Self-organizing Coordination of Multi-Agent Microgrid Networks

abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities.Dissertation/ThesisDoctoral Dissertation Systems Engineering 201

Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 51907031); 10.13039/100008398-Villum Fonden (Grant Number: 25920); Guangdong Basic and Applied Basic Research Foundation (Grant Number: 2021A1515410009)

Energy management of micro-grid using cooperative game theory

Micro-grid (MG) has been introduced as a low voltage and a very small power system connected to a distribution grid through the point of common coupling. It consists of distributed energy resources (DERs) such as solar Photovoltaic (PV), wind turbine, fuel cell, etc.), interconnected load and energy storage sources. It can operate in grid-connected (i.e. when connected to the main grid) or islanded (i.e. when not connected to the main grid) mode. It has an advantage of utilizing low carbon sources and the possibility of its use in the remote local environment, which means that the transmission infrastructures and their associated costs may be deferred. Although there has been a proliferation of optimization methods of energy management in the MG, most of these methods consider self-interest of the players in profit distribution. Moreover, only a few of them consider a fair profit distribution using Nash bargaining solution (NBS) (i.e. when utility function is linear) leading to even profit distribution and high degree of dissatisfaction. For the MG to achieve better economic outcomes, a novel method based on weighted fair energy management among the participants (i.e. building of different types, such as residential buildings, schools, and shops) is proposed. The novelty of the proposed method lies in the new profit sharing method to favour certain participant by assigning a weight to each participant with cooperative game theory (CGT) approach using generalized Nash bargaining solution (GNBS). The proposed approach achieves a fair (reasonable or just) profit allocation with negotiating power indicator. In this work, a case study of six different participant sites is proposed using the CGT method of energy management. The proposed method is able to cope with the drawbacks of the existing independent method, which negotiate directly with other participants for selfish profit distribution. It is demonstrated that the independent method results in (1) a reduction in the profit of each participant of MG when compared with CGT approach and (2) the variation of transfer prices in some participants having profit below the specified lower bound profit since the method does not take into consideration the lower profit bounds. The use of CGT method (i.e. when participants form a coalition) to finding multi-partner profit level subject to specified lower bounds is demonstrated. This results in (1) increase in the profit of the MG participants (2) maintaining the profit level of all the participants above status-quo profit (lower specified profit bounds) with variation in transfer prices and (3) allowing certain participant to be favoured by assigning higher negotiating power to such participant. To achieve the optimal solution in the proposed method, a teaching-learning-based optimization (TLBO) algorithm is presented to efficiently solve the problem. For TLBO algorithm, no specific control parameters are needed except the number of generations and population size. This is in contrast with other heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) that require other control parameters (i.e. GA requires selection and crossover operation, while PSO makes use of social parameters and cognitive weight). To demonstrate the effectiveness of the proposed TLBO method, the profit allocations are tested in the grid-connected and the islanded mode using both the CGT and the independent method. In this work, the proposed TLBO method is compared with one traditional method, i.e. Lambda iteration method and two heuristic methods, i.e. PSO and GA. Thus, by using TLBO a considerable amount of computation time is saved. Using the same parameter setting for all the heuristic algorithms used, 20 trials are performed to be able to compare the quality of solution and convergence characteristics. The investigation reveals that TLBO gives the highest quality solutions and better convergence characteristics compared to PSO and GA

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence-based methods

Abstract: Energy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI‐based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems

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

Modelling Of An Architecture For Local Energy Generation And Distribution With Peer-To-Peer Electricity Sharing In A South African Context

DissertationThe increasing share of variable renewable energy sources, strict targets set for the reduction of greenhouse gas emissions and the requirements on the improvement of system security and reliability, are calling for important changes in our energy systems. In South Africa, distributed renewable energy systems have emerged as effective ways in improving the quality of energy service. The integration of distributed renewable energy, such as solar photovoltaic systems (PV) and micro-grids, is significantly increasing the coupling and interactions between sources and between supply and end use, at various scales, from multinational, national, and community scale, down to building level. In a South African context, power produced from the renewable energy that is not consumed by the load, needs to be stored for later use, or discarded, as the power utility, as well as the municipalities do not generally allow the power to be sold, or shared through the national grid. In the case where various small generation units residing on the same land (estates or a block of townhouses), the power generated from the PV may be shared between the various consumers on the same land. Consumers on the same land having different load patterns as not everyone uses electricity simultaneously connecting them in a micro-grid may allow the power to flow between the different generation systems and consumers. This will decrease the size of the storage systems, as well as the amount of power dumped and lost when it is not in use. On the other hand, the reliance on the grid power will further decrease. With the increasing installation of distributed generation at the demand side, more and more consumers become prosumers, that may both generate and consume energy. The high penetration of sporadic renewable energy may cause severe problems to power systems. Therefore, in order to facilitate the self-consumption of local generation, the export price at which the prosumers sell electricity to the utility grid is usually designed to be significantly lower than the retail price at which electricity is being purchased. This is the major motivation for prosumers to share excess electrical energy amongst each other, rather than to feed it back to the utility grid at a significantly reduced cost. The decreasing tariff rate of the feed-in tariff in most countries, does make this incentive a significantly more attractive approach. The mathematical modelling of the operation of Peer-to-peer (P2P) energy sharing model between two dissimilar load profiles, will be discussed. These profiles are of typical commercial and residential nature. The P2P system consists of two prosumers: the residential prosumer that has a roof mounted PV system that is fixed at a 30° angle, with energy storage capabilities and commercial prosumer, with a solar tracking system. A description of the system is discussed in detail, with all the relevant components outlined. In order to evaluate the cost effectiveness of the hybrid system, in terms of money spent, a baseline system was established, consisting solely of energy supplied by the grid. The optimal operation of the proposed system was simulated and compared to the baseline system. A life cycle cost (LCC) analysis was conducted for a period of 20 years, for both the baseline and the optimally controlled P2P energy sharing scheme. In addition, two electrical energy storage technologies were evaluated for the proposed system. These technologies include lead acid and lithium ion energy storage configurations. Results from the analysis indicated that, if the system were to use lead acid batteries as a storage medium, the proposed system would break-even in 5.304 years, with an approximate saving of 57%, translating into savings of R 1,972,277.98. The proposed system with Li-ion battery storage, indicated a break-even point of 5.131 years, with an expected saving of 54%, translating into cost savings of approximately R 1,861,939.36 at the end of the evaluated life cycle period. Based on the results from the study, it was observed that the optimally controlled P2P energy sharing scheme has shown to be economically feasible, in the South African context

Performance Optimisation of Standalone and Grid Connected Microgrid Clusters

Remote areas usually supplied by isolated electricity systems known as microgrids which can operate in standalone and grid-connected mode. This research focus on reliable operation of microgrids with minimal fuel consumption and maximal renewables penetration, ensuring least voltage and frequency deviations. These problems can be solved by an optimisation-based technique. The objective function is formulated and solved with a Genetic Algorithm approach and performance of the proposal is evaluated by exhaustive numerical analyses in Matlab

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

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