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Co-Planning of Transmission Expansion Planning and Distributed Energy Resources Considering Power System Resiliency

Ranjbar, Hossein | 2021

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 53822 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Hosseini, Hamid
  7. Abstract:
  8. The loads in power systems are increasing mainly due to the development of new technologies, population growth, and the arrival of new consumers. This load growth necessitates the planning for expansion of power systems to meet the future load. Transmission expansion planning (TEP) is one of the main power system planning studies that is performed to meet the future load with an acceptable reliability level. On the other hand, in recent years, the integration of distributed energy resources (DERs) into power systems is growing, mainly driven by energy policies and the associated incentives. The impact of the growing integration of DERs into power grids on distribution network planning is well understood. However, there is growing evidence that large-scale integration of DERs also impacts high voltage transmission systems expansion planning so that the installing DERs in inappropriate places may cause serious problems for power system planners and operators such as transmission congestion. Moreover, given the limitations of building new lines, such as right-of-way challenges and environmental issues, transmission planners usually try to avoid as much as possible building new transmission lines in the first place. Furthermore, it has been proven that DERs are a powerful tool for increasing the resiliency of the power system by locally supplying critical loads. To this end, co-optimizing models for TEP and DERs may prove helpful to meet the future load and to enhance the power system resiliency.This dissertation first develops a bi-level multi-stage model for co-planning of transmission systems and merchant DERs. The upper level of the model determines the optimal expansion plan for transmission lines and merchant DERs by minimizing the line investment cost and cost of buying energy from conventional generators and DERs, while ensuring a given rate of return on DERs investments. The lower level simulates market-clearing to find nodal prices for calculating DERs' profit and cost of buying energy. The uncertainties in the future load and generators' bids are captured by the scenario technique. The proposed bi-level model is recast to a single-level MINLP optimization problem using the strong duality technique. The MINLP problem is then reformulated as a MILP problem using bigM method and the complementary slackness conditions. The proposed model has been finally applied on the IEEE 24-bus test system and the results showed that installing DERs can decrease investment in transmission lines and defer some of them.In order to consider different types of DER technologies, including renewable DERs (wind and solar) and storage devices, a new model is proposed which uses the robust optimization method to model uncertainties. The proposed model in this part is a two-stage tri-level optimization problem in which the first stage determines the optimal expansion plan for transmission lines and merchant DERs and the second stage tries to find the worst situation of uncertain parameters (second level) considering the best possible operation strategies of energy providers to meet the demand (third level). The model considers an acceptable rate of return for investing in DERs to make them potentially attractive for private investors. The proposed problem is recast to a min-max bi-level equivalent model with a MILP master problem and a bi-linear subproblem and is solved using the C&CG method and an iterative algorithm dealing with the subproblem. The proposed model has been tested by the IEEE 24-bus system and the results confirmed that the co-planning of DERs and transmission lines can reduce the number of new transmission lines and improve the flexibility of the power system. Also, the combination of wind, solar, and ESS units in each bus can reach better results. Moreover, the results showed that solar resources are more dependent on the expected rate of return than wind and ESS resources.
    Considering the impact of DER on the resiliency of the power system, we present a new model for joint planning of transmission network development and DER considering power system resiliency. The proposed model is a two-stage stochastic planning model that takes into accounts both normal and emergency conditions as well as the duration of each condition. The proposed model is formulated as a MILP optimization problem to minimize the total investment cost of transmission lines and DERs, expected values of generators’ operation cost in normal and emergency situations, and load shedding cost in emergency conditions. The Benders decomposition technique is utilized to solve the optimization problem. In this section, we proposed a new probabilistic algorithm based on the Monte-Carlo simulation to obtain the emergency scenarios. In this algorithm, first, the behavior of hurricanes is modeled by HAZUS software and the fragility curves are utilized for determining the damage status of transmission assets in each emergency scenario. The emergency scenarios are considered as moderate, severe, and complete damage states, which have different recovery times for transmission assets. Numerical results are demonstrated based on the IEEE 118-bus test system which has been mapped to the state of Texas and compared with previous planning models. We also applied the data from hurricanes IKE and Harvey to the proposed expansion plan to analyze and validate its response. The simulation results show that the proposed planning method in addition to benefiting from DERs in normal operating conditions (reducing the need and delaying investment in the transmission system), also leads to significantly lower load shedding owed to the placement of DER units in more vulnerable spots in the system
  9. Keywords:
  10. Uncertainty ; Private Investor ; Natural Disasters ; Distributed Energy Resorurces (DER) ; Dispersed Generation ; Co-planning in Power System ; Equipments Damage Probability Function ; Power System Resiliency

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