A Review of Energy Management of Renewable Multisources in Industrial Microgrids

This review aims to consolidate recent advancements in power control within microgrids and multi-microgrids. It specifically focuses on analyzing the comparative benefits of various architectures concerning energy sharing and demand cost management. The paper provides a comprehensive technical analysis of different architectures found in existing literature, which are designed for energy management and demand cost optimization. In summary, this review paper provides a thorough examination of power control in microgrids and multi-microgrids and compares different architectural approaches for energy management and demand cost optimization

Two-stage co-optimization for utility-social systems with social-aware P2P trading

Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model’s effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water–energy–social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas

Planning of multi-hub energy system by considering competition issue

Energy hub concept has been emerged as a suitable tool to analyze multi-carrier energy systems. Deregulation and increasing competition in the energy industry have provided a suitable platform for developing the multi-agent energy systems. Planning of energy hubs considering the competition between the hubs has not been sufficiently addressed, yet. A model has been proposed in this study for planning of a multi-hub energy system considering the competition between the hubs.  The hubs are interconnected via an electric transmission system. A linear model has been developed to determine the optimal planning/operation strategy for energy hubs in a multi-period planning horizon to meet the heat and electricity demand for the defined load zone. The problem has been formulated and solved using Karush–Kuhn–Tucker (KKT) conditions. The proposed model has been applied to 3-Hub and 5-Hub energy systems. The effect of renewable generation and storage system has also been evaluatedIt has also been observed that inclusion of renewable generation or storage technologies can reduce the conventional electricity generation capacity by 63 percent in HUB2

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

Socially governed energy hub trading enabled by blockchain-based transactions

Decentralized trading schemes involving energy prosumers have prevailed in recent years. Such schemes provide a pathway for increased energy efficiency and can be enhanced by the use of blockchain technology to address security concerns in decentralized trading. To improve transaction security and privacy protection while ensuring desirable social governance, this article proposes a novel two-stage blockchain-based operation and trading mechanism to enhance energy hubs connected with integrated energy systems (IESs). This mechanism includes multienergy aggregators (MAGs) that use a consortium blockchain and its enabled proof-of-work (PoW) to transfer and audit transaction records, with social governance principles for guiding prosumers’ decision-making in the peer-to-peer (P2P) transaction management process. The uncertain nature of renewable generation and load demand are adequately modeled in the two-stage Wasserstein-based distributionally robust optimization (DRO). The practicality of the proposed mechanism is illustrated by several case studies that jointly show its ability to handle an increased renewable generation capacity, achieve a 16.7% saving in the audit cost, and facilitate 2.4% more P2P interactions. Overall, the proposed two-stage blockchain-based trading mechanism provides a practical trading scheme and can reduce redundant trading amounts by 6.5%, leading to a further reduction of the overall operation cost. Compared to the state-of-the-art benchmark methods, our mechanism exhibits significant operation cost reduction and ensures social governance and transaction security for IES and energy hubs

An Integrated Framework for Modelling and Control of eP2P Interactions based on Model Predictive Control

