The dairy sector in the Netherlands aims for a 30% increase in efficiency and 30% carbon dioxide emission reduction compared to the reference year of 1990, and a 20% share of renewable energy, all by the year 2020. Anaerobic Digestion (AD) can play a substantial role in achieving these aims. However, results from this study indicate that the AD system is not fully optimized in combination with farming practices regarding sustainability. Therefore, the Industrial Symbiosis concept, combined with energy and environmental system analysis, Life Cycle Analysis and modeling is used to optimize a farm-scale AD system on four indicators of sustainability (i.e., energy efficiency, carbon footprint, environmental impacts and costs). Implemented in a theoretical case, where a cooperation of farms share biomass feedstocks, a symbiotic AD system can significantly lower external energy consumption by 72 to 92%, carbon footprint by 71 to 91%, environmental impacts by 68 to 89%, and yearly expenditures by 56 to 66% compared to a reference cooperation. The largest reductions and economic gains can be achieved when a surplus of manure is available for upgrading into organic fertilizer to replace fossil fertilizers. Applying the aforementioned symbiotic concept to the Dutch farming sector can help to achieve the stated goals indicated by the Dutch agricultural sector for the year 2020.

With the integration of Intermitted Renewables Energy (I-RE) electricity production, capacity is shifting from central to decentral. So, the question is if it is also necessary to adjust the current load balancing system from a central to more decentral system. Therefore, an assessment is made on the overall effectiveness and costs of decentralized load balancing, using Flexible Renewable Energy (F-RE) in the shape of biogas, Demand Side Management (DSM), Power Curtailment (PC), and electricity Storage (ST) compared to increased grid capacity (GC). As a case, an average municipality in The Netherlands is supplied by 100% I-RE (wind and solar energy), which is dynamically modeled in the PowerPlan model using multiple scenarios including several combinations of balancing technologies. Results are expressed in yearly production mix, self-consumption, grid strain, Net Load Demand Signal, and added cost. Results indicate that in an optimized scenario, self-consumption of the municipality reaches a level of around 95%, the total hours per year production matches demand to over 90%, and overproduction can be curtailed without substantial losses lowering grid strain. In addition, the combination of balancing technologies also lowers the peak load to 60% of the current peak load in the municipality, thereby freeing up capacity for increased demand (e.g., electric heat pumps, electric cars) or additional I-RE production. The correct combination of F-RE and lowering I-RE production to 60%, ST, and PC are shown to be crucial. However, the direct use of DSM has proven ineffective without a larger flexible demand present in the municipality. In addition, the optimized scenario will require a substantial investment in installations and will increase the energy cost with 75% in the municipality (e.g., from 0.20€ to 0.35€ per kWh) compared to 50% (0.30€ per kWh) for GC. Within this context, solutions are also required on other levels of scale (e.g., on middle or high voltage side or meso and macro level) to ensure security of supply and/or to reduce overall costs.

Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics.

Residential distribution networks with high photovoltaic penetration are increasingly exposed to bidirectional transformer congestion, with export-driven violations during high solar production and import-driven violations during residential demand peaks. This paper presents the COBRA framework, a coordinated control approach for residential battery energy storage systems aimed at reducing low-voltage transformer congestion while considering electricity cost, PV self-consumption, and battery degradation. The framework was evaluated using measured and predicted Transformer data from a rural real network in the Netherlands, focusing on nine households equipped with 10.5 kWh battery each. Five control strategies were compared under a common evaluation structure against a no-battery baseline: local greedy control, Model Predictive Control (MPC) with perfect forecast, MPC with forecast error, Proximal Policy Optimization (PPO) and MAPPO (Multi-Agent PPO). The test set consisted of the seven most congested summer and seven most congested winter days representing LV transformer violation extremities. The no-battery baseline produced 588 kWh of transformer violation energy and 162 violation steps, dominated by summer PV export congestion and winter demand peaks. MPC with perfect forecast achieved the lowest total violation energy, reducing violations by 66%, and is interpreted as an ideal upper-bound benchmark. Among practical controllers, the multi-agent reinforcement learning MAPPO achieved the strongest overall performance, reducing violation energy by 60%, mainly through improved battery coordination during summer export-congestion periods. MPC with forecast error remained competitive, while PPO performed strongly for winter import peaks. Local greedy control provided the least transformer congestion relief highlighting that household level self-consumption control does not necessarily contribute to transformer-level congestion management. The results demonstrated that coordinated, transformer-aware battery scheduling can substantially reduce LV grid congestion. It also showed that controller performance may be seasonal and objective-dependent: summer congestion requires coordinated charging to absorb PV surplus, whereas winter congestion requires well-timed discharge for peak shaving. The COBRA framework therefore provides a transparent basis for comparing optimisation-based and learning-based control strategies for residential battery coordination in distribution networks aimed at LV congestion relief.