Evaluating the Potential of Hosting Capacity Enhancement Using Integrated Grid Planning Modelling Methods

Connection of a significant amount of distributed generation, such as solar photovoltaic (PV) capacity, may lead to problems in distribution networks due to violations of distribution network hosting capacity (HC) limits. HC enhancement techniques, such as energy storage, could increase the allowable PV penetration level in the distribution network, reducing the need for transmission and large-scale generation expansion. However, current approaches for transmission and generation expansion planning do not account for distribution network HC limits. As a consequence, it is hard to quantify the impact and benefits of HC enhancement in the context of long-term grid expansion planning. This paper presents a novel integrated planning approach, combining a two-stage transmission and generation expansion planning model with a distribution network hosting capacity assessment, which allows for inclusion of detailed distribution network constraints We test this method on a stylized representation of the Malaysian grid. Our results show that distribution constraints have a significant impact on optimal transmission expansion plans and significantly increase overall system costs. HC enhancement in the form of battery storage does not significantly mitigate this but does lead to a cost decrease regardless of distribution network constraints. We also show how our approach can identify the key interactions between transmission and distribution networks in systems with high levels of renewable and storage technologies. In particular, HC enhancement with battery storage can act as a substitute or complement to line investment, depending on the renewable energy penetration, the storage location and the level of coordination in the network

Optimal Scheduling for Energy Storage Systems in Distribution Networks

[EN] Distributed energy storage may play a key role in the operation of future low-carbon power systems as they can help to facilitate the provision of the required flexibility to cope with the intermittency and volatility featured by renewable generation. Within this context, this paper addresses an optimization methodology that will allow managing distributed storage systems of different technology and characteristics in a specific distribution network, taking into account not only the technical aspects of the network and the storage systems but also the uncertainties linked to demand and renewable energy variability. The implementation of the proposed methodology will allow facilitating the integration of energy storage systems within future smart grids. This paper's results demonstrate numerically the good performance of the developed methodology.This research was funded by European Regional Development Fund (Comunidad Valenciana FEDER 2014-2020 PO, CCI number: 2014ES16RFOP013) and the ITE-IVACE collaboration agreement corresponding to the annuity 2019 (file: IMDEEA-2019-38).Escoto Simó, M.; Montagud, M.; González-Cobos, N.; Belinchón, A.; Trujillo, AV.; Romero-Chavarro, JC.; Diaz-Cabrera, JC.... (2020). Optimal Scheduling for Energy Storage Systems in Distribution Networks. Energies. 13(15):1-13. https://doi.org/10.3390/en13153921S1131315The Impact of the Covid-19 Crisis on Clean Energy Progresshttps://www.iea.org/articles/the-impact-of-the-covid-19-crisis-on-clean-energy-progressSustainable Development Goalshttps://www.un.org/sustainabledevelopment/Mesarić, P., & Krajcar, S. (2015). Home demand side management integrated with electric vehicles and renewable energy sources. 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(2020). Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks. Applied Energy, 261, 114412. doi:10.1016/j.apenergy.2019.114412Saboori, H., & Jadid, S. (2020). Optimal scheduling of mobile utility-scale battery energy storage systems in electric power distribution networks. Journal of Energy Storage, 31, 101615. doi:10.1016/j.est.2020.101615Kassai, M. (2017). Prediction of the HVAC Energy Demand and Consumption of a Single Family House with Different Calculation Methods. Energy Procedia, 112, 585-594. doi:10.1016/j.egypro.2017.03.1121Zheng, Y., Zhao, J., Song, Y., Luo, F., Meng, K., Qiu, J., & Hill, D. J. (2018). Optimal Operation of Battery Energy Storage System Considering Distribution System Uncertainty. IEEE Transactions on Sustainable Energy, 9(3), 1051-1060. doi:10.1109/tste.2017.2762364Jayasekara, N., Masoum, M. A. S., & Wolfs, P. J. (2016). Optimal Operation of Distributed Energy Storage Systems to Improve Distribution Network Load and Generation Hosting Capability. IEEE Transactions on Sustainable Energy, 7(1), 250-261. doi:10.1109/tste.2015.2487360Mehrjerdi, H., & Hemmati, R. (2019). Modeling and optimal scheduling of battery energy storage systems in electric power distribution networks. Journal of Cleaner Production, 234, 810-821. doi:10.1016/j.jclepro.2019.06.195Macedo, L. H., Franco, J. F., Rider, M. J., & Romero, R. (2015). Optimal Operation of Distribution Networks Considering Energy Storage Devices. IEEE Transactions on Smart Grid, 6(6), 2825-2836. doi:10.1109/tsg.2015.2419134Lunci Hua, Jia Wang, & Chi Zhou. (2014). Adaptive Electric Vehicle Charging Coordination on Distribution Network. IEEE Transactions on Smart Grid, 5(6), 2666-2675. doi:10.1109/tsg.2014.2336623Guo, X., Guo, X., & Su, J. (2013). 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Climate policy costs of spatially unbalanced growth in electricity demand: the case of datacentres. ESRI Working Paper No. 657 March 2020

We investigate the power system implications of the anticipated expansion in electricity demand by datacentres. We perform a joint optimisation of Generation and Transmission Expansion Planning considering uncertainty in future datacentre growth under various climate policies. Datacentre expansion imposes significant extra costs on the power system, even under the cheapest policy option. A renewable energy target is more costly than a technology-neutral carbon reduction policy, and the divergence in costs increases non-linearly in electricity demand. Moreover, a carbon reduction policy is more robust to uncertainties in projected demand than a renewable policy. High renewable targets crowd out other low-carbon options such as Carbon Capture and Sequestration. The results suggest that energy policy should be reviewed to focus on technology-neutral carbon reduction policies

Market and Economic Modelling of the Intelligent Grid: End of Year Report 2009

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy

Investigating the Impacts of Distributed Generation on Transmission Expansion Cost: An Australian Case Study

Distributed generation (DG) is rapidly increasing its penetration level in Australia, and is expected to play a more important role in the power industry. An important benefit of DG is its ability to defer transmission investments. In this paper, a simulation model is implemented to conduct quantitative analysis on the effect of DG on transmission investment deferral. The transmission expansion model is formulated as a multi-objective optimization problem with comprehensive technical constraints, such as AC power flow and system security. The model is then applied to study the Queensland electricity market in Australia. Simulation results show that, DG does show the ability to reduce transmission investments. This ability however is greatly influenced by a number of factors, such as the locations of DG, the network topology, and the power system technical constraints.