Microgrid energy management system for smart home using multi-agent system

This paper proposes a multi-agent system for energy management in a microgrid for smart home applications, the microgrid comprises a photovoltaic source, battery energy storage, electrical loads, and an energy management system (EMS) based on smart agents. The microgrid can be connected to the grid or operating in island mode. All distributed sources are implemented using MATLAB/Simulink to simulate a dynamic model of each electrical component. The agent proposed can interact with each other to find the best strategy for energy management using the java agent development framework (JADE) simulator. Furthermore, the proposed agent framework is also validated through a different case study, the efficiency of the proposed approach to schedule local resources and energy management for microgrid is analyzed. The simulation results verify the efficacy of the proposed approach using Simulink/JADE co-simulation

Smart Grid Ecosystem Modeling Using a Novel Framework for Heterogenous Agent Communities

The modeling of smart grids using multi-agent systems is a common approach due to the ability to model complex and distributed systems using an agent-based solution. However, the use of a multi-agent system framework can limit the integration of new operation and management models, especially artificial intelligence algorithms. Therefore, this paper presents a study of available open-source multi-agent systems frameworks developed in Python, as it is a growing programming language and is largely used for data analytics and artificial intelligence models. As a consequence of the presented study, the authors proposed a novel open-source multi-agent system framework built for smart grid modeling, entitled Python-based framework for heterogeneous agent communities (PEAK). This framework enables the use of simulation environments but also allows real integration at pilot sites using a real-time clock. To demonstrate the capabilities of the PEAK framework, a novel agent ecosystem based on agent communities is shown and tested. This novel ecosystem, entitled Agent-based ecosystem for Smart Grid modeling (A4SG), takes full advantage of the PEAK framework and enables agent mobility, agent branching, and dynamic agent communities. An energy community of 20 prosumers, of which six have energy storage systems, that can share energy among them, using a peer-to-peer market, is used to test and validate the PEAK and A4SG solutions.The authors acknowledge the work facilities and equipment provided by the GECAD research center (UIDB/00760/2020) to the project team.info:eu-repo/semantics/publishedVersio

Energy flexibility assessment of a multi agent-based smart home energy system

Power systems worldwide are complex and challenging environments. The increasing necessity for an adequate integration of renewable energy sources is resulting in a rising complexity in power systems operation. Multi-agent based simulation platforms have proven to be a good option to study the several issues related to these systems. In a smaller scale, a home energy management system would be effective for the both sides of the network. It can reduce the electricity costs of the demand side, and it can assist to relieve the grid congestion in peak times. This paper represents a domestic energy management system as part of a multi-agent system that models the smart home energy system. Our proposed system consists of energy management and predictor systems. This way, homes are able to transact with the local electricity market according to the energy flexibility that is provided by the electric vehicle, and it can manage produced electrical energy of the photovoltaic system inside of the home.his work has been supported by the European Commission H2020 MSCA-RISE-2014: Marie Sklodowska-Curie project DREAM-GO Enabling Demand Response for short and real- time Efficient And Market Based Smart Grid Operation - An intelligent and real-time simulation approach ref. 641794.info:eu-repo/semantics/publishedVersio

A Distributed Optimization Method for Optimal Energy Management in Smart Grid

This chapter presents a distributed optimization method named sequential distributed consensus-based ADMM for solving nonlinear constrained convex optimization problems arising in smart grids in order to derive optimal energy management strategies. To develop such distributed optimization method, multi-agent system and consensus theory are employed. Next, two smart grid problems are investigated and solved by the proposed distributed algorithm. The first problem is called the dynamic social welfare maximization problem where the objective is to simultaneously minimize the generation costs of conventional power plants and maximize the satisfaction of consumers. In this case, there are renewable energy sources connected to the grid, but energy storage systems are not considered. On the other hand, in the second problem, plug-in electric vehicles are served as energy storage systems, and their charging or discharging profiles are optimized to minimize the overall system operation cost. It is then shown that the proposed distributed optimization algorithm gives an efficient way of energy management for both problems above. Simulation results are provided to illustrate the proposed theoretical approach

Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management

With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction. Traditional DSM works focusing on optimizing the energy activities for a single household can not scale up to large-scale home energy management problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential way to solve the problem of scalability, where modern homes interact together to reduce energy consumers consumption while striking a balance between energy cost and peak loads reduction. However, it is difficult to solve such an environment with the non-stationarity, and existing MA-DRL approaches cannot effectively give incentives for expected group behavior. In this paper, we propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid. To mitigate the non-stationarity of the microgrid environment, a novel predictive model is proposed to measure the collective market behavior. Besides, a collective behavior entropy is introduced to reduce the high peak loads incurred by the collective behaviors of all householders in the smart grid. Empirical results show that our approach significantly outperforms the state-of-the-art methods regarding power cost reduction and daily peak loads optimization

An event-based resource management framework for distributed decision-making in decentralized virtual power plants

The Smart Grid incorporates advanced information and communication technologies (ICTs) in power systems, and is characterized by high penetration of distributed energy resources (DERs). Whether it is the nation-wide power grid or a single residential building, the energy management involves different types of resources that often depend on and influence each other. The concept of virtual power plant (VPP) has been proposed to represent the aggregation of energy resources in the electricity market, and distributed decision-making (DDM) plays a vital role in VPP due to its complex nature. This paper proposes a framework for managing different resource types of relevance to energy management for decentralized VPP. The framework views VPP as a hierarchical structure and abstracts energy consumption/generation as contractual resources, i.e., contractual offerings to curtail load/supply energy, from third party VPP participants for DDM. The proposed resource models, event-based approach to decision making, multi-agent system and ontology implementation of the framework are presented in detail. The effectiveness of the proposed framework is then demonstrated through an application to a simulated campus VPP with real building energy data

