The impact of energy storage modeling in coordination with wind farm and thermal units on security and reliability in a stochastic unit commitment

Vatanpour, Mohsen; Yazdankhah, Ahmad Sadeghi

doi:10.1016/j.energy.2018.07.181

Cited by 35 publications

(6 citation statements)

References 28 publications

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“…Adequate lifetime is important for economic and stable operation. For example, an SUC problem considering the uncertainty of WTs focused only on the impact of an ESS model on system stability and reliability [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

Lee

Kim

2021

Energies

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As renewable penetration increases in microgrids (MGs), the use of battery energy storage systems (BESSs) has become indispensable for optimal MG operation. Although BESSs are advantageous for economic and stable MG operation, their life degradation should be considered for maximizing cost savings. This paper proposes an optimal BESS scheduling for MGs to solve the stochastic unit commitment problem, considering the uncertainties in renewables and load. Through the proposed BESS scheduling, the life degradation of BESSs is minimized, and MG operation becomes economically feasible. To address the aforementioned uncertainties, a scenario-based method was applied using Monte Carlo simulation and the K-means clustering algorithm for scenario generation and reduction, respectively. By implementing the rainflow-counting algorithm, the BESS charge/discharge state profile was obtained. To formulate the cycle aging stress function and examine the life cycle cost (LCC) of a BESS more realistically, the nonlinear cycle aging stress function was partially linearized. Benders decomposition was adopted for minimizing the BESS cycle aging, total operating cost, and LCC. To this end, the general problem was divided into a master problem and subproblems to consider uncertainties and optimize the BESS charging/discharging scheduling problem via parallel processing. To demonstrate the effectiveness and benefits of the proposed BESS optimal scheduling in MG operation, different case studies were analyzed. The simulation results confirmed the superiority and improved performance of the proposed scheduling.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

Lee

Kim

2021

Energies

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“…Stochastic unit commitment problem deals with the uncertainty. In the literature, several studies discuss the scenario based stochastic programming for solving unit commitment with different methods like progressive hedging [52][53][54][55][56], dual decomposition [57][58][59], benders decomposition [60][61][62][63][64], spatial decomposition [57], directly solved method [65][66][67][68][69][70], cutting plane [71], dynamic formulation [72] and heuristic methods [73][74][75].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Parvez

Shen

Khan

et al. 2019

Water

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The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

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“…The use of ESS can mitigate some of the problems associated with the unpredictable nature of wind power generation [8]. ESS can function as a virtual power generation device by implementing peak-shaving and load following by absorbing and discharging energy to meet the demands of the grid.…”

Section: Introductionmentioning

confidence: 99%

Unit Commitment Accommodating Large Scale Green Power

Ju¹,

Wang²,

Ge³

et al. 2019

Applied Sciences

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As more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment (UC) problem involving the characteristics of both wind and nuclear generation are urgently needed. This paper first describes a model of nuclear power and wind power for the UC problem, and then establishes an objective function for the total cost of nuclear and thermal power units, including the cost of fuel, start-stop and peak-shaving. The operating constraints of multiple generation unit types, the security constraints of the transmission line, and the influence of non-gauss wind power uncertainty on the spinning reserve capacity of the system are considered. Meanwhile, a model of an energy storage system (ESS) is introduced to smooth the wind power uncertainty. Due to the prediction error of wind power, the spinning reserve capacity of the system will be affected by the uncertainty. Over-provisioning of spinning reserve capacity is avoided by introducing chance constraints. This is followed by the design of a UC model applied to different power sources, such as nuclear power, thermal power, uncertain wind power, and ESS. Finally, the feasibility of the UC model in the scheduling of a multi-type generation unit is verified by the modified IEEE RTS 24-bus system accommodating large scale green generation units.

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The impact of energy storage modeling in coordination with wind farm and thermal units on security and reliability in a stochastic unit commitment

Cited by 35 publications

References 28 publications

Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Unit Commitment Accommodating Large Scale Green Power

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