Distributed Algorithms for Peer-to-Peer Energy Trading

A S the proliferation of the ’sharing economy’ increases, its phenomenon is actively extending to the power grid, where energy consumers are motivated to use, produce, trade or share energy with the main grid and themselves. To optimise the potential of this changing era in smart grid, considering the complexity requirements of the individual distributed connected components, a distributed coordination algorithm is required to manage the large influx of energy as well as the altruistic goal of diverse energy producers. Furthermore, a trading platform is actively needed to implement these distributed algorithms to match the prosumers, coordinate their resources and maximise their utilities for increased profits and cost savings. This research investigates distributed algorithms for peer-to-peer energy trading and sharing (P2P-ETS) to facilitate discovery, communication and utility maximisation of peers who are trading energy in a P2P fashion. To begin, a four-layer system architectural model is proposed to categorise the key elements and technologies associated with the P2P-ETS. Then, constrained by as few assumptions as possible, while showing promising performance and key metrics, three distributed algorithms are developed to facilitate discovery, peer’s matching, data routing, energy transfer, and utility maximisation of the trading entities. These algorithms utilise only local information to solve the problem with promising results, complementing their presentation with simulations that demonstrate their effectiveness over imperfect communication links. Finally, based on these distributed algorithms, a software platform is developed to support the pairing of prosumers on the P2P-ETS platform. A case study based on real microgrid data is used to verify the performance of the platform which demonstrate increase in local energy consumption. Simulation results show that the developed platform is able to balance local generation and consumption and increase cost savings of 45% for prosumers that trade energy among themselves compared to trading with the power grid. This savings however varies depending on the participants on the platform

Prosumers Matching and Least-Cost Energy Path Optimisation for Peer-to-Peer Energy Trading

Potential benefits of peer-to-peer energy trading and sharing (P2P-ETS) include the opportunity for prosumers to exchange flexible energy for additional income, whilst reducing the carbon footprint. Establishing an optimal energy routing path and matching energy demand to supply with capacity constraints are some of the challenges affecting the full realisation of P2P-ETS. In this paper, we proposed a slime-mould inspired optimisation method for addressing the path cost problem for energy routing and the capacity constraint of the distribution lines for congestion control. Numerical examples demonstrate the practicality and flexibility of the proposed method for a large number of peers (15 – 2000) over existing optimised path methods. The result shows up to 15% cost savings as compared to a non-optimised path. The proposed method can be used to control congestion on distribution links, provide alternate paths in cases of disruption on the optimal path, and match prosumers in the local energy market

VirtElect: A Peer-to-Peer Trading Platform for Local Energy Transactions

An average UK electricity bill is made up of at least 60% service charge, with approximately 22% related to network characteristics including distance charge. This makes distance and network constraints important factors in matching prosumers on any peer-to-peer energy trading platform as assessed in this paper. To realise that, A platform -VirtElect, based on a double auction market is developed to support the matching interaction between prosumers. Case studies based on real microgrid data are used to verify the performance of the platform in demonstrating the potential of local energy consumption. Results show that it is possible to balance local energy generation and consumption, with little or no interaction with the utility grid. We also show that local energy trading is not only beneficial to the environment but also leads to a significant amount of cost savings of up to 45%, depending on the number of participants and their ratios on the platform

Consensus Algorithms and Deep Reinforcement Learning in Energy Market: A Review

Blockchain (BC) and artificial intelligence (AI) are often utilised separately in energy trading systems (ETS). However, these technologies can complement each other and reinforce their capabilities when integrated. This paper provides a comprehensive review of consensus algorithms (CA) of BC and deep reinforcement learning (DRL) in ETS. While the distributed consensus underpins the immutability of transaction records of prosumers, the deluge of data generated paves the way to use AI algorithms for forecasting and address other data analytic related issues. Hence, the motivation to combine BC with AI to realise secure and intelligent ETS. This study explores the principles, potentials, models, active research efforts and unresolved challenges in the CA and DRL. The review shows that despite the current interest in each of these technologies, little effort has been made at jointly exploiting them in ETS due to some open issues. Therefore, new insights are actively required to harness the full potentials of CA and DRL in ETS. We propose a framework and offer some perspectives on effective BC-AI integration in ETS

