Abstract
In this paper, a planning model for active distribution network (ADN) is proposed considering distributed energy resources (DERs) and distribution network expansion planning (DNEP) to cope with load growth and to maximize profits to the distribution system operator (DSO). The DERs considered in this paper include distributed renewable energy resources (RESs), distributed thermal generators (TGs), and battery energy storage system (BESS). DNEP options, such as adding new lines to the distribution network (DN) and upgrading existing lines are taken into account. The main motivation of this research is to find the optimal sizing and siting of DERs with considering DNEP to meet the load growth requirements and to maximize the total payoff to the DSO. The DSO trades energy externally with an independent system operator (ISO) at the market-clearing price (MCP) and internally with consumers at a predetermined cost. The overall performance of the proposed model is investigated on a 38-bus radial distribution test system. Simulation results indicate that considering DERs contributes to maximizing the total payoff to DSO and meeting the load growth requirements. In addition to that DERs can relieve the congestion in the DN's lines; therefore, it can mitigate or postpone DNEP's investment.
Original language | English |
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Title of host publication | 2021 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665433396 |
DOIs | |
State | Published - 2021 |
Publication series
Name | 2021 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2021 |
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Bibliographical note
Publisher Copyright:© 2021 IEEE
Keywords
- MILP
- Optimal bidding
- distribution network expansion planning
- distribution system operator
- linear power flow
ASJC Scopus subject areas
- Artificial Intelligence
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
- Control and Optimization