arXiv subjects

Malcolm McCulloch

Publications and source records attributed to Malcolm McCulloch.

At least 19 recordsLinked to original sources

arXiv2025

Understanding long-term energy use in off-grid solar home systems in sub-Saharan Africa

Solar home systems provide low-cost electricity access for rural off-grid communities. As access to them increases, more long-term data becomes available on how these systems are used throughout their lifetime. This work analyses a dataset of 1,000 systems across sub-Saharan Africa. Dynamic time warping clustering was applied to the load demand data from the systems, identifying five distinct archetypal daily load profiles and their occurrence across the dataset. Temporal analysis reveals a general decline in daily energy consumption over time, with 77% of households reducing their usage compared to the start of ownership. On average, there is a 33% decrease in daily consumption by the end of the second year compared to the peak demand, which occurs on the 96th day. Combining the load demand analysis with payment data shows that this decrease in energy consumption is observed even in households that are not experiencing economic hardship, indicating there are reasons beyond financial constraints for decreasing energy use once energy access is obtained.

eess.SY↗

arXiv2024

Quantifying national space heating flexibility potential at high spatial resolution with heating consumption data

Decarbonizing the building stock in cold countries by replacing fossil fuel boilers with heat pumps is expected to drastically increase electricity demand. While heating flexibility could reduce the impact of additional demand from heat pumps on the power system, characterizing the national spatial distribution of heating flexibility capacity to incorporate into sophisticated power system models is challenging. This paper introduces a novel method for quantifying at large scale and high spatial resolution the energy capacity and duration of heating flexibility in existing building stock based on historical heating consumption and temperature data. This method can reflect the geographic diversity of the national building stock in sophisticated power system models. The proposed heating consumption-based method was tested in Britain using national residential gas data. The results demonstrate the potential of this approach to characterize the heterogeneous distribution of heating flexibility capacity at the national scale. Assuming a 3$^\circ$C temperature flexibility window, a total thermal energy storage capacity of 500 GWh$_{th}$ is identified in the British housing stock. For an illustrative cold weather COP value of 2.5, this thermal energy storage capacity is equivalent to 200 GWh of electricity storage. Regarding heating flexibility duration, gas-heated homes have a median of 5.9 heat-free hours for 20th percentile regional daily winter temperatures from 2010 to 2022. However, extreme cold days nearly halve flexibility duration to a median of 3.6 heat-free hours. These high spatial resolution energy capacity and self-discharge parameters can account for geographic diversity at the national scale and provide a new data-based layer of information for sophisticated power system models to support energy transition.

physics.soc-ph↗

arXiv2023

Lead-acid battery lifetime extension in solar home systems under different operating conditions

Solar home systems (SHS) provide low-cost electricity access for rural off-grid communities. Batteries are a crucial part of the system, however they are often the first point of failure due to shorter lifetimes. Using field data, this work models the degradation of lead-acid batteries for different SHS use-cases, finding the dominant ageing mechanisms in each case. Corrosion is the dominant ageing mechanisms in all cases apart from the highest use case. This is caused by extended time at high state of charge (SOC) and hence high voltage. A new voltage control scheme is proposed for one of the use cases dominated by corrosion, whereby the number of days between full recharges varies depending on the degradation mechanisms the battery experiences. Simulating the new voltage control scheme yields a 25% increase in battery lifetime whilst ensuring no loss of load to the user.

eess.SY↗

arXiv2023

Home Electricity Data Generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles

In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability, based on real-life UK datasets. The lack of usable data is a major hurdle for research on residential distributed energy resources characterisation and coordination, especially when using data-driven methods such as machine learning-based forecasting and reinforcement learning-based control. A key issue is that while large data banks are available, they are not in a usable format, and numerous subsequent days of data for a given single home are unavailable. We fill these gaps with the open-access HEDGE tool which generates data sequences of energy data for several days in a way that is consistent for single homes, both in terms of profile magnitude and behavioural clusters. From raw datasets, pre-processing steps are conducted, including filling in incomplete data sequences and clustering profiles into behaviour clusters. Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups consistent with real-life behavioural and physical patterns.