Distributed Adaptive Primal Algorithm for P2P-ETS over Unreliable Communication Links

Algorithms for distributed coordination and control are increasingly being used in smart grid applications including peer-to-peer energy trading and sharing to improve reliability and efficiency of the power system. However, for realistic deployment of these algorithms, their designs should take into account the suboptimal conditions of the communication network, in particular the communication links that connect the energy trading entities in the energy network. This study proposes a distributed adaptive primal (DAP) routing algorithm to facilitate communication and coordination among proactive prosumers in an energy network over imperfect communication links. The proposed technique employs a multi-commodity flow optimization scheme in its formulation with the objective to minimize both the communication delay and loss of energy transactional messages due to suboptimal network conditions. Taking into account realistic constraints relating to network delay and communication link capacity between the peers, the DAP routing algorithm is used to evaluate network performance using various figures of merit such as probability of signal loss, message delay, congestion and different network topologies. Further, we address the link communication delay problem by redirecting traffic from congested links to less utilized ones. The results show that the proposed routing algorithm is robust to packet loss on the communication links with a 20% reduction in delay compared with hop-by-hop adaptive link state routing algorith

Comparative Analysis of P2P Architectures for Energy Trading and Sharing

Rising awareness and emergence of smart technologies have inspired new thinking in energy system management. Whilst integration of distributed energy resources in micro-grids (MGs) has become the technique of choice for consumers to generate their energy, it also provides a unique opportunity to explore energy trading and sharing amongst them. This paper investigates peer-to-peer (P2P) communication architectures for prosumers’ energy trading and sharing. The performances of common P2P protocols are evaluated under the stringent communication requirements of energy networks defined in IEEE 1547.3-2007. Simulation results show that the structured P2P protocol exhibits a reliability of 99.997% in peer discovery and message delivery whilst the unstructured P2P protocol yields 98%, both of which are consistent with the requirements of MG applications. These two architectures exhibit high scalability with a latency of 0.5 s at a relatively low bandwidth consumption, thus, showing promising potential in their adoption for prosumer to prosumer communication

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation

Interlinked Computing in 2040 : Safety, Truth, Ownership and Accountability

Computer systems are increasingly linked together, with systems controlled by different parties cooperating to deliver services. Such links offer both huge benefits and possible risks. Both the potential benefits and risks may be magnified as novel technologies such as Artificial Intelligence are integrated into these toolchains. What are these risks, and how might we begin to address them? Using a Delphi-based method, we interviewed twelve experts at envisaging technology futures to gain insight into likely trends, their impact on society, and how we might start to mitigate negative impacts. From the results, we highlight five forecasts, and six possible interventions that could help. The forecasts include major challenges related to Artificial Intelligence and system complexity, particularly where these involve interactions between independent systems. Addressing these challenges using the suggested interventions offers a good strategy to prepare ourselves for 2040

Virtual microgrids: A management concept for peer-to-peer energy trading

The proliferation of distributed energy resources (DERs) and smart technologies has enabled the integration of microgrid generation into the energy supply chain. This paper proposes the use of energy trading agents (ETA) in the overlaying communication system in a neighbourhood area network (NAN) in which a number of microgrids (MGs) are grouped together into logical clusters called virtual MGs (VMGs) to minimize operational costs. To decouple the communication network from the grid topology and study the communication performance, each VMG is assigned an ETA such that prosumers in a VMG exchange messages only with the ETA rather than uncontrolled messaging in the network. Although this reduces the amount of network traffic, a key question is on how to determine the optimal location of the ETAs. For VMGs of regular shapes, we formulate this as a simple minimization problem of Euclidean distances of regular shapes. Our results show that by employing ETAs, the model reduces the distance traversed by a prosumer by 40%