Experiences with mixed integer linear programming based approaches on short-term hydro scheduling

Chang, G.W.; Aganagic, M.; Waight, J.G.; Medina, J.; Burton, T.; Reeves, Stuart; Christoforidis, M.

doi:10.1109/59.962421

Cited by 233 publications

(96 citation statements)

References 15 publications

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“…法有动态规划 [2]、逐步优化 [3]、网络流 [4]、线性规划 [5]、非线性规划 [6]、混合整数规划 [7] [8]等。与此同时，一些启发式算法也越来越多的应用于水库群优化调度，例如遗传算法 [9] …”

Section: 梯级水电站群短期发电调度是复杂的混合整数非线性规划问题，求解的主要困难在于水位-库容曲线、水头-耗水率-出力曲线等非线性unclassified

A Fast Algorithm for Short Term Scheduling of Cascaded Hydropower Reservoir Systems

武¹

2018

JWRR

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With the development of hydropower systems, more and more complex demands are encountered by hydropower scheduling. To obtain near optimal schedules of hydropower systems effectively without solving complex mathematic programming, a fast algorithm is proposed. Without an objective function, the basic scheduling concepts of saving water, servicing for power system balance, coordination of up and down stream reservoirs, together with most of the constrains of optimization models, are used to direct the scheduling of cascaded hydropower systems. The method is composed of a series of operations for whole time horizon requirements such as final reservoir level, total energy or total turbine water of a plant, total energy of several cascaded reservoirs, and reducing spill water. To achieve one target, a successive power scheduling method is proposed in which temporal order of power rise/fall based on load characters and a local scheduling algorithm that can satisfy period coupled constraints are combined. Case study of hydropower system in Hongshui River operated by China Southern Power Grid (CSG) shows that the proposed method is effective in obtaining power schedules and complex power distribution rules can be achieved.

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Section: 梯级水电站群短期发电调度是复杂的混合整数非线性规划问题，求解的主要困难在于水位-库容曲线、水头-耗水率-出力曲线等非线性unclassified

A Fast Algorithm for Short Term Scheduling of Cascaded Hydropower Reservoir Systems

武¹

2018

JWRR

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“…Under this consideration, profit t,w > 0, and substituting Eqs. (17), (18) and (19) in (20), it is possible to get Eq. (21).…”

Section: Mathematical Model For the Combined Bidmentioning

confidence: 99%

“…This relationship is strongly non-linear but it can be represented through a concave piecewise linearization [19][20][21], as shown in Fig. 2.…”

Section: Hydro Modelmentioning

confidence: 99%

Combined hydro-wind generation bids in a pool-based electricity market

Angarita

Usaola

Martínez-Crespo

2009

Electric Power Systems Research

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a b s t r a c tPresent regulatory trends are promoting the direct participation of wind energy in electricity markets. The final result of these markets sets the production scheduling for the operation time, including a power commitment from the wind generators. However, wind resources are uncertain, and the final power delivered usually differs from the initial power committed. This imbalance produces an overcost in the system, which must be paid by those who produce it, e.g., wind generators among others. As a result, wind farm revenue decreases, but it could increase by allowing wind farms to submit their bids to the markets together with a hydro generating unit, which may easily modify its production according to the expected imbalance. This paper presents a stochastic optimization technique that maximizes the joint profit of hydro and wind generators in a pool-based electricity market, taking into account the uncertainty of wind power prediction.

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“…In the past few decades, many methods are implemented for solving the SHTS problem such as dynamic programming (DP) [1], linear programming (LP) [2] and Lagrange relaxation (LR) [3]. DP algorithm can actually tackle a quite general class of dynamic optimization problems, including the ones with nonlinear constraints.…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

Chen¹,

Huang²,

Zhi³

2017

TOEEJ

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This study proposes a novel chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm for solving shortterm hydrothermal scheduling problem with a set of equality and inequality constraints. In the proposed method, chaotic local search technique is employed to enhance the local search capability and convergence rate of the algorithm. In addition, a novel constraint handling strategy is presented to deal with the complicated equality constrains and then ensures the feasibility and effectiveness of solution. A system including four hydro plants coupled hydraulically and three thermal plants has been tested by the proposed algorithm. The results are compared with particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) and other population-based artificial intelligence algorithms considered. Comparison results reveal that the proposed method can cope with short-term hydrothermal scheduling problem and outperforms other evolutionary methods in the literature.

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Experiences with mixed integer linear programming based approaches on short-term hydro scheduling

Cited by 233 publications

References 15 publications

A Fast Algorithm for Short Term Scheduling of Cascaded Hydropower Reservoir Systems

A Fast Algorithm for Short Term Scheduling of Cascaded Hydropower Reservoir Systems

Combined hydro-wind generation bids in a pool-based electricity market

A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

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