Optimal and Secure Electricity Market Framework for Market Operation of Multi-Microgrid Systems

Traditional power systems were typically based on bulk energy services by large utility companies. However, microgrids and distributed generations have changed the structure of modern power systems as well as electricity markets. Therefore, restructured electricity markets are needed to address energy transactions in modern power systems. In this dissertation, we developed a hierarchical and decentralized electricity market framework for multi-microgrid systems, which clears energy transactions through three market levels; Day-Ahead-Market (DAM), Hour-Ahead-Market (HAM) and Real-Time-Market (RTM). In this market, energy trades are possible between all participants within the microgrids as well as inter-microgrids transactions. In this approach, we developed a game-theoretic-based double auction mechanism for energy transactions in the DAM, while HAM and RTM are cleared by an optimization algorithm and reverse action mechanism, respectively. For data exchange among market players, we developed a secure data-centric communication approach using the Data Distribution Service. Results demonstrated that this electricity market could significantly reduce the energy price and dependency of the multi-microgrid area on the external grid. Furthermore, we developed and verified a hierarchical blockchain-based energy transaction framework for a multi-microgrid system. This framework has a unique structure, which makes it possible to check the feasibility of energy transactions from the power system point of view by evaluating transmission system constraints. The blockchain ledger summarization, microgrid equivalent model development, and market players’ security and privacy enhancement are new approaches to this framework. The research in this dissertation also addresses some ancillary services in power markets such as an optimal power routing in unbalanced microgrids, where we developed a multi-objective optimization model and verified its ability to minimize the power imbalance factor, active power losses and voltage deviation in an unbalanced microgrid. Moreover, we developed an adaptive real-time congestion management algorithm to mitigate congestions in transmission systems using dynamic thermal ratings of transmission lines. Results indicated that the developed algorithm is cost-effective, fast, and reliable for real-time congestion management cases. Finally, we completed research about the communication framework and security algorithm for IEC 61850 Routable GOOSE messages and developed an advanced protection scheme as its application in modern power systems

An overview of the operation architectures and energy management system for multiple microgrid clusters

The emerging novel energy infrastructures, such as energy communities, smart building-based microgrids, electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system. It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters. In order to efficiently, optimally, and flexibly control multi-microgrid clusters, cross-disciplinary technologies such as power electronics, control theory, optimization algorithms, information and communication technologies, cyber-physical, and big-data analysis are needed. This paper introduces an overview of the relevant aspects for multi-microgrids, including the outstanding features, architectures, typical applications, existing control mechanisms, as well as the challenges

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

An ADMM approach for day-ahead scheduling of a local energy community

The paper deals with the day-ahead operational planning of a grid-connected local energy community (LEC) consisting of several prosumers each equipped with generating units, loads and battery energy storage units. The prosumers are connected to the same low-voltage distribution network. In order to preserve, as much as possible, the confidentiality of the features and forecast of prosumers' equipment, the problem is addressed by designing a specific distributed procedure based on the alternating direction method of multipliers (ADMM). The distributed procedure provides the scheduling of the batteries to limit the balancing action of the external grid. Results obtained for various case studies are compared with those obtained by a centralized approach. The values of the objective function, the profiles of the power exchanged with the utility grid and the profile of the energy stored in the batteries provided by the distributed approach are in close agreement with those calculated by the centralized one

Peer-to-Peer Energy Trading for Networked Microgrids

Considering the limitations of the existing centralized power infrastructure, research interests have been directed to decentralized smart power systems constructed as networks of interconnected microgrids. Therefore, it has become critical to develop secure and efficient energy trading mechanisms among networked microgrids for reliability and economic mutual benefits. Furthermore, integrating blockchain technologies into the energy sector has gained significant interest among researchers and industry professionals. Considering these trends, the work in this thesis focuses on developing Peer-to-Peer (P2P) energy trading models to facilitate transactions among microgrids in a multiagent network. Price negotiation mechanisms are proposed for both islanded and grid-connected microgrid networks. To enable a trusted settlement of electricity trading transactions, a two-stage blockchain-based settlement consensus protocol is also developed. Simulation results have shown that the model has successfully facilitated energy trading for networked microgrids

A three-stage optimal operation strategy of interconnected microgrids with rule-based deep deterministic policy gradient algorithm

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.In part by the National Natural Science Fund, the Basic Research Project of Leading Technology of Jiangsu Province, the National Natural Science Fund of Jiangsu Province and the National Natural Science Key Fund.https://ieeexplore.ieee.org/servlet/opac?punumber=5962385hj2023Electrical, Electronic and Computer Engineerin

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

Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

The influence of the DC infrastructure on the control of power-storage flow in micro- and smart grids has gained attention recently, particularly in dynamic vehicle-to-grid charging applications. Principal effects include the potential loss of the charge–discharge synchronization and the subsequent impact on the control stabilization, the increased degradation in batteries’ health/life, and resultant power- and energy-efficiency losses. This paper proposes and tests a candidate solution to compensate for the infrastructure effects in a DC microgrid with a varying number of heterogeneous battery storage systems in the context of a multiagent neighbor-to-neighbor control scheme. Specifically, the scheme regulates the balance of the batteries’ load-demand participation, with adaptive compensation for unknown and/or time-varying DC infrastructure influences. Simulation and hardware-in-the-loop studies in realistic conditions demonstrate the improved precision of the charge–discharge synchronization and the enhanced balance of the output voltage under 24 h excessively continuous variations in the load demand. In addition, immediate real-time compensation for the DC infrastructure influence can be attained with no need for initial estimates of key unknown parameters. The results provide both the validation and verification of the proposals under real operational conditions and expectations, including the dynamic switching of the heterogeneous batteries’ connection (plug-and-play) and the variable infrastructure influences of different dynamically switched branches. Key observed metrics include an average reduced convergence time (0.66–13.366%), enhanced output-voltage balance (2.637–3.24%), power-consumption reduction (3.569–4.93%), and power-flow-balance enhancement (2.755–6.468%), which can be achieved for the proposed scheme over a baseline for the experiments in question.</p