Energy Management and Control for Islanded Microgrid Using Multi-Agents

Frank Ibarra Hernández, Carlos Alberto Canesin Power Electronics Laboratory - LEP

São Paulo State University – UNESP – FE/IS Ilha Solteira - SP, Brazil

Ramon Zamora, Fransiska Martina, Anurag K Srivastava

Smart Grid Demonstration and Research Investigation Lab Washington State University - WSU

Pullman - WA, USA

Abstract — This paper presents a multi-agent system for real-time operation of simulated microgrid using the Smart-Grid Test Bed at Washington State University. The multi-agent system (MAS) was developed in JADE (Java Agent DEvelopment Framework) which is a Foundation for Intelligent Physical Agents (FIPA) compliant open source multi-agent platform. The proposed operational strategy is mainly focused on using an appropriate energy management and control strategies to improve the operation of an islanded microgrid, formed by photovoltaic (PV) solar energy, batteries and resistive and rotating machines loads. The focus is on resource management and to avoid impact on loads from abrupt variations or interruption that changes the operating conditions. The management and control of the PV system is performed in JADE, while the microgrid model is simulated in RSCAD/RTDS (Real-Time Digital Simulator). Finally, the outcome of simulation studies demonstrated the feasibility of the proposed multi-agent approach for real-time operation of a microgrid.

Index Terms — Distributed energy resources, Microgrid, Multi-agent system, Real-time digital simulator, Real-time operation, Photovoltaic power systems.

I. INTRODUCTION With the electric power system development toward the

smart grid to improve reliability, security and economic operation [1], the integration of Distributed Energy Resources (DERs) into the future Smart Distribution Network (SDN) has challenging issues [2]: • The Smart Distribution Network (SDN) requires periodic

and fast estimations of network security as well as real-time information from the network components.

• The interconnection of the DER units increases the complexity of Smart Distribution Management System (SDMS).

These DER units may have a cooperative function to improve the reliability and quality of the supplied electric power. The DER units in the SDN have advantages in power quality and reliability improvement and economic benefits [2]. The effective integration of DER units in distribution networks demands powerful simulation and test methods in order to determine both system and component behavior, and

to understand their interaction [3]. According to the U.S. Department of Energy, a Microgrid is defined as an integrated energy system consisting of interconnected loads and distributed energy resources which as integrated system can operate in parallel with the grid or in an intentional islanded mode [4]. In islanded mode: • The unavailability of grid requires specific management

of micro-sources and loads, since the micro-sources have maximum capacity limits [5];

• The microgrid is operated like an isolated island and should meet the load-generation balance by adjusting load and generation accordingly [6].

The above description means that intelligent control of microgrid with distributed generation and energy storage is important to keep the reliability, stability and security of system as required. One of the intelligent control, Multi-Agent System (MAS) is defined as a collection of autonomous computational entities (agents), which makes decisions based on goals within an environment that can be difficult to define analytically. Often MAS agents work with a limited system-wide perspective and focus on localized task achievement. Although each agent’s ability to affect the system environment is limited to the capabilities of their immediately controllable system or component, agents can communicate information about their goal achievement to other independent agents inside the MAS. In this way, MAS development is a compromise between agents acting in self-interest way and in a cooperative manner [7]. In this paper, the interconnection between MAS (in JADE) and electrical system (in RSCAD) is in offline model. In this case, the outputs of JADE will be used as new set points for electrical system and vice versa until the best possible conditions are determined for the operation of a specific microgrid, as is explained in detail in section III. This offline approach is also commonly used in other MAS application such as market operation [8].

Finally, considering that most of the power outages and disturbances take place in the distribution network, therefore, the first step towards the smart grid should start at the bottom of the chain, in the distribution system [9].

