Multi-agent architecture for local electricity trading in power distribution systems

[ES] En la última década, los mercados eléctricos han desarrollado entornos competitivos para sistemas eléctricos completos. El rápido crecimiento de los recursos energéticos distribuidos ha dificultado mantener la credibilidad y estabilidad del sistema. Sin embargo, debido a la volatilidad de los recursos energéticos distribuidos las estrategias convencionales de gestión de la energía son incapaces de resolver estos problemas de forma centralizada. Además, los mercados centralizados de electricidad no son capaces de adaptarse al comportamiento flexible de los consumidores que ocurre en los programas de respuesta de demanda. Por lo tanto, se requieren nuevas estructuras de comercio de electricidad que proporcionen energía a las redes de distribución de forma descentralizada y distribuida. Este trabajo presenta un enfoque ascendente de gestión energética basado en una arquitectura multiagente para el comercio local de la electricidad. La estructura propuesta consiste en una clase de organización basada en sistemas multiagente, en la cual cada agente cumple diferentes tareas. Estos agentes est_an formados por recursos energéticos distribuidos, consumidores eléctricos, prosumidores, vehículos eléctricos (Electricit Vehicles (EV)), agregadores, un operador del sistema de distribución, coordinadores locales y los coordinadores de los EV del sistema. Además, proponemos un enfoque ascendente para el comercio de energía desde los usuarios finales, como agentes prosumidores capaces de proporcionar transacciones energéticas bidireccionales a los agregadores y al gestor de la red de distribución (Distibution System Operator (DSO)). En este contexto, se presenta una arquitectura basada en sistemas multiagente, para el sistema eléctrico de las casas inteligentes (como ejemplo de usuario final). A continuación, se define el sistema de gestión de la energía en el hogar (HEMS por sus siglas en ingles) para modelar el comportamiento flexible de los usuarios finales residenciales y su incertidumbre basándose en diferentes métodos de optimización (por ejemplo, intervalo, estocástico e intervalo-estocástico). Además, presentamos un método basado en escenarios probabilísticos para la gestión de la energía residencial y el comercio de energía con el mercado local de electricidad basado en una estrategia de licitación óptima. De acuerdo con nuestro modelo de oferta óptimo, el HEMS es capaz de realizar transacciones de energía con otros actores en su vecindario como un agente de fijación de precios basado en los enfoques de intercambio de energía entre pares o enfoques basados en la comunidad. Conforme al enfoque ascendente propuesto en nuestro trabajo de doctorado, las decisiones de los agentes en la capa inferior tienen prioridad en comparación con las decisiones de los agentes en las capas superiores. De esta manera, la estrategia propuesta gestiona la energía localmente para lograr una optimización social global. Además, en la red de distribución se pueden comercializar localmente diferentes tipos de productos básicos de electricidad, como la energía y la flexibilidad. A continuación, hemos propuesto varios enfoques (por ejemplo, descentralizado, monopolístico y basado en juegos) para la gestión de la flexibilidad energética entre los agentes de la red de distribución de energía, teniendo en cuenta el comportamiento flexible de los usuarios finales y los agregadores. Por último, se ha estudiado el impacto de los futuros sistemas de transporte en las redes inteligentes. Así, la gestión de la flexibilidad energética de los usuarios finales y las operaciones de recarga de los vehículos eléctricos se modelan en la red de distribución. Se han presentado tres estrategias de gestión de la energía para abordar la flexibilidad energética y el funcionamiento de los vehículos eléctricos entre los actores de la capa inferior del sistema eléctrico. Además, la incertidumbre causada por la movilidad de los vehículos eléctricos se ha modelado mediante una programación estocástica. Aquí, el reto es modelar un problema multinivel basado en la función objetiva de los agentes considerando la incertidumbre de los parámetros estocásticos del sistema. De esta forma, cada agente puede participar en diferentes tipos de transacciones eléctricas según sus funciones objetivas correspondientes. Se evalúa el rendimiento del sistema propuesto de gestión de la energía en el hogar (HEMS) comparándolo con los métodos de optimización de intervalos estocásticos propuestos y de bandas estocásticas predichas medicadas. Evaluamos el impacto del modelo de flexibilidad energética y su exactitud de predicción. Además, evaluamos el programa de respuesta de demanda en términos de las ganancias esperadas, de la energía eléctrica tramitada y de la credibilidad de los resultados. Para ello, proponemos un modelo de oferta óptima para el sistema de gestión de la energía en el hogar. Así, el sistema puede participar en el comercio local de electricidad. El rendimiento del modelo de oferta _optima propuesto se evalúa en dos casos diferentes. El Caso 1 evalúa el impacto de los coeficientes de optimismo y flexibilidad en el HEMS, considerando la estrategia de licitación óptima. En el caso 2, sin embargo, el rendimiento de los dos métodos de optimización diferentes -llamados InterStoch e Hybrid- en el HEMS se evalúa sin considerar la estrategia de licitación _optima. Posteriormente, se evalúa el funcionamiento de nuestros enfoques descentralizados, monopolísticos y basados en juegos en términos de su impacto en la incertidumbre de la línea de distribución y el comportamiento flexible de los usuarios finales. Por último, modelamos la gestión de la flexibilidad energética de los usuarios finales y la operación de carga de los EV en la red de distribución. Se presentan tres estrategias de gestión de la energía para abordar la flexibilidad energética y el funcionamiento de los EV entre los actores de la capa inferior del sistema eléctrico

