Demand-side management (DSM) is a strategy enabling the power supplying companies to effectively manage the increasing demand for electricity and the quality of the supplied power. The main objectives of DSM programs are to improve the financial performance and customer relations. The idea is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times. The DSM controls the match between the demand and supply of electricity. Another objective of DSM is to maintain the power quality in order to level the load curves. In this chapter, a genetic algorithm is used in conjunction with demand-side management techniques to find the optimal scheduling of energy consumption inside N buildings in a neighborhood. The issue is formulated as multi-objective optimization problem aiming at reducing the peak load as well as minimizing the energy cost. The simulations reveal that the adopted strategy is able to plan the daily energy consumptions of a great number of electrical devices with good performance in terms of computational cost.
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Traditional power grids face some challenges that limit their efficiency. Among these deficiencies are the non-optimal dimensioning and usage of grid resources. In order to meet customers’ demand of electric energy, power capacity must be able to meet the worst case scenario that is the peak of demand. Moreover, additional capacity must be available to deal with the uncertainty in generation and consumption. Based on that, the grids resources are, for most of the time, underutilized (Barbato and Capone, 2014).
Another proliferating issue in the current power grids is the integration of medium- to small-sized renewable energy source (RES) plants. The tremendous employment of RESs is motivated by the enormous socio-economic benefits obtainable with these sources. These include: reduction of greenhouse gas emissions and air pollution, diversification of energy supply, reduced dependence on imported fuels, economic development and jobs in manufacturing, installation and management of RESs plants (Ortega et al., 2013; Pfeiffer and Mulder, 2013; Chellali et al., 2011). Despite these benefits, industry and consumers themselves, there are barriers that can limit renewable sources’ integration (Richards et al., 2012).
Solutions to the previous dilemma have been found in the revolutionizing aspect of Smart grids (SGs). SGs can make grids more efficient and smarter by means of facilitating the deployment of renewable energy sources, decreasing oil consumption by reducing the need for inefficient generation during peak usage periods, optimizing resources utilization and construction of back-up (peak load) power plants and enabling the integration of plug-in electric vehicles(PEVs) and energy storage systems (ESSs) (NIST, 2010). The energy produced is dispatched through the transmission and distribution sectors, which are controlled by the operation domain. The balance between supply and the demand is guaranteed by the market domain, which consists of suppliers of bulk electricity, retailers who supply electricity to users and traders who buy electricity from suppliers and sell it to retailers and aggregators of distributed small-scale power plants. The service provider manages services for utilities companies and end-users, like billing and consumers’ account management. Finally, customers consume energy, but can also generate and store electricity locally. This domain includes residential, commercial and industrial customers, who can actively contribute to the efficiency of the grid (Barbato and Capone, 2014).
Demand management mechanisms can be classified into two main categories: demand-response (DR) and demand-side management (DSM). DR methods are reactive solutions designed to encourage consumers to dynamically change their electricity demand in the short term, according to signals provided by the grid/utilities, such as prices or emergency condition requests. Typically, these techniques are used to reduce the peak demand or to avoid system emergencies, such as blackouts. On the other hand, DSM is a proactive approach aimed at making consumers energy-efficient in the long-term. In the literature, demand-response and demand-side management terms are often used interchangeably. Thus, in some works on DSM, proposed solutions are called DR methods and vice-versa. Actually, demand-response and demand-side management are two different methodologies, which can also be used in conjunction with each other (Barbato and Capone, 2014)..
Demand management mechanisms can be designed to control the electric resource of individual users. However, this approach may have some undesirable effects (Strbac, 2008). In fact, consumers are characterized by diversity in terms of appliance usage. This feature is fully exploited by the power system to optimize its efficiency in generating and distributing energy. Demand management mechanisms for individual users may actually disturb this diversity. As an example, in the case of systems for consumers’ payment reduction, all users would shift their loads to periods of the day where the electricity prices are low. Unfortunately, this would determine large peaks of demand during such low-cost periods and, possibly, service interruptions (i.e., blackout or brownouts) (Barbato and Capone, 2014).. To contain these unwanted side effects, management mechanisms can be designed to control the community of users, thus managing their resources based on a system-wide perspective.