A Multi-Agent Framework for Operation of a Smart Grid

doi:10.4236/epe.2013.54B252

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Energy and Power Engineering, 2013, 5, 1330-1336

doi:10.4236/epe.2013.54B252 Published Online July 2013 (http://www.scirp.org/journal/epe)

A Multi-Agent Framework for Operation of a Smart Grid

Ruchi Gupta1, Deependra Kumar Jha1, Vinod Kumar Yadav1, Sanjeev Kumar2

1School of Electrical, Communication and Electronics Engineering, Galgotias University, Uttar Pradesh, India

2School of Basic and Applied Sciences, Department of Mathematics, Galgotias University, Uttar Pradesh, India

Email: [email protected]

Received March, 2013

ABSTRACT

This paper presents the operation of a Multi-agent system (MAS) for the control of a smart grid. The proposed Mul-

ti-agent system consists of seven types of agents: Single Smart Grid Controller (SGC), Load Agents (LAGs), a Wind

Turbine Agent (WTAG), Photo-Voltaic Agents (PVAGs), a Micro-Hydro Turbine Agent (MHTAG), Diesel Agents

(DGAGs) and a Battery Agent (BAG). In a smart grid LAGs act as consumers or buyers, WTAG, PVAGs, MHTAG &

DGAGs acts as producers or sellers and BAG act as producer/consumer or seller/buyer. The paper demonstrates the use

of a Multi-agent system to control the smart grid in a simulated environment. In order to validate the performance of the

proposed system, it has been applied to a simple model system with different time zone i.e. day time and night time and

when power is available from the grid and when there is power shedding. Simulation results show that the proposed

Multi-agent system can perform the operation of the smart grid efficiently.

Keywords: Multi-agent System; Smart Grid; Micro-grid; Distributed Generation; Renewable Energy

1. Introduction

The security and resiliency of electric power supply to

serve critical facilities are of high importance in today’s

world. In India, with the increasing complexity of power

grids, growing demand and growing concerns for envi-

ronment accentuate the leap towards something ‘smarter’.

Instead of building large electric power grids and high

capacity transmission lines an intelligent approach is

essential for transforming the existing power grid to a

‘smarter grid’ widely referred as ‘smart grid’. Smart grid

technologies have the potential to transform the existing

grids to more efficient, self healing, reliable, safer and

less constrained grids [1].

A smart micro-grid can be defined as a low voltage

distribution network with distributed energy resource

(DER) units, such as the distributed generation (DG)

units and distributed storage (DS) units and loads. The

DG units utilize Diesel Engines, Micro turbines, Fuel Cells,

Photovoltaic (PV) panels, small wind turbines, and com-

bined heat and power (CHP) systems. The capacity of the

DG sources varies from few kW to 1-2 MW. The DS

units could be flywheels, energy capacitors and batteries.

The micro-grid can be made smarter by integrating

advanced sensing technologies, control methods and

communication techniques [1-3]. Smart grids can benefit

customers through providing uninterruptible power, en-

hancing local reliability, reducing transmission loss, and

supporting local voltage and frequency.

As the DER units typically operate at a distribution

voltage level and geographically close to loads, smart

micro-grids are developed to interconnect the energy

sources and loads in a relatively small area, such as a

suburban community, a university or school, a commer-

cial area, and an industrial site. Besides the environ-

mental benefit of using more renewable energy sources, a

smart grid can either be interconnected to the main grid

as a single aggregated load (or generator) or in case of

external faults or periods of emergency, it has the capa-

bility to separate, or island, from the main grid and oper-

ate independently, within limits.

Significant research works have been carried out on

operation and control of smart grid. Current trends to

control and monitor the operation of electrical power

systems are however moving towards the use of an

automated agent technology, known as multi-agent sys-

tem. In recent years, multi-agent based approach is pro-

posed/adopted to provide intelligent energy control and

management systems in smart grids. Multi-agent systems

have become the focus of intense research in European

countries [4, 5].

