Location Awareness in Multi-Agent Control of Distributed Energy Resources

The integration of Distributed Energy Resource (DER) technologies such as heat pumps, electric vehicles and small-scale generation into the electricity grid at the household level is limited by technical constraints. This work argues that location is an important aspect for the control and integration of DER and that network topology can inferred without the use of a centralised network model. It addresses DER integration challenges by presenting a novel approach that uses a decentralised multi-agent system where equipment controllers learn and use their location within the low-voltage section of the power system. Models of electrical networks exhibiting technical constraints were developed. Through theoretical analysis and real network data collection, various sources of location data were identified and new geographical and electrical techniques were developed for deriving network topology using Global Positioning System (GPS) and 24-hour voltage logs. The multi-agent system paradigm and societal structures were examined as an approach to a multi-stakeholder domain and congregations were used as an aid to decentralisation in a non-hierarchical, non-market-based approach. Through formal description of the agent attitude INTEND2, the novel technique of Intention Transfer was applied to an agent congregation to provide an opt-in, collaborative system. Test facilities for multi-agent systems were developed and culminated in a new embedded controller test platform that integrated a real-time dynamic electrical network simulator to provide a full-feedback system integrated with control hardware. Finally, a multi-agent control system was developed and implemented that used location data in providing demand-side response to a voltage excursion, with the goals of improving power quality, reducing generator disconnections, and deferring network reinforcement. The resulting communicating and self-organising energy agent community, as demonstrated on a unique hardware-in-the-loop platform, provides an application model and test facility to inspire agent-based, location-aware smart grid applications across the power systems domain

Creating protective space for innovation in electricity distribution networks in Great Britain: the politics of institutional change

Innovation in electricity distribution networks will be an important element in the transition to a sustainable low-carbon energy system. The nature of networks as regulated monopolies means that the locus of the evolution of protective space for innovation is regulatory institutions, and that the politics of creating protective space is the politics of institutional change. In this paper, I examine the case of Britain, where protective space for research, development and demonstration projects was created over the course of the 2000s in the form of funding mechanisms within the regulatory regime. The case study is used to test structural and discursive theories of gradual institutional change. I conclude that these theoretical frameworks are consistent with the evidence, but that the characterisations of regime actors and of dominant paradigms are insufficiently flexible. I also conclude that the framework for innovation in the British regulator remains incomplete

Mathematical optimization techniques for demand management in smart grids

The electricity supply industry has been facing significant challenges in terms of meeting the projected demand for energy, environmental issues, security, reliability and integration of renewable energy. Currently, most of the power grids are based on many decades old vertical hierarchical infrastructures where the electric power flows in one direction from the power generators to the consumer side and the grid monitoring information is handled only at the operation side. It is generally believed that a fundamental evolution in electric power generation and supply system is required to make the grids more reliable, secure and efficient. This is generally recognised as the development of smart grids. Demand management is the key to the operational efficiency and reliability of smart grids. Facilitated by the two-way information flow and various optimization mechanisms, operators benefit from real time dynamic load monitoring and control while consumers benefit from optimised use of energy. In this thesis, various mathematical optimization techniques and game theoretic frameworks have been proposed for demand management in order to achieve efficient home energy consumption scheduling and optimal electric vehicle (EV) charging. A consumption scheduling technique is proposed to minimise the peak consumption load. The proposed technique is able to schedule the optimal operation time for appliances according to the power consumption patterns of the individual appliances. A game theoretic consumption optimization framework is proposed to manage the scheduling of appliances of multiple residential consumers in a decentralised manner, with the aim of achieving minimum cost of energy for consumers. The optimization incorporates integration of locally generated and stored renewable energy in order to minimise dependency on conventional energy. In addition to the appliance scheduling, a mean field game theoretic optimization framework is proposed for electric vehicles to manage their charging. In particular, the optimization considers a charging station where a large number of EVs are charged simultaneously during a flexible period of time. The proposed technique provides the EVs an optimal charging strategy in order to minimise the cost of charging. The performances of all these new proposed techniques have been demonstrated using Matlab based simulation studies

A Critical Investigation into Whole System Transitions to Low Carbon Futures and New Sources of Energy Flexibility in Great Britain's Electricity Sector

Great Britain’s (GB) electricity sector is transitioning to low carbon futures in response to various pressures including legally binding carbon emission targets while ensuring security of supply. Such transitions are likely to focus on a mix of inflexible low carbon generation and new sources of energy flexibility, e.g. demand side flexibility, storage and/or interconnection. Existing studies recognise that transitions are uncertain with actors across the whole sector playing a role. However, they suggest tidy and clearly delineated futures and fail to fully capture the messiness emerging from actor interactions. Drawing on transitions research concepts including the Multi-level Perspective, whole system analysis, architectural innovation, power and discourses, this study critically investigates whole system transitions to low carbon futures and new sources of energy flexibility in GB’s electricity sector. Data were collected via semi-structured interviews with 28 senior figures across the sector and analysed using thematic coding and discourse analysis. This study shows that five futures are articulated representing five discourse coalitions (1) ‘Market-based’, (2) ‘Network-focussed’, (3) ‘Policy-driven’, (4) ‘Consumer-centric’; and (5) ‘Prosumer-led’. These futures are messy because actors hold a plurality of views and cannot be simply marshalled into discourse coalitions. This underscores the complexity of electricity sector transitions and reveals important issues such as different ontologies and framings of energy flexibility. By investigating contemporary energy transition discourses, the study argues that a system level understanding of transitions and changes in future making practices currently dominated by quantitative modelling analyses and fixed transition frameworks are essential to effectively manage transitions. Further research is needed to investigate and find ways to better attend to the messiness and multiplicity of energy transitions from a whole systems perspective. This exploratory study is situated in a broader landscape of transitions research about energy futures and provides useful recommendations for both industry and academic communities

A Case Study of Existing Peer-to-Peer Energy Trading Platforms: Calling for Integrated Platform Features

The emergence of distributed energy has led to a change in the role of the consumer in the traditional sense over the past decade. The proliferation of emerging generators and distributors has created opportunities for a more decentralised and open energy market. In particular, the emergence of peer-to-peer (P2P) energy trading models, challenged by the surge in demand for sustainable energy, has eliminated the need for intermediaries in energy transactions between consumers, producers, and sellers. Due to the great promise of sustainable energy, both in terms of its contribution to the environment and production costs, this paper reviews a number of well-known P2P energy trading platforms to understand what makes P2P energy trading platforms more functional. As a result, areas for consideration were identified and grouped into five themes: (1) set-up, (2) market, (3) information, (4) price, and (5) regulation

Multi-agent reinforcement learning for the coordination of residential energy flexibility

This 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

The governance of retail energy services in the UK: a framework for the future

This Working Paper is the first output of research on retail market governance in the theme on “Decision making”. Retail markets are the main commercial interface for most people with the energy system. Current retail energy market governance in the UK is characterized by a quite complex mix of arrangements that have evolved over time. The scale of governance is increasingly complex, for both technical and political reasons, with a trend towards multi-level governance. The role of EU institutions has increased and this seemed set to continue until the EU Referendum; some energy governance is now devolved (although to different extents in Scotland, Wales and Northern Irelandii); and some local government is beginning to play a more active role. However, the principal level is still the nation state, and that is therefore our predominant focus