1,720,990 research outputs found
Inverter reactive power control for the integration of distributed generation
Full text linkThis thesis sets out to answer the following question:
To what extent can inverter reactive power control, combined with voltage regulators, increase network effciency and hosting capacity of distributed generation?
The first part of this thesis is concerned with analytic methods for studying this question; therefore, a two-bus network model is used. Two aspects of the problem are considered using this model. Firstly, the maximum power transfer capabilities of distributed generation are considered. This bounds the maximum generation of a network, even if the marginal costs of both real and reactive power are zero. It is shown that losses caused by the reactive power and generation result in maximum power transfer before stability limits, in networks with high impedance (R=X) ratios.
Secondly, analytic bounds on a proposed 'relative energy loss fraction' are derived in closed form, using the LinDistFlow approximate solution. The bounds are validated by comparison with both the exact solution of the two-bus equations, and on a set of unbalanced distribution networks. The relative energy loss fraction was found to be as high as 30%, with the approximate bounds able to estimate the true bounds to within 5%.
The next part of this thesis studies how reactive power and taps can be used to increase hosting capacity under uncertainty in the locations and sizes of domestic generation. A linear method is proposed that is shown to reduce the computing runtime by as much as 1000 times. A proposed hosting capacity sensitivity is shown empirically to correlate well with the error caused by linearization. Centralised control of taps and regulators are shown to increase hosting capacity by as much as 70%.
Finally, an optimization is proposed to study the impact of taps and domestic inverter control on the feeder total power draw. The use of regulator control in isolation results in benefits of up to a 5% reduction in feeder power; the use of inverter reactive power control further reduces the feeder power by up to 0.5% of the load. A control scheme is proposed with a reduced communications overhead, which can obtain up to 97% of the potential benefits of full inverter control
Multi-scale spatio-temporal framework for characterising design and operational parameters of the electrical power system
Full text linkThis thesis explores the key research question as stated below:
• KEY RESEARCH QUESTION: Given the increasing adoption of distributed variable energy resources and smart appliances onto the electrical power system, what methods can be developed to help electrical power system planners and operators gain insight into the design and operational parameters of the electrical power system for purposes of enabling greater adoption of distributed variable energy resources and smart appliances in an efficient and sustainable way?
This key research question covers a number of important elements that are essential for enabling the adoption of efficient and sustainable design and operational techniques to be applied on the modern electrical power system. Therefore, in order to arrive at logical answers in respect of the key research question, three further sub-questions were explored, namely:
• SUB-QUESTION 1: In view of increasing presence of distributed variable energy resources and smart appliances on the electrical power system, how do the planning and design parameters of a modern electrical power system vary when such parameters are characterised at different customer aggregation levels?
• SUB-QUESTION 2: With the objective to accurately estimate the planning and design parameters of a modern electrical power system, what effect does the use of time-series customer load data with different time granularities have on the estimated parameters?
• SUB-QUESTION 3: When considering a real electrical power system with a high presence of distributed variable energy resources and smart appliances, how do the design and operational parameters of a modern electrical power system vary when such parameters are characterised at different customer aggregation levels when using time-series customer load data with different time granularities?
It is important to note that sub-questions 1 and 2 relate to characterising planning and design parameters of the power system, which are derived from characterisations based on customer load profiles. Sub-question 1 relates to performing such characterisations at different customer aggregation levels, whereas sub-question 2 relates to the characterisations considered when using customer load profiles with different time granularities. The difference in the focus of sub-question 3 in relation to sub-questions 1 and 2 is that sub-question 3 seeks to characterise design and operational parameters on a real network with a composition of customer load profiles added to and/or removed from the network at different customer aggregation levels when using customer load profiles with different time granularities. So, sub-question 3 pertains to characterisations based on a real network with the inclusion of distributed variable energy resources and smart appliances.
The research work covered in this thesis pertains to the characterisation of design and operational parameters of the electrical power system, with a particular focus on distribution networks with significant penetration of distributed variable renewable energy and smart appliances.
The characterisation technique contributed by this thesis bears two major focus areas, namely: 1) to characterise the planning and design parameters of the electrical power system, and also, 2) to characterise the operational parameters of the electrical power system. For both focus areas, i.e., planning & design and operation, a characterisation of specific parameters is considered at different customer aggregation levels, using time-series customer load data with different time granularities. It is because of the spatial/sizing consideration of customer load aggregation and the temporal aspect of the time granularity of time-series customer load data that the over-arching characterisation technique which forms the integral contribution of this thesis is termed spatio-temporal characterisation framework.
