Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach

Microgrids (MG) are anticipated to be important players in the future smart grid. For proper operation of MGs an Energy Management System (EMS) is essential. The EMS of an MG could be rather complicated when renewable energy resources (RER), energy storage system (ESS) and demand side management (DSM) need to be orchestrated. Furthermore, these systems may belong to different entities and competition may exist between them. Nash equilibrium is most commonly used for coordination of such entities however the convergence and existence of Nash equilibrium can not always be guaranteed. To this end, we use the correlated equilibrium to coordinate agents, whose convergence can be guaranteed. In this paper, we build an energy trading model based on mid-market rate, and propose a correlated Q-learning (CEQ) algorithm to maximize the revenue of each agent. Our results show that CEQ is able to balance the revenue of agents without harming total benefit. In addition, compared with Q-learning without correlation, CEQ could save 19.3% cost for the DSM agent and 44.2% more benefits for the ESS agent.Comment: Accepted by 2020 IEEE International Conference on SmartGridComm, 978-1-7281-6127-3/20/$31.00 copyright 2020 IEE

Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing Approach

With the rapidly increased penetration of renewable generations, incentive-based demand side management (DSM) shows great value on alleviating the uncertainty and providing flexibility for microgrid. However, how to price those demand resources becomes one of the most significant challenges for promoting incentive-based DSM under microgrid environments. In this paper, a flexible demand resource pricing scheme is proposed. Instead of using the utility function of end users like most existing literatures, the economic benefit of flexible demand resources is evaluated by the operation performance enhancement of microgrid and correspondingly the resource is priced based on a benefit sharing approach. An iteration-based chance-constrained method is established to calculate the economic benefit and shared compensation for demand resource providers. Meanwhile, the financial risks for the microgrid operator due to uncertain factors are mitigated by the chance-constrained criterion. The proposed scheme is examined by an experimental microgrid to illustrate its effectiveness.Comment: 10 pages, 16 figure

Optimal Operation Strategy for Multi-Energy Microgrid Participating in Auxiliary Service

Since multi-energy microgrid (MEMG) can coordinate various resources to operate as a virtual power plant (VPP), it is an important way to maintain the stable and economic operation of the power systems and decrease the impact of intermittence of distributed energy resources (DERs). However, the potential of MEMG as a VPP has not been thoroughly explored since auxiliary service (AS) market is not fully open for MEMG at present. The relevant challenges include balancing conflict of interests among multiple energy entities, motivating users to adjust flexible loads, integrating multiple flexible resources in energy supply/demand sides and formulating specific policies, etc. To handle these tasks, an optimal operation strategy for MEMG participating in AS is proposed by considering Stackelberg game theory and integrated demand response (IDR). The feasibility of the proposed strategy is validated by a practical MEMG in Hunan, China. The results show that the economic benefits of energy entities are effectively raised and the peak-shaving AS is realized while user satisfaction is also maintained. This work would give reference to the constructor of future AS market to formulate polices about the operation modes and pricing schemes of MEMG

A review of constraints and adjustable parameters in microgrids for cost and carbon dioxide emission reduction

In a world grappling with escalating energy demand and pressing environmental concerns, microgrids have risen as a promising solution to bolster energy efficiency, alleviate costs, and mitigate carbon emissions. This article delves into the dynamic realm of microgrids, emphasizing their indispensable role in addressing today's energy needs while navigating the hazards of pollution. Microgrid operations are intricately shaped by a web of constraints, categorized into two essential domains: those inherent to the microgrid itself and those dictated by the external environment. These constraints, stemming from component limitations, environmental factors, and grid connections, exert substantial influence over the microgrid's operational capabilities. Of particular significance is the three-tiered control framework, encompassing primary, secondary, and energy management controls. This framework guarantees the microgrid's optimal function, regulating power quality, frequency, and voltage within predefined parameters. Central to these operations is the energy management control, the third tier, which warrants in-depth exploration. This facet unveils the art of fine-tuning parameters within the microgrid's components, seamlessly connecting them with their surroundings to streamline energy flow and safeguard uninterrupted operation. In essence, this article scrutinizes the intricate interplay between microgrid constraints and energy management parameters, illuminating how the nuanced adjustment of these parameters is instrumental in achieving the dual objectives of cost reduction and Carbon Dioxide emission minimization, thereby shaping a more sustainable and eco-conscious energy landscape. This study investigates microgrid dynamics, focusing on the nuanced interplay between constraints and energy management for cost reduction and Carbon Dioxide minimization. We employ a three-tiered control framework—primary, secondary, and energy management controls—to regulate microgrid function, exploring fine-tuned parameter adjustments for optimal performance