eess.SY↗

arXiv2023

Spatial clustering of temporal energy profiles with empirical orthogonal functions and max-p regionalization

This paper presents a spatial clustering method to create regions with similar time-varying energy characteristics. This method combines empirical orthogonal functions (EOFs) for dimensionality reduction and max-p regionalization for spatial clustering. The proposed approach creates regions that each have a similar value of a spatially extensive attribute, such as available land area, population, or GDP, as well as similar weather-dependent temporal energy profiles, such as wind and solar generation potential or heating and cooling demand, within each region. We demonstrate this technique using hourly wind and solar generation potential in 2019 in Ireland and Britain. Solar generation clusters are best-defined at a smaller land area threshold compared to wind generation.

physics.soc-ph↗

arXiv2023

Optimal sizing of solar photovoltaic and lithium battery storage to reduce grid electricity reliance in buildings

In alignment with the Paris Agreement, the city of Oxford in the UK aims to become carbon neutral by 2040. Renewable energy help achieve this target by reducing the reliance on carbon-intensive grid electricity. This research seeks to optimally size solar photovoltaic and lithium battery storage systems, reducing Oxford's grid electricity reliance in buildings. The analysis starts with modeling the electricity demand. The model uses Elexon electricity settlement profiles, and assembles them into the demand profile according to the quantity and types of buildings in Oxford. Then, solar generation is modeled using Pfenninger and Staffell's method. Solar photovoltaic and lithium storage systems are sized using a hybridized analytical and iterative method. First, the method calculates the solar system size search range, then iterates through the range. At each solar size, the method calculates and iterates through the storage system size search range. Within each iteration, the renewable system is simulated using demand and generation data with a simplified system set-up and the conventional operation strategy. The method outputs combinations of solar system capacity, storage system capacity, and grid electricity import. Each combination's levelized cost of electricity is calculated, and the lowest cost combination is the optimal sizing. Solar and storage system costs are projected from 2019 to 2100, and the optimal sizing is calculated for each year. The result shows that solar photovoltaic is economically competitive, but lithium storage cost is still too high. As solar and storage prices continue to drop, they will take up greater portions of the energy system. However, there will always be a need for the grid, as it provides flexibility and can meet demands that are too costly for solar and storage

eess.SY↗

arXiv2023

Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility

This paper investigates how deep multi-agent reinforcement learning can enable the scalable and privacy-preserving coordination of residential energy flexibility. The coordination of distributed resources such as electric vehicles and heating will be critical to the successful integration of large shares of renewable energy in our electricity grid and, thus, to help mitigate climate change. The pre-learning of individual reinforcement learning policies can enable distributed control with no sharing of personal data required during execution. However, previous approaches for multi-agent reinforcement learning-based distributed energy resources coordination impose an ever greater training computational burden as the size of the system increases. We therefore adopt a deep multi-agent actor-critic method which uses a \emph{centralised but factored critic} to rehearse coordination ahead of execution. Results show that coordination is achieved at scale, with minimal information and communication infrastructure requirements, no interference with daily activities, and privacy protection. Significant savings are obtained for energy users, the distribution network and greenhouse gas emissions. Moreover, training times are nearly 40 times shorter than with a previous state-of-the-art reinforcement learning approach without the factored critic for 30 homes.

eess.SY↗

arXiv2023

An Insurance Paradigm for Improving Power System Resilience via Distributed Investment

Extreme events, exacerbated by climate change, pose significant risks to the energy system and its consumers. However there are natural limits to the degree of protection that can be delivered from a centralised market architecture. Distributed energy resources provide resilience to the energy system, but their value remains inadequately recognized by regulatory frameworks. We propose an insurance framework to align residual outage risk exposure with locational incentives for distributed investment. We demonstrate that leveraging this framework in large-scale electricity systems could improve consumer welfare outcomes in the face of growing risks from extreme events via investment in distributed energy.

econ.GN↗

arXiv2022

Coordination of resources at the edge of the electricity grid: systematic review and taxonomy

