A multi-agent model for assessing electricity tariffs

This paper describes the framework for modelling a multi-agent approach for assessing dynamic pricing of electricity and demand response. It combines and agent-based model with decision-making data, and a standard load-flow model. The multi-agent model described here represents a tool in investigating not only the relation between different dynamic tariffs and consumer load profiles, but also the change in behaviour and impact on low-voltage electricity distribution networks.The authors acknowledge the contribution of the EPSRC Transforming Energy Demand Through Digital Innovation Programme, grant agreement numbers EP/I000194/1 and EP/I000119/1, to the ADEPT project

The user support programme and the training infrastructure of the EGI Federated Cloud

The EGI Federated Cloud is a standards-based, open cloud system as well as its enabling technologies that federates institutional clouds to offer a scalable computing platform for data and/or compute driven applications and services. The EGI Federated Cloud is based on open standards and open source Cloud Management Frameworks and offers to its users IaaS, PaaS and SaaS capabilities and interfaces tuned towards the needs of users in research and education. The federation enables scientific data, workloads, simulations and services to span across multiple administrative locations, allowing researchers and educators to access and exploit the distributed resources as an integrated system. The EGI Federated Cloud collaboration established a user support model and a training infrastructure to raise visibility of this service within European scientific communities with the overarching goal to increase adoption and, ultimately increase the usage of e-infrastructures for the benefit of the whole European Research Area. The paper describes this scalable user support and training infrastructure models. The training infrastructure is built on top of the production sites to reduce costs and increase its sustainability. Appropriate design solutions were implemented to reduce the security risks due to the cohabitation of production and training resources on the same sites. The EGI Federated Cloud educational program foresees different kind of training events from basic tutorials to spread the knowledge of this new infrastructure to events devoted to specific scientific disciplines teaching how to use tools already integrated in the infrastructure with the assistance of experts identified in the EGI community. The main success metric of this educational program is the number of researchers willing to try the Federated Cloud, which are steered into the EGI world by the EGI Federated Cloud Support Team through a formal process that brings them from the initial tests to fully exploit the production resources. © 2015 IEEE

Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles

There is growing interest in discerning behaviors of electricity users in both the residential and commercial sectors. With the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from the computational viewpoint. One of the popular techniques is clustering, but depending on the algorithm the resolution of the data can have an important influence on the resulting clusters. This paper shows how temporal resolution of power demand profiles affects the quality of the clustering process, the consistency of cluster membership (profiles exhibiting similar behavior), and the efficiency of the clustering process. This work uses both raw data from household consumption data and synthetic profiles. The motivation for this work is to improve the clustering of electricity load profiles to help distinguish user types for tariff design and switching, fault and fraud detection, demand-side management, and energy efficiency measures. The key criterion for mining very large data sets is how little information needs to be used to get a reliable result, while maintaining privacy and security

A reduced-dimension feature extraction method to represent retail store electricity profiles

Copyright © 2022 The Author(s). Characterising the inter-seasonal energy performance of buildings is a useful tool for a business to understand what is ‘normal’ for its portfolio of premises and to detect anomalous patterns of energy demand. When adding a new building to the portfolio, it will be useful to predict what will be the likely energy use as part of on-going monitoring of the site. For a large portfolio of buildings with, say, half-hourly energy use measurements (48 dimensions), analysis and prediction will require machine learning tools. Even so, it is advantageous to minimise the amount of data and number of dimensions and features required to find useful patterns in the measurement stream. Our aim is to devise a reduced feature set that can generate a statistically reasonable representation of daily electricity load profiles of retail stores and small supermarkets. We then test if our method is sufficiently accurate to predict and cluster measured patterns of demand. We propose an automatic method to extract features such as times and average demands from electricity load profiles. We used four regression models for prediction and six clustering methods to compare with the results obtained using all of the readings in the load profile. We found that the reduced feature set gave a good representation of the load profile, with only small prediction and clustering errors. The results are robust as prediction is supervised learning and clustering is unsupervised. This simplified feature set is a concise way to represent profiles without using small variances of the demand that do not add useful information to the overall picture. As modern sensor systems increase the volume, availability, and immediacy of data, using reduced dimensional datasets will be key to extracting useful information from high-resolution data streams

Optimizing the utilisation of EV light goods vehicles for supermarket delivery services

