Advanced model for joint generation and transmission expansion planning including reactive power and security constraints of the network integrated with wind turbine

Khob, Sayed Akbar Eghbali; Moazzami, Majid; Hemmati, Reza

doi:10.1002/etep.2799

Cited by 8 publications

(7 citation statements)

References 31 publications

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“…The upper active, reactive, and lower reactive power for generation units is 80%, 50%, and −50% of the unit's total capacity. Three shunt FACTS devices, including static var compensator (SVC) and static synchronous compensator (STATCOM), are placed in buses 3, 4, and 5. the specifications of FACTS devices, such as installation cost, type, and capacity, are mentioned in [44]. These devices under the headings P1 to P3 can be installed in buses 3–5.…”

Section: Simulation Resultsmentioning

confidence: 99%

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Seyyedi

Rashid

Akbari

et al. 2022

IET Generation Trans & Dist

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Generation and transmission expansion increase the flexibility of power systems and hence their ability to deal with contingency. This paper presents a resilient‐constrained generation and transmission expansion planning (RCGTEP) model considering the occurrence of earthquakes and floods. The proposed model minimizes the investment and operation costs of resiliency sources (RSs) and resiliency (blackout) costs arising from the outage of the network against the occurrence of extreme conditions. For further consideration, uncertainties of load and RSs availability are included as a Stochastic programming model. A hybrid solver of teaching‐learning‐based optimization (TLBO) and krill herd optimization (KHO) is used to solve the proposed problem and achieve the optimal solution, including a low standard deviation in the final optimal response. The model is tested using a modified version of the IEEE 6‐Bus and IEEE 89‐Bus transmission networks. Numerical results show the potential of the mentioned approach to improve indices of operation, economics, and resiliency in the transmission network.

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Section: Simulation Resultsmentioning

confidence: 99%

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Seyyedi

Rashid

Akbari

et al. 2022

IET Generation Trans & Dist

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“…Operation constraints of the electricity network are presented in ( 13)-( 21) [35,36]. Constraints ( 13)-( 16) refer to the equations of AC power flow in this network [35], which respectively represent the active and reactive power balance in electrical buses, and active and reactive power flow through transmission lines.…”

Section: Lower-level Problemmentioning

confidence: 99%

Resilience‐oriented expansion planning of multi‐carrier microgrid utilizing bi‐level technique

Dehshiri Badi,

Amir,

Shariatmadar

2023

IET Renewable Power Gen

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This paper presents the generation and transmission expansion planning (GTEP) in electricity and gas networks by considering their resilience against floods and earthquakes. These networks supply electricity, heat, and gas consumption energies as a multi‐carrier microgrid. The scheme is expressed in the form of bi‐level optimization, the upper level of which is the minimization of generation and transmission planning cost (total investment cost and expected operating cost) in the mentioned networks constrained to the investment budget and the planning model of the mentioned elements. Lower‐level formulation minimizes the total expected annual operating cost of these networks and the expected outage cost of electricity, heat, and gas consumers in the event of floods and earthquakes. This formulation is bound by the power flow equations of electricity and gas networks, the operation and resilience constraints of the networks, and the limitation on generation capability. In this problem, the expected energy not‐supplied and the outage cost during natural disasters are considered resilience indicators. Next, a single‐level model for the proposed design is extracted from the Karush–Kuhn–Tucker (KKT) method. The basic requirement of this method is the convexity of the lower‐level constraints. For this purpose, first, a linear approximation model is obtained for the lower‐level constraints of the problem. Furthermore, stochastic optimization is adopted to model the uncertainty of load, renewable power, and network equipment availability during floods and earthquakes. Finally, the extracted numerical results confirm the capability of the proposed scheme in improving the operation and resilience of the mentioned networks using optimal generation and transmission planning.

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“…[21]. The specifications of transmission lines were extracted from [28], the data for generation units and parallel FACTSs are listed in Table 2 [28,[37][38]. The term R P is the product of the capacity of wind farms and the daily power rate curve.…”

Section: A the Modified Ieee 6-bus Networkmentioning

confidence: 99%

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Baziar

Ghotbabadi

et al. 2021

IEEE Access

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An AC security constrained unit commitment (AC-SCUC) in the presence of the renewable energy sources (RESs) and parallel flexible AC transmission system (FACTS) devices is conventionally modeled as a deterministic optimization problem to minimize the operation cost of conventional generation units (CGUs) subject to AC optimal power flow (AC-OPF) equations, operation constraints of RESs, parallel FACTS devices, and CGUs. To cope with the uncertainties of load and RES generation, robust and stochastic optimization and linearized formulation have been used to achieve a sub-optimal solution. To achieve the optimal solution, this paper proposed an evolutionary algorithm-based adaptive robust optimization (EA-ARO) approach to solve the non-linear and non-convex optimization problem. A hybrid solver of grey wolf optimization (GWO) and teaching learning-based optimization (TLBO) obtained the robust and reliable optimal solution for the proposed AC-SCUC in the worst-case scenario. Finally, the proposed method was simulated on standard IEEE test systems to demonstrate its capabilities, and the results showed the proposed hybrid solver obtained robust optimal solutions with reduced computation time and standard deviation. Moreover, the numerical results proved the proposed strategy's capabilities of improving the economics of generation units, such as lower operational cost, and technical performance of the transmission networks, such as improved voltage profile and reduced energy losses.INDEX TERMS AC security constrained unit commitment, Evolutionary algorithm-based adaptive robust optimization, Renewable energy sources, Parallel FACTS devices.

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Advanced model for joint generation and transmission expansion planning including reactive power and security constraints of the network integrated with wind turbine

Cited by 8 publications

References 31 publications

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Resilience‐oriented expansion planning of multi‐carrier microgrid utilizing bi‐level technique

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

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