This paper proposes a novel taxonomy of coordination strategies for distributed energy resources at the edge of the electricity grid, based on a systematic analysis of key literature trends. The coordination of distributed energy resources such as decentralised generation and flexibility sources is critical for decarbonising electricity and achieving climate goals. The literature on the topic is growing exponentially; however, there is ambiguity in the terminology used to date. We seek to resolve this lack of clarity by synthesising the categories of coordination strategies in a novel exhaustive, mutually exclusive taxonomy based on agency, information and game type. The relevance of these concepts in the literature is illustrated through a systematic literature review of 84,741 publications using a structured topic search query. Then 93 selected coordination strategies are analysed in more detail and mapped onto this framework. Clarity on structural assumptions is key for selecting appropriate coordination strategies for differing contexts within energy systems. We argue that a plurality of complementary strategies is needed to coordinate energy systems' different components and achieve deep decarbonisation.

eess.SY↗

arXiv2021

On the Design of an Insurance Mechanism for Reliability Differentiation in Electricity Markets

Securing an adequate supply of dispatchable resources is critical for keeping a power system reliable under high penetrations of variable generation. Traditional resource adequacy mechanisms are poorly suited to exploiting the growing flexibility and heterogeneity of load enabled by advancements in distributed resource and control technology. To address these challenges this paper develops a resource adequacy mechanism for the electricity sector utilising insurance risk management frameworks that is adapted to a future with variable generation and flexible demand. The proposed design introduces a central insurance scheme with prudential requirements that align diverse consumer reliability preferences with the financial objectives of an insurer-of-last-resort. We illustrate the benefits of the scheme in (i) differentiating load by usage to enable better management of the system during times of extreme scarcity, (ii) incentivising incremental investment in generation infrastructure that is aligned with consumer reliability preferences and (iii) improving overall reliability outcomes for consumers.

eess.SY↗

arXiv2020

Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering

Among the various market structures under peer-to-peer energy sharing, one model based on cooperative game theory provides clear incentives for prosumers to collaboratively schedule their energy resources. The computational complexity of this model, however, increases exponentially with the number of participants. To address this issue, this paper proposes the application of K-means clustering to the energy profiles following the grand coalition optimization. The cooperative model is run with the "clustered players" to compute their payoff allocations, which are then further distributed among the prosumers within each cluster. Case studies show that the proposed method can significantly improve the scalability of the cooperative scheme while maintaining a high level of financial incentives for the prosumers.

cs.CE↗

arXiv2020

Electric Vehicle transition in the UK

This paper provides an overview of the electric vehicle transition in the UK. The spatial disparity in the uptake of BEVs across the different regions is analysed using historic BEV sales. A forecast for future growth in BEVs (ignoring the impact of Covid-19) is performed using an s-curve model. Currently, South East England and Greater London have the highest BEV sales as a percentage of new vehicle sales. The spatial distribution of EV chargers across the different regions is also analysed. The spatial analysis clearly shows the regional disparity in the uptake of EV. South East England has the highest number of public chargers excluding Greater London. However, if we consider the number of EVs in that region, it has the second-lowest ratio of approx. 1 charger per 10 BEV. The lowest ratio being 0.8 in the West Midlands.

cs.CY↗

arXiv2020

Predictive Control of Rural Microgrids with Temperature-dependent Battery Degradation Cost

Off-grid systems have emerged as a sustainable and cost-effective solution for rural electrification. In sub-Sarahan Africa (SSA), a great number of solar-hybrid microgrids have been installed or planned, operating stand-alone or grid-tied to a weak grid. Presence of intermittent energy sources necessitates the provision of energy storage for system balancing. Reliability and economic performance of those rural microgrids strongly depend on specific control strategies. This work develops a predictive control framework dedicated to rural microgrids incorporating a temperature-dependent battery degradation model. Based on a scalable DC PV-battery microgrid, the realistic simulation shows its superior performance in the reliability improvement and cost reduction. Compared with the day-ahead control without the temperature-dependent battery degradation model, this control strategy can improve the reliability by 5.5% and extend the lead-acid battery life time by 26%, equivalent to lowering the levelised cost of electricity (LCOE) by 13%.

