A distributed load scheduling mechanism for micro grids

A review on Multi-Agent system based energy management systems for micro grids, 2019

Over the last century, there has been no significant change in the centrally controlled structure of electrical power grids. The economic and population growth throughout the planet increases the pressure on the power sector. Power electrical grid, as the main structure for power transmission has to reconsider its concepts. The flexible controlling of DER (Distributed Energy Resources) such as wind power, solar power, hydropower, wave power, geo-thermal power and biomass power has to be considered to overcome these challenges. The demand for electricity is steadily increasing and current centrally controlled structure is ill-suited to fulfill the modern complex power requirements. As a result the concepts like micro grids, smart grids, multi-energy systems, virtual power plants are emerging combined with new concepts like MAS (Multi-Agent Systems) and IoT (Internet of Things). They move our electrical power grid to a more decentralized, highly efficient energy management system. The objective of this paper is to discuss how multi-agent concept based energy management systems will behave for micro grids. The paper contributes a description of smart micro grids, MAS based Energy management systems and controlling of DER in micro grids, and future of power electrical grid structure with MAS.

Energy, 2019

The inaccurate prediction of solar irradiance, wind speed and demand load may significantly influence the operation of micro-grids. This condition causes unbalances between generation and load that lead to variations in DC and AC bus voltages, and may affect system stability. A multi-agent system (MAS) is proposed in this study to achieve optimal energy management for voltage regulation and to enhance the stability of a system under different weather conditions and load perturbations for two connected micro-grids. Optimum operation is achieved through two stages. The first stage is the optimum day-ahead energy from each source based on historical data. The second stage is implemented to maintain the balance between generation and load by considering economic operation during real-time operation. This stage can be realized by controlling converters related to each source in both micro-grids. Different scenarios are presented to evaluate the effectiveness of the proposed MAS. The simulation results demonstrate that the performance of the proposed energy management system is efficient.

As the power generation paradigm is going green by involvement of renewable energy sources, the energy management process also has to evolve towards new more powerful, intelligent control methods. The inter-connection of a large number of renewable energy sources to the grid, along with their obvious advantages, makes the grid highly distributed. This may cause problems of efficient control, stability, reliability, power quality and security, thus represent one of the greatest engineering challenges. To enable greater amounts of distributed and renewable energy resources across the power grid backbone for providing better demand management, the next-generation electricity grid is emerging as a system of organically integrated intelligent Micro-Grids. In order to make them intelligent the application of Information & Communication Technology (ICT), remote sensing and communication technology to power systems is gaining momentum making Micro-Grids smart. Agent-oriented software engineering paradigm represents an interesting means of analyzing, designing and building complex software systems, quite suitable to the requirements of making Micro-Grids smart. Multi-Agent System (MAS) seems to have marvelous features that meet the operation and control requirements of distributed energy resources in Micro-Grids. This paper presents an overview of the emerging sub-field of Distributed Artificial Intelligence, Multi-Agent System to control and manage the changing face of electric power grid by inculcating the intelligent agents into Micro-Grids.

—High penetration of distributed generation leads to the importance of adapting the new trend of Smart Grid to divide the grid into intelligent Micro-Grid zones and facilitate its operation using decentralized distributed control schema. In this paper a practical Microgrid model has been designed and simulated using MATLAB/SIMULINK (MATrix LABoratory). JADE (Java Agent DEvelopment Framework) platform was used to design an agent-based control system that uses a certain algorithm to control both of the load and the supply units by switching on and off the breakers according to specified power reliability criteria. TCP/IP (Transmission Control Protocol/Internet Protocol) protocol was used to link the SIMULINK environment and the JADE Platform. Simulation of typical daily scenarios were tested to verify the effectiveness of the control system.

