In the SERENE project, innovations to help improve integrated energy systems in local communities are developed. A key objective is to increase local self-consumption, particularly during peak solar production hours. These innovations are tested and validated in The Aardehuizen community, a neighbourhood which aims to be sustainable and self-sufficient. In this work the aims, development and preliminary results of three innovations are discussed 1) An energy storage system and associated smart control, 2) Smart control of e-boilers, and 3) An energy awareness app.

Flexible loads such as EV chargers and heat pumps have the potential to reduce the amount of energy storage required to handle local power imbalances. However, when sizing energy storage these loads are often neglected. Thereby it is unclear how intelligently controlled flexible loads influence energy storage sizing. Therefore, this paper presents an empirical study into the effect of controlling four different classes of flexible loads on energy storage sizing, under which fall existing technologies such as EV chargers, heat pumps and white-goods. We use a Profile Steering control strategy to minimize the power imbalance over the considered period. Furthermore, we use parameter sweeps to investigate the influences of load availability and load demand on the energy storage sizing, and visualize the energy storage reduction with heat maps. The results show that significant energy storage sizing reductions are possible if flexible loads are considered, including a reduction in capacity of more than 60%. Furthermore, if flexible loads can also supply their buffered power to the local microgrid (such as with V2G applications), the need for energy storage is almost completely removed. We conclude that it is important to consider local flexible devices when sizing storage in order to avoid over-dimensioning.

This paper investigates the influences of cut-off frequency selection and data resolution on the flexibility requirements found from an imbalance load profile. A filter-based signal technique is used to create sub-profiles representing a grid connection and an energy storage. The method is evaluated based on a use case of an EV charging hub microgrid for a parcel distribution center with 100% PV energy generation. Hereby several load profile data resolutions are used for the analysis. It is found that using a resolution of 60 minutes to find storage capacity requirements yields a 208 times larger capacity requirement compared to using a resolution of 10 seconds. Additionally, using a resolution of 10 seconds find the used storage discharging cycles per year yields 148 times more cycles used when compared to a resolution of 60 minutes. Finally, an ESS capacity of 5.4 MWh and a power rating of 8.9 MW is selected.

This paper explores the effects of active energy communities, as defined by EU Directive 2019/944, on the energy system using simulations modelled based on a real-world ecological community. The energy flexibility of this community is optimized towards the objectives peak reduction, cost minimization, and CO2 emission minimization. The resulting effects for different stakeholders, such as network operators and the community itself, are investigated using different performance indicators. The results show that energy imports can be reduced by 44% when peak reduction is applied. Conflicting objectives may lead to peak synchronization however, with the risk of deteriorating business cases if the network operator needs to intervene. This results in a risk where energy communities abstain from participation in energy market and its benefits.

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.

Recently, smart energy hubs with hydrogen conversion and storage have received increased attention in the Netherlands. The hydrogen is to be used for vehicle filling stations, industrial processes and heating. The scientific problem addressed in this paper is the proper sizing of capacities for renewable energy generation, hydrogen conversion and storage in relation to a feasible business case for the energy hub while achieving security of supply. Scenario analysis is often used during the early stages of the energy planning process, and for this an easy-to-use analysis model is required. This paper investigates available modelling approaches and develops an algorithmic modelling method which is worked out in Microsoft Excel and offers ease of use for scenario analysis purposes. The model is applied to case study, which leads to important insights such as the expected price of hydrogen and the proper sizing of electrolyser and hydrogen storage for that case. The model is made available open-source. Future work is proposed in the direction of application of the model for other project cases and comparison of results with other available modelling tools.

This paper investigates the effect of adding energy storage to a metro DC microgrid in order to recuperate otherwise dissipated energy produced through regenerative braking. For this, a power load profile is constructed for a metro station using existing measured railcar data, which is then used to derive the requirements for storing regenerative braking energy. It is found that introducing a storage can lead to an almost complete recuperation of otherwise dissipated energy. The derived requirements for the storage are then used to investigate a selection of possible scenarios more in depth. These scenarios include both smart controlled and uncontrolled scenarios, and with the usage of an ideal storage device as well as a (non-ideal) flywheel. Simulation results confirm that all braking energy can be recuperated through the addition of energy storage to the microgrid.

Due to the growing influence of sustainable energy sources, fluctuations in supply peaks are increasing, and thus, an imbalance in the grid may occur. This paper uses a basic smart control and battery algorithm to deal with these energy flow issues on a micro-grid level. Furthermore, a realistic model of a neighborhood located in Olst, the Netherlands is created in order to demonstrate the impact of smart control and battery storage in a practical setting. This model consists of PV generation, a washing machine, dishwasher together with various other devices which are assumed uncontrollable, as well as a grid connection. Three different scenarios were investigated to determine the effectiveness of the smart control algorithm and battery storage in terms of self-consumption, self-sufficiency and peak reduction. The results are shown by means of simulation and indicate a significant increase in self-consumption, self-sufficiency and peak reduction.