Topology-based Approximations for N−1\mathcal{N}-1 Contingency Constraints in Power Transmission Networks

It is crucial for maintaining the security of supply that transmission networks continue to operate even if a single line fails. Modeling $\mathcal{N} - 1$ security in power system capacity expansion problems introduces many extra constraints if all possible outages are accounted for, which leads to a high computational burden. Typical approaches to avoid this burden consider only a subset of possible outages relevant to a given dispatch situation. However, this relies on knowing the dispatch situation beforehand, and it is not suitable for investment optimization problems where the generation fleet is not known in advance. In this paper, we introduce a heuristic approach to model the fully secured $\mathcal{N}-1$ feasible space using a smaller number of constraints in a way that only depends on the topology of transmission networks. In our proposed approach, the network's security is modelled by comparing the polytope of the feasible space of nodal net power obtained from the security-constrained linearized AC optimal power flow problem. To approximate this polytope, a buffer capacity factor is defined for transmission lines in the $\mathcal{N}-0$ secure case, thereby avoiding the introduction of many additional constraints. In this way, three approaches are introduced for obtaining a buffer capacity factor consisting of approximate, robust and line-specific approaches. Finally, the performance of our proposed approaches is assessed in different scales of transmission networks for determining the proposed buffer capacity factors, contingency analysis and economic evaluation. Moreover, we find that our proposed heuristics provide excellent approximations of the fully secured $\mathcal{N}-1$ solutions with a much lower computational burden

Two-Layer Game-Based Framework for Local Energy Flexibility Trading

A new configuration is required to model the behavior of customers, aggregators, the distribution system operator (DSO), and their interactions due to the active participation of customers in the local flexibility market. To this end, we propose a two-layer game-based framework that models agents’ behavior and their interactions. Thus, firstly, at the inner layer, customers and aggregators set their decision variables considering the decisions of each other performing an iterative game. After the inner layer game concludes, in the outer layer, the DSO determines its decision variable according to the decision of aggregators and customers. If the convergence condition is satisfied, the game of the outer layer concludes. Otherwise, there is another inner game and subsequent outer game until the satisfaction of convergence condition. Therefor, customers, aggregators, and the DSO have similar decision-making power. Since all of them can make their own decisions and modify them according to others’ decisions. To study our model, we consider three scenarios with different levels of freedom while decision-making for customers that is resulted from different levels of limitation for arbitrage avoidance. Our results illustrate that our iterative approach is converged after few iterations in both the inner and the outer layer. Moreover, customers who have a contract with the same aggregator behave similarly. Furthermore, aggregators benefit from customers’ freedom, while it is very destructive for the DSO and increases its objective function.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading

A framework for participation of prosumers in peer-to-peer energy trading and flexibility markets

As the owners of distributed energy resources (DER), prosumers can actively manage their power supply and consumption and partake in new energy services. In order to enable prosumers to benefit from their participation in energy services, innovative market models need to be designed. This paper proposes a framework for local energy and flexibility trading within distribution networks, in which prosumers participate in a peer-to-peer (P2P) market to trade energy with each other based on their preferences. The P2P market is cleared in a decentralized manner with direct interaction of seller and buyer prosumers. Then, the distribution system operator (DSO) checks the network constraints based on the energy scheduling of prosumers. If the network constraints are not satisfied, the DSO calculates the flexibility that is required in each feeder to avoid network issues. Triggered by the requested flexibility by the DSO, prosumers in each feeder form a community and participate in a flexibility market, in which they can offer their flexibility in response to the DSO’s request. An iterative auction is employed to clear the flexibility market, which enables the prosumers to independently decide on their offered flexibility, while the DSO adjusts the flexibility price to minimize its costs. The proposed framework is tested on a real-world distribution network. Simulations based on a number of case studies indicate that through the proposed framework, the DSO can avoid network constraints violation by employing prosumers’ flexibility. Besides, participation in the P2P and flexibility trading reduces the net energy costs of the prosumers in different community by an average of 17.09%.©2022 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Monopolistic and game-based approaches to transact energy flexibility