eess.SY↗

arXiv2019

A Stochastic Model for Uncontrolled Charging of Electric Vehicles Using Cluster Analysis

This paper proposes a probabilistic model for uncontrolled charging of electric vehicles (EVs). EV charging will add significant load to power systems in the coming years and, due to the convenience of charging at home, this is likely to occur in residential distribution systems. Estimating the size and shape of the load will allow necessary reinforcements to be identified. Models predicting EV charging are usually based on data from travel surveys, or from small trials. Travel surveys are recorded by hand and typically describe conventional vehicles, but represent a much larger and more diverse sample of the population. The model here utilizes both sources: trial data to parameterize the model, and survey data as the model input. Clustering is used to identify modes of vehicle use, thus reducing vehicle use to a single parameter -- which can be incorporated into the model without adding significant computational burden. Two case studies are included: one investigating the aggregated charging of 50 vehicles, and one predicting the increase in after diversity maximum demand for different regions of the UK.

eess.SP↗

arXiv2019

Estimation of the Shapley Value of a Peer-to-Peer Energy Sharing Game using Coalitional Stratified Random Sampling

Various peer-to-peer energy markets have emerged in recent years in an attempt to manage distributed energy resources in a more efficient way. One of the main challenges these models face is how to create and allocate incentives to participants. Cooperative game theory offers a methodology to financially reward prosumers based on their contributions made to the local energy coalition using the Shapley value, but its high computational complexity limits the size of the game. This paper explores a stratified sampling method proposed in existing literature for Shapley value estimation, and modifies the method for a peer-to-peer cooperative game to improve its scalability. Finally, selected case studies verify the effectiveness of the proposed coalitional stratified random sampling method and demonstrate results from large games.

cs.GT↗

arXiv2019

Stochastic Hosting Capacity in LV Distribution Networks

Hosting capacity is defined as the level of penetration that a particular technology can connect to a distribution network without causing power quality problems. In this work, we study the impact of solar photovoltaics (PV) on voltage rise. In most cases, the locations and sizes of the PV are not known in advance, so hosting capacity must be considered as a random variable. Most hosting capacity methods study the problem considering a large number of scenarios, many of which provide little additional information. We overcome this problem by studying only cases where voltage constraints are active, with results illustrating a reduction in the number of scenarios required by an order of magnitude. A linear power flow model is utilised for this task, showing excellent performance. The hosting capacity is finally studied as a function of the number of generators connected, demonstrating that assumptions about the penetration level will have a large impact on the conclusions drawn for a given network.

math.OC↗

arXiv2018

The Value of Reactive Power for Voltage Control in Lossy Networks

Reactive power has been proposed as a method of voltage control for distribution networks, providing a means of increasing the amount of energy transferred from distributed generators to the bulk transmission network. The value of reactive power can therefore be measured according to an increase in transferred energy, where the transferred energy is defined as the total generated energy, less the total network losses. If network losses are ignored, an error in the valuation of a given amount of reactive power will be observed (leading to reactive power provision being under- or over-valued). The non-linear analytic solution of a two-bus network is studied, and non-trivial upper and lower bounds are determined for this `valuation error'. The properties predicted by this two-bus network are demonstrated to hold on a three-phase unbalanced distribution test feeder with good accuracy. This allows for an analytic assessment of the importance of losses in the valuation of reactive power in arbitrary networks.

math.OC↗

arXiv2017

Numerical Analysis of National Travel Data to Assess the Impact of UK Fleet Electrification

Accurately predicting the future power demand of electric vehicles is important for developing policy and industrial strategy. Here we propose a method to create a representative set of electricity demand profiles using survey data from conventional vehicles. This is achieved by developing a model which maps journey and vehicle parameters to an energy consumption, and applying it individually to the entire data set. As a case study the National Travel Survey was used to create a set of profiles representing an entirely electric UK fleet of vehicles. This allowed prediction of the required electricity demand and sizing of the necessary vehicle batteries. Also, by inferring location information from the data, the effectiveness of various charging strategies was assessed. These results will be useful in both National planning, and as the inputs to further research on the impact of electric vehicles.

physics.soc-ph↗