Modelo y desarrollo de un sistema de gestión óptima para una microrred empleando algoritmos bio-inspirados

Tesis por compendio[ES] Las fuentes de energía renovable (ER) permiten una alta disgregación, por lo que hacen posible generar la energía que se utilizará en el mismo sitio de su aprovechamiento. Esto favorece un cambio en la estructura de las redes eléctricas, permitiendo pasar de un esquema de generación centralizado a un esquema distribuido. Sin embargo, las fuentes de ER son altamente dependientes de las condiciones medioambientales como la radiación solar, la nubosidad, el viento, entre otros, por lo que lograr un sistema de generación basado en energías renovables es un reto en la actualidad. Los sistemas de generación que integran fuentes renovables tienen que ser capaces de establecer estrategias de control y gestión de la energía que para hacer un uso eficiente de ella e intentar cubrir la demanda de energía de forma óptima al combinar más de un tipo de fuente y sistema de almacenamiento, siendo posible operar de manera aislada o conectada a la red eléctrica. En la actualidad es de interés el estudio, desarrollo e implementación de sistemas gestores de la energía (SGE) para microrredes eléctricas híbridas, que permitan aumentar su eficiencia, fiabilidad, y disminuir los costes de instalación, operación y mantenimiento. Diversos estudios de investigación han probado múltiples estrategias, desde SGE basados en reglas, algoritmos comparativos, controladores clásicos, y en años recientes, la integración de algoritmos de optimización bio-inspirados e inteligencia artificial. Estos algoritmos han mostrado ser una alternativa interesante a las técnicas clásicas para la solución de problemas de optimización y control en diversos problemas de ingeniería, su aplicación en el ámbito de las microrredes sigue en estudio y en ello se basa este trabajo de investigación. Los algoritmos bio-inspirados se fundamentan en imitar matemáticamente los mecanismos y estrategias que la naturaleza ha implementado a lo largo de millones de años para lograr un equilibrio en su funcionamiento, por ejemplo, imitando el cómo las aves vuelan en parvada buscando alimento, o como las hormigas y los lobos siguen patrones para la búsqueda de su alimento, o como las especies llevan a cabo mecanismos de cruce con el objetivo de mejorar su raza haciéndolas una especie optimizada y mejorando su supervivencia. Por tanto, se puede hacer una analogía con los sistemas artificiales para la mejora de controladores y diseño de sistemas en microrredes eléctricas. En este trabajo de investigación se muestra el modelo y desarrollo de un sistema de gestión óptima para una microrred empleando algoritmos bio-inspirados con el objetivo de mejorar su desempeño, partiendo desde el control primario, con la mejora de los convertidores de potencia, hasta el control terciario con las transacciones energéticas de la microrred. Se exploran diversos algoritmos, evaluando su desempeño. Los resultados para las diferentes etapas de esta investigación se encuentran plasmados en cuatro diferentes publicaciones científicas que se detallan en el Capítulo 2 del presente documento, donde se explica la metodología y los principales resultados y hallazgos para cada una de ellas.[CA] Les fonts d'energia renovables (ER) permeten una alta desagregació, pel que fan possible generar l'energia que s'utilitzarà en el mateix lloc del seu aprofitament. Això afavoreix un canvi en l'estructura de les xarxes elèctriques, permetent passar d'un esquema de generació centralitzat a un esquema distribuït. No obstant, les fonts d'ER són altament dependents de les condicions mediambientals com la radiació solar, la nuvolositat, el vent, entre altres; pel que aconseguir un sistema de generació basat en energies renovables és un repte. Els sistemes de generació que integren energies renovables han de ser capaços de: establir estratègies de control i gestió de l'energia que es genera per fer un ús eficient d'ella i intentar cobrir la demanda d'energia de la millor manera possible al combinar més d'un tipus de font d'energia, i sistemes d'emmagatzemament. Aquest esquema es coneix com a microxarxa elèctrica, la qual és capaç d'operar de manera aïllada de la xarxa elèctrica principal, o de manera interconnectada. Actualment s'està interessant en l'estudi, desenvolupament i implementació de sistemes gestors de l'energia (SGE) per a microxarxes elèctriques híbrides, que permeten augmentar la seua eficiència, fiabilitat i reduir els costos de la seua instal·lació i d'operació i manteniment. S'han provat múltiples estratègies, des de SGE basats en regles, algorismes comparatius, controladors clàssics i, en anys recents, la integració d'algorismes d'optimització bio-inspirats i intel·ligència artificial. Aquests algorismes han demostrat ser una alternativa interessant a les tècniques clàssiques per a la solució de problemes d'optimització i control en diversos problemes d'enginyeria, la