9 research outputs found

    Electric Vehicle (EV)-Assisted Demand-Side Management in Smart Grid

    Get PDF
    While relieving the dependency on diminishing fossil fuels, Electric Vehicles (EVs) provide a promising opportunity to realise an eco-friendly and cost-effective means of transportation. However, the enormous electricity demand imposed by the wide-scale deployment of EVs can put power infrastructure under critical strain, potentially impacting the efficiency, resilience, and safety of the electric power supply. Interestingly, EVs are deferrable loads with flexible charging requirements, making them an ideal prospect for the optimisation of consumer demand for energy, referred to as demand-side management. Furthermore, with the recent introduction of Vehicle-to-Grid (V2G) technology, EVs are now able to act as residential battery systems, enabling EV customers to store energy and use them as backup power for homes or deliver back to the grid when required. Hence, this thesis studies Electric Vehicle (EV)-assisted demand-side management strategies to manage peak electricity demand, with the long-term objective of transforming to a fully EV-based transportation system without requiring major upgrades in existing grid infrastructure. Specifically, we look at ways to optimise residential EV charging and discharging for smart grid, while addressing numerous requirements from EV customer's perspective and power system's perspective. First, we develop an EV charge scheduling algorithm with the objective of tracking an arbitrary power profile. The design of the algorithm is inspired by water-filling theory in communication systems design, and the algorithm is applied to schedule EV charging following a day-ahead renewable power generation profile. Then we extend that algorithm by incorporating V2G operation to shape the load curve in residential communities via valley-filling and peak-shaving. In the proposed EV charge-discharge algorithm, EVs are distributedly coordinated by implementing a non-cooperative game. Our numerical simulation results demonstrate that the proposed algorithm is effective in flattening the load curve while satisfying all heterogeneous charge requirements across EVs. Next, we propose an algorithm for network-aware EV charging and discharging, with an emphasis on both EV customer economics and distribution network aspects. The core of the algorithm is a Quadratic Program (QP) that is formulated to minimise the operational costs accrued to EV customers while maintaining distribution feeder nodal voltage magnitudes within prescribed thresholds. By means of a receding horizon control approach, the algorithm implements the respective QP-based EV charge-discharge control sequences in near-real-time. Our simulation results demonstrate that the proposed algorithm offers significant reductions in operational costs associated with EV charging and discharging, while also mitigating under-voltage and over-voltage conditions arising from peak power flows and reverse power flows in the distribution network. Moreover, the proposed algorithm is shown to be robust to non-deterministic EV arrivals and departures. While the previous algorithm ensures a stable voltage profile across the entire distribution feeder, it is limited to balanced power distribution networks. Therefore, we next extend that algorithm to facilitate EV charging and discharging in unbalanced distribution networks. The proposed algorithm also supports distributed EV charging and discharging coordination, where EVs determine their charge-discharge profiles in parallel, using an Alternating Direction Method of Multipliers (ADMM)-based approach driven by peer-to-peer EV communication. Our simulation results confirm that the proposed distributed algorithm is computationally efficient when compared to its centralised counterpart. Moreover, the proposed algorithm is shown to be successful in terms of correcting any voltage violations stemming from non-EV load, as well as, satisfying all EV charge requirements without causing any voltage violations

    Modelling the transition to a low-carbon energy supply

    Get PDF
    PhD ThesisA transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world - especially in problematic regions unable to cope with these conditions. However, the movement to a low-carbon energy supply can not happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required, however, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. A well choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods. Specifically, we investigate the following applications: 1. Predictions are made to predict electricity demand in the short-term. We model the impact that poor predictions have on investments in electricity generators and utilisation over the long-term. We find that poor short-term predictions lead to a higher proportion of coal, gas, and nuclear power plants. 2. We devise a long-term carbon tax for the United Kingdom using a genetic algorithm approach. We find multiple strategies that can minimise both long-term carbon emissions and electricity cost. 3. Oligopolies can have a detrimental effect on an electricity market by raising electricity prices without an increase in benefit to users. Reinforcement learning can be used to devise intelligent bidding strategies which are based upon forecasts and predictions of other agent behaviour to maximise revenues. These behaviours can not be modelled through traditional rule-based algorithms. We use reinforcement learning to model strategic bidding into the electricity market, and find ways to limit the impact of this strategic bidding through a market cap. We find that introducing a market cap can significantly reduce the ability for oligopolies to manipulate the market. These studies require a number of core challenges to be addressed to ensure our agent-based model, ElecSim, is fit for purpose. These are: 1. Development of the ElecSim model, where the replication of the pertinent features of the electricity market was required. For example, generation company investment behaviour, electricity market design and temporal granularity. We find that the temporal granularity of the model has a large impact on accuracy of the model, but with suitably chosen representative days calibration is possible to accurately model a time period. 2. The complexity of a model increases with the replication of increasing market features. Therefore, optimisation of the code was required to maintain computational tractability, to allow for multiple scenario runs. This enabled us to run multiple iterations to train different machine learning techniques. 3. Once the model has been developed, its long-term behaviour must be verified to ensure accuracy. In this work, cross-validation was used to both validate and calibrate ElecSim. We are able to accurately model a historic period observed in the real-world with this approach 4. To ensure that the salient parameters are found, a sensitivity analysis was run. In addition, various example scenarios were generated to show the behaviour of the model. We find that the input parameters, such as the cost of capital have a disproportionate effect on the long-term electricity mix. The findings outlined previously demonstrate the ability for artificial intelligence, machine learning and agent-based models to perform complex analyses in an uncertain system. We find that solely focusing on the accuracy of machine learning techniques, for instance, misses out on a significant amount research potential. We instead argue, that by further developing these research themes, we are able to better understand the electricity market system of the United Kingdom

