238 research outputs found
Sustainability Matchmaking: Exploration into using excess renewable energy to deliver ‘free’ energy to fuel poor homes – a preliminary case study in Ireland
The aggregated fuel cost of domestic hot water (DHW) generation in Ireland, in 2022, was €529M with associated emissions/load of 1.3MtCO2/289GWh. The shadow price of carbon monetises the negative impact of emissions, rising with time; DHW generation has an associated shadow carbon cost of €13M in 2022, rising to €42M in 2030 and €335M in 2050.
In 2020, c12%/€441M of wind was curtailed or wasted as inter alia, there was no demand at times of high wind. Meanwhile, a ‘silent crisis’ is occurring in Ireland wherein one-in-two dwellings were considered in fuel poverty in 2022. Households in fuel poverty are known to limit DHW generation, impacting hygiene and well-being.
As most Irish households have an electrical immersion already installed in DHW tanks, this research develops a preliminary (first round) wind allocation model to assess the potentials and economics of redeploying excess wind to heat DHW and, in the interest of a just-transition, focuses on households at risk of fuel poverty.
It is found that fuel-poor households in Ireland could be theoretically provided with a ‘free’ full tank of hot water, once in every 3 weeks, redeploying 89% of overnight curtailed wind energy in 2019, realising a potential carbon cost saving to the Irish state of c€4M in 2030, rising to c€11M in 2050 along with a better quality of life for fuel-poor citizens.
This research concludes this massive, readily deployable, shared, citizen-owned dispatch-down resource should be utilised and further research into redeployment of dispatch-down as a service is merited
Wind and solar intermittency and the associated integration challenges : a comprehensive review including the status in the Belgian power system
Renewable Energy Sources (RES) have drawn significant attention in the past years to make the transition towards low carbon emissions. On the one hand, the intermittent nature of RES, resulting in variable power generation, hinders their high-level penetration in the power system. On the other hand, RES can aid not only to supply much more eco-friendly energy but also it allows the power system to enhance its stability by ancillary service provision. This article reviews the challenges related to the most intermittent RES utilised in Belgium, that is, wind energy and solar energy. Additionally, wind speed and solar irradiance variations, which are the cause of wind and solar intermittency, are studied. Then, recent techniques to forecast their changes, and approaches to accommodate or mitigate their impacts on the power system, are discussed. Finally, the latest statistics and future situation of RES in the Belgian power system are evaluated
A Blockchain-Enabled Model to Enhance Disaster Aids Network Resilience
The disaster area is a true dynamic environment. Lack of accurate information from the affected area create several challenges in distributing the supplies. The success of a disaster response network is based on collaboration, coordination, sovereignty, and equality in relief distribution. Therefore, a trust-based dynamic communication system is required to facilitate the interactions, enhance the knowledge for the relief operation, prioritize, and coordinate the goods distribution. One of the promising innovative technologies is blockchain technology which enables transparent, secure, and real-time information exchange and automation through smart contracts in a distributed technological ecosystem. This study aims to analyze the application of blockchain technology on disaster management resilience. Blockchain technology, the Internet of Things (IoT), and Dynamic Voltage Frequency Scaling (DVFS) algorithm are integrated in a network-based simulation. The results indicate an advancement in disaster-aids network strategies using smart contracts for collaborations. From the investigations, insights have been derived for researchers in the field and the managers interested in practical implementation
Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review
Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area
A Scalable Feature Selection and Opinion Miner Using Whale Optimization Algorithm
Due to the fast-growing volume of text documents and reviews in recent years,
current analyzing techniques are not competent enough to meet the users' needs.
Using feature selection techniques not only support to understand data better
but also lead to higher speed and also accuracy. In this article, the Whale
Optimization algorithm is considered and applied to the search for the optimum
subset of features. As known, F-measure is a metric based on precision and
recall that is very popular in comparing classifiers. For the evaluation and
comparison of the experimental results, PART, random tree, random forest, and
RBF network classification algorithms have been applied to the different number
of features. Experimental results show that the random forest has the best
accuracy on 500 features. Keywords: Feature selection, Whale Optimization
algorithm, Selecting optimal, Classification algorith
Artificial intelligence for decision making in energy demand-side response
This thesis examines the role and application of data-driven Artificial Intelligence
(AI) approaches for the energy demand-side response (DR). It follows the point of
view of a service provider company/aggregator looking to support its decision-making
and operation. Overall, the study identifies data-driven AI methods as an essential
tool and a key enabler for DR. The thesis is organised into two parts. It first provides
an overview of AI methods utilised for DR applications based on a systematic review
of over 160 papers, 40 commercial initiatives, and 21 large-scale projects. The reviewed work is categorised based on the type of AI algorithm(s) employed and the DR
application area of the AI methods. The end of the first part of the thesis discusses
the advantages and potential limitations of the reviewed AI techniques for different
DR tasks and how they compare to traditional approaches. The second part of the
thesis centres around designing machine learning algorithms for DR. The undertaken
empirical work highlights the importance of data quality for providing fair, robust,
and safe AI systems in DR — a high-stakes domain. It furthers the state of the art
by providing a structured approach for data preparation and data augmentation in
DR to minimise propagating effects in the modelling process. The empirical findings
on residential response behaviour show better response behaviour in households with
internet access, air-conditioning systems, power-intensive appliances, and lower gas
usage. However, some insights raise questions about whether the reported levels of
consumers’ engagement in DR schemes translate to actual curtailment behaviour and
the individual rationale of customer response to DR signals. The presented approach
also proposes a reinforcement learning framework for the decision problem of an aggregator selecting a set of consumers for DR events. This approach can support an
aggregator in leveraging small-scale flexibility resources by providing an automated
end-to-end framework to select the set of consumers for demand curtailment during
Demand-Side Response (DR) signals in a dynamic environment while considering a
long-term view of their selection process
Transitioning power distribution grid into nanostructured ecosystem : prosumer-centric sovereignty
PhD ThesisGrowing acceptance for in-house Distributed Energy Resource (DER) installations at lowvoltage
level have gained much significance in recent years due to electricity market liberalisations
and opportunities in reduced energy billings through personalised utilisation
management for targeted business model. In consequence, modelling of passive customers’
electric power system are progressively transitioned into Prosumer-based settings where presidency
for Transactive Energy (TE) system framework is favoured. It amplifies Prosumers’
commitments into annexing TE values during market participations and optimised energy
management to earn larger rebates and incentives from TE programs. However, when dealing
with mass Behind-The-Meter DER administrations, Utility foresee managerial challenges
when dealing with distribution network analysis, planning, protection, and power quality
security based on Prosumers’ flexibility in optimising their energy needs.