seua aplicació en l'àmbit de les microxarxes continua en estudi. Els algorismes bio-inspirats es basen en imitar matemàticament els mecanismes i estratègies que la Natura ha implementat al llarg de milions d'anys per aconseguir equilibri en el seu funcionament, per exemple, imitant com les aus volen en ramat buscant menjar, o com les formigues i els llops segueixen patrons per a la recerca del seu menjar, o com les espècies porten a terme mecanismes de creuament amb mira a millorar la seua raça fent-les una espècie més apta per a la supervivència;, el qual es pot fer una analogia a sistemes artificials per a la millora de controladors i disseny de sistemes en microxarxes elèctriques. En aquest treball de recerca es mostra el model i desenvolupament d'un sistema de gestió òptima per a una microxarxa emprant algorismes bio-inspirats amb l'objectiu de millorar el seu rendiment, partint des del control primari, amb la millora dels convertidors de potència, fins al control terciari amb les transaccions energètiques de la microxarxa. S'exploren diversos algorismes, avaluant el seu rendiment. Els resultats per a les diferents etapes d'aquesta recerca es troben plasmats en quatre diferents publicacions científiques que es detallen al Capítol 2 del present document, on s'explica la metodologia i els principals resultats i troballes per a cada una d'elles.[EN] Renewable energy sources (RES) allow for high disaggregation, making it possible to generate energy at the site of its use. This favors a change in the structure of electrical grids, allowing for a transition from a centralized generation scheme to a distributed scheme. However, RES are highly dependent on environmental conditions such as solar radiation, cloudiness, wind, among others, making the creation of a renewable energy generation system a challenge. Generation systems that integrate renewable energies must be able to establish control and energy management strategies to make efficient use of the energy generated and try to meet the energy demand in the best possible way by combining more than one type of energy source and storage systems. This scheme is known as a microgrid, which is capable of operating independently from the main electrical grid or interconnecting with it. Currently, the study, development, and implementation of energy management systems (EMS) for hybrid microgrids are of interest in order to increase their efficiency, reliability, and reduce installation, operation, and maintenance costs. Multiple strategies have been tested, including rule-based EMS, comparative algorithms, classical controllers, and in recent years, the integration of bio-inspired optimization algorithms and artificial intelligence. These algorithms have shown to be an interesting alternative to classical techniques for solving optimization and control problems in various engineering problems, although their application in the field of microgrids is still under study. Bio-inspired algorithms are based on mathematically imitating the mechanisms and strategies that Nature has implemented over millions of years to achieve balance in its operation, for example, by imitating how birds fly in flocks in search of food, or how ants and wolves follow patterns to search for food, or how species carry out crossing mechanisms in order to improve their breed and make them more suitable for survival; in other words, they are based on how Nature optimizes its resources to prosper. Therefore, an analogy can be made with artificial systems for improving controllers and designing systems in microgrids. In this research work, the model and development of an optimal management system for a microgrid using bio-inspired algorithms is presented with the aim of improving its performance, starting from primary control, with the improvement of power converters, to tertiary control with the energy transactions of the microgrid. Various algorithms are explored, evaluating their performance. The results for the different stages of this research are reflected in four different scientific publications that are detailed in Chapter 2 of this document, where the methodology and main results and findings for each of them are explained.The authors wish to acknowledge the National Council of Science and Technology of Mexico (CONACYT) for funding this work through the Ph.D. scholarship number 486670. The authors would also thank the Institute of Energy Engineering of the Polytechnic University of Valencia, Spain, and the Department of Water and Energy Studies of the University of Guadalajara, Mexico, for all their support and collaboration. This study has also been supported by Food and Agriculture Organization of the United Nations through the project “Design of a Hybrid Renewable Microgrid System”.Águila León, J. (2023). Modelo y desarrollo de un sistema de gestión óptima para una microrred empleando algoritmos bio-inspirados [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/196747Compendi

