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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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 need for increasing further the penetration of Renewable Energy Sources (RESs) is demanding a change in the way distribution grids are managed. In particular, the RESs intermittent and stochastic nature is finding in Battery Energy Storage (BES) systems its most immediate countermeasure. This work presents a reality-based assessment and comparison of the impact of three different BES technologies on distribution grids with high RES penetration, namely Li-ion, Zn-Air and Redox Flow. To this end, a benchmark distribution grid with real prosumers’ generation and load profiles is considered, with the RES penetration purposely scaled up in such a way as to violate the grid operational limits. Then, further to the BES(s) placement on the most affected grid location(s), the impact of the three BES types is assessed considering two Use Cases: 1) Voltage & Congestion Management and 2) Peak Shaving & Energy shifting. Assessment is conducted by evaluating a set of technical Key Performance Indicators (KPIs), together with a simplified economic analysis.
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The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed.
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The application of DC grids is gaining more attention in office applications. Especially since powering an office desk would not require a high power connection to the main AC grid but could be made sustainable using solar power and battery storage. This would result in fewer converters and further advanced grid utilization. In this paper, a sustainable desk power application is described that can be used for powering typical office appliances such as computers, lighting, and telephones. The desk will be powered by a solar panel and has a battery for energy storage. The applied DC grid includes droop control for power management and can either operate stand-alone or connected to other DC-desks to create a meshed-grid system. A dynamic DC nano-grid is made using multiple self-developed half-bridge circuit boards controlled by microcontrollers. This grid is monitored and controlled using a lightweight network protocol, allowing for online integration. Droop control is used to create dynamic power management, allowing automated control for power consumption and production. Digital control is used to regulate the power flow, and drive other applications, including batteries and solar panels. The practical demonstrative setup is a small-sized desktop with applications built into it, such as a lamp, wireless charging pad, and laptop charge point for devices up to 45W. User control is added in the form of an interactive remote wireless touch panel and power consumption is monitored and stored in the cloud. The paper includes a description of technical implementation as well as power consumption measurements.
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The Johan Cruijff ArenA (JC ArenA) is a big events location in Amsterdam, where national and international football matches, concerts and music festivals take place for up to 68,000 visitors. The JC ArenA is already one of the most sustainable, multi-functional stadia in the world and is realizing even more inspiring smart energy solutions for the venue, it’s visitors and neighbourhood. The JC ArenA presents a complex testbed for innovative energy services, with a consumption of electricity comparable to a district of 2700 households. Thanks to the 1 MWp solar installation on the roof of the venue, the JC ArenA already produces around 8% of the electricity it needs, the rest is by certified regional wind energy.Within the Seev4-City project the JC ArenA has invested in a 3 MW/2.8 MWh battery energy storage system, 14 EV charging stations and one V2G charging unit. The plan was to construct the 2.8 MWh battery with 148 2nd life electric car batteries, but at the moment of realisation there were not enough 2nd life EV batteries available, so 40% is 2nd life. The JC ArenA experienced compatibility issues installing a mix of new and second-life batteries. Balancing the second-life batteries with the new batteries proved far more difficult than expected because an older battery is acting different compared to new batteries.The EV-based battery energy storage system is unique in that it combines for the first time several applications and services in parallel. Main use is for grid services like Frequency Containment Reserve, along with peak shaving, back-up services, V2G support and optimization of PV integration. By integrating the solar panels, the energy storage system and the (bi-directional) EV chargers electric vehicles can power events and be charged with clean energy through the JC ArenA’s Energy Services. These and other experiences and results can serve as a development model for other stadiums worldwide and for use of 2nd life EV batteries.The results of the Seev4-City project are also given in three Key Performance Indicators (KPI): reduction of CO2-emission, increase of energy autonomy and reduction in peak demand. The results for the JC ArenA are summarised in the table below. The year 2017 is taken as reference, as most data is available for this year. The CO2 reductions are far above target thanks to the use of the battery energy storage system for FCR services, as this saves on the use of fossil energy by fossil power plants. Some smaller savings are by replacement of ICEby EV. Energy autonomy is increased by better spreading of the PV generated, over 6 instead of 4 of the 10 transformers of the JC ArenA, so less PV is going to the public grid. A peak reduction of 0.3 MW (10%) is possible by optimal use of the battery energy storage system during the main events with the highest electricity demand.
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The need to reduce carbon emissions calls for more use of renewable generation, particularly distributed resources. The intermittency of renewable generation, and concerns about energy security, require us to become more independent of central grid operation by use of local or regional (micro-grid) electricity systems. Distributed generation, allied to the commercial availability of battery storage products, permits this–the pathway to energy autonomy. This paper reviews the contribution of different renewable energy sources (RES), trends in energy storage technologies to enable energy autonomy, and the centralised and decentralised techniques that coordinate the associated energy management. The paper covers energy autonomy at different scales, ranging from household levels to district levels. The improvements in grid independency are measured accordingly. There is discussion of this measurement and of the economic and ecological benefits from energy autonomy in the context of policy frameworks.
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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This paper investigates smart charging strategies for battery-electric construction machinery (non-road mobile machinery, NRMM) through a case study of a large-scale housing project in The Hague, Netherlands. The study develops a methodology to estimate energy demands and simulate charging profiles during various construction phases. Using a combination of smart charging and temporary battery storage, the paper demonstrates that peak grid loads can be significantly reduced—by up to 46%—compared to conventional charging strategies. Simulations reveal that grid limitations, especially during early construction phases, can be overcome with optimized load management and supplemental battery systems. The findings highlight the importance of smart charging infrastructure and energy planning in enabling the transition to zero-emission construction practices. This research contributes to the practical implementation of electric NRMM in urban construction projects, addressing one of the key bottlenecks in decarbonizing the construction sector.
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