978-1-4799-1255-1/13/$31.00 ©2013 IEEE

II. STATEMENT OF THE PROBLEM Smart grid (SG) represents a vision for the future power

distribution systems, which integrates advanced sensing technologies, control methodologies and communication technologies into current electricity grid. Microgrid is an innovative control and management architecture at distribution level, which makes SG implement techniques at distribution level easier [9]. Since the past decade, the PV systems have experienced tremendous growth in manufacturing scale and technology advancement, and is expected to continue growing rapidly [11], [12], [13]. Many research works have been carried out on grid connected PV focusing on the development of various technologies and control strategies. PV system has two major problems: low conversion efficiency especially under low irradiation and dependency of the electric power generated by solar arrays on weather conditions [14]. It is very important to use the Real-Time system studies to find the best operating conditions to ensure the reliability of the system, considering the interactions between PV an microgrid in islanded mode. On the other hand, the feasibility of the microgrid islanding mode concept was laboratory tested in a prototype installed in the National Technical University of Athens (NTUA), which comprises a photovoltaic (PV) generator, battery energy storage, loads, and a controlled interconnection to a low voltage grid [15]. According to Fig. 1, Multi Agent System in JADE and microgrid simulation in RSCAD/RTDS can be used to coordinate the interactions of Distributed Energy Resources in islanded mode. Taking into account [2], [3], [4], [5], [6] and [14], this work is aimed to improve the interactions among PV and islanded microgrid, without any other generating technology included.

Fig. 1: Flowchart of the PV System Real-Time Simulation in an Islanded Microgrid

According to Fig. 1, the interaction between the PV management and control in JADE and the microgrid simulation in RSCAD/RTDS is theoretically a continuous cyclic process, until the best electrical conditions are met. The MAS management and control in JADE is responsible to provide initial electrical information (PV module specifications and load conditions) for RSCAD/RTDS simulation.

Taking into account the above, this paper contributes to the current state of the art in the fact that, using multi-agent system, was found an electrically reliable approach to model and simulate a real-time system formed only by photovoltaic sources in an islanded microgrid, which serves as a basis for studying electrical abnormalities in islanded photovoltaic

systems such as: faults, voltage fluctuations, power outages, among others.

III. MICROGRID ARCHITECTURE The microgrid architecture presented in this paper is based

on the CERTS Microgrid Concept and IEEE standard P1547.4, “Guide for Design, Operation, and Integration of Distributed Resource Island Systems with Electric Power Systems”. The CERTS microgrid has no “master” controller. Each source is connected in a peer-to-peer fashion with a localized control scheme implemented for each component. Each micro-source can seamlessly balance the loads when the microgrid islands using a power vs. frequency droop controller [16]. An islanded mode with excess generation will experience an increase in frequency which autonomously reduces the output of generation, moves storage to a charging mode and smoothly supports the PV output as necessary [17].

A. Microgrid based on CERTS Concept (Case Study) According to Fig. 2, our case study consists of three

photovoltaic systems (3 micro sources of 60 kW each) and a group of four radial feeders, in which three of them (Feeders A, B, and C) contains “sensitive” loads [18]. These sensitive loads require local generation. Each of the four bank loads can be controlled from 0-90 kW. The micro sources control the microgrid operation using only local voltage and current measurements. Feeder D does not have a sensitive load and therefore, no local generation is required for this feeder. When the microgrid is grid-connected, power from the local generation can be directed to the non-sensitive loads of Feeder D [19]. The power conditioning system is shown in Fig. 3 [16].

Fig. 2: Microgrid based on CERTS Concept

Fig. 3: Power conditioning system

Each micro-source has a number of PV modules (connected in series and parallel), and according to [14], the considered photovoltaic module is a Shell SQ160PC module with a capacity of 160W as given in the datasheet (Number of cells: 72). The main specifications of each module are:

• Open Circuit Voltage Voc: 43.5 V • Vmpp: 35 V • Impp: 4.58 A • Short Circuit Current Isc: 4.90 A • Panel Efficiency: 12.1%. • Maximum System Voltage Vmax: 120 V • Dimensions: 1622.0 × 814.0 × 40.0mm (32.0 × 63.9 ×

1.6 inch). • Weight: 17.2Kg (37.9 lbs).