In market operations, distributed generators (DGs) and price-sensitive loads participate in a microgrid energy market implemented in JADE. Each DG and each price-sensitive load is represented by the respective agents which perform various functions such as scheduling, coordination and market clearing subject to system, DG and load constraints. Each agent is assigned to one of the several agent objectives which maximizes either DG or load surpluses or both. In simulated operation of a microgrid, hourly power reference signals and load control signals from JADE are passed to DG and load models developed in MATLAB/Simulink using MACSimJX. Simulated operation of DGs and loads are studied by performing simulations under different agent objectives. Results from simulation studies demonstrate the effectiveness of implementing multi-agent system (MAS) in the distributed management of microgrids.

Energy, 2015

Providing a cost-efficient and reliable energy is one of the main issues in human societies of the 21st century. In response to this demand, new features of micro grid technology have provided huge potentials, specifically by the capability of having an interactive coordination between energy suppliers and consumers. Accordingly, this paper offers an improved model for achieving an optimal Demand Response programing. To solve the proposed multi-objective optimization problem, Artificial Bee Colony algorithm and quasi-static technique are utilized. The considered objectives in this paper are minimizing the overall cost of energy consumption and also improving the technical parameters of micro grid over a time horizon. This optimization is subject to several constraints such as satisfying the energy balance and the operating constraints of each energy supply sources. Manageable load or load as source is another enabling feature existing in smart energy networks, which is considered in this paper and its effect on cost reduction and reliability improvement is studied. Trying to examine the performance of the proposed Demand Response Programing in real conditions, the uncertainties are also analyzed by stochastic methods. The results show significant improvements which are obtained by applying just intelligent programming and management.

Technology and Economics of Smart Grids and Sustainable Energy

With the increasing integration of Distributed Energy Resources (DER) in the power grid, a decentralized approach becomes essential for scheduling and allocation of resources in a smart grid. Economic Dispatch (ED) and Unit Commitment (UC) are the two major resource allocation problems that play critical role in the safe and stable operation of a grid system. The uncertainty associated with renewable energy sources have made the resource allocation problems even more challenging for grid operators. The future grid will have a higher generation mix of renewable energy sources and a large load of Electrical vehicles, with the possibility of bi-directional power flow. This complex smart grid system necessitates the development of a decentralized approach to resource allocation problem, which allows inter-node communication and decision making. Multi-agent systems (MAS) is a promising platform to decentralize the traditional centralized resource allocation aspects of smart grid. This paper presents a comprehensive literature review on the application of MAS to Economic Dispatch (ED) and Unit Commitment (UC) in smart grids.

2012 IEEE Network Operations and Management Symposium, 2012

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power.

Unlike existing centralized grids, smart grids (SGs) have the ability to reduce fossil fuel combustion and carbon emissions up to a significant mark. Smart homes and smart building are becoming more attractable due to low energy consumption and high comfort. Different demand side management (DSM) programs have been proposed to involve users in decision making process of SGs. Power consumption pattern of shiftable home appliances is modified in response of some rebates to achieve certain benefits. In this paper, an energy management model is proposed using genetic algorithm (GA), teaching learning based optimization (TLBO), enhanced differential evolution (EDE) algorithm and our novel proposed EDTLA. The main objectives include: daily electricity bill minimization, peak to average ratio reduction and user comfort maximization. Simulation results validate the performance and applicability of our proposed model.

Multi Agent Based Energy Management System for Microgrids, 2020

This paper presents an implementation of a Multi Agent System based Energy Management System for a microgrid. In addressing the emerging distributed generation concept, microgrids have been identified as a suitable platform for integration. The continuous controlling and monitoring is essential for distributed renewable sources in a microgrid platform. Multi Agent System concept provides a plug and play controlling nature to a microgrid unlike complex and conventional controlling techniques. Here a MAS is proposed with five main agents, which mainly consists of a wind agent, a solar agent, load agents and a server agent. Proposed system operation is based on the optimum supply of loads through the available renewable sources. As presented in this paper the interaction between agents in the system is simulated through Java Agent Development Environment. A microgrid test bed is implemented, in order to apply Multi Agent System operation with physically implemented agents. The main purpose of this paper is to discuss the physical implementation of a MAS for a micro grid application.