A neural network model for optimal demand management contract design

The ever increasing need for energy efficient systems has led to various ingenious ideas about energy management. A major offshoot of this search for energy efficient solutions is demand management in power systems. The goal of any demand management program is to control the demand for electric power among customers thereby creating load relief for electric utilities and improving system security. Typically demand management contract formulations reward customers who willingly sign up for load interruption with incentives. These forms of contracts are termed incentive compatible contracts and the incentive offered the customer should exceed interruption cost and at the same time should be beneficial to the utility. There are different systems to design these kind of contracts and in the past mechanism design from Game theory, has been used in the design of such contracts. In this work we propose an artificial neural network which is trained to determine the optimal contract. The learning algorithm utilized by the artificial neural network is the back propagation learning algorithm where useful power system parameters serve as the neural networks input while the neural systems output is the contract value. Game theory's mechanism design serves as the target for results obtained from the artificial neural network. Our proposed neural system is tested on the IEEE 14 bus test system.

I. Introduction

In most nations of the world, electrical energy demand has increased tremendously. It is common for electrical utilities to react to this increase in demand by attempting to generate more electricity. The problem with this kind of response however is the fact that it is capital intensive and is often harmful to the environment. An alternative and preferred strategy is Demand Side Management (DSM). Demand side management seeks to influence customer use of electricity by altering the magnitude and pattern of the customer's load. This is achieved either by peak clipping, valley filling, load shifting, strategic conservation, strategic load growth and flexible load shape [1]. DSM strategies are used whenever the utility foresees disturbing loading patterns and can be applied either system wide or at specific locations. A fundamental requirement of demand management programs is that customer must participate in these programs voluntarily. DSM strategies have been designed using a host of methods and techniques including artificial neural networks amidst other soft computing techniques [2]–[3]. They can also be used in conjunction with or to complement other programs.