Abstract
In most smart grids, load management techniques are required to handle multiple loads of several types. This paper studies decentralized demand-side management (DSM) in a grid with different types of appliances in two service areas: one with many residential households, and one bus with commercial customers. Each building runs an individual optimal DSM to reschedule the usage time of its flexible appliances to reduce its electric energy cost at a manageable sacrifice of inconvenience according to the forecasted time-varying electricity price. Using the developed model, we examined the effectiveness of decentralized DSM by comparing its performance on the operation status of the grid in terms of electricity cost saving, rooftop photovoltaic (PV) utilization efficiency, voltage fluctuation, power loss, voltage rises, and reverse power flows, which can easily be seen at the commercial load bus.
Highlights
Demand-side management (DSM) refers to the response taken by the consumer to manage the energy usage based on the electricity price over 24 h [1,2,3,4]
It is important to mention that the algorithm allows a shift whenever cost saving is possible elsewhere (Csaving > Cp ), and the total cost paid by the consumer is represented by Ce − Csaving + Cp, where Csaving is the cost saving, Cp is the penalty cost, and Ce is the total electricity
To examine the impact of the decentralized demand-side management (DSM) on the operation status of the grid, the DSM was firstly applied to some households with and without PV-generated power
Summary
Demand-side management (DSM) refers to the response taken by the consumer to manage the energy usage based on the electricity price over 24 h [1,2,3,4]. For a more realistic study, this paper takes into consideration that residential and commercial loads have different time-varying billing rates and exhibit different characteristics (e.g., power consumption profile, electric devices settings, and customer willingness for DSM participation). Residential and commercial loads have different time-varying billing rates and exhibit different characteristics, such as load profile, appliance settings, and customer willingness for DSM participation; they may have different impacts on electricity cost savings and distribution network operation. Our model was verified using the clonal selection algorithm (CSA) This algorithm can deal with multiple types of household appliances in two areas (residential and commercial), despite each type of load having different characteristics such as load profile, electricity price, power rate of the appliances, and customer willingness for DSM participation.
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