In India, a lot is yet to be done towards making power

grids smarter and reliable, capable of taking self correct-

ing decisions for optimal scheduling of generation re-

sources. In this paper the operation of a multi-agent sys-

tem for the control of a smart grid is described in an In-

dian scenario.

Present power scenario of India is described in Section

II. The rest of the paper is organized as follows: Section

R. GUPTA ET AL. 1331

III gives the Multi-agent approach to smart grid opera-

tion. Multi-agent technology is shortly introduced. An

overview of the related work, general management and

control concept based on the related work is presented

with the help of a process flow-chart. In Section IV

Simulation results are presented based on the proposed

approach. Section V concludes the paper.

2. Present Power Scenario of India

The relationship between power consumption and na-

tional economic development has a great significance.

Indian power sector is facing challenges and despite sig-

nificant growth in generation over the years, it has been

suffering from shortages and supply constraints. All In-

dia region-wise generating installed capacity (MW) of

power utilities is given in Table 1 [6] and the corre-

sponding pie chart is shown in Figure 1.

India’s electricity sector is amongst the world’s most

active players in renewable energy utilization, especially

wind energy. As of December 2011, India had an in-

stalled capacity of about 22.4 GW of renewal technolo-

gies-based electricity. In1990, the capacity of renewable

energy sources (RES) was 18 MW whereas the genera-

tion during the year 1989-1990 was 6 MU in India. Ini-

tially, the annual capacity addition was very slow, but

from 2008 onwards the contribution from RES is consid-

erable. In 2012, the RES capacity was 24503. 45 MW

and the percentage share of RES in total generation ca-

pacity was 12.26% which is expected to increase to

17.12% by 2017. The percentage share of RES in total

generation in India during 2011-12 was around 5.5 %.

The category-wise details of electricity generation in

the country during August 2012 and during April 2012 to

August 2012 are given below in Table 2 [7].

Figure 1. All India Generating Installed Capacity (MW).

Table 1. Region –wise Electricity Generating Capacity of India.

Thermal

S.N. Region Coal Gas Diesel Total

Nuclear Hydro RES Total

1 Northern 31623.50 4671.26 12.99 36307.75 1620.00 15467.75 4623.24 58018.74

2 Western 43537.00 8254.81 17.48 51809.29 1840.00 7447.50 8450.04 69546.83

3 Southern 23782.50 4962.78 939.32 29684.60 1320.00 11353.03 12096.78 54454.41

4 Eastern 22607.88 190.00 17.20 22815.08 0.00 3948.12 436.71 27199.91

5 North Eastern 60.00 824.20 142.74 1026.94 0.00 1200.00 243.28 2470.22

6 Islands 0.00 0.00 70.02 70.02 0.00 0.00 6.10 76.12

7 Total 121610.88 18903.00 1199.75 141713.68 4780.00 39416.40 25856.14 211766.22

Capacitive Generation Capacity in Industries having demand of 1 MW and above, Grid interactive (as on 31-03-2011) = 34444.12 MW

Table 2. Electricity generation in India (category-wise).

Category Monitored capacity

(MW) Actual Generation (MU) during

August2012 Actual Gen (MU) during

April2012 to August201 2

RES

Wind 14870.955 4508.687 18909.007

Solar 976.904 83.483 539.205

Biomass 707.250 105.995 781.449

Biogases 2881.210 243.555 2184.995

Small Hydro 1290.293 330.083 1059.842

Others 131.760 7.900 83.246

Total RES 20858.372 5279.702 23557.744

Total (conventional) 181710.520 74498.360 382466.050

Total 202568.892 79778.062 406023.794

R. GUPTA ET AL.

1332

3. Multi-Agent Based Operation of Smart

Grid

3.1. Multi-Agent System

A multi-agent system is a combination of several agents

working in collaboration pursuing assigned tasks to

achieve the overall goal of the system. The multi-agent

system has become an increasingly powerful tool in de-

veloping complex systems that take advantages of agent

properties: autonomy, sociality, reactivity and pro-activ-

ity. The multi-agent system is social-able in that they

interact with other agents via some kind of agent com-

munication language. The agents also perceive and react

to their environment. Lastly, the multi-agent system is

proactive in that they are able to exhibit goal-oriented

behavior by taking initiatives [1-4, 8-12].

