Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters

Abstract Nowadays, large numbers of renewable energy generators have been installed world-widely to reduce carbon dioxide emissions. Hydrogen-based storage system has the largest energy density, which is with a great potential suitable to address the intermittence of these renewable energy outputs. In addition, the HVDC (high voltage direct current) transmission is often deployed to transmit the remote located abundant renewable energy resources to terminal consumers due to its lower losses. However, building a low-carbon emission power system through hydrogen-based microgrid clusters and the HVDC transmission still lacks investigations thus is an essential problem. In this paper, a probabilistic sizing methodology considering the uncertainties is developed. First, both the hydrogen-based storage system model and the HVDC model are analytically modeled. Second, a mixed integer programming optimal strategy is deployed to operate the hydrogen-based microgrid. Third, operation model of the renewable energy power station-HVDC transmission-IEEE 30 nodes network-microgrid clusters are presented. Last, the best sizing value for each component is obtained by a genetic algorithm. In addition, the uncertainties of the data profiles are modeled using scenario method. Within different scenarios, the probability density function of each sizing component is calculated. The results demonstrate that the sizing values based on the probabilistic method can reduce the adverse impacts of the uncertainties on the utility grids. Specifically, the mean value of the utility grid operation cost is reduced by 0.005 %-0.08 %.

[1]  Zhao Yang Dong,et al.  Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications , 2020 .

[2]  J. Andrews,et al.  Energy and cost analysis of a solar-hydrogen combined heat and power system for remote power supply using a computer simulation , 2010 .

[3]  Abdellatif Miraoui,et al.  Microgrid sizing with combined evolutionary algorithm and MILP unit commitment , 2017 .

[4]  Ching-Ming Lai,et al.  Reliability impacts of the dynamic thermal rating and battery energy storage systems on wind-integrated power networks , 2019 .

[5]  Liuchen Chang,et al.  Review on distributed energy storage systems for utility applications , 2017 .

[6]  Bin Lu,et al.  100% renewable electricity in Australia , 2017 .

[7]  K. Agbossou,et al.  Characterization of a Ballard MK5-E Proton Exchange Membrane Fuel Cell Stack , 2001 .

[8]  Jamshid Aghaei,et al.  Robust planning of distributed battery energy storage systems in flexible smart distribution networks: A comprehensive study , 2020 .

[9]  Oriol Gomis-Bellmunt,et al.  Control of multi-terminal HVDC networks towards wind power integration: A review , 2016 .

[10]  Hedayat Saboori,et al.  Reliability improvement in radial electrical distribution network by optimal planning of energy storage systems , 2015 .

[11]  Jaesung Jung,et al.  Optimal planning and design of hybrid renewable energy systems for microgrids , 2017 .

[12]  Michael Kurrat,et al.  Challenges and opportunities for a European HVDC grid , 2017 .

[13]  Li Sun,et al.  Life Cycle Optimization of Renewable Energy Systems Configuration with Hybrid Battery/Hydrogen Storage: A Comparative Study , 2020 .

[14]  E.F. El-Saadany,et al.  Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization , 2010, IEEE Transactions on Power Systems.

[15]  D. Retzmann,et al.  From Smart Grid to Super Grid: Solutions with HVDC and FACTS for grid access of renewable energy sources , 2011, 2011 IEEE Power and Energy Society General Meeting.

[16]  Javier Contreras,et al.  Optimization of control strategies for stand-alone renewable energy systems with hydrogen storage , 2007 .

[17]  Jiashen Teh,et al.  Probabilistic Peak Demand Matching by Battery Energy Storage Alongside Dynamic Thermal Ratings and Demand Response for Enhanced Network Reliability , 2020, IEEE Access.

[18]  Irene Moser,et al.  Modelling and optimisation of microgrid configuration for green data centres: A metaheuristic approach , 2020, Future Gener. Comput. Syst..

[19]  Vincent Wai Sum Wong,et al.  Direct Energy Trading of Microgrids in Distribution Energy Market , 2020, IEEE Transactions on Power Systems.

[20]  Gregory S. Pavlak,et al.  Sizing and dispatch of an islanded microgrid with energy flexible buildings , 2020 .

[21]  Grain Philip Adam,et al.  HVDC Transmission: Technology Review, Market Trends and Future Outlook , 2019, Renewable and Sustainable Energy Reviews.

[22]  Tilman Weckesser,et al.  Market Integration of HVDC Lines: Internalizing HVDC Losses in Market Clearing , 2018, IEEE Transactions on Power Systems.