CHARACTERISATION BASED ON LOAD PROFILES
The spatio-temporal characterisation framework is thus applied to several load profiles datasets, namely: a synthetic dataset (generated using an Excel Workbook model developed by the Centre for Renewable Energy Systems Technology (CREST) of Loughborough University) and real datasets from four different jurisdiction areas (i.e., the United Kingdom (UK, Northern Grid), United States of America (USA, Texas), Belgium (Mons), and Australia (Ausgrid)). This study characterises planning and design parameters such as the after-diversity maximum demand (ADMD, a measure for diversity), load variance (a measure for load variability), and the load factor (which is useful for calculating the expected network power losses). All of these parameters are characterised in relation to changing aggregation levels and time-scales. All the time-series customer load profiles datasets used in this thesis are representative of residential customers. Both the ADMD (i.e. per customer capacity requirement) and load variance were found to asymptotically decrease toward a settling value, when both the size of customer groupings and averaging time intervals approached large numbers. Conversely, the load factor asymptotically increases toward a settling value, when both the size of home groupings and averaging time intervals increase.
CHARACTERISATION BASED ON A DISTRIBUTION NETWORK
In order to understand the impact of the size of customer load aggregation and the time granularities of load profiles datasets on the design and operational parameters of a distribution network, the spatio-temporal characterisation framework was further applied to a UK low voltage (LV) network with a high presence of distributed solar PV based renewable energy and plug-in electric vehicles (PEVs) based flexible demand. This study characterises the network diversified peak demand, load variability, power losses, and the load-dependency loss factor, when different sizes of home groupings are either added to or disconnected from the network at a time, using time-series profiles (for the load, solar PV, and PEV charging) with different time granularities. The load-dependency loss factor is a newly introduced theoretical parameter, defined as the differential change in losses at a given aggregation level divided by the total demand at full load, which quantifies how the change in losses implicitly compares to the differential change in network load when varying aggregation levels for a given time granularity. The results for the characterisations based on a real distribution network are very comprehensive and they shed light on the appropriate time granularity of customer load data that can be used for purposes of accurately planning and designing a power system with a high composition of distributed variable energy resources and smart appliances. The results also give insight into the requisite time scales of managing and operating the modern power system. A detailed summary of these results is presented in Chapter 6 under sub-section 6.2.2. What is most noteworthy is that, additional to the aspects of customer aggregation levels and the time granularity of customer load profiles, the inclusion of solar PV and electric vehicles had a great impact on the characterisations of network diversified peak demand, load variability, power losses, and the load-dependency loss factor, particularly when the composition of such resources is varied on the network at different aggregation levels.
The spatio-temporal characterisation framework developed in this thesis provides a useful tool for distribution network planners and operators to derive planning and design parameters of a distribution network with a particular load size on the basis of the per-customer capacity requirement, and devise network-specific active management schemes with carefully tailored control time scales on the basis of load variability. The characterisation of network power losses allows the distribution network operators to assess the network performance and implement appropriate network interventions for optimal network operation
Spatial planning of a bulk power system with high residential heat pump adoption
Full text linkThis thesis investigates the impact of widespread residential heat pump use on bulk power system planning at high spatial resolution.
The electrification of residential heating offers a challenge for power systems in drastically increased peak demand. At the same time, flexible heating could reduce demand peaks and increase use of renewable generation. While studies have identified spatial variation in heating demand and flexibility potential, these differences have not yet been incorporated into bulk power system planning. By incorporating spatial heating data and projections into capacity expansion planning, this thesis accounts for spatial variation in a future with high residential heat pump adoption. The research contributions are divided into four chapters. The first two research chapters focus on heating demand, and the final two research chapters focus on heating flexibility.
Reanalysis weather data is used to scale limited heat pump demand data from field trials to regional hourly demand projections. A power system planned with regional hourly heat pump demand is compared to a system planned with conventionally used, spatially uniform heat pump demand. Cost-optimal placement of generation and storage capacity requires regional hourly heat pump demand data, and using spatially uniform heat pump demand for planning leads to load shedding.
Space heating flexibility potential in the current residential housing stock is quantified using a top-down, data-driven method based on historical weather data and sub-national annual gas demand. This heating flexibility potential is incorporated into a capacity expansion model to assess where flexibility offers the most value to the bulk power system. Heating flexibility is most valuable in regions with moderate thermal energy storage losses near demand centers; in low-carbon scenarios, heating flexibility is most valuable in regions with the lowest thermal
energy storage losses. A wide range of regional flexibility participation can achieve at least 95% of optimal flexibility savings.
