Murat Fahrioglu

Using Utility Information to Calibrate Customer Demand Management Behavior Models

In times of stress customers can help a utility by means of voluntary demand management programs if they are offered the right incentives. The incentives offered can be optimized if the utility can estimate the outage or substitution costs of its customers. This paper illustrates how existing utility data can be used to predict customer demand management behavior. More specifically, it shows how estimated customer cost functions can be calibrated to help in designing efficient demand management contracts.

Designing cost effective demand management contracts using game theory

Demand relief from customers can help an electric utility solve a variety of problems. There exist all sorts of different demand management programs that utilities use. A critical issue is the incentive paid to the customer to participate in demand management programs and provide load relief. The utility has to design cost effective yet attractive demand management contracts. The main goal is to get load relief when needed. If the contracts are designed to be cost effective they can help the utility reduce costs. Customers sign up for programs when the benefits they derive in the form of up front payments and interruption payments exceeds their cost of interruption. In order to design such contracts, mechanism design with revelation principle is adopted from game theory and applied to the interaction between a utility and its customers. The idea behind mechanism design is to design a program incentive structure that encourages customers to reveal their true value of power (and thus, the value of power interruptibility) without the need to explicitly have customers declare the value. This economic analysis is combined with power system sensitivity analysis to help determine the value of interruptibility for each system location.

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 theorys 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.

A soft computing approach to projecting locational marginal price

The increased deregulation of electricity markets in most nations of the world in recent years has made it imperative that electricity utilities design accurate and efficient mechanisms for determining locational marginal price (LMP) in power systems. This paper presents a comparison of two soft computing-based schemes: Artificial neural networks and support vector machines for the projection of LMP. Our system has useful power system parameters as inputs and the LMP as output. Experimental results obtained suggest that although both methods give highly accurate results, support vector machines slightly outperform artificial neural networks and do so with manageable computational time costs.

Integrating distributed generation technology into demand management schemes

Recent developments in distributed generation technology bring about extra flexibility into existing demand management schemes. Many utilities have implemented various demand management programs to help in problematic times of the power system. This paper explains how existing demand management ideas can be supplemented by the use of distributed generation technology. More specifically, it compares economic aspects of using demand management contracts with the use of distributed generation. A key observation of this paper is that there are cases where it is more beneficial to use distributed generation rather than demand management contracts.

Using utility information to calibrate customer demand management behavior models

Summary form only given as follows. In times of stress customers can help a utility by means of voluntary demand management programs if they are offered the right incentives. The incentives offered can be optimized if the utility can estimate the outage or substitution costs of its customers. This paper illustrates how existing utility data can be used to predict customer demand management behavior. More specifically, it shows how estimated customer cost functions can be calibrated to help in designing efficient demand management contracts.

Feasibility study of a grid connected hybrid PV-wind power plant in Gwanda, Zimbabwe

The depletion of fossil fuel resources on worldwide basis has necessitated an urgent search for alternative energy sources to meet up the present day demands. Energy demand is growing in developing nations which makes a hybrid power system, consisting of a hybrid Solar Photovoltaic together with wind energy to be considered one of the best alternatives in renewable energy. These sources of energy can partially or fully meet Gwandas demand with little or no disturbance on the countrys stability. The objective of this study is to convert the wind and solar resources in Gwanda into electrical energy to meet the growing demand. This system ensures a suitable utilization of resources and hence improves the efficiency as compared with their individual modes of generation. The annual energy generated by the hybrid system is calculated and energy accounting is performed according to the demand. The main goal is to have a hybrid system with a suitable Levelized Cost of Electricity (LCOE). Comparative analysis was made with the existing grid tariff of Zimbabwe to investigate the feasibility of such a system.

Climate and size correction in European Union’s Waste Framework Directive and R1 energy efficiency criteria:

This article presents an analysis on the use of the R1 formula to determine the recovery status of some energy from waste plants. Detailed R1 computations are provided to demonstrate the application of R1 guidelines in incineration and gasification facilities. Climate and size correction methods are proposed in consideration of the disadvantage faced by smaller-sized energy from waste plants or those located in warmer regions in meeting the set threshold. A key highlight is the case-based application of climate and size correction factors to three case study plants in scaling the R1 value in consideration of external variants. The proposed size and climate correction factors are compared with the climate correction factor defined in the Waste Framework Directive of the European Union. The application of the proposed correction factors lead to conservative R1 scaling when compared with the application of the Waste Framework Directive climate correction factor. The introduction of the size correction factor addresses an important gap in the current Waste Framework Directive.

Environmental feasibility analysis of a coal fired power plant project in Gadani, Pakistan

The electricity demand and supply gap in Pakistan has reached 6,000 MW, for which the Government of Pakistan has planned some power generating projects. Environmental feasibility analysis is done for one of these projects. The Government of Pakistan has planned to build a coal-based mega power plant project of 6,600 MW capacity at Gadani, Karachi. Coal for this project will be imported from South Africa, Indonesia and Australia, even though Pakistan also has huge reserves of coal at Thar, Sindh. CO2 of 208.92-804.02 tons/GWh and SO2 emissions of 3.73-14.55 tons/GWh are estimated from this project using stoichiometric calculations. Renewable energy potential of Pakistan is presented in order to discourage the Government of Pakistan on these environmental unfriendly projects. Solar, wind, biomass and hydro electric energy options are encouraged to invest in as they are sustainable and environment friendly.

An analysis on the potential of solar photovoltaic power

ABSTRACT In this present paper, the potential of solar photovoltaic power in Zimbabwe so as to cater for the rising energy demand is assessed. The main objective of this present study is to convert solar resources in 28 different locations scattered all over the country into electrical energy. This investment requires a capital cost of US