Search

The 10th International Conference on Sustainable Energy Information Technology (SEIT 2020), Leuven, Belgium, 9-12 August 2020 The Irish government is implementing policies to transition Ireland to a low carbon and environmentally sustainable economy by 2050. Ireland has sectoral targets of 600,000 installed heat pumps by 2030, currently roughly 28,000 are installed. Such a high target of heat pumps will not only have a significant effect on electricity demand but also on the management and operation of the grid. In this paper we explore the demand from homes heated by air source heat pumps using an innovative dataset from a field trial in Ireland. To assess the impact of large-scale adoption of heat pumps, this paper estimates the after diversity maximum demand per heat pump heated home. In particular we explore statistical distributions to best model coincident demand, and estimate after diversity maximum demand per home. We use the software package RStudio to model several different distributions. Based on goodness-of-fit statistics and criteria, a Gamma distribution is the best fit. We apply our methodology to data from a similar heat pump trial in the UK to complement our results. Enterprise Ireland Sustainable Energy Authority of Ireland (SEAI) The International Energy Research Centre (IERC)

This chapter provides an introduction to exact methods and their application to solving vehicle routing problems in sustainable transportation. We provide an overview of existing methods and focus on providing an intuition as to how these approaches work, rather than providing rigorous mathematical proofs. We demonstrate the use of the vehicle flow formulation on a sample of benchmark instances, and on some new problem instances that incorporate geospatial information to aid vehicle routing decision making in sustainable transportation. We outline the use of emissions models to estimate the CO2 emissions on solution routes.

We propose a Genetic Algorithm (GA) to address a Green Vehicle Routing Problem (G-VRP). Unlike classic formulations of the VRP, this study aims to minimise the CO2 emissions per route. The G-VRP is of interest to policy makers who wish to reduce greenhouse gas emissions. The GA is tested on a suite of benchmark, and real-world instances which include road speed and gradient data. Our solution approach incorporates elements of local and population search heuristics. Solutions are compared with routes currently used by drivers in a courier company. Reductions in emissions are achieved without incurring additional operational costs.

International Network Optimization Conference, Warsaw, Poland, 18-20 May 2015 Smart metering in electricity markets offers an opportunity to explore more diversetariff structures. In this article a Genetic Algorithm (GA) is used to design Time ofUse tariffs that minimise the wholesale risk to the supplier in residential markets.Residential demand and the System Marginal Price of Ireland's Single ElectricityMarket are simulated to estimate the wholesale risk associated with each tariff.

Demand-Side Management systems aim to modulate energy consumption at the customer side of the meter using price incentives. Current incentive schemes allow consumers to reduce their costs, and from the point of view of the supplier play a role in load balancing, but do not lead to optimal demand patterns. In the context of charging fleets of electric vehicles, we propose a centralised method for setting overnight charging schedules. This method uses evolutionary algorithms to automatically search for optimal plans, representing both the charging schedule and the energy drawn from the grid at each time-step. In successive experiments, we optimise for increased state of charge, reduced peak demand, and reduced consumer costs. In simulations, the centralised method achieves improvements in performance relative to simple models of non-centralised consumer behaviour.