Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

Realizing the potential of distributed energy resources and peer-to-peer trading through consensus-based coordination and cooperative game theory

Driven by environmental and energy security concerns, a large number of small-scale distributed energy resources (DERs) are increasingly being connected to the distribution network. This helps to support a cost-effective transition to a lower-carbon energy system, however, brings coordination challenges caused by variability and uncertainty of renewable energy resources (RES). In this setting, local flexible demand (FD) and energy storage (ES) technologies have attracted great interests due to their potential flexibility in mitigating the generation and demand variability and improving the cost efficiency of low-carbon electricity systems. The combined effect of deregulation and digitalization inspired new ways of exchanging electricity and providing management/services on the paradigm of peer-to-peer (P2P) and transparent transactions. P2P energy trading enables direct energy trading between prosumers, which incentivizes active participation of prosumer in the trading of electricity in the distribution network, in the meantime, the efficient usage of FD and ES owned by the prosumers also facilitates better local power and energy balance. Though the promising P2P energy trading brings numerous advancements, the existing P2P mechanisms either fail to coordinate energy in a fully distributed way or are unable to adequately incentivize prosumers to participate, preventing prosumers from accessing the highest achievable monetary benefits and/or suffering severely from the curse of dimensionality. Therefore, this thesis aims at proposing three P2P energy trading enabling mechanisms in the aspect of fully distributed efficient balanced energy coordination through consensus-based algorithm and two incentivizing pricing and benefit distribution mechanisms through cooperative game theory. Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of DER due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of FD and ES resources. This thesis demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability. Moreover, this thesis proposes two computationally efficient pricing and benefit distribution mechanisms to construct a stable grand coalition of prosumers participating in P2P trading, founded on cooperative game-theoretic principles. The first one involves a benefit distribution scheme inspired by the core tatonnement process while the second involves a novel pricing mechanism based on the solution of single linear programming. The performance of the proposed mechanisms is validated against state-of-the-art mechanisms through numerous case studies using real-world data. The results demonstrate that the proposed mechanisms exhibit superior computational performance than the nucleolus and are superior to the rest of the examined mechanisms in incentivizing prosumers to remain in the grand coalition.Open Acces

Optimal coalition structure generation on large-scale renewable energy smart grids

Most renewable energy sources are dependent on unpredictable weather conditions, which have considerable variation over space and time. The intermittent nature of this production means that any renewable energy prosumer may sometimes produce an amount of energy in excess of its local consumption needs and sometimes in deficiency. This thesis is concerned with developing methods that can improve the effectiveness and widespread adoption of renewable energy usage. In order for renewable energy to be more economically viable, there needs to be a scheme for sharing energy among the prosumers so that those with excess energy can give their excess amounts to those in energy deficiency. That is the task addressed in this thesis. The way to deal with this problem is to setup an optimal arrangement of local coalitions of renewable energy prosumers such that energy is shared within the coalitions in an optimally efficient manner. As is formally explained early on in this work, finding such an optimal coalition arrangement is an example of a Coalition Structure Generation (CSG) problem. The most straightforward way to find an optimal solution for a given pool of prosumer agents in these circumstances is to examine every possible coalition partition (coalition structure) and evaluate its comparative utility. This is known as ``exhaustive search'' (ES) and can be computationally expensive. As has been shown earlier, the number of such evaluations in ES even for a pool of twenty agents can be in the tens of trillions. The problem for us in the renewable energy domain is that, because of the constantly changing weather conditions among the scattered prosumers, the CSG optimization calculation must be carried out every hour of the day. This means that the ES approach in the CSG optimization calculation for a reasonable number of prosumer agents is computationally intractable. So a more computationally feasible stochastic optimization method must be used, which searches through the coalition structure search space in order to find a reasonably good solution even if it is not the global optimum. To this end, a number of stochastic optimization search methods have been investigated in this thesis, including some of our own novel extensions to existing approaches. These search methods have been examined with respect to two different connection arrangements with respect to the outside world – (1) when the local prosumer networks have a connection to a public utility power grid and can therefore buy needed energy (at a high price) from the grid and sell excess energy (at a low price) to the grid and (2) when the local prosumer networks are isolated from any public utility, which is referred to as ``island mode''. The overall goal of these investigations has been to find an optimization approach that arrives at a near-optimal (near the global optimum of the given search space) that is computationally efficient (i.e. it does not require a vast amount of computer memory or running time). Based on these empirical examinations, which have employed realistic parameters drawn from existing consumption and renewable energy data sets, the following conclusions concerning renewable energy can be drawn from this study: • It is feasible to employ ordinary computer resources to obtain on an hourly basis near-optimal energy-sharing coalition structures that will lead to the more effective and economical use of renewable energy. • This energy-sharing approach will contribute to more rapid adoption and proliferation of existing renewable energy equipment and infrastructure. The principal contributions towards these end that this thesis work has made are as follows: • A modelling framework has been set up that can be used for extensive empirical determinations of near-optimal energy-sharing coalition structures. • A detailed empirical study has been carried out that has examined the relative capabilities in this context of various optimal coalition structure search methods, including genetic algorithms (GA), dynamic programming (DP), particle swarm optimization (PSO), population-based incremental learning (PBIL), and several variants to PBIL. • The novel extensions to basic PBIL optimization have included Top-k Merit Weighting PBIL (PBIL-MW), Set-ID Encoding Schemes, and Hierarchical PBIL-MW

