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.

This study explores the relationship between electricity consumption, real gross domestic product per capita and carbon dioxide emissions in Zimbabwe. To achieve this, the study set off by examining the stationarity properties of the variables under review with the Zivot-Andrews ( 1992 Zivot, E. , and D.W.K. Andrews. 1992. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal Of Business and Economic Statistics 10:251-270. doi: http://dx.doi.org/10.2307/1391541. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar] ) unit root test that accounts for a single structural break. Subsequently, Maki ( 2012 Maki, Daiki. 2012. “Tests for Cointegration Allowing for an Unknown Number of Breaks.” Economic Modelling 29 (5): 2011–15. https://doi.org/10.1016/J.ECONMOD.2012.04.022 [Google Scholar] ) cointegration test, which accounts for multiple structural breaks, is applied for equilibrium relationship between the variables under review while the long run regression of dynamic ordinary least square (DOLS) is employed for long-run coefficients as estimation procedures. In order to account for the direction of causality flow, the Toda-Yamamoto ( 1995 Toda, H Y.. , and Taku Yamamoto. 1995. Statistical inference in vector autoregressions with possibly integrated processes. Journal Of Econometrics 66 (1–2):225–50. doi: 10.1016/0304-4076(94)01616-8. [Crossref], [Web of Science ®] , [Google Scholar] ) causality test is used for annual frequency data set spanning from 1971–2014. Empirical evidence from the Maki cointegration test shows that there exists a long-run equilibrium relationship between electricity consumption, carbon dioxide emissions and real gross domestic product per capita over the sampled period. The long-run regression suggests that there exist a positive statistically significant relationship between real income and electricity consumption. Thus, corroborating the electricity-led growth hypothesis. This result is supported by the causality test, as one-way causality is observed running from electricity consumption to real gross domestic product. Thus, this is suggestive to government administrators and policymakers that the Zimbabwean economy is electricity dependent. However, there is a tradeoff for environmental quality. As the increase in electricity consumption increases carbon dioxide emissions. The need for diversification of Zimbabwe energy portfolio to cleaner and environmentally friendly energy sources is recommended, given the world global consciousness for cleaner energy consumption.

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.