This report describes the creation and use of a database for energy storage technologies which was developed in conjunction with Netbeheer Nederland and the Hanze University of Applied Sciences. This database can be used to make comparisons between a selection of storage technologies and will provide a method for ranking energy storage technology suitability based on the desired application requirements. In addition, this document describes the creation of the energy storage label which contains detailed characteristics for specific storage systems. The layout of the storage labels enables the analysis of different storage technologies in a comprehensive, understandable and comparative manner. A sampling of storage technology labels are stored in an excel spreadsheet and are also compiled in Appendix I of this report; the storage technologies represented here were found to be well suited to enable flexibility in energy supply and to potentially provide support for renewable energy integration [37] [36]. The data in the labels is presented on a series of graphs to allow comparisons of the technologies. Finally, the use and limitations of energy storage technologies are discussed. The results of this research can be used to support the Dutch enewable Energy Transition by providing important information regarding energy storage in both technically detailed and general terms. This information can be useful for energy market parties in order to analyze the role of storage in future energy scenarios and to develop appropriate strategies to ensure energy supply.
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A transition of today’s energy system towards renewableresources, requires solutions to match renewable energy generationwith demand over time. These solutions include smartgrids, demand-side management and energy storage. Energycan be stored during moments of overproduction of renewableenergy and used from the storage during moments ofinsufficient production. Allocation in real time of generatedenergy towards controlled appliances or storage chargers, isdone by a smart control system which makes decisions basedon predictions (of upcoming generation and demand) andinformation of the actual condition of storages.
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Energy cooperatives are beginning to expand their role from stimulating small-scale electricity production to developing local energy systems, including cooperatively owned energy storage solutions. However, many technical, social and financial obstacles are encountered in this process. It is as yet unclear how new roles of citizens, building owners, grid operators and energy cooperatives will develop. Furthermore, it is difficult to assess if a feasible business case is at all possible given present context conditions in the Netherlands.
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To reduce greenhouse gas emissions, countries around the world are pursuing electrification policies. In residential areas, electrification will increase electricity supply and demand, which is expected to increase grid congestion at a faster rate than grids can be reinforced. Battery energy storage (BES) has the potential to reduce grid congestion and defer grid reinforcement, thus supporting the energy transition. But, BES could equally exacerbate grid congestion. This leads to the question: What are the trade-offs between different battery control strategies, considering battery performance and battery grid impacts? This paper addresses this question using the battery energy storage evaluation method (BESEM), which interlinks a BES model with an electricity grid model to simulate the interactions between these two systems. In this paper, the BESEM is applied to a case study, wherein the relative effects of different BES control strategies are compared. The results from this case study indicate that batteries can reduce grid congestion if they are passively controlled (i.e., constraining battery power) or actively controlled (i.e., overriding normal battery operations). Using batteries to reduce congestion was found to reduce the primary benefits provided by the batteries to the battery owners, but could increase secondary benefits. Further, passive battery controls were found to be nearly as effective as active battery controls at reducing grid congestion in certain situations. These findings indicate that the trade-offs between different battery control strategies are not always obvious, and should be evaluated using a method like the BESEM.
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With increased share of energy generated from variable renewable sources, storagebecomes a critical issue to ensure constantly balanced supply/demand.Methane is a promising vector for energy storage and transport.
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The intermittency of renewable energy technologies requires adequate storage technologies. Hydrogen systems consisting of electrolysers, storage tanks, and fuel cells can be implemented as well as batteries. The requirements of the hydrogen purification unit is missing from literature. We measured the same for a 4.5 kW PEM electrolyser to be 0.8 kW for 10 min.A simulation to hybridize the hydrogen system, including its purification unit, with lithium-ion batteries for energy storage is presented; the batteries also support the electrolyser. We simulated a scenario for operating a Dutch household off-electric-grid using solar and wind electricity to find the capacities and costs of the components of the system.Although the energy use of the purification unit is small, it influences the operation of the system, affecting the sizing of the components. The battery as a fast response efficient secondary storage system increases the ability of the electrolyser to start up.
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The increase in renewable energy sources will require an increase in the operational flexibility of the grid, due to the intermittent nature of these sources. This can be achieved for the gas and the electricity grid, which are integrated by means of power-to-gas and vice versa, by applying gas and other energy storages. Because renewables are applied on a decentralized scale level and syngas and biogas are produced at relatively low pressures, we study the application of a decentralized (bio)gas storage system combined withMicro Turbine Technology (MTT), Compressed Air Energy Storage (CAES) and Thermal Energy Storage (TES) units, which are designed to optimize energy efficiency.In this study we answer the following research questions:a. What is the techno-economical feasibilty of applying a decentralized (bio)gas storage with a MTT/CAES/TES system to balance the integrated renewable energy network?b. How should the decentralized (bio)gas storage with MTT/CAES/TES system be designed, so that the energy efficient application in such networks is optimized?Note that:c. We verify the calculations for the small scale MTT unit with measurements on our proof-of-principle set-up of part of the system that includes two MTTs in parallel.Based on wind speed, irradiance patterns and electricity and heat demand patterns for a case of 100 households, we found the optimum dimensions for the decentralized (bio)gas storage based on guaranteed supply. We concluded that a decentralized (bio)gas storage of 85 000 Nm3 was needed to provide the heat demand. LNG was the most energy efficient storage technology for such dimensions.The use of (bio)gas directly in a CHP (P/Q ratio = 2/3) that was mainly heat driven, resulted in a continuous overproduction of electricity due to the dominant heat demand of the 100 households in the Netherlands.This does not leave any room for the increase in the application of PV and wind generators, nor is there a purpose for electricity storage.For that reason we will further investigate the application of a decentralized (bio)gas storage with MTT/CAES/TES as a solution to balance a renewable integrated network. Using an MTT in the system offers a more useful P/Q ratio for households of 1/5.
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Hybrid Energy Storage System (HESS) have the potential to offer better flexibility to a grid than any single energy storage solution. However, sizing a HESS is challenging, as the required capacity, power and ramp rates for a given application are difficult to derive. This paper proposes a method for splitting a given load profile into several storage technology independent sub-profiles, such that each of the sub-profiles leads to its own requirements. This method can be used to gain preliminary insight into HESS requirements before a choice is made for specific storage technologies. To test the method, a household case is investigated using the derived methodology, and storage requirements are found, which can then be used to derive concrete storage technologies for the HESS of the household. Adding a HESS to the household case reduces the maximum import power from the connected grid by approximately 7000 W and the maximum exported power to the connected grid by approximately 1000 W. It is concluded that the method is particularly suitable for data sets with a high granularity and many data points.
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