With the rise of online grocery shopping, a centralized delivery model has the potential to improve sustainability. Growing demand though brings challenges to grocery retailers’ net-zero targets with many retailers’ sustainability plans detailing the transition to electric vehicles (EV) within logistics as essential. In this study, a feasibility analysis on replacing internal combustion engine vehicles (ICEV) with EVs for Sainsbury's grocery delivery business in Oxfordshire was conducted. As grocery delivery has stricter timing requirements and considerations must be made for EV charging and load management, several methodologies were proposed and used in simulations. Using Genetic Algorithms combined with Dynamic Programming for vehicle fleet route optimization, we were able to achieve optimizations of 48.8 % under the 4h Saver slot and 20.8 % under the 1h Standard slot. Furthermore, strategies were developed to optimise the number and type of EVs deployed at each store and the location of EV charging stations (EVCS). The optimal strategy provided a reduction of 458.8 km for daily fleet operation under the Saver slot. Emissions analysis shows that the initial deployment of the EV fleet results in 303.5 tons more greenhouse gas (GHG) than the ICE fleet. However, during operation, the accumulated GHG emissions of the ICE fleet exceed those of the EV fleet after the 4th year of deployment. Due to the battery and EVCS installation costs, the upfront cost of the EV fleet is €474,600 higher than the ICE fleet but would see operational cost savings of €609698 due to the lower cost of electricity. Technical, environmental, and economic analysis proved the feasibility of EV fleet deployment for Sainsbury’s grocery delivery business based on the specifications of existing electric vehicles and the current technologies & standards of EV charging. Increasing carbon prices and decreasing carbon intensity of electricity generation would expand the benefits of EV usage even further in the future.</p

Investigating in-route energy consumption profiles of battery-electric buses using open-source transportation simulation

In the context of ambitious net-zero targets and decarbonisation of transport, bus fleet electrification has emerged as a likely significant transformation. This study proposes a multi-step model for investigating the effect of route structure on electric buses' energy consumption. Initial bus fleets were selected based on the route demand and characteristics, using a Multi-Criteria Decision Matrix. Energy consumption modelling of buses was differentiated into auxiliary and powertrain components to consider select operational characteristics. Travel flow analysis was conducted using the Simulation of Urban Mobility (SUMO) software through importing transport networks, defining bus stop locations and routes. A case study of Oxfordshire's existing bus system was conducted given worst-case seasonal temperatures including optimized electric bus selection for five selected routes, and a flow simulation to determine the energy consumption patterns of different route types. Powertrain modelling showed strong monotonic relationships between route length and propulsion energy consumption, ranging from 19.1 kWh to 105 kWh for the shortest and longest routes respectively. Higher levels of congestion, proxied through average inter-stop speeds, correlated with lower instantaneous energy increases. Overall, auxiliary systems constituted a maximum of 11 % of total energy consumption across all routes simulated. Auxiliary system modelling also showed that total energy consumption and the proportion spent on auxiliary services were only weakly influenced by route length; instead, stronger correlations were observed with the total number of stops, due to a significant impact on door opening times. Future work could investigate the optimised location of depot and in-route charging infrastructure to best support electric bus fleets considering additional constraints of grid congestion. These findings contribute to research on the infrastructural needs of a zero-carbon bus fleet by providing local authorities with a high-level understanding of e-bus energy demands across the region.</p

Utilising Amazon Web Services to provide an on demand urgent computing facility for climateprediction.net

Climateprediction.net has traditionally been an activity that requires a large amount of computing resources from its volunteer network, whilst allowing a time-frame of weeks to months for simulations to be returned for each project. However, there is an increasing trend of projects requiring results in shorter and shorter timescales. Under no project is this clearer than in the World Weather Attribution (WWA) initiative, where we are aiming to provide in near to real-time an answer to how anthropogenic climate change has altered the frequency of occurrence of a particular type of extreme weather event, either as it happens or as soon after as is practical. As such we need the ability to run simulations on alternate resources when volunteer resources will not provide results within the necessary timeframe. This paper describes a workflow to distribute ensembles of climateprediction.net simulations in the Amazon Elastic Compute Cloud, to provide urgent compute capability for projects such as WWA. We propose a method of optimizing the use of cloud resources to minimize cost while maximising throughput. A case study is presented to provide a proof of concept of this methodology. As such, this is a clear example of beneficial utilisation of cloud resources to supplement those available through our volunteer community

Predicting electricity demand profiles of new supermarkets using machine learning

Predicting the electricity consumption of proposed new supermarkets is helpful to design and plan future energy management. Instead of creating complex site-specific thermal engineering models, data-driven energy prediction models can be useful to energy managers. We have designed and implemented a data-driven method to predict the future ’electricity daily load profile’ (EDLP) of new supermarkets using historical EDLPs of existing supermarkets of the same type. The supermarket features used for the prediction are 10 types of floor areas divided by usage (m2) and its location. Four data-driven regression models are used and compared to predict EDLPs: Artificial Neural Networks, Support Vector Machines, k-Nearest Neighbours and OLS. Prediction computational experiments were performed over 1-h electricity readings of 213 UK supermarkets gathered during six years. Prediction error mainly varies between 12 and 20% depending on method, year, supermarket type, and division of the data (season or temperature intervals). EDLPs computed over warm periods are better predicted than over cold periods and supermarkets only with electricity are better predicted than supermarkets with electricity and gas. The three features with more weight in the prediction are Food, Chilled produce and Cafeteria areas. The limitations of machine learning methods to solve this problem are discussed