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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A new Risk-Managed planning of electric distribution network incorporating customer engagement and temporary solutions

The connection of renewable-based distributed generation (DG) in distribution networks has been increasing over the last few decades, which would result in increased network capacity to handle their uncertainties along with uncertainties associated with demand forecast. Temporary non-network solutions (NNSs) such as demand response (DR) and temporary energy storage system/DG are considered as promising options for handling these uncertainties at a lower cost than network alternatives. In order to manage and treat the risk associated with these uncertainties using NNSs, this paper presents a new risk-managed approach for multi-stage distribution expansion planning (MSDEP) at a lower cost. In this approach, the uncertainty of available DR is also taken into account. The philosophy of the proposed approach is to find the “optimal level of demand” for each year at which the network should be upgraded using network solutions while procuring temporary NNSs to supply the excess demand above this level. A recently developed forward-backward approach is fitted to solve the risk-managed MSDEP model presented here for real sized networks with a manageable computational cost. Simulation results of two case studies, IEEE 13-bus and a realistic 747-bus distribution network, illustrate the effectiveness of the proposed approach

Distributed Resources Shift Paradigms on Power System Design, Planning, and Operation: An Application of the GAP Model

Power systems have evolved following a century-old paradigm of planning and operating a grid based on large central generation plants connected to load centers through a transmission grid and distribution lines with radial flows. This paradigm is being challenged by the development and diffusion of modular generation and storage technologies. We use a novel approach to assess the sequencing and pacing of centralized, distributed, and off-grid electrification strategies by developing and employing the grid and access planning (GAP) model. GAP is a capacity expansion model to jointly assess operation and investment in utility-scale generation, transmission, distribution, and demand-side resources. This paper conceptually studies the investment and operation decisions for a power system with and without distributed resources. Contrary to the current practice, we find hybrid systems that pair grid connections with distributed energy resources (DERs) are the preferred mode of electricity supply for greenfield expansion under conservative reductions in photovoltaic panel (PV) and energy storage prices. We also find that when distributed PV and storage are employed in power system expansion, there are savings of 15%-20% mostly in capital deferment and reduced diesel use. Results show that enhanced financing mechanisms for DER PV and storage could enable 50%-60% of additional deployment and save 15 /MWh in system costs. These results have important implications to reform current utility business models in developed power systems and to guide the development of electrification strategies in underdeveloped grids

Linear Optimal Power Flow Using Cycle Flows

Linear optimal power flow (LOPF) algorithms use a linearization of the alternating current (AC) load flow equations to optimize generator dispatch in a network subject to the loading constraints of the network branches. Common algorithms use the voltage angles at the buses as optimization variables, but alternatives can be computationally advantageous. In this article we provide a review of existing methods and describe a new formulation that expresses the loading constraints directly in terms of the flows themselves, using a decomposition of the network graph into a spanning tree and closed cycles. We provide a comprehensive study of the computational performance of the various formulations, in settings that include computationally challenging applications such as multi-period LOPF with storage dispatch and generation capacity expansion. We show that the new formulation of the LOPF solves up to 7 times faster than the angle formulation using a commercial linear programming solver, while another existing cycle-based formulation solves up to 20 times faster, with an average speed-up of factor 3 for the standard networks considered here. If generation capacities are also optimized, the average speed-up rises to a factor of 12, reaching up to factor 213 in a particular instance. The speed-up is largest for networks with many buses and decentral generators throughout the network, which is highly relevant given the rise of distributed renewable generation and the computational challenge of operation and planning in such networks.Comment: 11 pages, 5 figures; version 2 includes results for generation capacity optimization; version 3 is the final accepted journal versio