This thesis demonstrates the importance of considering regional differences in heating demand and flexibility potential when planning bulk power systems. While this thesis focuses on the case study of Britain, the methods can be applied across cold and temperate countries transitioning from fossil fuel-based heating systems to electric heat pumps
High-level control algorithms for maximising energy efficiency of solar nano-grids designed for energy access
Full text linkPeer-to-peer interconnection of households having on-site batteries, four-port DC-DC converters and solar panels to form four-port DC-DC converter-enabled solar nano-grids is an emerging bottom-up approach for providing affordable energy access to rural areas. However, power losses which include battery charge and discharge losses, distribution losses and converter losses are one of the technical barriers hindering the practical deployment and operation of solar nano-grids in rural areas. Power losses reduce the energy efficiency of solar nano-grids. This research aimed to answer the following research question:
What control algorithms can be developed to address the problem of maximising the energy efficiency of four-port DC-DC converter-enabled solar nano-grids that are designed for energy access?
Solar nano-grids with star and hybrid distribution network configurations were considered in this research. Three novel high-level control algorithms were developed to answer the research question. The first one was a centralised control algorithm, the second one was a quasi-consensus based distributed control algorithm and the third one was a distributed state-of-charge based droop control algorithm. Numerical and simulation results carried out in Python showed the effectiveness of the developed control algorithms for addressing the research question through the considered solar nano-grid configurations.
The first and second control algorithms addressed the research question for the star solar nano-grid configuration through optimisation. Firstly, a high fidelity steady-state model of the solar nano-grid was developed and a new two-stage convex power loss optimisation problem was formulated. The formulated optimisation problem did not require either linearisation or convex relaxation of power flow equations, making it scalable and computationally efficient. Then, the first and second control algorithms were developed to solve the formulated optimisation problem in a centralised and quasi-distributed manner respectively.
The third algorithm addressed the research question for both the star and hybrid solar nano-grid configurations through balancing the state-of-charge of the batteries in a distributed manner. In this algorithm, household distribution voltages were adjusted in proportional to the state-of-charge of their local batteries, creating an automatic power flow between the households each time there was a slight change in the battery state-of-charge. Due to the automatic power flow between the households, the magnitude of current flow at every time instant in the solar nano-grid was low, thus, keeping the power losses to a minimum.
The present work aimed to contribute towards the universal energy access agenda by focussing specifically on the third target of the United Nation’s Sustainable Development Goal 7 - doubling the global rate of improvement in energy efficiency by 2030. There is a lot of work that still needs to be done to achieve this goal. Nonetheless, the present research has made a significant step towards improving energy efficiency by developing control algorithms that minimise the solar nano-grid power losses
Scalable spatial design of electricity access systems
Full text linkThis thesis explores spatially specific data and methods to design community-tailored electricity access systems at scale. It is motivated by the need to close the electricity access gap in rural low- and middle-income country contexts quickly and cheaply in line with the Sustainable Development Goals.
The majority of the 760 million people currently lacking electricity access live in rural areas of sub-Saharan Africa and South Asia. Electrifying these areas is challenging given their cultural diversity, remote nature, and sensitivity to affordability. Context-specific electrical designs are required to achieve uptake in these communities; however, such specificity can come at high cost. This thesis therefore tackles the challenge of the local specificity and global scale of the electricity access problem through practical spatial design methods. Two key data gaps are identified which must be filled in order to design least-cost energy access systems: locations of potential connection points, and their anticipated demands.
Home-level location data which are publicly available for potential connection points in off-grid areas are either aggregated at inadquate resolution for topology design or contain significant gaps, particularly in off-grid areas. To address this, citizen science and computer vision are applied to accelerate accurate home detection in satellite imagery. Through a large-scale online citizen science project, approximately 1,267 km2 of rural Kenya, Sierra Leone and Uganda was mapped at an average rate of 7 km2/day and an estimated cost of $20.84/km2. Home annotations produced through this work achieve a recall of 93% and precision of 49%, which can be increased to 69% through clustering. The clustered annotations were used to train a Faster R-CNN object detection algorithm, which achieves a precision of 67% and recall of 36%; this can be increased to 57% by training on raw annotations instead of clusters. The trained detector was found to map at a rate of 42,938 km2/day, proving the rapid mapping of rural unelectrified areas to be feasible at a global scale and low cost.
