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Top1. Introduction
Deregulation of power markets and establishment of new type of competition based markets have introduced new dynamics and behaviors in the power sector (Salas & Saurina, 2003). In many markets, electricity purchases follow specific market rules that allow very-short-term contracts while prices do not remain the same but vary according to the current market conditions. Realization of competition based power markets requires utilization of advanced information, computing and communication technologies, and may be implemented by bringing together power system and communication infrastructures (Tsoukalas & Gao, 2008 August).
The electric power grid is the essential infrastructure for transmission and distribution of generated power. It is a complex system with dynamic characteristics – continuously changing and extended. However, its dynamic characteristics make it vulnerable to instabilities that may result to blackouts and pauses of electricity flow with severe financial impact. To avoid that, the ahead-of-time prediction of destabilizing events is crucial in preventing catastrophic system failures and disturbance propagation through the power infrastructure (Alamaniotis et al., 2012). Integration of power grid with information and communication technologies (ICT), conceptualized as a smart grid, promises the highest energy utilization and enhanced reliable grid operation. Grid participants are able to receive and transmit information over the grid, and subsequently design their purchase strategy and plan their electricity consumption schedule. Information flow may be utilized for managing power flow in such a way to stabilize the power grid and subsequently ensure the non-stop electricity supply to consumers (Alamaniotis et al., 2010).
Demand side management (DSM) has been recognized as a suitable solution for regulating power flow and avoiding grid instabilities (Strbac, 2008). In particular, ICT may be used for managing the load consumption either in a direct or indirect way. The direct way includes the management of specific electric appliances directly from the supplier (i.e., direct load control). In that case the supplier has remote access to appliances and may control their operation in order to reduce the consumption at peak demand hours (Moura & De Almeida, 2010). The indirect way includes load management by offering incentives to consumers either to curtail or shift their consumption at a later lime (off-peak demand hours) (Gabaldon et al., 2003 June). In price-directed energy utilization consumers are connected to the power grid via intelligent meters. Consumers are recipients of pricing signals via their intelligent meters, and react with their consumption schedule that is formed based on their personal needs and judgement. Therefore, it is in the interest of grid manager and suppliers to incentivize electricity consumers to alter their consumption pattern.
Demand Response (DR) has been identified as an effective way in electricity power markets for balancing demand and supply (Balijepalli, et al., 2011 December). In particular, balance is attained by reducing the amount of demanded load instead of increasing the power generation, which is the case in supply driven markets. Hence, DR may become a valuable tool in emergencies for stabilizing the power grid in emergencies and in peak demand time. There have been proposed several means for implementing DR in smart grids including time-of-use tariffs, real-time pricing and critical peak pricing (Gellings, 2009). It should be noted that participation of consumers in DR programs is based on volunteering. As a result incentivized payments and market-driven prices are the key components to attract consumers to participate in the demand response programs.
To that end, it is essential to identify the inherent relations between the incentives and the conditions of participation in DR (Chao & DePillis, 2013). Incentives should be taken into consideration several factors such as the market clearing price (MCP), consumers’ intention to participate, expected cost of participation, and balance between forecasted demand and generated power (Albadi & El-Saadany, 2007, June). As a result, several methods have been developed and presented for implementing Incentive based Demand Response (IbDR) schemes in smart grids.