Abstract:
Demand side management (DSM) is one of the important functions in a smart
grid that allows customers to make informed decisions regarding their energy
consumption, and helps the energy providers reduce the peak load demand and
reshape the load profile. This thesis presents a demand side management
strategy based on load shifting technique for demand side management of
future smart grids with a large number of controllable devices of several types.
The day-ahead load shifting technique proposed in this paper is mathematically
formulated as a minimization problem. A heuristic-based particle swarm
optimization (PSO) that easily adapts heuristics in the problem was developed
for solving this minimization problem. Simulations were carried out on a smart
grid which contains a variety of loads in three service areas, one with
residential customers, another with commercial customers, and the third one
with industrial customers. Further in thesis we havediscussed two cases which
changes the load profile and customer‟s convenience. This is allabout passive
DSM.
Further Renewable hybrid system, which can explore solar or wind sources at
low cost, is a popular choice for this purpose nowadays. In this thesis optimal
energy management for a grid-connected photovoltaic-battery hybrid system is
proposed to sufficiently explore solar energy and to benefit customers at
demand side. The management of power flow aims to minimize electricity cost
subject to a number of constraints, such as power balance, solar output and
battery capacity. With respect to demand side management, an optimal control
method (open loop) is developed to schedule the power flow of hybrid system
over 24 hours. A small residential load is taken for two seasons summer and
winter and linear programming model is used to solve the power flows
between lines. And the power profile adjusted so that the power taken from the
grid is minimized.
