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Figure 1 Distribution of surveyed references in the different sections of this paper. Table 1 Distribution of the referenced literature based on country

Figure 1 Distribution of surveyed references in the different sections of this paper. Table 1 Distribution of the referenced literature based on country

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Charles River Associates (2005) reflect on case studies from China, Thailand, Tunisia, Turkey, Uruguay and Vietnam that relate to the implementation of dynamic price schemes (mostly TOU) in these countries. The paper concludes that: (1) Rates should benefit both—the utility and the consumers. (2) residential, agricultural, and small and medium industries, it is income inelastic. Tiwari (2000) studies household survey data from India and confirms that short-run income and _ price elasticity of demand are low and the upper-middle class provides maximum response price signals. Filippini and Pachauri (2004) develop three electricity demand functions, one for each season, from real data of 30,000 households in India. Results show that electricity demand is price and income inelastic but varies with household, demographic and geographical variables.
Charles River Associates (2005) reflect on case studies from China, Thailand, Tunisia, Turkey, Uruguay and Vietnam that relate to the implementation of dynamic price schemes (mostly TOU) in these countries. The paper concludes that: (1) Rates should benefit both—the utility and the consumers. (2) residential, agricultural, and small and medium industries, it is income inelastic. Tiwari (2000) studies household survey data from India and confirms that short-run income and _ price elasticity of demand are low and the upper-middle class provides maximum response price signals. Filippini and Pachauri (2004) develop three electricity demand functions, one for each season, from real data of 30,000 households in India. Results show that electricity demand is price and income inelastic but varies with household, demographic and geographical variables.
Figure 2 Risk—-reward mapping of dynamic tariff types.
Figure 2 Risk—-reward mapping of dynamic tariff types.
Table 2 Comparison of the various pricing policies simple or complex bids, price determination timing with respect to actual delivery, capacity payments, geographically differentiated pricing and price capping. self-organizing pricing scheme, called Sepia, to compute the unit price of electricity based on consumption history, grid load and type of consumer. This pricing scheme is decen- tralized, and a grid frequency is used for grid load measure- ment in smart meters for determining the subsequent unit price of electricity. McDonald and Lo (1990) mention that an appropriate social basis of price designs for retail electricity includes welfare considerations for both consumers and suppliers. Li et al (2003) express the price-deriving objective as a nonlinear optimization problem leading to welfare, yet reflecting the competitive relationship among generation companies, utilities and customers.
Table 2 Comparison of the various pricing policies simple or complex bids, price determination timing with respect to actual delivery, capacity payments, geographically differentiated pricing and price capping. self-organizing pricing scheme, called Sepia, to compute the unit price of electricity based on consumption history, grid load and type of consumer. This pricing scheme is decen- tralized, and a grid frequency is used for grid load measure- ment in smart meters for determining the subsequent unit price of electricity. McDonald and Lo (1990) mention that an appropriate social basis of price designs for retail electricity includes welfare considerations for both consumers and suppliers. Li et al (2003) express the price-deriving objective as a nonlinear optimization problem leading to welfare, yet reflecting the competitive relationship among generation companies, utilities and customers.
8. Demand forecasting by performance cuts to minimize infeasibilities and improve efficiency.
8. Demand forecasting by performance cuts to minimize infeasibilities and improve efficiency.
Related topics:
Computer ScienceEconomicsdynamic pricing in electricity
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