Hydro unit commitment and loading problem for day-ahead operation planning problem

Finardi, Erlon Cristian; Scuzziato, Murilo Reolon

doi:10.1016/j.ijepes.2012.07.023

Cited by 70 publications

(6 citation statements)

References 27 publications

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“…Virmani, Adrian, Imhof and Mukherjee [91], Norouzi, Ahmadi, Nezhad and Ghaedi [76] and Finardi and Scuzziato [153] The linear variability is involved between the time of execution and time stages. This method gives efficient results and it is more capable in precision and execution time for large level systems.…”

Section: Lagrange Relaxationmentioning

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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Section: Lagrange Relaxationmentioning

confidence: 99%

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Parvez

Shen

Khan

et al. 2019

Water

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“…where q in,s , q out,s represent water inflow rate and outflow rate in scenario s at time period t, separately (m 3 /s); q s,t denotes the volume flow ratio passing through the turbine in scenario s at time period t (m 3 /s); q spill,s,t represents water spillage rate (m 3 /s) in scenario s at time period t. As for SHPP's power output P H i,s,t , according to previous research [28], it can be formulated as:…”

Section: Shppmentioning

confidence: 99%

“…Constraints (14) and (28). As can be seen from the reformulation, the problem mentioned above is divided into two stages, which includes a master problem (MP) and a sub-problem (SP).…”

Section: Model Reformulationmentioning

confidence: 99%

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Day-Ahead Scheduling Model of the Distributed Small Hydro-Wind-Energy Storage Power System Based on Two-Stage Stochastic Robust Optimization

2019

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With renewable energy sources (RESs) highly penetrating into the power system, new problems emerge for the independent system operator (ISO) to maintain and keep the power system safe and reliable in the day-ahead dispatching process under the fluctuation caused by renewable energy. In this paper, considering the small hydropower with no reservoir, different from the other hydro optimization research and wind power uncertain circumstances, a day-ahead scheduling model is proposed for a distributed power grid system which contains several distributed generators, such as small hydropower and wind power, and energy storage systems. To solve this model, a two-stage stochastic robust optimization approach is presented to smooth out hydro power and wind power output fluctuation with the aim of minimizing the total expected system operation cost under multiple cluster water inflow scenarios, and the worst case of wind power output uncertainty. More specifically, before dispatching and clearing, it is necessary to cluster the historical inflow scenarios of small hydropower into several typical scenarios via the Fuzzy C-means (FCM) clustering method, and then the clustering comprehensive quality (CCQ) method is also presented to evaluate whether these scenarios are representative, which has previously been ignored by cluster research. It can be found through numerical examples that FCM-CCQ can explain the classification more reasonably than the common clustering method. Then we optimize the two stage scheduling, which contain the pre-clearing stage and the rescheduling stage under each typical inflow scenario after clustering, and then calculate the final operating cost under the worst wind power output scenario. To conduct the proposed model, the day-ahead scheduling procedure on the Institute of Electrical and Electronics Engineers (IEEE) 30-bus test system is simulated with real hydropower and wind power data. Compared with traditional deterministic optimization, the results of two-stage stochastic robust optimization structured in this paper, increases the total cost of the system, but enhances the conservative scheduling strategy, improves the stability and reliability of the power system, and reduces the risk of decision-making simultaneously.

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“…Compared with a single algorithm, hybrid algorithms use the advantages of each algorithm to obtain a better optimal solution [13]. As will be shown, the STLD for HPMTT is modeled by means of a mixed 0-1 Nonlinear Programming (NP) problem, and, to solve it efficiently, a two-phase decomposition approach is proposed [23]. A hybrid algorithm for solving the STLD for HPMTT problems that combines a heuristic searching method and a progressive optimal algorithm is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Dispatching Method for a Diversion Hydropower Plant with Multiple Turbines in One Tunnel Using a Two-Stage Model

Liao

Zhao

et al. 2019

Energies

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Short-term load dispatching (STLD) for a hydropower plant with multiple turbines in one tunnel (HPMTT) refers to determining when to startup or shutdown the units of different tunnels and scheduling the online units of each tunnel to obtain optimal load dispatch while simultaneously meeting the hydraulic and electric system constraints. Modeling and solving the STLD for a HPMTT is extremely difficult due to mutual interference between units and complications of the hydraulic head calculation. Considering the complexity of the hydraulic connections between multiple power units in one tunnel, a two-phase decomposition approach for subproblems of unit-commit (UC) and optimal load dispatch (OLD) is described and a two-stage model (TSM) is adopted in this paper. In the first stage, an on/off model for the units considering duration constraints is established, and the on/off status of the units and tunnels is determined using a heuristic searching method and a progressive optimal algorithm. In the second stage, a load distribution model is established and solved using dynamic programming for optimal load distribution under the premise of the on/off status of the tunnel and units in the first stage. The proposed method is verified using the load distribution problem for the Tianshengqiao-II reservoir (TSQII) in dry season under different typical load rates. The results meet the practical operation requirements and demonstrate the practicability of the proposed method.

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Hydro unit commitment and loading problem for day-ahead operation planning problem

Cited by 70 publications

References 27 publications

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Day-Ahead Scheduling Model of the Distributed Small Hydro-Wind-Energy Storage Power System Based on Two-Stage Stochastic Robust Optimization

Short-Term Load Dispatching Method for a Diversion Hydropower Plant with Multiple Turbines in One Tunnel Using a Two-Stage Model

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