Models for efficient control and fair sharing of assets in energy communities

In recent times, energy communities have gained significant interest. These communities empower citizen prosumers by leveraging their own renewable energy generation and storage assets to manage their energy requirements and engage in the broader energy market. Such communities offer a promising solution for sustainable energy systems, promoting renewable integration and active user involvement. Within energy communities, members can engage in energy trading and invest in shared assets like production units, energy storage, and network infrastructure. However, efficiently controlling these assets in real-time and equitably distributing energy outputs among diverse members with varying needs remains a vital challenge. Addressing this concern is of both research and practical importance. It is essential to consider technical constraints like local low-voltage network characteristics and power ratings during this process. To tackle these challenges, this thesis presents a model that examines the techno-economic benefits of community-owned versus individually-owned energy assets, accounting for physical asset degradation and network constraints. Employing cooperative game theory principles, the thesis proposes a redistribution model for community benefits based on the marginal contribution of each household. This redistribution mechanism utilizes the concept of marginal value from coalitional game theory and distributed AI (specifically the multi-agent system). Study results demonstrate that the proposed marginal cost redistribution mechanism is fairer and more computationally manageable than existing state-of-the-art methods, thus providing a scalable approach for economic sharing of joint assets in community energy systems. However, integration of centrally shared community-owned energy assets may face limitations due to network/grid constraints. To address this issue, the thesis proposes a novel framework for a local peer-to-community (P2C) market mechanism as an alternative solution to investing in community-owned assets. The dynamics of the P2C market mechanism are studied for three different types of P2C sellers with non-uniform pricing schemes and tested across various community settings (comprising a mix of prosumers and consumers) and different rates of renewable energy adoption. All proposed models are validated and applied to a real case study from a large-scale smart energy demonstration project in the UK, using a substantial dataset of real renewable generation and demand. This practical case study provides confidence in the robustness of the experimental comparison results presented in the thesis.Engineering and Physical Sciences Council (EPSRC) Doctoral Training Programme (DTP) grant (EP/R513040/1

A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks

In order to coordinate the economic desire of microgrid (MG) owners and the stability operation requirement of the distribution system operator (DSO), a multi-market participation framework is proposed to stimulate the energy transaction potential of MGs through distributed and centralized ways. Firstly, an MG equipped with storage can contribute to the stability improvement at special nodes of the distribution grid where the uncertain factors (such as intermittent renewable sources and electric vehicles) exist. The DSO is thus interested in encouraging specified MGs to provide voltage stability services by creating a distribution grid service market (DGSM), where the dynamic production-price auction is used to capture the competition of the distributed MGs. Moreover, an aggregator, serving as a broker and controller for MGs, is considered to participate in the day-ahead wholesale market. A Stackelberg game is modeled accordingly to solve the price and quantity package allocation between aggregator and MGs. Finally, the modified IEEE-33 bus distribution test system is used to demonstrate the applicability and effectiveness of the proposed multi-market mechanism. The results under this framework improve both MGs and utility

Enabling cooperative and negotiated energy exchange in remote communities

Energy poverty at the household level is defined as the lack of access to electricity and reliance on the traditional use of biomass for cooking, and is a serious hindrance to economic and social development. It is estimated that 1.3 billion people live without access to electricity and almost 2.7 billion people rely on biomass for cooking, a majority of whom live in small communities scattered over vast areas of land (mostly in the Sub-Saharan Africa and the developing Asia). Access to electricity is a serious issue as a number of socio-economic factors, from health to education, rely heavily on electricity. Recent initiatives have sought to provide these remote communities with off-grid renewable microgeneration infrastructure such as solar panels, and electric batteries. At present, these resources (i.e., microgeneration and storage) are operated in isolation for individual home needs, which results in an inefficient and costly use of resources, especially in the case of electric batteries which are expensive and have a limited number of charging cycles. We envision that by connecting homes together in a remote community and enabling energy exchange between them, this microgeneration infrastructure can be used more efficiently. Against this background, in this thesis we investigate the methods and processes through which homes in a remote community can exchange energy. We note that remote communities lack general infrastructure such as power supply systems (e.g., the electricity grid) or communication networks (e.g., the internet), that is taken for granted in urban areas. Taking these challenges into account and using insights from knowledge domains such game theory and multi-agent systems, we present two solutions: (i) a cooperative energy exchange solution and (ii) a negotiated energy exchange solution, in order to enable energy exchange in remote communities.Our cooperative energy exchange solution enables connected homes in a remote community to form a coalition and exchange energy. We show that such coalition a results in two surpluses: (i) reduction in the overall battery usage and (ii) reduction in the energy storage losses. Each agents's contribution to the coalition is calculated by its Shapley value or, by its approximated Shapley value in case of large communities. Using real world data, we empirically evaluate our solution to show that energy exchange: (i) can reduce the need for battery charging (by close to 65%) in a community; compared with when they do not exchange energy, and (ii) can improve the efficient use of energy (by up to 10% under certain conditions) compared with no energy exchange. Our negotiated energy exchange solution enables agents to negotiate directly with each other and reach energy exchange agreements. Negotiation over energy exchange is an interdependent multi-issue type of negotiation that is regarded as very difficult and complex. We present a negotiation protocol, named Energy Exchange Protocol (EEP), which simplifies this negotiation by restricting the offers that agents can make to each other. These restrictions are engineered such that agents, negotiation under the EEP, have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes (within restricted the set of offers) in a decentralised manner. Using real world data, we empirically evaluate our protocol and show that, in this instance, a society of agents can: (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%, compared to when they do not exchange energy