Stochastic transmission expansion planning in the presence of wind farms considering reliability and N-1 contingency using grey wolf optimization technique

Ghadimi, Ali; Amani, Mohammad; Bayat, Mohammad; Ahmadi, Saeid; Miveh, Mohammad Reza

doi:10.1007/s00202-021-01339-w

Cited by 19 publications

(6 citation statements)

References 39 publications

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“…where x is decision variables and (32) denotes the objective function (3). Equation (33) indicates deterministic constraints.…”

Section: Scenario-basedmentioning

confidence: 99%

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Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

Ghadimi,

Ahmadi,

Miveh

2024

International Journal of Energy Research

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In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model.

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“…where x is decision variables and (32) denotes the objective function (3). Equation (33) indicates deterministic constraints.…”

Section: Scenario-basedmentioning

confidence: 99%

“…So far, many papers are published to address the GEP-TEP problem either by the AC [1][2][3] or DC power flow equations [4]. The AC power flow provides a nonlinear model; therefore, it is not easy to deal with such complicated equations, especially in a large-scale power system.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

Ghadimi,

Ahmadi,

Miveh

2024

International Journal of Energy Research

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“…Also, this table indicates that corridors (6-10) and ( 14 problem. Also, several corridors, including (3-24), (6-10), (10)(11), (11)(12)(13)(14), (14-16), (15)(16)(17)(18)(19)(20)(21), (16)(17), and (16)(17)(18)(19)(20)(21)(22)(23), are among the selected corridors to install new transmission lines in the proposed co-planning and TEP problems which In the last step of the simulation study, a sensitivity analysis is conducted to measure the influence of private investors' minimum RoR on wind power's installed capacity and the transmission network's investment expenditure. In this regard, running the proposed robust TEP model, the total capacity of wind farms, and the investment cost of newly added lines for different values of the minimum RoR are determined and illustrated in Figure 7.…”

Section: B Robust Tepmentioning

confidence: 99%

“…The SP method is extensively utilized to model the uncertainties in TEP. Authors in [10] proposed a stochastic TEP model to deal with the uncertainties associated with demand and wind power production. In that study, the aim of the optimization problem is minimizing the total planning cost while satisfying techno-economic constraints.…”

Section: Introductionmentioning

confidence: 99%

Developing a Robust Expansion Planning Approach for Transmission Networks and Privately-Owned Renewable Sources

et al. 2023

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Power system restructuring has changed transmission expansion planning (TEP) and caused many complications due to conflicting and contradictory objectives. The transmission capacity expansion would significantly affect the revenue of investor-owned renewable energy sources (RESs). Thus, the investment decisions on merchant RESs must be considered in the TEP studies conducted by the transmission system operator (TSO). In this regard, this paper aims to propose a multi-objective co-planning of investment in transmission networks and merchant RESs with three objective functions: minimizing the investment cost of newly deployed transmission lines, minimizing transmission congestion cost, and minimizing load curtailment in N-1 conditions. Moreover, the TSO guarantees a desirable rate of return for private investors to finance renewable energy projects. Further, a robust optimization (RO) technique is employed to cope with the uncertainties associated with demand and renewable energy production. Also, a posteriori multi-objective optimization algorithm, i.e., the non-dominated sorting genetic algorithm (NSGAII), is applied to solve the advanced optimization problem, followed by the fuzzy min-max method to acquire the final optimal solution. Finally, the IEEE RTS 24-bus test system is utilized to demonstrate the effectiveness and applicability of the suggested approach.

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“…In the solution of TEP, novel metaheuristic algorithms such as constructive metaheuristic algorithm (CMA) [25], tree searching heuristic algorithm (TSHA) [26], orthogonal crossover-based diferential evolution (OXDE) [27], teaching learning-based optimization (TLBO) [28], shufed frog leap algorithm (SFLA) [29], and salp swarm algorithm (SSA) [30] have been widely used in recent years. In addition to novel approaches, relatively old metaheuristic algorithms such as particle swarm optimization (PSO) [31], nondominated sorting genetic algorithm-2 (NSGA-2) [32], ant colony optimization (ACO) [33], artifcial bee colony (ABC) [34], and grey wolf optimization (GWO) [35] continue to be used in TEP studies.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Multiyear Transmission Expansion Planning in Turkish Power System

Ova,

Dogan,

Demirbas

2024

International Transactions on Electrical Energy Systems

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To sustain the clean energy transition without interruption and to ensure the reliable operation of the transmission system, it is required to have enough additional transmission capacity in the future horizons. The transmission expansion planning (TEP) problem is a core issue in deciding additional transmission capacity in the planning activities. TEP aims to find the best expansion plan while satisfying technical and economic constraints. In this study, a new binary version of the original FBI algorithm called the BFBI (binary forensic-based investigation) algorithm is developed to solve the binary TEP problem. The effectiveness and performance of the developed BFBI are assessed by implementing it in two different test systems: the standard Garver 6-bus test system and the modified 400 kV Turkish grid. Seasonal scenarios are created for 5- and 10-year planning periods to cover all possible generation and load conditions and to assess the impact of the increased share of RES on the grid in the TEP studies conducted for the modified 400 kV Turkish grid created as a bulk realistic grid. The TEP problem is solved by including investment, reliability, and operational costs in two different objective functions for cases while considering the N-1 contingency criterion. The efficacy and robustness of the BFBI algorithm are justified by comparing it with well-known algorithms such as GA and PSO.

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Stochastic transmission expansion planning in the presence of wind farms considering reliability and N-1 contingency using grey wolf optimization technique

Cited by 19 publications

References 39 publications

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

Developing a Robust Expansion Planning Approach for Transmission Networks and Privately-Owned Renewable Sources

Long-Term Multiyear Transmission Expansion Planning in Turkish Power System

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