This dissertation contributes prepositions into modelling Distributed Energy Resources
Management System (DERMS) as an aggregator designed for Prosumer-centered cooperation,
interoperating TE control and coordination as key parameters to market for both
optimised energy trading and ancillary services in a Community setting. However, Prosumers
are primarily driven to create a profitable business model when modelling their
DERMS aggregator. Greedy-optimisation exploitations are negative concerns when decisions
made resulted in detrimental-uncoordinated outcomes on Demand-Side Response (DSR)
and capacity market engagements. This calls for policy decision makers to contract safe (i.e.
cooperative yet competitive tendency) business models for Prosumers to maximise TE values
while enhancing network’s power quality metrics and reliability performances.
Firstly, digitalisation and nanostructuring of distribution network is suggested to identify
Prosumer as a sole energy citizen while extending bilateral trading between Prosumer-to-
Prosumer (PtP) with the involvements of other grid operators−TE system. Modelling of
Nanogrid environment for DER integrations and establishment of local area network infrastructure
for IoT security (i.e. personal computing solutions and data protection) are committed
for communal engagements in a decentralise setting. Secondly, a multi-layered Distributed
Control Framework (DCF) is proposed using Microsoft Azure cloud-edge platform that cascades energy actors into respective layers of TE control and coordination. Furthermore,
modelling of flexi-edge computing architecture is proposed, comprising of Contract-Oriented
Sensor-based Application Platform (COSAP) employing Multi-Agent System (MAS) to
enhance data-sharing privacy and contract coalition agreements during PtP engagements.
Lastly, the Agents of MAS are programmed with cooperative yet competitive intelligences
attributed to Reinforcement Learning (RL) and Neural Networks (NN) algorithms to solve
multimodal socio-economical and uncertainty problems that corresponded to Prosumers’
dynamic energy priorities within the TE framework. To verify the DERMS aggregator
operations, three business models were proposed (i.e. greedy-profit margin, collegial-peak
demand, reserved-standalone) to analyse comparative technical/physical and economic/social
dimensions. Results showed that the proposed TE-valued DERMS aggregator provides
participation versatility in the electricity market that enables competitive edginess when utilising
Behind-The-Meter DERs in view of Prosumer’s asset scheduling, bidding strategy, and
corroborative ancillary services. Performance metrics were evaluated on both domestic and
industrial NG environments against IEEE Standard 2030.7-2017 & 2030.8-2018 compliances
to ensure deployment practicability.
Subsequently, proposed in-house protection system for DER installation serves as an
add-on monitoring service which can be incorporated into existing Advance Distribution
Management System (ADMS) for Distribution Service Operator (DSO) and field engineers
use, ADMS aggregator. It provides early fault detections and isolation processes from allowing
fault current to propagate upstream causing cascading power quality issues across
the feeder line. In addition, ADMS aggregator also serves as islanding indicator that distinguishes
Nanogrid’s islanding state from unintentional or intentional operations. Therefore, a
Overcurrent Current Relay (OCR) is proposed using Fuzzy Logic (FL) algorithm to detect,
profile, and provide decisional isolation processes using specified OCRs. Moreover, the
proposed expert knowledge in FL is programmed to detect fault crises despite insufficient
fault current level contributed by DER (i.e. solar PV system) which conventional OCR fails
to trigger
Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid
This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources
Towards Flexible Distribution Systems : Future Adaptive Management Schemes
During the ongoing evolution of energy systems toward increasingly flexible, resilient, and digitalized distribution systems, many issues need to be developed. In general, a holistic multi-level systemic view is required on the future enabling technologies, control and management methods, operation and planning principles, regulation as well as market and business models. Increasing integration of intermittent renewable generation and electric vehicles, as well as industry electrification during the evolution, requires a huge amount of flexibility services at multiple time scales and from different voltage levels, resources, and sectors. Active use of distribution network-connected flexible energy resources for flexibility services provision through new marketplaces will also be needed. Therefore, increased collaboration between system operators in operation and planning of the future power system will also become essential during the evolution. In addition, use of integrated cyber-secure, resilient, cost-efficient, and advanced communication technologies and solutions will be of key importance. This paper describes a potential three-stage evolution path toward fully flexible, resilient, and digitalized electricity distribution networks. A special focus of this paper is the evolution and development of adaptive control and management methods as well as compatible collaborative market schemes that can enable the improved provision of flexibility services by distribution network-connected flexible energy resources for local (distribution system operator) and system-wide (transmission system operator) needs.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed
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