Optimized day-ahead hydrothermal wind energy systems scheduling using parallel PSO

Zadeh, Alimorad Khajeh; Abdel‐Akher, Mamdouh; Wang, M.; Senjyu, Tomonobu

doi:10.1109/icrera.2012.6477311

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…Equations (11)- (15) are also the constraints of MPC 1 because the prediction horizon is reduced significantly, i.e., it is smaller than that of the single-level MPC, as described in Table 2. The result of the third sub-objective is shown as follows: Figure 11 shows the wind power that is directly sent to the grid wd t p (wd).…”

Section: Two-level Mpc Programming Model (A) Programming Model Of Mpc2mentioning

confidence: 99%

“…These benefits are possible because the wind power is directly injected into the grid and the insufficient part of the demand is compensated by the energy storage system when the wind power is less than power plan. Moreover, when the wind power exceeds the power plan, wd t p can only be close to the generation plan to meet the sub-objective and Equation (15). Assumption 1 is that pumped storage can change its operating state at the beginning of a new half-hour period and then remain in this state until the half-hour period ends.…”

Section: Single-level Mpc Programming Model (A) Objective Function Ofmentioning

confidence: 99%

“…However, the PID and fuzzy control do not belong to optimal algorithm, and their scheduling results are less than the results of MPC. Meanwhile, in this paper the Equations (15) and (16) are logic constrains, these logic constraints can be easily fulfilled using a CPLEX optimal tool, which is a common tool used in building MPC model. In contrast, they are hard to perform with the PID and fuzzy controller methods.…”

Section: Introductionmentioning

confidence: 99%

“…Wind farm scheduling can be classified into the day-ahead scheduling and the real-time scheduling according to the influence of wind power on the grid [15]. The day-ahead scheduling is employed to address the long-term influence, and the real-time scheduling is applied to address the short-term influence.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

et al. 2015

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Abstract:Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC) method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long-and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.

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Section: Two-level Mpc Programming Model (A) Programming Model Of Mpc2mentioning

confidence: 99%

Section: Single-level Mpc Programming Model (A) Objective Function Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

et al. 2015

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“…But the objective function about different purpose of the hydrothermal scheduling is not always derivable and differentiable. As a member of the modern optimization algorithm, particle swarm optimization(PSO) [10] algorithm has no request about the derivability and differentiability of the objective function and it is more flexible and scalable, which gives PSO algorithm advantages to solve the STOHS problem. PSO algorithm has been developed a lot for its fast rate of convergence, simple principle and convenience to be programmed since it was put forward.…”

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confidence: 99%

Short-term optimal hydrothermal scheduling with power flow constraint

Lin

Huang

Zhang

et al. 2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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Short-term optimal hydrothermal scheduling(STOHS) problem [1] is usually supposed to find a reasonable scheduling scheme in a period of time(mostly 24h) while satisfying various constraints. The scheduling scheme aims to make full use of water resources, reduce the proportion of the thermal generation, minimize the operating cost of the hydrothermal system. Scholars have done many mathematical model researches about the hydrothermal power system since Ricard first put forward a mathematical model of optimal coordination of hydrothermal power system in 1940s. These researches mainly focus on the effect of head variation, electrical efficiency [2][3][4], value point of thermal generator [5] and the transmission loss of the active power [6] without consideration of the electricity network structure and effect of the reactive power. Power flow constraint is an important method to consider the effect of the electricity network and reactive power. The key point of the paper is to introduce the power flow constraint into the hydrothermal scheduling for the advantages to reflect the real working states of the hydrothermal power systems. The STOHS problem is a dynamic optimal power flow(OPF) problem essentially after taking the power flow constraint into account. There are only a few scholars who have added power flow constraint in the optimal hydrothermal scheduling model [7].Although Newton method and interior point method are usually used to solve the OPF problem [8-9] , both of the two methods have difficulties in solving the STOHS problem. One vital point of the difficulties is that the two methods are base on the condition that the objective function is derivable or differentiable. But the objective function about different This work was supported by National Natural Science Foundation of China (No. 61403321) and Knowledge Innovation Program of Shenzhen City (No. JCYJ20130327150859765).purpose of the hydrothermal scheduling is not always derivable and differentiable. As a member of the modern optimization algorithm, particle swarm optimization(PSO)[10] algorithm has no request about the derivability and differentiability of the objective function and it is more flexible and scalable, which gives PSO algorithm advantages to solve the STOHS problem. PSO algorithm has been developed a lot for its fast rate of convergence, simple principle and convenience to be programmed since it was put forward. An improved particle swarm optimization is applied to the STOHS problem in this study. The algorithm is verified by the IEEE nine buses test system, and the results show the feasibility and efficiency of the algorithm. This paper is organized as follows. Section 2 gives the formulation procedure of the STOHS problem. Section 3 takes an overview of the improved PSO algorithm. Section 4 shows the solving procedures of the improved PSO algorithm. Section 5 shows the simulation result of the IEEE nine buses test system. Finally, section 6 makes a conclusion of the paper. PROBLEM FORMULATIONSThe purpose of the STOHS is to make full...

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Representation of Wind Energy Scenarios in the Mid-Term Hydrothermal Systems Operation Scheduling

Fernandes

Oliveira

Marcato

et al. 2019

J Control Autom Electr Syst

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This paper presents a modified stochastic dual dynamic programming methodology in order to include the wind energy scenarios in the mid-term hydrothermal systems operation planning with an hourly representation of the demand. As the wind power intermittence adds more complexity for this class of problem, the conventional stochastic dual dynamic programming (SDDP) is not suitable to solve it due the excessive computational effort. Then the proposed approach includes in a conventional SDDP an analytical representation of immediate cost function for each stage and inflow/wind scenarios. Analysis results considering different cases of the South Brazilian system will be used to show the effectiveness and robustness of the proposed methodology.

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Optimized day-ahead hydrothermal wind energy systems scheduling using parallel PSO

Cited by 13 publications

References 13 publications

Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

Short-term optimal hydrothermal scheduling with power flow constraint

Representation of Wind Energy Scenarios in the Mid-Term Hydrothermal Systems Operation Scheduling

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