A multimicrogrid energy management model implementing an evolutionary game-theoretic approach

[EN] Microgrids (MGs) are widely increasing to manage unequal electrical load requirements based on the infrastructure. The goal of this article is to manage energy in a centralized controller multimicrogrid (MMG) system operated at islanded mode. Renewable energy fluctuations in MG due to weather conditions build oscillation in MG operation modes. To solve this, a three-stage energy management MMG system is proposed. The proposed system is composed of operating mode prediction by measuring the weather conditions. In islanded mode, energy management is incorporated using a two-round fuzzy-based speed (TRFS) algorithm followed by evolutionary game theory and status updating by Markov chain. The TRFS algorithm takes into account voltage, frequency, power factor, total harmonic distortion, and loss of produced power probability parameters. The parallel processing of the TRFS algorithm reduces processing time, then a Stackelberg game with a quasi-oppositional symbiotic organisms search approach is carried out for power exchange. Markov chain based future prediction of MG states ensures detection of MG operating mode along with weather changes. Simulations are developed in MATLAB Simulink, and their outcomes show better performance than previous work whose results are evaluated in terms of load and generator output at two modes, power generated at individual MG and exchanged power.Consejo Nacional de Ciencia y Tecnologia, Grant/Award Number: 486670Aguila-Leon, J.; Chiñas-Palacios, C.; García, EXM.; Vargas-Salgado, C. (2020). A multimicrogrid energy management model implementing an evolutionary game-theoretic approach. International Transactions on Electrical Energy System. 30(11):1-19. https://doi.org/10.1002/2050-7038.12617S1193011Pinzon, J. A., Vergara, P. P., da Silva, L. C. P., & Rider, M. J. (2019). 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State of charge and state of power management among the energy storage systems by the fuzzy tuned dynamic exponent and the dynamic PI controller. Journal of Energy Storage, 19, 348-363. doi:10.1016/j.est.2018.08.004Bhowmik, P., Chandak, S., & Rout, P. K. (2019). Frequency superimposed energy bifurcation technology for a hybrid microgrid. Sustainable Cities and Society, 45, 607-618. doi:10.1016/j.scs.2018.12.027Pouryekta, A., Ramachandaramurthy, V. K., Mithulananthan, N., & Arulampalam, A. (2018). Islanding Detection and Enhancement of Microgrid Performance. IEEE Systems Journal, 12(4), 3131-3141. doi:10.1109/jsyst.2017.2705738Talapur, G. G., Suryawanshi, H. M., Xu, L., & Shitole, A. B. (2018). A Reliable Microgrid With Seamless Transition Between Grid Connected and Islanded Mode for Residential Community With Enhanced Power Quality. IEEE Transactions on Industry Applications, 54(5), 5246-5255. doi:10.1109/tia.2018.2808482Guo, Y., & Zhao, C. (2018). Islanding-Aware Robust Energy Management for Microgrids. IEEE Transactions on Smart Grid, 9(2), 1301-1309. doi:10.1109/tsg.2016.2585092Mahmood, H., & Jiang, J. (2019). Decentralized Power Management of Multiple PV, Battery, and Droop Units in an Islanded Microgrid. IEEE Transactions on Smart Grid, 10(2), 1898-1906. doi:10.1109/tsg.2017.2781468Zhou, J., Zhang, J., Cai, X., Shi, G., Wang, J., & Zang, J. (2019). Design and Analysis of Flexible Multi-Microgrid Interconnection Scheme for Mitigating Power Fluctuation and Optimizing Storage Capacity. Energies, 12(11), 2132. doi:10.3390/en12112132Nguyen, A.