon methodology to solve problems over many time-periods. The choice of rolling horizon parameters is addressed, and the approach is applied to a model consisting of a multiple energy hubs. This work highlights the potential to reduce the computational burden for the simulation of detailed optimal operating strategies without using typical-periods representations. Results demonstrate the possibility to improve by 15 to 100 times the computational time required to solve energy optimisation problems without affecting the quality of the results.

A bi-level design and operation optimization process applied to an energy centre

This paper presents the methods and results of a bi-level optimization process used to select and size the components of an energy centre as well as to determine their optimal operation. The optimization process used a single-objective optimization of operational control nested within a two-objective optimization of design parameters that minimized capital costs and carbon emissions. Different cases were examined in which the ‘credit’ assigned for the export of excess electricity to the grid was varied. Results are presented including the overall trade-off front, trends amongst design variables, operational schedules for a particular solution, and aggregated parameters like total energy supplied by each device. Constraints on roof area were critical at the design-level, and complex interactions between demand and supply were identified at the operational level. Comparison of the two cases highlights the impact of the export of excess power to the grid.

Multi-material Compositional Pattern-Producing Networks for Form Optimisation

CPPN-NEAT (Compositional Pattern Producing Networks and NeuroEvolution for Augmented Topologies) is a representation and optimisation approach that can generate and optimise complex forms without any pre-defined structure by using indirect, implicit representations. CPPN is based on an analogy to embryonic development; NEAT is based on an analogy to neural evolution. We present new developments that extend the approach to include multi-material objects, where the material distribution must be optimised in parallel with the form.

Facilitating Model Reuse and Integration in an Urban Energy Simulation Platform

Abstract The need for more sustainable, liveable and resilient cities demands improved methods for studying urban infrastructures as integrated wholes. Progress in this direction would be aided by the ability to effectively reuse and integrate existing computational models of urban systems. Building on the concept of multi-model ecologies, this paper describes ongoing efforts to facilitate model reuse and integration in the Holistic Urban Energy Simulation (HUES) platform - an extendable simulation environment for the study of urban multi-energy systems. We describe the design and development of a semantic wiki as part of the HUES platform. The purpose of this wiki is to enable the sharing and navigation of model metadata – essential information about the models and datasets of the platform. Each model and dataset in the platform is represented in the wiki in a structured way to facilitate the identification of opportunities for model reuse and integration. As the platform grows, this will help to ensure that it develops coherently and makes efficient use of existing formalized knowledge. We present the core concepts of multi-model ecologies and semantic wikis, the current state of the platform and associated wiki, and a case study demonstrating their use and benefit.

Impact of electrical storage and grid upgrade on the optimal design and operation of a microgrid

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.