IV. MULTI-AGENT MODELING AND IMPLEMENTATION

An agent is merely a software (or hardware) entity that is situated in some environment and is able to react autonomously in response to environmental changes, therefore it is able to schedule action based on environmental observations. The environment is simply everything external to the agent. An agent can operate usefully in any environment which supports the tasks the agent intends to perform. An intelligent agent is an agent with a flexible autonomy, instead of only autonomy. An intelligent agent has the following three characteristics: reactivity (react to changes in its environment in a timely fashion), pro-activeness (goal-directed behavior) and social ability (ability to negotiate and interact in a cooperative manner). MAS is a system containing two or more autonomous entities that can detect and react to its environment and communicate with each other to optimize its local goal, and therefore system’s global goal is obtained. The role of implementing MAS is to break down a complicated problem into several small simple problems, and each simple problem will be handled by an agent. Agents have a certain degree of autonomy to make decisions based on the information it acquires from environment without a central controller [20]. MAS development is a compromise between agents acting in a self-interested way and in a cooperative manner. At each instant, the MAS agents must evaluate their local situation, determine a local best solution to the problem dictated by the agent’s goals, and if necessary, communicate their intended action to the MAS, participate in MAS prioritization, and adjust its action based on collective group decision [7]. To implement a MAS, there are a number of open-source agent platforms available in the literature that aid developers to build a complex agent system in a simplified fashion. These open-source agent platforms include: Aglets software development kit, Voyager, Zeus, JADE, Tracy, SPRINGS and Skeleton Agent. [4].

In this work, the MAS will be developed in JADE which is a Foundation for Intelligent Physical Agents (FIPA) compliant open source multi-agent platform. Also, JADE is an agent software framework fully implemented in Java language. It simplifies the application of the agent systems through a middleware that comply with the FIPA specifications [21]. JADE facilitates the development of multi-agent peer-to-peer applications. It supports an asynchronous agent programming

model, communication between agents either on the same or different platforms, mobility, security, and other utilities [22]. JADE (Java Agent DEvelopment framework) was selected for calculating the operation parameters of the microgrid which aims for: a) optimal use of local distributed resources; b) feeding of local loads; c) operation simplicity. JADE allows the development of unique software agents that can perform a myriad of tasks, control functions, and supports decentralized control architectures. The JADE-based agent platform directly supports “plug-and-play” connectivity, as agents come on/off-line asynchronously. Distributed multi-agent systems can be difficult to develop because of tradeoffs between strict definitions of autonomous behavior and the need for agents to interact [23]. According to Fig. 4, the Concept of Operations for Simulated Scenario uses three kinds of agents: a) Producer Agent, responsible for calculating each photovoltaic system and communicate the value of the nominal battery capacity to the Storage Agent (PVM1, PVM2 and PVM3); b) Storage Agent, monitors the storage capacity of each photovoltaic system (Storage1, Storage2, and Storage3); c) Observer Agent, monitors all the Simulated Scenario (Sniffer Agent).

Fig. 4: Concept of Operations for Simulated Scenario

The most important work is done by the Producer Agent, because, according to Fig. 5, it uses four PV Module Specifications (Open Circuit Voltage [V], Voltage mpp of Module [V], Current mpp of Module [A], Short Circuit Current of a Module [A]) and four Load Specifications (Total Power of AC Resistive loads [W], Total Power of AC Rotating machines loads [W], Operating hours per day of Resistive loads [hours], Operating hours per day of Rotating machines loads [hours]) for calculating each photovoltaic system [24], that is to say, for calculating ten variables: Average Consumption [Wh/Day], Total Number of Modules (panels) per branch required, Total Number of branches in Parallel Required, Total Number of Modules Required, Instant Power of Load [W], Instant Power of Photovoltaic System [W], Calculated Regulator Output Current [A], Calculated Inverter Power [W], Maximum Power that Can Receive the Battery to be Charged [W] and Power Discharge Battery [W]. The foregoing calculations are performed taking into account

the following two assumptions: a) All simulations were done with the lowest solar irradiation, which occurs in December, with a value of: 3320 (Wh/m2/day) and b) The photovoltaic module NOT always works in the maximum power point tracking (MPPT).

