Unit commitment with load uncertainty by joint chance-constrained programming

Peralta, J.; Pérez-Ruiz, Juan; Torre, S. de la

doi:10.1109/ptc.2013.6652433

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…Some information about the forecast precision can be here taken into account 18 . 19 At least, it necessarily relies on the number of involved vehicles, including batteries features, and rated power of PV plant, thus on the sizing of the collaborative system. This sizing of the collaborative system in terms of PV rated power and EV fleet size is the very first decision which is made in the collaborative system life and it impacts every downstream decision.…”

Section: Breaking Down Of the System Management Into Sub-problemsmentioning

confidence: 99%

“…The grid commitment profile computation is handled one day ahead, on the basis of available forecasts for the PV production f P pv and the charging demand f P ev . Some information about the forecast precision can be here taken into account (Gonzalez Vaya and Andersson, 2013;Peralta et al, 2013). At least, it necessarily relies on the number of involved vehicles, including batteries features, and rated power of PV plant, thus on the sizing of the collaborative system.…”

Section: Breaking Down Of the System Management Into Sub-problemsmentioning

confidence: 99%

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Impact of the management strategy on the sizing of a collaborative system

Latimier

Multon

Ahmed

2015

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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PURPOSE-To foster the grid integration of both electric vehicles (EV) and renewable generators, this paper investigates the possible synergies between these players so as to jointly improve the production predictability while ensuring a green mobility. It is here achieved by the mean of a grid commitment over the overall power produced by a collaborative system which here gathers a PV plant with an EV fleet. The scope of the present contribution is to investigate the conditions to make the most of such an association, mainly regarding to the management strategies and optimal sizing, taking into account forecast errors on PV production. METHODOLOGY-To evaluate the collaboration added value, several concerns are aggregated into a primary energy criterion: the commitment compliance, the power spillage, the vehicle charging, the user mobility and the battery aging. Variations of these costs are computed over a range of EV fleet size. Moreover, the influence of the charging strategy is specifically investigated throughout the comparison of three managements: a simple rule of thumb, a perfect knowledge deterministic case and a charging strategy computed by stochastic dynamic programming. The latter is based on an original modeling of the production forecast error. This methodology is carried out to assess the collaboration added value for two operators points of view: a virtual power plant (VPP) and a balance responsible party (BRP). FINDINGS-From the perspective of a BRP, the added value of PV-EV collaboration for the energy system has been evidenced in any situation even when the charging strategy is very simple. On the other hand, for the case of a VPP operator, the coupling between the optimal sizing and and the management strategy is highlighted. ORIGINALITY-A co-optimization of the sizing and the management of a PV-EV collaborative system is introduced and the influence of the management strategy on the collaboration added value has been investigated. This gave rise to the presentation and implementation of an original modeling tool of the PV production forecast error. Finally, to widen the scope of application, two different business models have been tackled and compared.

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Section: Breaking Down Of the System Management Into Sub-problemsmentioning

confidence: 99%

Section: Breaking Down Of the System Management Into Sub-problemsmentioning

confidence: 99%

Impact of the management strategy on the sizing of a collaborative system

Latimier

Multon

Ahmed

2015

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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“…Each one of the aforementioned issues is a complex question which is currently being investigated in other contexts and other systems. Among others, Peralta et al [12] propose to determine production commitments within a framework where the consumption of domestic users is uncertain, using a joint chance-constrained programming method. Such an approach could be transposed to a situation where uncertainty is shifted from consumption to production.…”

Section: A a Collaborative Systemmentioning

confidence: 99%

Preliminary sizing of a collaborative system: Photovoltaic power plant and electric vehicle fleet

Latimier

Kovaltchouk

Hamed

et al. 2014

2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER)

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Simultaneous upcoming of photovoltaic generation and electric vehicles increases constraints on electric power system. This paper explores the possible synergy between these players so as to jointly improve the production predictability while ensuring a low carbon mobility. First a context is defined for this collaboration. It consists in the association of a photovoltaic producer and some electric vehicles owners so as to both manage the EV recharge and meet a day-ahead production commitment. Several currently studied questions such as commitment strategies or optimal charging can be transposed into the proposed context called collaborative system. Here, we mainly focus on its sizing in terms of PV rated power and number of vehicles. A simplified model of the system is thus realised, including a day ahead commitment and an optimal vehicle charging planning, based on deterministic vehicle characteristics.First results show a strong influence of the sizing on the potential added value of vehicles in this association. Then, we assess the impact of day-ahead production forecast quality by comparing persistence forecast with some meteorological data. It appears that other things remaining equal, an imprecise forecast will increase the optimal number of vehicles that are supposed to get into the collaborative system. Finally, the robustness of the charging planning is investigated.

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“…In the literature, a common approach to considering risk is imposing chance constraints, which is equivalent to bounding the Value at Risk (VaR) of the loss. Chance-constrained UC models are used to find commitment schedules that are able to satisfy the power demand of the system with a user-defined reliability level [27,28,31,33]. Wang et al [42], however, proposed a UC model that includes features of both the two-stage stochastic program and the chanceconstrained stochastic program.…”

Section: Introductionmentioning

confidence: 99%

Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation

2017

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Unit commitment seeks the most cost effective generator commitment schedule for an electric power system to meet net load, defined as the difference between the load and the output of renewable generation, while satisfying the operational constraints on transmission system and generation resources. Stochastic programming and robust optimization are the most widely studied approaches for unit commitment under net load uncertainty. We incorporate risk considerations in these approaches and investigate their comparative performance for a multi-bus power system in terms of economic efficiency as well as the risk associated with the commitment decisions. We explicitly account for risk, via Conditional Value at Risk (CVaR) in the stochastic programming objective function, and by employing a CVaR-based uncertainty set in the robust optimization formulation. The numerical results indicate that the stochastic program with CVaR evaluated in a low-probability tail is able to achieve better cost-risk trade-offs than the robust formulation with less conservative preferences. The CVaR-based uncertainty set with the most conservative parameter settings outperforms an uncertainty set based only on ranges. Industrial Engineering | Systems Engineering CommentsThis is an accepted manuscript published as Kazemzadeh, Narges, Sarah M. Ryan, and Mahdi Hamzeei. "Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation." Energy Systems: 1-25.Abstract Unit commitment seeks the most cost effective generator commitment schedule for an electric power system to meet net load, defined as the difference between the load and the output of renewable generation, while satisfying the operational constraints on transmission system and generation resources. Stochastic programming and robust optimization are the most widely studied approaches for unit commitment under net load uncertainty. We incorporate risk considerations in these approaches and investigate their comparative performance for a multi-bus power system in terms of economic efficiency as well as the risk associated with the commitment decisions. We explicitly account for risk, via conditional value at risk (CVaR) in the stochastic programming objective function, and by employing a CVaR-based uncertainty set in the robust optimization formulation. The numerical results indicate that the stochastic program with CVaR evaluated in a low-probability tail is able to achieve better cost-risk trade-offs than the robust formulation with less conservative preferences. The CVaR-based uncertainty set with the most conservative parameter settings outperforms an uncertainty set based only on ranges.

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Unit commitment with load uncertainty by joint chance-constrained programming

Cited by 8 publications

References 10 publications

Impact of the management strategy on the sizing of a collaborative system

Impact of the management strategy on the sizing of a collaborative system

Preliminary sizing of a collaborative system: Photovoltaic power plant and electric vehicle fleet

Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation

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