TY - JOUR
T1 - Residential Demand Response for Renewable Energy Resources in Smart Grid Systems
AU - Park, Laihyuk
AU - Jang, Yongwoon
AU - Cho, Sungrae
AU - Kim, Joongheon
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2017/12
Y1 - 2017/12
N2 - With the current state of development in demand response (DR) programs in smart grid systems, there have been great demands for automated energy scheduling for residential customers. Recently, energy scheduling in smart grids have focused on the minimization of electricity bills, the reduction of the peak demand, and the maximization of user convenience. Thus, a user convenience model is proposed under the consideration of user waiting times, which is a nonconvex problem. Therefore, the nonconvex is reformulated as convex to guarantee optimal solutions. Moreover, mathematical formulations for DR optimization are derived based on the reformulated convex problem. In addition, two types of pricing policies for electricity bills are designed in the mathematical formulations, i.e., real-time pricing policy and progressive policy. With real-time pricing policy, convexity is guaranteed whereas progressive policy cannot. Then, heuristic algorithms are finally designed for obtaining approximated optimal solutions in progressive policy.
AB - With the current state of development in demand response (DR) programs in smart grid systems, there have been great demands for automated energy scheduling for residential customers. Recently, energy scheduling in smart grids have focused on the minimization of electricity bills, the reduction of the peak demand, and the maximization of user convenience. Thus, a user convenience model is proposed under the consideration of user waiting times, which is a nonconvex problem. Therefore, the nonconvex is reformulated as convex to guarantee optimal solutions. Moreover, mathematical formulations for DR optimization are derived based on the reformulated convex problem. In addition, two types of pricing policies for electricity bills are designed in the mathematical formulations, i.e., real-time pricing policy and progressive policy. With real-time pricing policy, convexity is guaranteed whereas progressive policy cannot. Then, heuristic algorithms are finally designed for obtaining approximated optimal solutions in progressive policy.
KW - Convex optimization
KW - demand response
KW - residential energy resources
KW - smart grid
UR - http://www.scopus.com/inward/record.url?scp=85029366974&partnerID=8YFLogxK
U2 - 10.1109/TII.2017.2704282
DO - 10.1109/TII.2017.2704282
M3 - Article
AN - SCOPUS:85029366974
SN - 1551-3203
VL - 13
SP - 3165
EP - 3173
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 6
M1 - 7927719
ER -