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Renewable energy management of heat, power, water and hydrogen using hierarchical model predictive control
Dirk Vries, Oguzhan Kaya, Els van der Roest and Tamás Keviczky
Climate change calls for urgent action in the transition from a fossil to a renewable energy system. In this process, we need to ensure that the energy system remains reliable and economically feasible, while avoiding or minimising carbon dioxide emissions. For urban areas, energy surplus or energy needs can be covered by usage of heat storage systems in the underground, or a conversion of renewable energy to a clean energy carrier such as hydrogen gas. The idea of combining renewable energy production with conversion and storage of heat for domestic use, plus conversion to hydrogen for mobility linked to rainwater harvesting has been proposed in the Power-to-X (P2X) concept as a sustainable, (renewable) energy management solution for neighbourhoods. In order to guarantee an optimal and reliable allocation of energy resources throughout the year, an energy management system should take into account the availability of the various energy resources, stochastic (consumer) demand, electricity tariffs in the energy market, and the variable nature of weather and seasons. Furthermore, multiple stakeholders are involved in the production and distribution of the different energy (re)sources and have their own business case. These factors favour the use of a hierarchical model predictive control (MPC) scheme. We propose a temporal multi-level coordination mechanism to generate MPC strategies that consider objectives and time scales of neighbouring layers. In this study, historical data on energy tariffs, the weather and domestic demand for heat, mobility and water is used by a forecast generator to create stochastic predictions for the closed-loop system. Simulations of the hierarchical MPC controlled P2X system are compared to a rule-based energy management system.
Water quality modeling for optimizing the scheme to use reclaimed water for landscape water replenishment – A case study in China
Xiaochang Wang, Dong Ao and Mawuli Dzakpasu
With shortages of urban water resources, reclaimed water (RW) has become increasingly important as an alternative water source, especially for replenishing landscape waters. To meet the requirements of landscape quality control when RW is used for partial replacement of the surface water (SW) source to a channel-type urban water in a city in northwest China, scenario analyses were carried out by mathematical modelling. Water transparency (measured by SD) is used as an intuitive indicator to reflect the comprehensive influence of suspended solids (SS) and algae growth on the water’s aesthetic quality. Findings indicate that although the significantly higher nutrient concentrations of RW might bring about higher algae growth potential, in comparison with SW, its much lower SS concentration could offset the adverse effects on SD, to a large extent. By embedding the water quality model, calibrated and validated based on a two-year measurement data into MIKE 3 software for both SD and algae growth calculation, computer simulations were carried out to assist a series of scenario analyses of RW utilization and optimization of the water supply scheme. As a result, to meet the requirement of SD > 80 cm at the control section of the landscape water, the total water inflow required was not increased but decreased with the optimal application of RW. The advantages of lower SS in RW to reduce the demand inflow of total water was more evident with the higher requirement of SD. Replenishment of the channel-type urban water by RW was, thus, proven feasible from the viewpoint of landscape quality control.
User-Friendly Decision Support Tool for the Selection of Wastewater Treatment Technologies for the Removal of Emerging Contaminants
Seyed M.K. Sadr
Seyed M.K. Sadr, Fayyaz A. Memon, Mathew B. Johns, David Butler and Shaowei Zhang
Water recycling and reuse has become a common practice in order to alleviate pressures on the existing water sources. Over the last few years, Emerging Contaminants (ECs), such as pharmaceuticals and personal care products, have been increasingly detected in the treated wastewater in Wastewater Treatment (WWT) plants across the world. This is due to the fact that many CECs are persistent substances that are only partially degradable in WWT processes. Additionally, the existing WWT plants were only designed to remove Conventional Contaminants (CCs) e.g. biochemical oxygen demand, nitrogen, phosphorus, and faecal coliform. Many of these contaminants have negative impacts on the environment and human health; therefore, more advanced WWT solutions should be considered in order to effectively and efficiently remove both CCs and ECs. The identification and selection of such solutions requires a careful consideration of many factors and parameters such as investment and operational costs, energy consumption, land requirement, intended use of treated wastewater, and raw wastewater quantity and quality. To support decision makers with this complex task, we developed a user friendly Decision Support Tool (DST) capable of generating optimal WWT solutions for different water reuse scenarios and identifying the best solutions for the removal of ECs. The core of this tool is the optimisation engine i.e. Optimal Solution Generator (OSG), that systematically generates every possible (feasible) solution with an enumeration algorithm. OSG-generated solutions will further be assessed by a Multi-Criteria based approach which facilitates the prioritisation of solutions reflecting the user’s preferences. Ultimately the DST evaluates each solution’s performance in removing different ECs. The approach implemented in the DST is tested for different water reuse scenarios including for different types of raw wastewater sources and system implementation scales. Initial results suggested that the tool can generate (and prioritise) optimal solutions and contribute towards informed decision making.
Dynamic Modelling of Sewage Lagoons in the Arctic
Boris Tartakovsky, Andrew Colombo, Yehuda Kleiner, Shuang Liang and Ehsan Roshani
Sewage lagoons are the treatment method of choice in the Canadian Arctic due to their simple operation and lower capital costs; however, lagoons in the Arctic differ from their counterparts elsewhere. Whereas in non-Arctic locations the lagoon is almost always full and operated as a flow-through system, the extreme temperatures in the Arctic do not permit winter outflow, and ice cover several months of the year limits biodegradation. A multi-layer (e.g., sludge, anaerobic, aerobic or ice) computer model was developed to simulate Arctic sewage lagoon performance in terms of COD removal. Material balances and kinetic equations describing the hydrolysis of particulate organic materials and aerobic and anaerobic kinetics were adapted from ASM3. In particular, multiplicative Monod kinetic equations were used to describe aerobic and anaerobic (anoxic) growth of heterotrophic biomass, which is dependent on readily available dissolved substrate (COD) and dissolved oxygen concentrations. Initial efforts were based on very limited data from summer water quality sampling campaigns. As a result, field work is currently being undertaken in order to obtain year-round water quality data for better calibration and validation, as well as to expand the model for nitrogen, phosphorous and algae components.