Capacity configuration optimization for stand-alone microgrid considering the uncertainties of wind and solar resource

Li, Kangping; Zhang, Jingjing; Che, Jianfeng; Wang, Fei; Ren, Hui; Mi, Zengqiang

doi:10.1109/isgt.2018.8403371

Cited by 8 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To ensure that the discriminator function roughly fulfills the 1 − Lipschitz continuity, the gradient penalty function of function D in the domain can be inserted into (3), such that the discriminator loss function can properly describe the Wasserstein distance. Hence, the GAN objective function may be transformed into [31]:…”

Section: We Need To Create Another Game Value Function ( )mentioning

confidence: 99%

Optimal Configuration and Scheduling Model of a Multi-Park Integrated Energy System Based on Sustainable Development

Gao

Huang

et al. 2023

Electronics

View full text Add to dashboard Cite

To maximize the utilization of renewable energy (RE) as much as possible in cold areas while reducing traditional energy use and carbon dioxide emissions, a three-layer configuration optimization and scheduling model considering a multi-park integrated energy system (MPIES), a shared energy storage power station (SESPS), and a hydrogen refueling station (HRS) cooperation based on the Wasserstein generative adversarial networks, the simultaneous backward reduction technique, and the Quantity-Contour (WGAN-SBR_QC) method is proposed. Firstly, the WGAN-SBR_QC method is used to generate typical scenarios of RE output. Secondly, a three-layer configuration and schedule optimization model is constructed using MPIES, SESPS, and HRS. Finally, the model’s validity is investigated by selecting a multi-park in Eastern Mongolia. The results show that: (1) the typical scenario of RE output improved the overall robustness of the system. (2) The profits of the MPIES and HRS increased by 1.84% and 52.68%, respectively, and the SESPS profit increased considerably. (3) The proposed approach increased RE utilization to 99.47% while reducing carbon emissions by 32.67%. Thus, this model is a reference for complex energy system configuration and scheduling, as well as a means of encouraging RE use.

show abstract

Section: We Need To Create Another Game Value Function ( )mentioning

confidence: 99%

Optimal Configuration and Scheduling Model of a Multi-Park Integrated Energy System Based on Sustainable Development

Gao

Huang

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Therefore, reference [12] proposes a planning method involving commitment and economic scheduling of hourly robust transmission constraint units, which can better evaluate operating costs under specific planning decisions and reduce the conservatism of robust optimization to a certain extent. To further reduce the conservatism of optimization results, reference [13] generates wind power generation scenarios based on Latin Hypercube Sampling (LHS) based on the renewable energy generation probability density distribution function, adopts the randomly optimized energy storage capacity allocation method, and takes the expected optimal under an uncertain environment as the goal. The flexibility and economy of the system are taken into account, but the probability density distribution function of renewable energy generation is often unknown in the actual planning work.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation

Wei¹,

Li²,

Fang³

et al. 2023

Sustainability

View full text Add to dashboard Cite

The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a long dispatching period during the deployment of energy storage in the microgrid. To this end, a typical multi-day scenario set is used as the simulation operation scenario, and an optimal allocation method of microgrid energy storage capacity considering the uncertainty of renewable energy generation is designed. Firstly, the historical scenarios are clustered into K types of daily state types using the K-means algorithm, and the corresponding probability distribution is obtained. Secondly, the Latin hypercube sampling method is used to obtain the state type of each day in a multi-day scenario set. Then, the daily scenario generation method based on conditional generative adversarial networks is used to generate a multi-day scenario set, combining the day state type as a condition, and then the typical scenario set is obtained using scenario reduction. Furthermore, a double-layer optimization allocation model for the energy storage capacity of microgrids is constructed, in which the upper layer optimizes the energy storage allocation capacity and the lower layer optimizes the operation plans of microgrids in each typical scenario. Finally, the proposed model is solved using the PSO algorithm nested with the CPLEX solver. In the microgrid example, the proposed method reduces the expected annual total cost by 19.66% compared with the stochastic optimal allocation method that assumes the scenic power obeys a specific distribution, proving that it can better cope with the uncertainty of renewable energy generation. At the same time, the expected annual total cost is reduced by 6.99% compared with the optimal allocation method that generates typical daily scenarios based on generative adversarial networks, which proves that it can better cope with the high dimensionality of renewable energy generation.

show abstract

“…GHI and electricity tariffs are local aspects that determine rooftop PV economics. GHI also fluctuates based on weather conditions (Fan and Huang, 2018) which significantly affects the rooftop PV system's capacity and energy production (Li et al, 2018). Therefore, neglecting GHI uncertainty impacts the mismatch between the rooftop PV system's planned energy production and its reality, which causes the economic analysis to be less precise.…”

Section: Introductionmentioning

confidence: 99%

The Prospect of Rooftop Photovoltaic Development Considering Global Horizontal Irradiation Uncertainty and Government Policies: A Case of Java Island, Indonesia

Budi

Sarjiya²,

Hadi³

2022

IJEEP

View full text Add to dashboard Cite

Global horizontal irradiation uncertainty and government policy significantly affect the economics of rooftop photovoltaic development. Unfortunately, previous studies neglect these aspects. Therefore, it is necessary to undertake the economic analysis of rooftop photovoltaic development by considering its global horizontal irradiation uncertainty and government policies. This research creates a model that can be used to perform analysis and assess rooftop photovoltaic development prospects by considering irradiation uncertainty and government policies. The attractiveness of rooftop photovoltaic development is indicated by the value of the Levelized cost of electricity, net present value, and internal rate of return. Monte Carlo simulation models irradiation uncertainty. Java Island in Indonesia is used as a case study to show the performance of the proposed model. The research results show that the rooftop photovoltaic is not economically viable if built by the utility. Rooftop photovoltaic systems are only profitable when built by the private sector and is used for internal usage only. These rooftop photovoltaic investments provide an internal rate of return above 14%, which shows that investors with green capital funds and local investors are interested in investing. The emission reductions from a 100 m2 rooftop photovoltaic system vary from 214 to 384 tons of CO2 per year.

show abstract

Capacity configuration optimization for stand-alone microgrid considering the uncertainties of wind and solar resource

Cited by 8 publications

References 6 publications

Optimal Configuration and Scheduling Model of a Multi-Park Integrated Energy System Based on Sustainable Development

Optimal Configuration and Scheduling Model of a Multi-Park Integrated Energy System Based on Sustainable Development

Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation

The Prospect of Rooftop Photovoltaic Development Considering Global Horizontal Irradiation Uncertainty and Government Policies: A Case of Java Island, Indonesia

Contact Info

Product

Resources

About