Power Management in Microgrids: A Multi Agent Petri Nets Based Approach

Power Management in Microgrids: A Multi Agent Petri Nets Based Approach

Tarek Jellad (ENIS, Tunisia), Khaled Taouil (ENIS, Tunisia) and Zied Chtourou (ENIS, Tunisia)
DOI: 10.4018/978-1-4666-4450-2.ch005
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Abstract

Conventional power generation stations used to be centralized and located far from customers. The transport and distribution infrastructure incur power losses that are mainly due to cabling resistance. Distributed power generation resources located close to customers are sought as a solution to minimize transport power losses. They are also good alternatives in situations where connection to the grid is not possible due to geographical or economical reasons. Furthermore, the adoption of renewable energies as alternatives for the scarce fossil energy sources paves the way to more distributed energy production. These Distributed energy resources, when located in a limited region, can be interconnected with loads and eventually storages to form a microgrid. A microgrid can operate in off-grid, on-grid, or alternate between these modes while optimizing power quality and cost. Multi-Agent Systems (MAS) and Petri nets could be put into contribution for high level planning of energy exchanges within a microgrid. This strategy has been validated on the basis of a dynamic model for the simulation and optimization of power exchanges between different DERs.
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Introduction

Power generation facilities used to be centralized and located far from the demand. This is due to the gas emissions inherent to fossil energy sources or to the dependence to natural resources such as dams or lakes. The generated power is then conveyed to the customers through overhead or underground lines. These transport and distribution operations are accompanied with substantive power losses. Moreover, the scarcity of fossil energy resources is continually pushing towards adopting renewable energy resources to satisfy part or the totality the energy needs.

The development of decentralized production means of electric power particularly from renewable energy opens the way for new modes of operation within microgrids. These structures are a concept which refers to a small-scale power system with a cluster of loads and distributed generators operating together with energy management, control and protection devices and associated software (Ustun, Ozansoy, & Zayegh, 2011). Microgrids could work on-grid, off-grid or continuously alternate between both modes.

To ensure optimal management of the energy produced by the various units of the microgrid, many features could be envisaged as shedding, deletion and dispatching of potential loads (Huang, Wang, & Jiang, 2012). However these functionalities couldn’t be available only if we have the necessary information on production units mostly related to weather conditions and predicted loads of different consumers. Local facilities must be equipped by advanced sensors and smart meters to ensure local monitoring from IHM devices (Shekara, Wang, & Devabhaktuni, 2011).

Several approaches have been proposed for managing electric energy, (Huang, Lu, & Zhang, 2001; Kumar, Chowdhury, & Paul, 2011; Dursun, & Kilic, 2012; Alvarez, Campos, Arboleya, & Gutiérrez, 2012; Zhao, Chen, Zhong, Lin, Wang, & Zhang, 2012). Management of peak consumption remains among the main problems related to electrical energy distribution. Moreover, the difficulty of electrical energy storage (Toledo, Filho, & Diniz, 2010) and the lack of real time information on production and loads status complicate the maintenance of the energy balance within microgrids (Basak, Chowdhury, Halder, & S. Chowdhury, 2012).

For reasons related to energy saving and environment preservation, the final consumer is attracted by new pricing practices and services offers. He becomes an active actor in the electric energy system. He can proceed voluntary to change his consumption patterns exploiting new possibilities of information and communication technologies (Alvial-Palavicino, Garrido-Echeverría, Jiménez-Estévez, Reyes, & Palma-Behnke, 2011).

Demand-response strategy was adopted in the U.S. to avoid large-scale blackout (OpenADR protocol) (Haaser, 2011). This demand-response strategy can be applied in microgrid to ensure optimal management of the energy under conditions of intermittent renewable generation sources, mainly due to changes in climatic conditions, and keep use of non-renewable production sources to minimum(Lu, Fakham, Zhou, & François, 2010).

Key Terms in this Chapter

BDI: The BDI (Belief, Desire, Intentions) architecture is another approach used in the design of deliberative agents. Agents are therefore based on these three aspects to choose their actions. A BDI agent should update his beliefs with the information collected from its environment, it decide which options are available for it, filter these options to determine new intentions and make its actions based on its intentions.

Microgrid Modeling: An abstract graphical representation of the deployed microgrid using an eventual mathematical modeling language.

GA: Genetic Algorithm is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems.

Petri Nets: A modeling languages for the description of distributed systems, it is a set of places, transitions and relations between them called arcs. A petri net can be interpreted as a state graph, an event graph or a resources chart.

Open Automated Demand Response (OpenADR): Demand Response plays a vital role in grid stabilization during hot Summers, easing severely constrained electrical grids from coast-to-coast. Future energy crises caused by electricity demand exceeding system capacity can be postponed or even averted through Demand Response. The U.S. Federal Energy Regulatory Commission defines DR as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.” The ability of DR to avert an energy crisis is so promising that one FERC Commissioner has identified DR as the “killer application” for the Smart Grid.

Multi-Agent System (MAS): An intelligent environment of interactions between distributed processes witch trend to resolve a common problem. Agents seem to emulate a social behavior such as bee swarm.

DER(s): Distributed Energy Resources: Small-scale power generation sources located close to where electricity is used.

Storage Units Conservation: A trend to extend storage units life by optimizing charge/discharge cycling of local batteries and fuel cells.

Microgrid: A Gathering of distributed power units (DERs), which trends to cover specific local needs such as to be Autonomous and why not to delivers locally generated extra power to neighboring facility. It can be deployed as a private power grid which interconnects energy units in many ways.

Yasdi Platform: “ Y et A nother S MA D ata I mplementation” An open source platform of communication between local energy units (Implementation of the SMA Data Protocol). Based on master/slave architecture and specific commands this platform was adapted by adding a slave API to be able to interconnect microgrid components.

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