-D., Bui, V.-H., Hussain, A., Nguyen, D.-H., & Kim, H.-M. (2018). Impact of Demand Response Programs on Optimal Operation of Multi-Microgrid System. Energies, 11(6), 1452. doi:10.3390/en11061452Rui, T., Li, G., Wang, Q., Hu, C., Shen, W., & Xu, B. (2019). Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids. Applied Sciences, 9(4), 624. doi:10.3390/app9040624Choobineh, M., Silva-Ortiz, D., & Mohagheghi, S. (2018). An Automation Scheme for Emergency Operation of a Multi-Microgrid Industrial Park. IEEE Transactions on Industry Applications, 54(6), 6450-6459. doi:10.1109/tia.2018.2851210Zeng, J., Peng, J., Zhang, C., Zhang, W., & Zhou, S. (2019). Research on islanding partition algorithm for the multi‐microgrids. The Journal of Engineering, 2019(16), 3345-3348. doi:10.1049/joe.2018.8395Farzin, H., Fotuhi-Firuzabad, M., & Moeini-Aghtaie, M. (2018). Role of Outage Management Strategy in Reliability Performance of Multi-Microgrid Distribution Systems. IEEE Transactions on Power Systems, 33(3), 2359-2369. doi:10.1109/tpwrs.2017.2746180Dey, B., Bhattacharyya, B., & Sharma, S. (2018). Optimal Sizing of Distributed Energy Resources in a Microgrid System with Highly Penetrated Renewables. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 43(S1), 527-540. doi:10.1007/s40998-018-0141-xHu, B., Wang, H., & Yao, S. (2017). Optimal economic operation of isolated community microgrid incorporating temperature controlling devices. Protection and Control of Modern Power Systems, 2(1). doi:10.1186/s41601-017-0037-1Rezaei, N., Ahmadi, A., Khazali, A. H., & Guerrero, J. M. (2018). Energy and Frequency Hierarchical Management System Using Information Gap Decision Theory for Islanded Microgrids. IEEE Transactions on Industrial Electronics, 65(10), 7921-7932. doi:10.1109/tie.2018.2798616Liu, W., Gu, W., Wang, J., Yu, W., & Xi, X. (2018). Game Theoretic Non-Cooperative Distributed Coordination Control for Multi-Microgrids. IEEE Transactions on Smart Grid, 9(6), 6986-6997. doi:10.1109/tsg.2018.2846732Zeng, J., Wang, Q., Liu, J., Chen, J., & Chen, H. (2019). A Potential Game Approach to Distributed Operational Optimization for Microgrid Energy Management With Renewable Energy and Demand Response. IEEE Transactions on Industrial Electronics, 66(6), 4479-4489. doi:10.1109/tie.2018.2864714Ju, C., Wang, P., Goel, L., & Xu, Y. (2018). A Two-Layer Energy Management System for Microgrids With Hybrid Energy Storage Considering Degradation Costs. IEEE Transactions on Smart Grid, 9(6), 6047-6057. doi:10.1109/tsg.2017.2703126Azeem, F., Narejo, G. B., & Shah, U. A. (2018). Integration of renewable distributed generation with storage and demand side load management in rural islanded microgrid. Energy Efficiency, 13(2), 217-235. doi:10.1007/s12053-018-9747-0Al Badwawi, R., Issa, W. R., Mallick, T. K., & Abusara, M. (2019). Supervisory Control for Power Management