Системная эффективность функционирования энергетической системы с управляемой нагрузкой

В статті розглянуто основні фактори, які визначають необхідність суттєвих змін в енергетиці та доцільність перегляду традиційних підходів, принципів та механізмів їх функціонування. Приведені основні елементи стимулюючого регулювання RAB-регулювання. Представлено ценологічний підхід до моделювання прогнозних значень електроспоживання. Визначені питання підвищення енергетичної ефективності, розглянуто енергоекологічні, технічні та енергетичні критерії оптимальності енергетичної системи. Проаналізовано необхідність та особливості реалізації системного підходу до задач оптимізації енергетичної системи. Приведено основні принципи впровадження технології Smart Grid. Порушені питання впровадження мультиагентних систем управління та застосування клієнтоорієнтованого підходу. Представлена загальна математична модель енергетичної системи.The article reviews the main factors that determine the need for significant changes in energy and appropriateness of traditional approaches, principles and mechanisms of their functioning. Indicates main elements of incentive regulation RAB–regulation. Presented tsenologycal approach to modeling predictive values of power consumption. Identified areas of energy efficiency is considered enerhoekologycal, technical and energy optimality criteria grid. Analyzes the necessity and especially the implementation of a systematic approach to optimization problems of the energy system. Powered basic principles of implementing technology Smart Grid. Brought up questions implementation of multi-agent systems management and client-application approach. Submitted general mathematical model of the power system.В статье рассмотрены основные факторы, которые определяют необходимость существенных изменений в энергетике и целесообразность пересмотра традиционных подходов, принципов и механизмов их функционирования. Приведены основные элементы стимулирующего регулирования RAB–регулирования. Представлен ценологичний подход к моделированию прогнозных значений электропотребления. Определены вопросы повышения энергетической эффективности, рассмотрены энергоэкологические, технические и энергетические критерии оптимальности энергетической системы. Проанализированы необходимость и особенности реализации системного подхода к задачам оптимизации энергетической системы. Приведены основные принципы внедрения технологии Smart Grid. Затронуты вопросы внедрения мультиагентных систем управления и применения клиентоориентированного подхода. Представлена общая математическая модель энергетической системы

Heuristic Multi-Agent Control for Energy Management of Microgrids with Distributed Energy Sources

The increased integration of distributed Renewable Energy Sources (RESs) and adoption of Electric Vehicles (EVs) require appropriate control and management of energy sources and EV charging. This becomes critical at the distribution system level, especially at a microgrid (MG) level. This control is required not only to mitigate the negative impacts of intermittent generation from RESs but also to make better use of available energy, reduce carbon footprint, maximize the overall profit of microgrid and increase energy autonomy by effective utilization of battery storage. This paper proposes a heuristic multi-agent based decentralized energy management approach for grid-connected MG. The MG comprises of active (controlled) and passive (uncontrolled) electrical loads, a photovoltaic (PV) system, battery energy storage system (BESS) and a charging post for electric vehicles. The proposed approach is aimed at optimizing the use of local energy generation from photovoltaic and smart energy utilization to serve electrical loads and EV as well as maximizing MG profit. The aim of the energy management is to supply local consumption at minimum cost and less dependency on the main grid supply. Utilizing energy available from RESs (PV and BESS), customers satisfaction (fulfilling local demand), considering uncertainty of renewable generation and load consumption and also taking into account technical constraint are the main strengths of the presented framework. Performance of the proposed algorithm is investigated under different operating conditions and its efficacy is verified

A Novel Locality Algorithm and Peer-to-Peer Communication Infrastructure for Optimizing Network Performance in Smart Microgrids

[EN] Peer-to-Peer (P2P) overlay communications networks have emerged as a new paradigm for implementing distributed services in microgrids due to their potential benefits: they are robust, scalable, fault-tolerant, and they can route messages even with a large number of nodes which are frequently entering or leaving from the network. However, current P2P systems have been mainly developed for file sharing or cycle sharing applications where the processes of searching and managing resources are not optimized. Locality algorithms have gained a lot of attention due to their potential to provide an optimized path to groups with similar interests for routing messages in order to get better network performance. This paper develops a fully functional decentralized communication architecture with a new P2P locality algorithm and a specific protocol for monitoring and control of microgrids. Experimental results show that the proposed locality algorithm reduces the number of lookup messages and the lookup delay time. Moreover, the proposed communication architecture heavily depends of the lookup used algorithm as well as the placement of the communication layers within the architecture. Experimental results will show that the proposed techniques meet the network requirements of smart microgrids even with a large number of nodes on stream.This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (ERDF) under Grant ENE2015-64087-C2-2R. This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under BES-2013-064539.Marzal-Romeu, S.; González-Medina, R.; Salas-Puente, RA.; Figueres Amorós, E.; Garcerá, G. (2017). A Novel Locality Algorithm and Peer-to-Peer Communication Infrastructure for Optimizing Network Performance in Smart Microgrids. 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