Generation expansion planning in the age of green economy

Careri, Francesco; Genesi, C.; Marannino, P.; Montagna, Maria Teresa; Rossi, Stefano Marco Paolo; Siviero, I.

doi:10.1109/ptc.2011.6019441

Cited by 17 publications

(20 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, from the market perspective, similar considerations would hold for the modeling of all types of strategic behaviors of market competitors and the potential resulting equilibria as in [21]. Hence, in many of the previous studies, network effects which may be of less importance, have not been explicitly included (e.g., in the European context as discussed in [21] and [22]). Other cases without modeling network effects include short-term models [23,24] and longterm models [10].…”

Section: Model Assumptionsmentioning

confidence: 99%

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Zhan

Zheng

Wang

et al. 2017

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

Abstract-Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage, decisiondependent stochastic optimization model for long-term, largescale generation expansion planning where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming (MILP) model. The wind penetration, investment decisions, and the optimality of the decisiondependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.

show abstract

Section: Model Assumptionsmentioning

confidence: 99%

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Zhan

Zheng

Wang

et al. 2017

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

show abstract

“…The answers to these types of questions are useful to better coordinate a conventional synchronous generator fleet for the variable wind power generation. Also, the answers can contribute to developing methods for planning the power generation expansion towards lowcarbon economy [13,14]. Specifically, the investigation is based on the total fault injected energy, which is the additional energy injected into the power system during the fault period.…”

Section: Introductionmentioning

confidence: 99%

A preliminary study of impact of reduced system inertia in a low-carbon power system

Javadi

Jiang

2015

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

One of the important features of low-carbon electric power system is the massive deployment of renewable energy resources in the advent of a new carbon-strained economy. Wind generation is a major technology of generating electric power with zero carbon dioxide emission. In a power system with the high penetration of wind generation, the displacement of conventional synchronous generators with variable speed wind turbines reduces system inertia. This leads to larger system frequency deviation following a loss of large generation. In this paper, the impact of the reduction of system inertia on system frequency is analyzed as the result of the integration of a significant amount of wind generation into power systems. Furthermore, we present a preliminary study of the impact of the distribution of the inertia contributions from those online conventional synchronous generators on the rate of change of frequency (ROCOF) based on the total energy injected into the system due to the fault. The total fault energy is represented using Hamiltonian formulism. With the IEEE 39-bus system, it is shown that for a fault with the given injected total energy, clearing time, and location, the distribution of inertia contributions can significantly affect the magnitude of ROCOF. Moreover, for such a fault with different locations, the average of the magnitudes of ROCOF caused by the fault at different locations is larger when the distribution of the inertia contributions is more dispersed.

show abstract

“…As the in-service life of electric power infrastructures normally span over several decades, a "carbon lock-in" effect would arise if the system were built up. Therefore, there is a growing consensus that changes should be made in planning methods to achieve the delivery of low-carbon electric power systems [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the exploratory studies concerning the above issue generally focused on the generation side [4]- [6]. This situation is changed with the advent of distributed generation (DG).…”

Section: Introductionmentioning

confidence: 99%

Active Distribution System Planning for Low-carbon Objective using Cuckoo Search Algorithm

Zeng¹,

Zhang²,

Zhang³

et al. 2014

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-In this study, a method for the low-carbon active distribution system (ADS) planning is proposed. It takes into account the impacts of both network capacity and demand correlation to the renewable energy accommodation, and incorporates demand response (DR) as an available resource in the ADS planning. The problem is formulated as a mixed integer nonlinear programming model, whereby the optimal allocation of renewable energy sources and the design of DR contract (i.e. payment incentives and default penalties) are determined simultaneously, in order to achieve the minimization of total cost and CO 2 emissions subjected to the system constraints. The uncertainties that involved are also considered by using the scenario synthesis method with the improved Taguchi's orthogonal array testing for reducing information redundancy. A novel cuckoo search (CS) is applied for the planning optimization. The case study results confirm the effectiveness and superiority of the proposed method.

show abstract

Generation expansion planning in the age of green economy

Cited by 17 publications

References 0 publications

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

A preliminary study of impact of reduced system inertia in a low-carbon power system

Active Distribution System Planning for Low-carbon Objective using Cuckoo Search Algorithm

Contact Info

Product

Resources

About