High-resolution residential demand data are similarly scarce in off-grid communities. Costly local surveys to understand electrical aspirations tend to produce inaccurate results, given the unfamiliarity of respondents with electricity. To overcome this, a bottom-up demand estimation approach rooted in existing empirical data is developed to achieve spatially-specific and realistic demand estimates for off-grid areas. A case study application of this approach in Sierra Leone was undertaken using Multiple Indicator Cluster Survey data. The results of this work validate the underlying premise of spatial variance of demand amplitude. The load profiles generated using this approach are found to approximate a Tier 3 load, despite a lack of Tier 3 appliances, leading to a critique of the definition of the Multi-Tier Framework for Measuring Energy Access.
These location and demand data are finally applied to home-level spatial grid design. Home locations are clustered into electricity communities, grid topologies are estimated using graph theory, generation types are selected through spatial analysis, and generation and storage are optimally sized for least cost. An approach is also developed to map design pathways through modular community grid expansions which allow for demand growth and autonomy. This framework was applied in a case study region in the Northern Province of Sierra Leone. In this region, 12 local micro-grids are identified as the best electrification solution in the near-term, with 11 outlying homes receiving solar home systems. Infrastructure sizing is presented for one micro-grid, where an initial design point of 50 kW of PV and 108 kWh of battery storage is found to meet anticipated low-end and mid-term demands without energy poverty risk. Three modular expansions of 30 kW PV and 65 kWh of storage are specified for installation in this micro-grid as demands evolve.
The work in this thesis focuses on sub-Saharan Africa, with particular attention paid to Kenya, Uganda, and Sierra Leone. Case studies in the thesis primarily focus on Sierra Leone; however, the methods are purposefully intended to be practical and generalizable across low- and middle-income countries requiring electricity access system design
Multi-agent reinforcement learning for the coordination of residential energy flexibility
Full text linkThis thesis investigates whether residential energy flexibility can be coordinated without sharing personal data at scale to achieve a positive impact on energy users and the grid.
To tackle climate change, energy uses are being electrified at pace, just as electricity is increasingly provided by non-dispatchable renewable energy sources. These shifts increase the requirements for demand-side flexibility. Despite the potential of residential energy to provide such flexibility, it has remained largely untapped due to cost, social acceptance, and technical barriers. This thesis investigates the use of multi-agent reinforcement learning to overcome these challenges.
This thesis presents a novel testing environment, which models electric vehicles, space heating, and flexible household loads in a distribution network. Additionally, a generative adversarial network-based data generator is developed to obtain realistic training and testing data. Experiments conducted in this environment showed that standard independent learners fail to coordinate in the partially observable stochastic environment. To address this, additional coordination mechanisms are proposed for agents to practise coordination in a centralised simulated rehearsal, ahead of fully decentralised implementation.
Two such coordination mechanisms are proposed: optimisation-informed independent learning, and a centralised but factored critic network. In the former, agents lean from omniscient convex optimisation results ahead of fully decentralised coordination. This enables cooperation at scale where standard independent learners under partial observability could not be coordinated. In the latter, agents employ a deep neural factorisation network to learn to assess their impact on global rewards. This approach delivers comparable performance for four agents and more, with a 34-fold speed improvement for 30 agents and only first-order computational time growth.
Finally, the impacts of implementing implicit coordination using these multi- agent reinforcement learning methodologies are modelled. It is observed that even without explicit grid constraint management, cooperating energy users reduce the likelihood of voltage deviations. The cooperative management of voltage constraints could be further promoted by the MARL policies, whereby their likelihood could be reduced by 43.08% relative to an uncoordinated baseline, albeit with trade-offs in other costs. However, while this thesis demonstrates the technical feasibility of MARL-based cooperation, further market mechanisms are required to reward all participants for their cooperation
Analytical methods for energy storage design in hybrid renewable systems
Full text linkThe need for storage design stems from rising greenhouse gas emissions, a key contributor to climate change. A significant portion of global greenhouse gas emissions originates from the energy sector due to fossil fuel usage. An alternative to fossil fuels is renewable energy. Renewable generation produces no emissions during operation and have lower life-cycle emissions compared to fossil fuel power plants. However, renewables are weather-dependent, leading to intermittent and non-dispatchable generation. This intermittency induces instability in the electricity system, while the lack of dispatchability results in curtailed generation and unmet demand. These challenges can be effectively managed through energy storage. Storage captures surplus energy during periods of high generation, and releases that energy during low generation, resulting in a more consistent generation output that can match the demand. Proper storage design ensures that storage can effectively manage renewable intermittency and provide dispatchability.
