Mixed integer programming of multiobjective hydro-thermal self scheduling

Ahmadi, Abdollah; Aghaei, Jamshid; Shayanfar, Heidar Ali; Rabiee, Abdorreza

doi:10.1016/j.asoc.2012.03.020

Cited by 82 publications

(72 citation statements)

References 20 publications

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“…The model in [25] considers a classical deterministic unit commitment of both thermal and combined cycle units; this minimizes the generation (fuel) costs (no electricity market) by satisfying simple bounds on SO 2 and NO x emissions. A quite common approach by several recent papers to the handling of emission limits involves using multiobjective optimization techniques in which both profits and emissions are minimized [4,28,35], sometimes with additional emissions limit constraints [8]. Despite the interest of all these studies, it is worth mentioning that none of them are optimal-bid models, as their formulations do not incorporate the bid rules of the electricity market, and the influence of the electricity market is reduced either to a deterministic forecasting of the electricity prices [4,8,28] or to the use of spot price scenarios [35].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A quite common approach by several recent papers to the handling of emission limits involves using multiobjective optimization techniques in which both profits and emissions are minimized [4,28,35], sometimes with additional emissions limit constraints [8]. Despite the interest of all these studies, it is worth mentioning that none of them are optimal-bid models, as their formulations do not incorporate the bid rules of the electricity market, and the influence of the electricity market is reduced either to a deterministic forecasting of the electricity prices [4,8,28] or to the use of spot price scenarios [35]. Some recent works have taken into account CO 2 emission constraints in the self-scheduling of thermal units (not CCGT units) that operate in electricity markets through two-stage stochastic programming [16,22,29].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This appendix deals with the development of the explicit formulation of the polyhedron P U C in equation (4). Let u ti be the first-stage binary variable expressing the off-on operating status of the i th unit and let c u ti , c d ti be continuous variables representing the startup and shutdown costs, respectively, of unit i in interval t. Let also constant G i be the number of periods that unit i must be initially online, due to its minimum up-time t on i .…”

Section: Appendixa Thermal and Combined Cycle Unit Commitment Constrmentioning

confidence: 99%

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Stochastic optimal generation bid to electricity markets with emissions risk constraints

Heredia

Cifuentes-Rubiano

Corchero

2018

Journal of Environmental Management

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There are many factors that influence the day-ahead market bidding strategies of a generation company (GenCo) within the framework of the current energy market. Environmental policy issues are giving rise to emission limitation that are becoming more and more important for fossil-fueled power plants, and these must be considered in their management. This work investigates the influence of the emissions reduction plan and the incorporation of the medium-term derivative commitments in the optimal generation bidding strategy for the day-ahead electricity market. Two different technologies have been considered: the high-emission technology of thermal coal units and the low-emission technology of combined cycle gas turbine units. The Iberian Electricity Market (MIBEL) and the Spanish National Emissions Reduction Plan (NERP) defines the environmental framework for dealing with the day-ahead market bidding strategies. To address emission limitations, we have extended some of the standard risk management methodologies developed for financial markets, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), thus leading to the new concept of Conditional Emission at Risk (CEaR). This study offers electricity generation utilities a mathematical model for determining the unit's optimal generation bid to the wholesale electricity market such that it maximizes the long-term profits of the utility while allowing it to abide by the Iberian Electricity Market rules as well as the environmental restrictions set by the Spanish National Emissions Reduction Plan. We analyze the economic implications for a GenCo that includes the environmental restrictions of this National Plan as well as the NERP's effects on the expected profits and the optimal generation bid.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Appendixa Thermal and Combined Cycle Unit Commitment Constrmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic optimal generation bid to electricity markets with emissions risk constraints

Heredia

Cifuentes-Rubiano

Corchero

2018

Journal of Environmental Management

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“…Augmented-weighted epsilon-constraint technique is suggested to remedy this deficiency. The details of incorporating lexicographic optimization and augmented-weighted epsilon-constraint technique have been described in our previous paper in the area [37,38]. To avoid tautology in writing, these matters have not been repeated in this paper.…”

Section: Multiobjective Mathematical Programming (Mmp)mentioning

confidence: 99%

“…In order to choose the best compromise solution among the obtained Pareto optimal solutions by the ε-constraint method, a fuzzy decision maker is proposed which can softly select the most preferred compromise solution among the Pareto solutions [29,[37][38][39][40][41][42].For this purpose, the fuzzy decision maker calculates a linear membership function for each objective function in each Pareto optimal solution, which measures the relative distance between the value of the objective function in the Pareto optimal solution from its values in the respective utopia and pseudo nadir points. The mathematical formulation of these membership functions for the MMP market clearing problem is as follows:…”

Section: A Fuzzy Decision Makermentioning

confidence: 99%

Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

Aghaei

Ahmadi

Rabiee

et al. 2015

Applied Soft Computing

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In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units' contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany's (GENCO's) profit maximization and thermal units' emission minimization. Accordingly, the ε-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The performance of the presented method is verified in different case studies. The results obtained from ε-constraint method is compared with those reported by weighted sum method, evolutionary programming-based interactive Fuzzy satisfying method, differential evolution, quantumbehaved particle swarm optimization and hybrid multi-objective cultural algorithm, verifying the superiority of the proposed approach. Abstract-In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units' contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany's (GENCO's) profit maximization and thermal units' emission minimization. Accordingly, the ε-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The perfo...

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Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

Ehteshami

Hashemi‐Dezaki

Javadi

2022

Energy Science & Engineering

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Several studies have been reported for optimal operation of electrical railway systems (ERSs). However, the stochastic energy management of ERSs, including renewable energy resources (RERs), has received less attention. The RERs’ uncertainties might affect the ERSs. On the other hand, the calculation time of the Monte Carlo simulation (MCS)‐based approaches is an essential challenge, which should be solved, particularly in real‐time decisions and recursive optimization problems. Thus, it is crucial to study the ERSs' stochastic behaviors and uncertainties, including RERs. This paper tries to overcome the discussed concerns and challenges by proposing a novel ERS’s optimal stochastic energy management using clustering algorithms. In this paper, the backward scenario reduction algorithm has been used. In addition, regenerative braking energy (RBE) and energy management systems (ESSs) have been studied. Studying the impacts of changes in the number of passengers on the optimized operation of ERSs and investigating the interaction between the utility grid and the ERS are other contributions of this research. The proposed method is applied to an actual test system of Tehran Urban and Suburban Railway Operation Company (TUSROC). Test results are validated by comparing with available MCS‐based methods. Simulation results illustrate the accuracy and computation time advantages of the proposed method. Simulation results illustrate that <0.6% inaccuracy appears in the proposed method, while it would be 500 times faster than MCS‐based ones. The comparative test results show the advantages of the introduced method.

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Mixed integer programming of multiobjective hydro-thermal self scheduling

Cited by 82 publications

References 20 publications

Stochastic optimal generation bid to electricity markets with emissions risk constraints

Stochastic optimal generation bid to electricity markets with emissions risk constraints

Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

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