    Real-time and semantic energy management across buildings in a district configuration

    Get PDF
    Existing building and district energy management strategies are in urgent need of an overhaul to meet the energy and environmental challenges of the 21st Century. The immense growth in the availability of data through the Internet of Things (IoT), the decentralisation of energy generation, and the increasing power of Artificial Intelligence (AI) presents an opportunity to achieve a paradigm shift in the way energy is controlled and managed. To contribute to this field, this PhD project undertook a thorough literature review combined with a participatory, action research approach to identify and understand the key challenges faced by facility managers and to identify potential areas of improvement. Following this, the PhD thesis aims to tackle three key research areas using simulated case study experiments. These aim to optimise thermal energy management within buildings at a zone-level, control energy generation at a district-level, and combine the learnings from these two experiments with a holistic energy management solution that controls both the energy supply and demand at a building and district-level. At a building-level, a model predictive control approach combining a genetic algorithm and surrogate artificial neural network is used. A predictive and context aware controller is able to produce 24 hour heating set point schedules for each zone within a building. This approach achieved an energy saving of 18% whilst maintaining thermal comfort for users. The methodology also had the capability to adapt to dynamic energy pricing tariffs and capable of optimising for energy cost by shifting load to cheaper periods. At a district-level, a predictive, optimisation-based approach was developed to determine the operation of a multi-vector, district heating, energy centre. When thermal storage and several generation sources are available, alongside variable renewable energy generation and building demand, static, rulebased controllers cannot perform adequately in all conditions. Instead, the optimisation-based approach, developed in this thesis, was able to increase profit to the energy centre by 45% as well as decrease CO2 emissions whist adapting to errors in energy demand and supply forecasting. Finally, the most significant contribution of this thesis was provided by efvii fectively combining the approaches made at a building and district-level. This case study aimed to simultaneously control the energy generation of the district energy centre, alongside the thermal demand of one of the buildings within the district. The additional flexibility provided by partially controlling the building demand led to a further 8% increase in profit to the energy centre, compared to just optimising energy supply. This demonstrates the vital importance of treating the consumer as an integral, active component of the energy system. It is argued that the contributions made throughout this thesis will become more relevant when coupled with additional research fields. This includes the growth in available data from IoT sources, advanced AI including unsupervised learning, and utilising a shared semantic description of smart building, smart energy and smart city concepts. At its core, this thesis aims to demonstrate that ‘thinking’, predictive, control strategies, that are more context-aware, can achieve significant benefits over the traditional reactive, rule-based controllers of the past

    Market Engineering

    Get PDF
    This open access book provides a broad range of insights on market engineering and information management. It covers topics like auctions, stock markets, electricity markets, the sharing economy, information and emotions in markets, smart decision-making in cities and other systems, and methodological approaches to conceptual modeling and taxonomy development. Overall, this book is a source of inspiration for everybody working on the vision of advancing the science of engineering markets and managing information for contributing to a bright, sustainable, digital world. Markets are powerful and extremely efficient mechanisms for coordinating individuals’ and organizations’ behavior in a complex, networked economy. Thus, designing, monitoring, and regulating markets is an essential task of today’s society. This task does not only derive from a purely economic point of view. Leveraging market forces can also help to tackle pressing social and environmental challenges. Moreover, markets process, generate, and reveal information. This information is a production factor and a valuable economic asset. In an increasingly digital world, it is more essential than ever to understand the life cycle of information from its creation and distribution to its use. Both markets and the flow of information should not arbitrarily emerge and develop based on individual, profit-driven actors. Instead, they should be engineered to serve best the whole society’s goals. This motivation drives the research fields of market engineering and information management. With this book, the editors and authors honor Professor Dr. Christof Weinhardt for his enormous and ongoing contribution to market engineering and information management research and practice. It was presented to him on the occasion of his sixtieth birthday in April 2021. Thank you very much, Christof, for so many years of cooperation, support, inspiration, and friendship

    Secure and Sustainable Energy System

    Get PDF
    This special issue aims to contribute to the climate actions which called for the need to address Greenhouse Gas (GHG) emissions, keeping global warming to well below 2°C through various means, including accelerating renewables, clean fuels, and clean technologies into the entire energy system. As long as fossil fuels (coal, gas and oil) are still used in the foreseeable future, it is vital to ensure that these fossil fuels are used cleanly through abated technologies. Financing the clean and energy transition technologies is vital to ensure the smooth transition towards net zero emission by 2050 or beyond. The lack of long‐term financing, the low rate of return, the existence of various risks, and the lack of capacity of market players are major challenges to developing sustainable energy systems.This special collected 17 high-quality empirical studies that assess the challenges for developing secure and sustainable energy systems and provide practical policy recommendations. The editors of this special issue wish to thank the Economic Research Institute for ASEAN and East Asia (ERIA) for funding several papers that were published in this special issue

    Dayahead Electricity Pricing for a Heterogeneous Microgrid Under Arbitrary Utility and Cost Structures

    No full text
    corecore