The energy paradigm is undergoing substantial changes in recent years. In terms of production, it is observable how distributed generation, with an ever-increasing contribution from renewable sources, is displacing large concentrated generation plants. But the fundamental change is not so much about energy supply as about diluting the historical roles of producers and consumers to give way to the concept of prosumers. That is, instead of just being energy consumers, households and industries also become producers. In principle, the purpose of this production, which is inherently distributed, is self-consumption. However, when there is a surplus of production, prosumers can choose between storing the excess, if they have an energy storage system, or sell the unused fraction of energy. An obvious type of prosumers are those industries that have renewable generation facilities and which, as a consequence of their production process, generate by-products that can be used for cogeneration. In this case an obvious problem for the company is to select at all times the power sources that minimize the cost of production, which is known as Optimal Power Dispatch (OPD). If, in addition, the energy consumption time profile of the manufacturing process (per unit of raw material introduced) is known, it is also possible to make an optimal production schedule to minimize energy cost, which is called Optimal Power Scheduling (OPS). Chapter 3 presents an Economic Model Predictive Controller (EMPC) that simultaneously performs OPD and OPS using an olive mill as an example. The emergence of the role of energy prosumers makes it necessary to extend, improve or replace the traditional mechanisms of energy exchange. This thesis includes novel approaches for modelling the behaviour of prosumers. It also proposes new structures to facilitate energy trading, always from the perspective of the peerification of the energy paradigm. Thus, another line of research studies the establishment of peer-to-peer (P2P) markets for the exchange of energy between heterogeneous prosumers (homes, vehicles, intelligent buildings, etc.). The efficiency of markets based on both discrete double auctions (DDAs) and continuous double auctions (CDAs) is compared. An Energy Management System (EMS) is also introduced including market agent software that allows the necessary tasks for participation in the auctions to be carried out automatically (determination of private valuation, role selection and price adaptation). Chapter 4, Chapter 5 and Chapter 6 present some examples of such exchange markets stablished between different types of prosumers: i) energy market for electric vehicles that coincide parked in a large workplace, ii) power market for households within the same neighbourhood and iii) integrated energy and power markets for heterogeneous energy entities. The evolution of aforementioned mechanisms and the appearance of new market models must be accompanied by the development of control techniques that optimise and automate all the processes related to energy saving and trading, by a group of increasingly heterogeneous prosumers. This thesis deals with how different variants of predictive controllers can contribute to this last aspect. For industries with cogeneration capacity, the EMPC contributes to the optimal scheduling of production to maximise the return from energy reuse, either through self-consumption or through the trading of surpluses. The use of stochastic predictive control is proposed in order to maximise the expected return on the participation of prosumers, whatever their type, in continuous markets where the price of energy may undergo stochastic variations.El paradigma energético está experimentando cambios sustanciales en los últimos años. En cuanto a la producción, se observa cómo la generación distribuida, con un aporte cada vez mayor de fuentes renovables, está desplazando a las grandes plantas de generación concentrada. Pero el cambio fundamental no consiste tanto en el suministro de energía como en la dilución de la clasificación tradicional entre productores y consumidores para dar paso al concepto de prosumidores. Es decir, en lugar de ser simplemente consumidores de energía, los hogares y las industrias también se convierten en productores. En principio, el objetivo de esta producción, que es intrínsecamente distribuida, es el autoconsumo. Sin embargo, cuando hay un excedente de producción, los prosumidores pueden elegir entre almacenar el excedente, si tienen un sistema de almacenamiento de energía, o vender la fracción no utilizada de la energía. Un tipo obvio de prosumidores son aquellas industrias que cuentan con instalaciones de generación renovable y que, como consecuencia de su proceso de producción, generan subproductos que pueden ser utilizados para la cogeneración. En este caso, un problema obvio para la empresa es seleccionar en todo momento las fuentes de energía que minimizan el coste de producción, lo que se conoce como Optimal Power Dispatch (OPD). Si, además, se conoce el perfil temporal de consumo de energía asociado al proceso de fabricación (por unidad de materia prima introducida), también es posible realizar un programa de producción óptimo para minimizar el coste de la energía, lo cual se denomina Optimal Power Scheduling (OPS). El capítulo 3 presenta un Controlador Predictivo Económico basado en Modelo (EMPC) que realiza simultáneamente OPD y OPS utilizando como caso de estudio una almazara olivarera. La aparición de la figura de los prosumidores energéticos hace necesario ampliar, mejorar o sustituir los mecanismos tradicionales de intercambio energético. Esta tesis incluye enfoques novedosos para modelar el comportamiento de los prosumidores. También propone nuevas estructuras para facilitar el comercio de energía, siempre desde la perspectiva de la peerificación del paradigma energético. Así, otra línea de investigación estudia el establecimiento de mercados peer-to-peer (P2P) para el intercambio de energía entre prosumidores heterogéneos (viviendas, vehículos, edificios inteligentes, etc.). Se compara la eficiencia de los mercados basados tanto en subastas dobles discretas (Discrete Double Auction - DDA) como en subastas dobles continuas (Continuous Double Auctions - CDA). También se introduce un Sistema de Gestión Energética (Energy Management System - EMS) que incluye un software de agente de mercado que permite que las tareas necesarias para la participación en las subastas (determinación de la valoración privada, selección de roles y adaptación de precios) se lleven a cabo automáticamente. Los capítulos 4, 5 y 6 presentan algunos ejemplos de estos mercados de intercambio establecidos entre diferentes tipos de prosumidores: i) mercado de energía para vehículos eléctricos que coinciden aparcados en un gran lugar de trabajo, ii) mercado de energía para hogares dentro de un mismo barrio y iii) mercados integrados de energía y electricidad para entidades energéticas heterogéneas. La evolución de los mecanismos mencionados y la aparición de nuevos modelos de mercado deben ir acompañados del desarrollo de técnicas de control que optimicen y automaticen todos los procesos relacionados con el ahorro y la comercialización de la energía, por parte de un conjunto de prosumidores cada vez más heterogéneos. Esta tesis trata de cómo las diferentes variantes de los controladores predictivos pueden contribuir a este último aspecto. Para las industrias con capacidad de cogeneración, el EMPC contribuye a la programación óptima de la producción para maximizar el rendimiento de la reutilización de la energía, ya sea a través del autoconsumo o de la comercialización de excedentes. Por otro lado, se propone el uso del control predictivo estocástico para maximizar el rendimiento esperado de la participación de los prosumidores, cualquiera que sea su tipo, en mercados P2P donde el precio de la energía está sujeto a incertidumbres