In the context of power systems, multi-agent technolo-

gies can be applied in a variety of applications, such as to

perform power system disturbance diagnosis, power sys-

tem restoration, power system secondary voltage control

and power system visualization. Some of the recent re-

searchers have implemented multi-agent system to con-

trol the operation of a smart grid.

3.2. Agent Model of the Proposed Smart Grid

The proposed model of the smart grid consists of several

agents that communicate and coordinate with each other.

The smart grid model that is used in the proposed appli-

cation is simple, since we focus mainly on the coopera-

tion of agents. Figure 2 shows the architecture of the

smart grid. Smart grid is connected to the main grid. The

Smart Grid Controller announces the time zone (day/

night) and availability of grid. The production units that

belong to the smart grid adjust their set points based on

their availability, the time zone, availability of the grid,

grid price, their operational cost and the load demand [1].

a) Load Agent (LAG)

LAG is installed at each load centre. Main functions of

LAG are:

i. Load Forecasting: LAG makes a prediction about

the demand of next day. The forecast demand for

electricity is determined from the historical data and

the weather forecast for tomorrow.

ii. Sending Message: LAG sends a request message to

SGC for the power purchase. The message includes

the amount of power and its price.

iii. Receiving Message: LAG receives from SGC a

message that tells from where to purchase the elec-

tric power.

b) Battery Agent (BAG)

BAG, installed at each battery centre, has attributes

such as the amount of charge, the capacity and the status

(charge/discharge). The following describes the main

functions of BAG.

i. Measurement of the state of charge: BAG conducts

to measure the amount of battery charge. Battery is

charged when Grid is available and discharged when

Grid is not available.

ii. Sending Message: BAG sends a request message to

SGC for the power purchase or sale. The message

includes the amount of power and its price.

iii. Receiving Message: BAG receives from SGC a

message that tells when to purchase or sale the elec-

tric power.

c) Smart-Grid-Controller (SGC)

SGC, a special purpose agent facilitates the negotia-

tion process of the multi-agent system. The following

describes the main functions of the SGC.

i. Controlling the timing of the negotiation: SGC sug-

gests the timing of the negotiation by sending a

message to open the market to all agents in the smart

grid.

ii. Decision making of the operation: SGC makes a

decision of the operation of producer or pro-

ducer/consumer agents.

d) Wind Turbine Agent (WTAG)

WTAG, installed at each wind turbine generator, has

attributes such as the generator available power. The fol-

lowing describes the main functions of WTAG.

i. Sending Message: WTAG sends a request message

to SGC for the power sale. The message includes the

amount of power and its price.

ii. Receiving Message: WTAG receives from SGC a

message that tells where to sale the electric power.

e) Photo-Voltaic Agent (PVAG)

PVAG, installed at each photo-voltaic generator, has

attributes such as the generator available power. The fol-

lowing describes the main functions of PVAG.

i. Sending Message: PVAG sends a request message

to SGC for the power sale. The message includes

availability, the amount of power and its price.

ii. Receiving Message: PVAG receives from SGC a

message that tells where to sale the electric power.

f) Micro-Hydro Turbine Agent (MHTAG)

Figure 2. Agent model of the proposed smart grid

R. GUPTA ET AL. 1333

MHTAG, installed at each micro-hydro turbine gen-

erator, has attributes such as the generator available

power. The following describes the main functions of

MHTAG.

i. Sending Message: MHTAG sends to SGC a mes-

sage to request for the power sale. The message in-

cludes the amount of power and its price.

ii. Receiving Message: MHTAG receives from SGC a

message that tells where to sale the electric power.

g) Diesel Generator Agent (DGAG)

DGAG, installed at each diesel generator, has attrib-

utes such as the generator minimum/maximum output

value. The following describes the main functions of

DGAG.

i. Sending Message: DGAG sends to SGC a message

to request for the power sale. The message includes

the amount of power and its price.

ii. Receiving Message: DGAG receives from SGC a

message that tells where and when to sale the elec-

tric power.

h) Steps for Sorting Process

The sorting algorithm (flow chart as shown in Figure 3)

for available generating resources (producers) is explained

as below:

Step 1: Collect the following data of the available

sources/ producers:

(i) Power cost per unit

(ii) Availability of the producer

(iii) Power availability with the producer

Assign ID in numeric to the producer(s).