Fig. 5: Flowchart of Calculation of a Photovoltaic System

The JADE runtime in turn executes within a Java Virtual Machine [25]. The main reason for adopting JADE is because JADE is an Operating System (OS) independent platform which means that the coded algorithm can be implemented on any computer as long as the Java Virtual Machine (JVM) is installed [26]. For optimal operation of an islanded microgrid, the system under the study consists of a MAS and electrical network of the microgrid. The electrical network of the microgrid is modeled in RSCAD.

The Sniffer Diagram for the Multi-Agent Modeling and Implementation can be seen in Fig. 6. This tool is used for documenting conversations between agents. Our work shows that the Producer Agents (PVM1, PVM2 and PVM3) send one specific message to the Storage Agents (Storage1, Storage2 and Storage3); this communication is the maximum power that can receive the battery to be charged. We can also see that the Agent Management System (AMS) agent that is responsible for managing the operation of an Agent Platform is in constant communication with the Sniffer agent, which is responsible to monitor all the agent’s requirements of the JADE platform.

Fig. 6: Sniffer Diagram for the Multi-Agent Modeling and Implementation

V. SIMULATION AND RESULTS Using MAS in an islanded microgrid, with the help of a

Smart Grid Test Bed of Washington State University [1], the

proposed operational strategy is mainly focused on using an appropriate energy management and control to improve the operation of an islanded microgrid. This Microgrid is formed only by photovoltaic (PV) solar systems, batteries and resistive and rotating machines loads. The cyclic procedure to measure the interactions among three photovoltaic systems is illustrated in Fig. 7.

Fig. 7: Flowchart of the Real-Time Operation of a Microgrid

Using the best operating conditions met after several cyclic processes in the Fig. 7, the simulation of the electrical system in RSCAD/RTDS could be performed with the results as shown in Fig. 8. It is industrial load which changes fast as shown in the next figures (from Fig. 8 to Fig. 9).

0 0.03333 0.06667 0.1 0.13333 0.16667 0.20.02

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Fig. 8: Power of each Micro-source (blue) [kW] and Sensitive Load (black) [kW]

0 0.03333 0.06667 0.1 0.13333 0.16667 0.274.365

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Fig. 9: Battery Charging/Discharging Parameter

The simulation result in Fig. 8 shows that energy management and control is able to regulate the micro-sources to reach their maximum capacities. This figure also shows fluctuated loads, which is more than the PV capacities at certain times and less than PV capacities at the other times. This extra or shortage power is balanced by the

battery storage system. Fig. 9 (a) shows that the battery is discharging with various rate to accommodate this fluctuated load. Also, this SOC is clearly shown in Fig. 9 (b) and (c). When the rate of discharging is high, the battery voltage decreases, while the current increases.

VI. CONCLUSIONS AND FUTURE WORK This paper presents a multi-agent system for real time

simulation of interactions between three photovoltaic sources and power systems in an islanded microgrid, using RTDS [26]. The multi-agent system was developed in JADE [27], an open source IEEE FIPA compliant platform. The outcome of the simulation studies demonstrates the effectiveness of the proposed control and technical calculation, and shows the possibility of autonomous built-in operation of a microgrid with a multi-agent system. The simulation results show that the micro-sources, loads, and batteries are well coordinated, where exceeding power from micro-sources is stored in batteries. These results have reflected the scenario in multi-agent system.

This research is a preliminary work to show the feasibility and utility of MAS applications in energy management and control of islanded microgrid with high penetration of PV systems. In the future, authors will extend this work to include the following aspects: different scenarios of load-generation balance, electrical fault and contingency analysis of microgrid, and online interfacing between MAS and RTDS in order to better realize the real-time operation of a microgrid.

ACKNOWLEDGMENT The authors gratefully acknowledge the São Paulo

Research Foundation – FAPESP for financial support to carry out an internship at WSU to condut this research. Thanks to Fullbright fellowship and Washington State University also for supporting some of the work reported in this paper.

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