[23]  Jun Yang,et al.  Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty , 2021 .

[24]  Phil S. Jones,et al.  Calculation of power losses for MMC-based VSC HVDC stations , 2013, 2013 15th European Conference on Power Electronics and Applications (EPE).

[25]  T. Ma,et al.  Integrated sizing of hybrid PV-wind-battery system for remote island considering the saturation of each renewable energy resource , 2019, Energy Conversion and Management.

[26]  Ian Cotton,et al.  Reliability Impact of Dynamic Thermal Rating System in Wind Power Integrated Network , 2016, IEEE Transactions on Reliability.

[27]  Debapriya Das,et al.  Optimal power dispatch considering load and renewable generation uncertainties in an AC–DC hybrid microgrid , 2019, IET Generation, Transmission & Distribution.

[28]  P. M. Diéguez,et al.  Thermal performance of a commercial alkaline water electrolyzer: Experimental study and mathematical modeling , 2008 .

[29]  Pavol Bauer,et al.  Techno-Economical Model Based Optimal Sizing of PV-Battery Systems for Microgrids , 2020, IEEE Transactions on Sustainable Energy.

[30]  Zhao Yang Dong,et al.  Planning of solar photovoltaics, battery energy storage system and gas micro turbine for coupled micro energy grids , 2017 .

[31]  Debapriya Das,et al.  An integrated optimal operating strategy for a grid-connected AC microgrid under load and renewable generation uncertainty considering demand response , 2021 .

[32]  Xin Jin,et al.  K-Means Clustering , 2010, Encyclopedia of Machine Learning.

[33]  Qian Ai,et al.  Two kinds of decentralized robust economic dispatch framework combined distribution network and multi-microgrids , 2019, Applied Energy.

[34]  Ching-Ming Lai,et al.  Optimum allocation of battery energy storage systems for power grid enhanced with solar energy , 2021 .

[35]  V. Sivakumar,et al.  Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019) , 2020 .

[36]  Mostafa F. Shaaban,et al.  An Efficient Planning Algorithm for Hybrid Remote Microgrids , 2019, IEEE Transactions on Sustainable Energy.

[37]  Abdellatif Miraoui,et al.  Sizing of a stand-alone microgrid considering electric power, cooling/heating, hydrogen loads and hydrogen storage degradation , 2017 .

[38]  Feng Zhang,et al.  An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties , 2021 .

[39]  Luhao Wang,et al.  Robust distributed optimization for energy dispatch of multi-stakeholder multiple microgrids under uncertainty , 2019 .

[40]  Yan Li,et al.  Engineering practices for the integration of large-scale renewable energy VSC-HVDC systems , 2020 .

[41]  Dehong Liu,et al.  Optimal operation strategy for interconnected microgrids in market environment considering uncertainty , 2020 .

[42]  Romano Giglioli,et al.  Stochastic sizing of isolated rural mini-grids, including effects of fuel procurement and operational strategies , 2018 .

[43]  Andrea Tosatto,et al.  HVDC loss factors in the Nordic power market , 2019 .

[44]  M. Z. Mostafa,et al.  High voltage direct current transmission - A review, part I , 2012, 2012 IEEE Energytech.

[45]  Giovanni Lutzemberger,et al.  Economic multi-objective approach to design off-grid microgrids: A support for business decision making , 2020 .

[46]  Kamran Hafeez,et al.  High voltage direct current (HVDC) transmission: Future expectations for Pakistan , 2019 .

[47]  Matti Lehtonen,et al.  Optimal location-allocation of storage devices and renewable-based DG in distribution systems , 2019, Electric Power Systems Research.

[48]  Amjad Anvari-Moghaddam,et al.  A decentralized robust model for optimal operation of distribution companies with private microgrids , 2019, International Journal of Electrical Power & Energy Systems.

[49]  Ching-Ming Lai,et al.  Risk-Based Management of Transmission Lines Enhanced With the Dynamic Thermal Rating System , 2019, IEEE Access.

[50]  J. Catalão,et al.  Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources , 2021, Energy.

[51]  Robin Girard,et al.  Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm , 2017, Applied Energy.

[52]  Makbul A.M. Ramli,et al.  A review of optimization approaches for hybrid distributed energy generation systems: Off-grid and grid-connected systems , 2018 .

[53]  Mukesh Singh,et al.  Feasibility study of an islanded microgrid in rural area consisting of PV, wind, biomass and battery energy storage system , 2016 .

[54]  Student Member,et al.  Comparative Evaluation of HVDC and HVAC Transmission Systems , 2008 .