The thesis explores storage design, encompassing storage sizing, storage and solar sizing, and storage sizing and placement. First, an analytical method is proposed to size storage based on its largest cumulative charge or discharge. The method is applied to two case studies. The first study demonstrates that optimally sized storage does not have wasted capacity due to over-sizing, nor does it cause energy deficits due to under-sizing. The second study finds that increasing the storage size has diminishing returns on the additional storage energy provided to the system. Second, the thesis proposes a hybridized techno-economic method to size solar photovoltaic and lithium battery storage. The hybridized method uses an analytical method to define the size search space, and then employs an enumerative approach to explore the search space to find the lowest-cost system sizing. The method is applied to a solar-battery microgrid case study, which finds that solar and storage will take up a greater portion of the energy system as their costs come down, but the electricity grid remains essential for providing cost-effective flexibility. Third, the thesis proposes an analytical method for sizing and placing energy storage. The method uses optimal power flow to decide the power dispatch at each location. Then, the analytical method is employed to size and place storage based on the power dispatch. The method is applied to a case study on a village with wind and solar generation. The study finds that storage tends to be placed near large generation, large demand, or lines with high power flow. These works contribute to the purpose of the thesis, which is to gain a better understanding of storage design in the context of hybrid renewable systems
Rethinking private sector approaches to off-grid electrification. The case of Uganda
No full textThe lack of electricity access hits marginalized groups hardest, worsening poverty, inequality, and climate pressures. In Sub-Saharan Africa’s low-income countries, the private sector is a crucial actor in extending energy access. In Uganda for example, the growth of the private sector-led off-grid has significantly contributed to the rise in the national electrification rates from 5% in 2002 to 58% in 2023. However, as firms require profits, tough trade-offs persist between commercial viability and equitably reaching underserved communities who struggle to pay cost-reflective rates amidst deprivation. While well-meaning interventions from different stakeholders exist—NGOs, donors and public agencies supporting through subsidies and other measures—affordability limitations and differing institutional interests inevitably introduce critical tensions that stall progress. This thesis investigates pathways for private sector-led off-grid energy access initiatives to accelerate energy access at scale. With a specific focus on Uganda’s thriving off-grid ecosystem, the empirical research examines two interlinked challenges across three first-authored journal articles: i) aligning diverse stakeholder interests towards a shared goal of delivering financially viable electricity and ii) ensuring firm financial sustainability amidst financial limitations and external uncertainties. By addressing these issues, the thesis aims to shine a light on ways the private sector-driven off-grid sector can effectively contribute to expanding energy access in low-income countries while balancing profitability and social impact
The impact of domestic electric vehicle charging on electricity networks
Full text linkThis thesis investigates the impact that home charging of a large private fleet of electric vehicles would have on the power system.
A large multi-regional travel survey dataset is used to model vehicle use and charging spatially heterogeneously, and a selection of representative network models are used to assess the impact of charging on system operation. A stochastic data-driven model is proposed to model uncontrolled charging of vehicles, and convex optimisation is used to calculate the optimal smart charging strategy.
The power system is commonly broken down into the generation, transmission, and distribution systems. The operation of each of these systems will be impacted by the addition of EV charging to residential networks. A variety of objectives have been proposed for smart charging, each of which would protect the system in a different way. Existing research tends to focus on a single part of the system, and considers only the smart charging objective that most benefits that part of the system. Here, the three systems are modelled simultaneously, and a large range of smart charging objectives are investigated.
The value of explicit loss minimising smart charging is quantified, compared to a simpler and more standard load flattening algorithm. These results are used to propose a novel optimisation formulation which reduces losses without requiring extensive network information. The value of bi-directional smart charging is also quantified compared to uni-directional smart charging, in order to investigate the viability of residential vehicle-to-grid.
It is demonstrated that it is not possible to optimise the transmission level and distribution level systems simultaneously, and the penalty of only optimising for one is quantified. A method for finding a compromising solution between both system levels is proposed, which exploits the sections of the distribution where components are over-specified.
Two specific case studies are investigated. The majority of the analysis in the thesis is based on the GB power system, however the Texas system is also presented as a comparative case study
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