Step 2:

(i) Sort the array of producers in the order of price

(ii) The producers which are available and can give

power at that time slot are sorted in ascending

order of cost/price.

Step3: Read the Demand (Load).

Step 4: Identify the producers which can fulfill the

demand using the array sorted in Step 2, individually or

in combination.

Step 5: Send the producer ID and capacity to SGC.

i) Negotiation Process

Negotiation is one of the key processes for the multi-

agent system to successfully attain its goal. Flow-chart in

Figure 4 shows the negotiation process.

There are seven types of agents in the proposed system

viz. SGC, LAG, BAG, PVAG, WTAG, MHTAG and

DGAG.

The main objective is to fulfill the demand (load) at a

particular time by minimizing the use of diesel genera-

tors as their per unit price outreach other available

sources. To accomplish this, the agents are required to

cooperate and coordinate so that they make efficient use

of the power supplied by other sources at the time of

power shedding.

Figure 3. Flow-chart for sorting of producers (Generating

units) for SGC

SGC is informed about the demand, producer avail-

ability and the operating cost per unit of different pro-

ducers in one negotiation cycle. At the time of power

shedding, if the electrical power in the smart grid is in-

sufficient, demand is fulfilled by the diesel generators.

SGC sends a message of announcement of tender to all

agents in the smart grid every 10 minutes. SGC is sug-

gested to control the timing of the negotiation process.

All buyer agents in the smart grid reply a message of

purchase of electrical power. The message includes the

amount of electrical power i.e. Demand ‘D’. All producer

agents in the smart grid reply a message of sale of elec-

trical power. The message includes the generator avail-

able power, their availability at a particular time and their

operating cost per unit.

In the next step, sorting of producers and the grid is

done depending upon their availability and operating

costs in ascending order of price per unit. This sorted list

is then sent to the SGC that makes a decision on opera-

tion of different producers based on this generated list.

The demand is checked with the power available with

the producer. If the demand is not fulfilled by first pro-

ducer, SGC sends operation command to next producer

and so on until the demand is fulfilled. Finally, genera-

tion load pattern is displayed for a given time period and

next negotiation cycle begins.

R. GUPTA ET AL.

1334

4. Simulation Results

4.1. Assumptions

The case study considers a township located in India. It

comprises of 150 houses inhabited with modern ameni-

ties, education sector, health centre, super market, eleva-

tors, flour mill, laundry, a community hall and water

pump. Typical household appliances include lighting,

refrigerator, AC/heater, geyser, TV, washing machine,

micro wave, electric cooker i.e. a max load of 5kVA to

10kVA. Super market facilities include lighting, refrig-

erator/freezer (max consumption 15kW), AC/heater.

Medical health centre facilities include lighting, refrig-

erator, AC/heater. Education sector facilities include

lighting, AC/heater, laboratory equipments.

The Smart Grid consists of a Load Agent (LAG),

Wind Turbine Agent (WTAG), two Photo-Voltaic Agents

(PVAGs), Micro-Hydro Turbine Agent (MHTAG), two

diesel generator agents (DGAGs) and a Battery Agent

(BAG). The maximum capacity of each agent is shown

in Table 3.

Figure 4. Flow-chart of the negotiation process

Electrical power prices of different producers are giv-

en in Table 4 [13]. In India, tariff is same for peak as

well as off-peak hours. Due to shortage of electricity,

power cuts (power shedding) are common in India. To

avoid a total black out of the power system, electricity

delivery is stopped for non-overlapping periods of time

over different parts of the distribution regions.

