• Energy Research
• 7. Clean energy close

Abstract This study proposes a curtailment-minimization model to investigate the potential of urban bio-waste to provide flexible electricity to a wind and solar powered Amsterdam. The transition to solar and wind as primary sources of renewable energy is hampered by their intermittent nature. Being controllable, biomass energy holds the potential of providing both renewable and flexible power. For the transformation from urban bio-waste to electricity, a coupled gasifier and solid oxide fuel cell (SOFC) unit was used. An islanded microgrid for the residential area of Amsterdam was investigated on the basis of an average year, both in terms of weather and electricity consumption. The study aims at finding the optimal sizing of each component to provide sustainable and secure electricity supply. Security of electricity supply was guaranteed by ensuring a net positive daily energy balance while minimizing the total surplus energy to be curtailed during the year. All organic municipal solid waste (MSW) available was used representing 39% of the yearly electricity demand of Amsterdam; PV panels (20%) and wind turbines (41%) covered the remaining share. To this end, optimal PV and wind capacities of 186 MW and 165 MW were estimated, representing respectively 16.9% and 94.0% of the total potential capacity of Amsterdam. In this study, the use of urban bio-waste is proven to bring flexibility to the energy system: using more biomass allows lower curtailment values.

Activated sludge systems are commonly used for robust and efficient treatment of municipal wastewater. However, these systems cannot achieve their maximum potential to recover valuable resources from wastewater. This study demonstrates a procedure to design a feasible novel configuration for maximizing energy and nutrient recovery. A simulation model was developed based on literature data and recent experimental research using steady-state energy and mass balances with conversions. The analysis showed that in the Netherlands, proposed configuration consists of four technologies: bioflocculation, cold partial nitritation/Anammox, P recovery, and anaerobic digestion. Results indicate the possibility to increase net energy yield up to 0.24 kWh/m3 of wastewater, while reducing carbon emissions by 35%. Moreover, sensitivity analysis points out the dominant influence of wastewater organic matter on energy production and consumption. This study provides a good starting point for the design of promising layouts that will improve sustainability of municipal wastewater management in the future.

There is a significant interest in utilizing demand response (DR) programs to increase the flexibility of sustainable power systems. The DR operators (e.g., aggregator companies) need a robust means to assess the performance of a potential DR program that will be employed in the future. Such assessments should be based on data, some of which are hardly available. Knowledge about the DR providers (e.g., the behavior of proactive consumers) is key to the success of a DR program. In this paper, we devise a data-driven framework to assess the impact of uncertainties associated with future DR programs. The proposed framework comprises two modules: the DR simulation module, and the data analytics module. The DR module solves an optimization problem which simulates the operation of a hypothetical DR program. The data analytics module, firstly, selects subsets from historical load and price data. Secondly, it performs sensitivity analysis on the optimal solution to capture the impact of uncertainties. We consider two sources of uncertainty. First, we consider lack of information about DR providers due to the absence of a DR program in the current system. Second, we consider errors in load and price forecasts, whose impacts are investigated by formulating a sensitivity matrix from the perturbed KKT equations of the optimization problem solved by the DR module. The proposed framework provides insights regarding the potential of a prospective DR program. Such information can be useful for DR operators as a starting point to decide their position in the contractual agreement they will engage in with the (distribution) system operator and/or DR providers in the future.

Sensor Data Fusion (SDF) is a widely used means of monitoring electrochemical processes. The application of SDF contributes to solving challenges in process efficiency, control and reliability. Due to recent, stringent regulations, there is a need to monitor the formation of by-products in electrochlorination, such as chlorate. For this development, the knowledge of SDF produced in neighboring fields of research, such as on batteries or fuel cells, can be of great value. This paper presents an overview of the application of SDF algorithms to monitor electrochemical processes, and discusses how to best apply SDF to monitor by-product concentrations in the context of electrochlorination. Both first-principles and data-driven approaches are discussed. Successful application of SDF to electrochlorination monitoring will improve the safety of drinking water supply. In addition, this overview can inspire and improve the application of SDF in the monitoring of other electrochemical systems.

Many countries, including Indonesia, have abundant renewable energy sources (RES), but the share of RES in the current national energy supply is still insignificant. The study aimed to investigate and provide the most feasible combinations of RES that meet domestic electricity demand. For Java and Bali, Indonesia, initially, 35 scenarios, given 4 primary RES (solar, wind, hydropower, geothermal) and municipal solid waste, were assessed based on economic and environmental indicators. This explorative data-driven study found that the existing capacity could only meet 51% of the electricity demand. However, the proposed energy mixes could cover 100% of the electricity demand in 2020 with a required capacity of 8.32–19.10 GW, varying on each scenario. The feasible energy mixes can reduce CO2 emissions by 90–94% compared to a fossil energy mix with gas-fired power plants. The installation, and operation and maintenance costs per life cycle can range from 29–50 and 4–16 billion dollars. The wind-based energy mix, with installed capacities of geothermal (1.16 GW), hydropower (2.87 GW), solar (0.003 GW) and municipal solid waste (0.18 GW) in 2020, showed the highest return on investment (139% ROI) and smallest CO2 emission with highest CO2 reduction (94%). This study provides a scientific method of selecting, projecting, and evaluating viable RES combinations for generating electricity without using fossil fuels.