Changing the day-ahead gate closure to wind power integration: a simulation-based study

ABSTRACT: Currently, in most European electricity markets, power bids are based on forecasts performed 12 to 36 hours ahead. Actual wind power forecast systems still lead to large errors, which may strongly impact electricity market outcomes. Accordingly, this article analyzes the impact of the wind power forecast uncertainty and the change of the day-ahead market gate closure on both the market-clearing prices and the outcomes of the balancing market. To this end, it presents a simulation-based study conducted with the help of an agent-based tool, called MATREM. The results support the following conclusion: a change in the gate closure to a time closer to real-time operation is beneficial to market participants and the energy system generally.info:eu-repo/semantics/publishedVersio

Coordination of smart home energy management systems in neighborhood areas: A systematic review

High penetration of selfish Home Energy Management Systems (HEMSs) causes adverse effects such as rebound peaks, instabilities, and contingencies in different regions of distribution grid. To avoid these effects and relieve power grid stress, the concept of HEMSs coordination has been suggested. Particularly, this concept can be employed to fulfill important grid objectives in neighborhood areas such as flattening aggregated load profile, decreasing electricity bills, facilitating energy trading, diminishing reverse power flow, managing distributed energy resources, and modifying consumers' consumption/generation patterns. This paper reviews the latest investigations into coordinated HEMSs. The required steps to implement these systems, accounting for coordination topologies and techniques, are thoroughly explored. This exploration is mainly reported through classifying coordination approaches according to their utilization of decomposition algorithms. Furthermore, major features, advantages, and disadvantages of the methods are examined. Specifically, coordination process characteristics, its mathematical issues and essential prerequisites, as well as players concerns are analyzed. Subsequently, specific applications of coordination designs are discussed and categorized. Through a comprehensive investigation, this work elaborates significant remarks on critical gaps in existing studies toward a useful coordination structure for practical HEMSs implementations. Unlike other reviews, the present survey focuses on effective frameworks to determine future opportunities that make the concept of coordinated HEMSs feasible. Indeed, providing effective studies on HEMSs coordination concept is beneficial to both consumers and service providers since as reported, these systems can lead to 5% to 30% reduction in electricity bills