Demand power of the LAG, as shown in Figure 5 is

the data of township under consideration during day time

and night time. Four simulations are carried out on this

smart grid model. Table 6 shows the content of the case

studies.

Table 3. Parameters.

Name Max Capacity Type

LAG 2MW(peak),1.5MW (off-peak) Consumer

WTAG 300KW Producer

PVAG1 150KW Producer

PVAG2 100KW Producer

MHTAG 400KW Producer

DGAG1 200KW Producer

DGAG2 200KW Producer

BAG ±250KWh Producer/consumer**

GRID ∞/0* Producer

*power shedding (no power is available from the grid); **producer at the

time of power shedding & consumer during charging phase.

Table 4. Power cost per unit

Producers Price/unit(kWh)

GRID 4.00

BAG 4.50#

PVAG 9.18*

WTAG 3.84**

MHTAG 5.51

DGAG 13.00

#Pseudo price (As battery is charged from the grid, the price includes grid

price per unit in addition to some maintenance cost; *Solar PV Crystalline;

**wind Zone IV (>400W/m2).

Table 5. Availability of the producer(s)

Time zonePVAGGRIDMHTAG WTAG BAGDGAG

06:00-12:00Yes Yes Yes Yes No Yes

12:00-18:00Yes No Yes Yes Yes Yes

18:00-0:00No Yes Yes Yes No Yes

0:00-6:00No No Yes Yes Yes Yes

R. GUPTA ET AL. 1335

Table 6. Test Case Scenario

Case Time Zone

Availability of Power

from Grid

Case I Day Y

Case II Day N

Case III Night Y

Case IV Night N

Figure 5. Demand power of LAG (24 hrs)

Figure 6. Showing the availability of power from grid dur-

ing the day time

Figure 7. Showing the unavailability of power from grid

during the day time

Case1: Day time and power is available from the grid

This is the case when grid is available. Any amount of

power can be taken from the grid. WTAG price is less

compared to grid. SGC takes the decision to fulfill the

load demand by WTAG as shown in figure 6. When the

load demand is more compared to the power available by

WTAG, power is taken from grid. The demand is ful-

filled at a lower price.

Case 2: Day time and power is not available from the

grid

This is the case when load demand is more. Due to

power shedding power is not available from the grid.

Power is taken from WTAG & BAG and demand is ful-

filled as shown in figure 7. If the demand is not fulfilled

by these producers, SGC fetch power from MHTAG,

PVAGs & DGAGs. Load demand is fulfilled at a lower

price. Simulation results show that the multi-agent ap-

proach is promising in smart grid operations.

Case 3: Night time and power is available from the

grid

This is the case when load demand is fluctuating. Any

amount of power can be taken from the grid. WTAG

price is less compared to grid. SGC takes the decision to

fulfill the load demand by WTAG as shown in figure 8.

When the load demand is more compared to the power

available by WTAG, power is taken from the grid. Load

demand is fulfilled at a lower price.

Case 4: Night time and power is not available from the

grid

Figure 8. Showing the availability of power from grid dur-

ing the night time

Figure 9. Showing the unavailability of power from grid

during night time

R. GUPTA ET AL.

1336

This is the case when load demand is less. Due to

power shedding power is not available from the grid.

Power is taken from WTAG and BAG and demand is

fulfilled as shown in figure 9. If the demand is not ful-

filled by these producers SGC fetch power from

MHTAG and then from DGAG. Load demand is fulfilled

at a lower price. Simulation results show that the mul-

ti-agent approach is promising in smart grid operation.

5. Conclusions

This paper presents the benefits of multi-agent system for

smart grid operation. This is achieved effectively through

the negotiation skills and coordination of actions between

SGC and various agents. The proposed approach is vali-

dated in a simulated environment that considers both the

availability and outage conditions of the power grid.

Based on the method, the algorithm and various test case

scenarios, it is found that the proposed multi-agent ap-

proach is viable in smart grid operations.

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