Premium
Multi‐agent decentralized microgrids planning considering long‐term demand response model
Author(s) -
Nemati Bizhan,
Hosseini Seyed Mohammad Hassan
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6960
Subject(s) - demand response , term (time) , smart grid , investment (military) , mathematical optimization , operations research , linear programming , integer programming , game theory , computer science , investment decisions , microeconomics , economics , engineering , production (economics) , electricity , mathematics , physics , quantum mechanics , politics , political science , law , electrical engineering
Summary Demand‐side management resources, such as demand response programs (DRPs) are useful options for the reduction of energy cost according to the viewpoint of department of energy. Utilizing these resources in long‐term studies like generation and transmission expansion planning (GTEP) needs long‐term modeling. In this article, the GTEP was developed considering long‐term model of DRPs using a cooperative game theory approach to minimize the total cost of microgrids (MGs). A two‐level decision‐making method was used to develop this model. On the top level, investment decisions were cooperatively made by MGs and a stochastic chance‐constrained mixed integer linear programming formulation was modeled for investment decisions with operational uncertainties on the bottom level. The self‐sufficiency index was also considered that guarantees supply of sensitive loads in islanding mode. Effectiveness of the proposed model was shown by numerical studies in three cases where in Case 1, load of each smart MG (SMG) will be supplied using its own resources and trading with the retail market. In Case 2, SMGs can also trade with each other in non‐cooperative (profitable) approach and in Case 3, SMGs can trade with each other in cooperative approach. The total cost of SMGs was decreased in Case 2 compared to the previous case by 2.62%, which was due to the use of other SMGs' resources to provide load. Also, the total cost of SMGs was decreased in Case 3 compared to the two previous cases (2.92% and 0.3% decrease compared to the Cases 1 and 2, respectively), which was due to cooperative approach taken by SMGs in this case. In this approach, SMGs provide each other's load without additional cost and try to minimize the total cost of all the SMGs.