Intelligent Agent Based Auction by Economic Generation Scheduling for Microgrid Operation

Wu Wen-HaoOct 26th , 2013

Innovative Smart Grid Technologies (ISGT), 2010R.Bhuvaneswari , S.K.Srivastava , C.S.Edrington , D.A.Cartes , S.Subramanian

Outline

Introduction Market operation Multi Agent System Implementation Results and Discussion Conclusion

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Introduction

Microgrid concept provides an effective approach to integrating small-scale distributed energy resources (DERs) into the bulk electric grid.

The benefits of DERs are seen to be the reliability of service, especially for those in areas where outages are common.

DER(Distributed Energy Resources) solutions include a reduction in emissions for some technologies.

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Introduction From a centralized system to a distributed one has

necessitated , distributed control with decision making done locally with power source and load can potentially create a scalable and robust energy system.

The Multi Agent System technology is suitable for autonomous control of microgrid.

An Artificial Immune System (AIS) based optimization method is used in agent based architecture to determine the optimal generation schedule of the DG(Distributed Generation) sources that will aid the bidding process in an energy market.

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Market operation

The trading is taking place over regular intervals of time

The grid operator announces two prices : the price for selling energy and price for buying energy.

The generator units in the microgrid adjust their set points after negotiation with the other units based on the grid prices, their operational cost and local demands.

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Market operation

1. The grid operator announces the prices for selling or buying energy to the microgrid.

2. The local loads announce their demand

3. The generation units run an optimization routine to minimize the cost of generation and to determine the individual generations.

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Market operation

4. The generator agents bids and negotiates, the generators decrease their offer as long as the generator bid is more than the buying price suggested by the grid or the generator bid reaches the break even point

5. After the end of the negotiation period, all the units know their generations. If there is no generation unit of the microgrid to satisfy the load demand, the power is bought from the grid.

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Market operation

The objective function can be mathematically formulated as Minimize Cost

is the bid from the ith DG source

Subject to the constraint

N number of DG sources that offer bids for power production

x active power production of the ith DG sourceb variable cost (fuel cost)c hourly payback amount for the investmentX active power bought from the gridA open market active power pricePg the individual generations of DG sources

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Multi Agent System - introduction

The main element of MAS is the agent, which is a physical entity that acts in the environment or a virtual one without physical existence.

Intelligent autonomous agents are software or hardware entities that must have three properties or characteristics : reactivity, proactiveness and social ability.

Agent is characterized by a certain behavior , this behavior is formed from the tendency to accomplish the goals set, satisfy objectives and use the resources, skills and services available.

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Multi Agent System - introduction

Each agent is independent, once it joins the system, the logic enables it to interface itself to the other existing agents. One common method for the interface is through a directory service whereby agents register themselves to a common directory and then self-organize their activities.

The ability for agents to be self-organized contributes to the scalability and robustness of the microgrids.

The implementation of MAS using JADE agent management platform which is a software development framework for developing MAS and applications.

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Multi Agent System - Agents Four kinds of agents are developed :

Main Grid : The main grid agent announces the buying and selling price to all the participants.

Load agent : This agent knows the current demand and estimates the energy demand for the next 15 minutes.

Generator agent : This agent adjusts the power flow depending on the market prices and on the outcome of the optimization routine. Every 15 minutes, he bids to the auctioneer in order to cover the estimated needs.

Auctioneer : This agent has to coordinate, announce the beginning and end of a negotiation for a specific period and record final power exchanges between agents in every period.

Grid

MicroGrid

Production Unit

Consumption Unit

Power Flow

BP SP

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Multi Agent System - AIS

The production settings of the regulated DGs and power exchange with the grid are determined using Economic Dispatch (ED). In the proposed work, ED is solved using an Artificial Immune System (AIS) based approach that uses clonal selection principle and evolutionary approach.

The main strength of the AIS is the fact that it is able to learn without any negative training examples. Being able to train an anomaly detection system using only experience of what is "normal“.

AIS is used in this application for determination of optimal generation of DG sources

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The network comprises three feeders : serving a primarily residential area, one industrial feeder serving a small workshop and one feeder with commercial consumers.

Implementation - LV network

Test case LV network

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Implementation – DGs sources

In order to consider realistic numbers, the electrical efficiency of the fuel-consuming units, as well as the depreciation time for their installation have been taken into account.

The bid coefficients assumed by the DG sources expressed in cents of Euro (Ect).

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Implementation – Apx

Actual energy prices from the Amsterdam Power Exchange (ApX) for a day with rather volatile prices have been assumed to represent realistically the open market operation.

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Results and Discussion

Cost(Euro)Average

price(Ect/kWh)

kWh

Base Case (without any DG) 471.83 14.8 31.88MAS Application to MicroGrid 368.42 11.28 32.66

Difference with base case 21.92 %

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Results and Discussion

Between 9.00 and 16.00 and during 21.00, the proposed methodology favors local DG production.

DG MT Micro turbineFC Fuel cellPV PhotovoltaicWT Wind turbine

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Results and Discussion

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Results and Discussion

The main objective in this application is the minimization of operating cost of the microgrid. the agents are required to cooperate so that they make efficient use of the power supplied by the various DG sources.

The auctioneer is responsible for monitoring the microgrid and optimizes the microgrid operation according to the open market prices.

Simulation studies demonstrate that the control agents manage the power of each energy source properly and the microgrid works reliably. Results show that the proposed agent based framework is effective for the control and management of microgrids.

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Conclusion

This paper presented a new method for agent based optimum market operation of a microgrid using Multi Agent Systems combined with Artificial Immune System.

It is proven that under the test conditions simulated, it is economically beneficial to operate the microgrid leading to reduced energy prices for the consumers.

Adopting a decentralized approach allows every manufacturer of the DG unit to embed a programmable agent in the controller of his equipment. This would provide the required ‘plug and play’ capability of future DG units and loads.

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Thanks for your listening.