Asynchronous Distributed Power Control of Multimicrogrid Systems

Asynchrony widely exists in microgrids (MGs), such as nonidentical sampling rates and communication delays, which challenges the MG control. This article addresses the asynchronous distributed power control problem of hybrid microgrids, considering different kinds of asynchrony, such as nonidentical sampling rates, and random time delays. To this end, we first formulate the economic dispatch problem of MGs, and devise a synchronous algorithm. Then, we analyze the impact of asynchrony, and propose an asynchronous iteration algorithm based on the synchronous version. By introducing a random clock at each iteration, different types of asynchrony are fitted into a unified framework, where the asynchronous algorithm is converted into a fixed-point iteration problem with a nonexpansive operator, leading to a convergence proof. We further provide an upper bound estimation of the time delay. Moreover, the real-time implementation of the proposed algorithm in both ac and dc MGs is introduced. By measuring the frequency/voltage, the controller is simplified by reducing one order, and adapting to the fast varying load demand. Finally, simulations on a benchmark MG, and experiments are utilized to verify the effectiveness, and advantages of the proposed algorithm

Convergence Analysis of Dual Decomposition Algorithm in Distributed Optimization:Asynchrony and Inexactness

Dual decomposition is widely utilized in the distributed optimization of multi-agent systems. In practice, the dual decomposition algorithm is desired to admit an asynchronous implementation due to imperfect communication, such as time delay and packet drop. In addition, computational errors also exist when the individual agents solve their own subproblems. In this paper, we analyze the convergence of the dual decomposition algorithm in the distributed optimization when both the communication asynchrony and the subproblem solution inexactness exist. We find that the interaction between asynchrony and inexactness slows down the convergence rate from &lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\mathcal {O} (1 / k)$&lt;/tex-math&gt;&lt;/inline-formula&gt; to &lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\mathcal {O} (1 / \sqrt{k})$&lt;/tex-math&gt;&lt;/inline-formula&gt;. Specifically, with a constant step size, the value of the objective function converges to a neighborhood of the optimal value, and the solution converges to a neighborhood of the optimal solution. Moreover, the violation of the constraints diminishes in &lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\mathcal {O} (1 / \sqrt{k})$&lt;/tex-math&gt;&lt;/inline-formula&gt;. Our result generalizes and unifies the existing ones that only consider either asynchrony or inexactness. Finally, numerical simulations validate the theoretical results.</p

Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process

Scheduling of Resources in Renewable Energy Communities

This work presents a detailed study of the scheduling of power and energy resources in renewable energy communities (RECs). The study has been developed starting from the analysis of a single basic unit of the community, i.e., the prosumer and its microgrid, to the scheduling and expansion of the energy community concept with several prosumers through several scenarios. The individual scheduling problem of the prosumer has been studied as a day-ahead deterministic problem and as a multistage stochastic problem to consider uncertainties associated with energy generation and energy consumption. Furthermore, an approach has been formulated to consider the integration of bidirectional charging services of electrical vehicles within a local energy system with the presence of renewable generation. Moreover, this thesis focuses on the scenario in which direct energy transactions between prosumers located within a REC are allowed in addition to the energy transactions with the external energy provider. The day-ahead scheduling problem has been addressed by a centralized approach and by a distributed approach based on the alternating direction method of multipliers (ADMM). The developed approaches provide the scheduling of the available energy resources to limit the balancing action of the external grid and allocate the internal network losses to the corresponding energy transactions. Finally, the thesis presents a coordinated day-ahead and intra-day approach to provide the optimal scheduling of the resources in a REC. In this case, the ADMM-based procedure, which is aimed at minimizing the total energy procurement costs, is adapted to cope with the impact of the fluctuation of both the local energy generation and demand during the day. To achieve this, a day-ahead multistage stochastic optimization approach is combined with an intra-day decision-making procedure, able to adjust the scheduling of the energy resources according to the current operational conditions

Microgrids: Planning, Protection and Control

This Special Issue will include papers related to the planning, protection, and control of smart grids and microgrids, and their applications in the industry, transportation, water, waste, and urban and residential infrastructures. Authors are encouraged to present their latest research; reviews on topics including methods, approaches, systems, and technology; and interfaces to other domains such as big data, cybersecurity, human–machine, sustainability, and smart cities. The planning side of microgrids might include technology selection, scheduling, interconnected microgrids, and their integration with regional energy infrastructures. The protection side of microgrids might include topics related to protection strategies, risk management, protection technologies, abnormal scenario assessments, equipment and system protection layers, fault diagnosis, validation and verification, and intelligent safety systems. The control side of smart grids and microgrids might include control strategies, intelligent control algorithms and systems, control architectures, technologies, embedded systems, monitoring, and deployment and implementation

Asynchronous Distributed Control of Biogas Supply and Multienergy Demand

In this paper, we study the coordination between biogas producers who can use the biogas themselves, exchange biogas with their neighbors, or deliver it to the various energy grids, such as the low pressure gas grid or the power grid. These producers are called prosumers. In this setting, gas storage, fuel cells, microcombined heat power systems, and heat buffers are all part of the prosumers' node. We aim to optimize the imbalance, profit, and comfort levels per prosumer, while taking the constraints of the energy grids into account and while allowing prosumers to exchange energy with each other. This results in a two-layer optimization problem formulation. In addition, in practice, the communication between prosumers among each other and with grid operators is done in an asynchronous manner. In this paper, we study the problem of two-layer optimization for biogas prosumers embedded in multiple energy grids, while the (bidirectional) communication between the various partners is done asynchronously. We prove the convergence of the asynchronous coordination algorithm that uses both the inputs and the states. We conduct simulations for the biogas prosumer setting, using realistic data to illustrate the convergence of the algorithm and to study its practical implementation