of an Islanded AC Microgrid Using a Frequency Signalling-Based Fuzzy Logic Controller. IEEE Transactions on Sustainable Energy, 10(1), 94-104. doi:10.1109/tste.2018.2825655Hosseinnia, H., Nazarpour, D., & Talavat, V. (2018). Multi-objective optimization framework for optimal planning of the microgrid (MG) under employing demand response program (DRP). Journal of Ambient Intelligence and Humanized Computing, 10(7), 2709-2730. doi:10.1007/s12652-018-0977-yChoudhury, S., Bhowmik, P., & Rout, P. K. (2018). Robust dynamic fuzzy-based enhanced VPD/FQB controller for load sharing in microgrid with distributed generators. Electrical Engineering, 100(4), 2457-2472. doi:10.1007/s00202-018-0724-6Choudhury, S., Bhowmik, P., & Rout, P. K. (2018). Seeker optimization approach to dynamic PI based virtual impedance drooping for economic load sharing between PV and SOFC in an islanded microgrid. Sustainable Cities and Society, 37, 550-562. doi:10.1016/j.scs.2017.11.013Choudhury, S., Bhowmik, P., & Rout, P. K. (2018). Economic load sharing in a D-STATCOM Integrated Islanded Microgrid based on Fuzzy Logic and Seeker Optimization Approach. Sustainable Cities and Society, 37, 57-69. doi:10.1016/j.scs.2017.11.004Chaitanya, B. K., Yadav, A., & Pazoki, M. (2018). Wide area monitoring and protection of microgrid with DGs using modular artificial neural networks. Neural Computing and Applications, 32(7), 2125-2139. doi:10.1007/s00521-018-3750-4Wu, P., Huang, W., Tai, N., Ma, Z., Zheng, X., & Zhang, Y. (2019). A Multi-layer Coordinated Control Scheme to Improve the Operation Friendliness of Grid-Connected Multiple Microgrids. Energies, 12(2), 255. doi:10.3390/en12020255Uy, L., Uy, P., Siy, J., Chiu, A. S. F., & Sy, C. (2018). Target-oriented robust optimization of a microgrid system investment model. Frontiers in Energy, 12(3), 440-455. doi:10.1007/s11708-018-0563-1Hu, K., Li, W., Wang, L., Cao, S., Zhu, F., & Shou, Z. (2018). Energy management for multi-microgrid system based on model predictive control. Frontiers of Information Technology & Electronic Engineering, 19(11), 1340-1351. doi:10.1631/fitee.1601826Rahbar, K., Chai, C. C., & Zhang, R. (2018). Energy Cooperation Optimization in Microgrids With Renewable Energy Integration. IEEE Transactions on Smart Grid, 9(2), 1482-1493. doi:10.1109/tsg.2016.2600863Kumar, R. H., & Ushakumari, S. (2018). A Novel Control Strategy for Autonomous Operation of Isolated Microgrid with Prioritized Loads. Journal of The Institution of Engineers (India): Series B, 99(4), 323-330. doi:10.1007/s40031-018-0335-7Kaushal, J., & Basak, P. (2018). A Novel Approach for Determination of Power Quality Monitoring Index of an AC Microgrid Using Fuzzy Inference System. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 42(4), 429-450. doi:10.1007/s40998-018-0087-zSun, W., Ma, S., Alvarez‐Fernandez, I., Roofegari nejad, R., & Golshani, A. (2018). Optimal self‐healing strategy for microgrid islanding. IET Smart Grid, 1(4), 143-150. doi:10.1049/iet-stg.2018.0057Sandgani, M. R., & Sirouspour, S. (2018). Priority-Based Microgrid Energy Management in a Network Environment. IEEE Transactions on Sustainable Energy, 9(2), 980-990. doi:10.1109/tste.2017.276955