The appearance of the flexible behavior of end-users based on demand response programs makes the power distribution grids more active. Thus, electricity market participants in the bottom layer of the power system, wish to be involved in the decision-making process related to local energy management problems, increasing the efficiency of the energy trade in distribution networks. This paper proposes monopolistic and game-based approaches for the management of energy flexibility through end-users, aggregators, and the Distribution System Operator (DSO) which are defined as agents in the power distribution system. Besides, a 33-bus distribution network is considered to evaluate the performance of our proposed approaches for energy flexibility management model based on impact of flexibility behaviors of end-users and aggregators in the distribution network. According to the simulation results, it is concluded that although the monopolistic approach could be profitable for all agents in the distribution network, the game-based approach is not profitable for end-users.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Iterative Game Approach for Modeling the Behavior of Agents in a Competitive Flexibility Trading

The potential of end-users to modify their consumption pattern makes them an interesting resource for providing energy flexibility in energy communities. Thus, active end-users require sufficient incentives and automated trading and management schemes. In order to enable increased small-scale end-users participation for flexibility service provision, a new design for flexibility trading is required to model the behavior of different agents and their interactions in energy communities. The novelty of our work lies in proposing an iterative game-based approach in which all agents – consisting of the distribution system operator (DSO), aggregators, and customers– can determine their decision variables to optimize their own objective functions and interact with others to modify their decisions according to others’ decisions. In addition, three scenarios are considered to study the effects of agents’ freedom while setting their decision variables (by removing one of their constraints in their corresponding decision-making problem). Moreover, the impact of the presence of interruptible loads in comparison with shiftable loads is investigated in this paper. According to the simulation results, it is found that in the scenario where end-users have fewer constraints, in presence of interruptible loads, end-users gain greater income compared to the absence of interruptible loads.2021 CCBY - IEEE is not the copyright holder of this material. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/The work of Amin Shokri Gazafroudi was supported by the CoNDyNet2 Project funded by the German Federal Ministry of Education and Research under Grant 03EK3055E.fi=vertaisarvioitu|en=peerReviewed

Multi-Agent Architecture for Peer-to-Peer Electricity Trading based on Blockchain Technology

The security of smart grids is put at risk due to their automation and remote access features. Blockchain technology can be used as a distributed ledger where data is stored and all the data transactions between the different entities of a smart grid are signed to protect them from such attacks. This paper proposes a multi-agent system (MAS) that combines smart contracts and blockchain to enable Peer-to-Peer electricity trading in a MicroGrid (MG) scenario, without the need for human intervention. The use of blockchain technology helps reduce transaction costs and allows to make micro transactions in the proposed market. Blockchain also improves the security of the platform because all the involved actors can be certain about the authorship of the information produced in the system. Finally, the use of a MAS and the possibility of negotiating between the agents helps obtain an optimal state in the system in which the costs of energy are minimal and the local production of energy is profitable.©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Application of artificial immune system to domestic energy management problem

[EN] The connection of devices in a smart home should be done optimally, this helps save energy and money. Numerous optimization models have been applied, they are based on fuzzy logic, linear programming or bio-inspired algorithms. The aim of this work is to solve an energy management problem in a domestic environment by applying an artificial immune system. We carried out a thorough analysis of the different strategies that optimize a domestic environment system, in order to demonstrate the ability of an artificial immune system to find a successful optima that satisfies the problem constraints

Peer-to-Peer Electricity Market based on Local Supervision

The active participation of small-scale prosumers and consumers with demand-response capability and renewable resources can be a potential solution to the environmental issues and flexibility-related challenges. A local peer-to-peer market is proposed to exploit the maximum flexibility potential of prosumers. In this local market, network users can trade with each other as well as the grid. The proposed trading model includes two levels to consider both the democracy and the profitability of energy trading. At the first level, the model considers the trading preferences of each player to respect the peers’ choices. The second level matches the rest of the bids and offers of the local buyers and sellers aiming to maximize the social welfare of all of the players participating in the local market. Our proposed local market is implemented for a test system consisting of fifteen residential players, and the results are compared to other trading models through different comparison criteria such as social-welfare of all players and the net cost of each individual player from consuming electricity. Simulation results for the case study demonstrate that the proposed local market model can still be profitable and liquid while respecting the players’ trading preferences and choices.©2021 Institute of Electrical and Electronics Engineers. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/This work was undertaken as part of the FLEXIMAR project (novel marketplace for energy flexibility) with financial support provided by Business Finland (Grant No. 6988/31/2018) as well as Finnish companies.fi=vertaisarvioitu|en=peerReviewed