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The definition, characterization and implementation of Positive Energy Districts is crucial in the path towards urban decarbonization and energy transition. However, several issues still must be addressed: the need for a clear and comprehensive definition, and the settlement of a consistent design approach for Positive Energy Districts. As emerged throughout the workshop held during the fourth edition of Smart and Sustainable Planning for Cities and Regions Conference (SSPCR 2022) in Bolzano (Italy), further critical points are also linked to the planning, modeling and assessment steps, besides sustainability aspects and stakeholders' involvement. The "World Café" methodology adopted during the workshop allowed for simple—but also effective and flexible—group discussions focused on the detection of key PED characteristics, such as morphologic, socio-economic, demographic, technological, quality-of-life and feasibility factors. Four main work groups were defined in order to allow them to share, compare and discuss around five main PED-related topics: energy efficiency, energy flexibility, e-mobility, soft mobility, and low-carbon generation. Indeed, to properly deal with PED challenges and crucial aspects, it is necessary to combine and balance these technologies with enabler factors like financing instruments, social innovation and involvement, innovative governance and far-sighted policies. This paper proposes, in a structured form, the main outcomes of the co-creation approach developed during the workshop. The importance of implementing a holistic approach was highlighted: it requires a systematic and consistent integration of economic, environmental and social aspects directly connected to an interdisciplinary cross-sectorial collaboration between researchers, policymakers, industries, municipalities, and citizens. Furthermore, it was reaffirmed that, to make informed and reasoned decisions throughout an effective PED design and planning process, social, ecological, and cultural factors (besides merely technical aspects) play a crucial role. Thanks to the valuable insights and recommendations gathered from the workshop participants, a conscious awareness of key issues in PED design and implementation emerged, and the fundamental role of stakeholders in the PED development path was confirmed. [ABSTRACT FROM AUTHOR]

The design, operational planning, and integration of wind power plants with other renewables and the grid face challenges attributed to the intermittent nature of wind power generation. Addressing this issue necessitates the development of a smart wind power (and in particular wind speed) forecasting approach. This is a complex task due to substantial fluctuations in wind speed. To overcome the inherent stochastic nature of wind speed and mitigate related challenges, traditionally, numerical weather prediction (NWP) models are employed for wind speed forecasting. However, the applicability of NWP models is limited to short-term forecasting due to their computational constraints. In this study, a hybrid AI-based approach is proposed to improve forecast accuracy over a 48 h horizon for the city of Montreal. The results demonstrate that by integrating the probability distribution of wind speed with a deep learning model, the forecasted values align closely with the observed values in terms of seasonality and trend, exhibiting enhanced accuracy. Evaluation metrics reveal a substantial reduction in the root mean squared error (13–31%) across three prediction horizons (summer, fall, and winter) compared to a single long, short-term memory model. Furthermore, integrating the improved model with the numerical weather prediction model yields increased accuracy and decreased error compared to the LSTM–Weibull model. [ABSTRACT FROM AUTHOR]

Decarbonizing buildings is crucial in addressing pressing climate change issues. Buildings significantly contribute to global greenhouse gas emissions, and reducing their carbon footprint is essential to achieving sustainable and low-carbon cities. Retrofitting buildings to become more energy efficient constitutes a solution. However, building energy retrofits are complex processes that require a significant number of simulations to investigate the possible options, which limits comprehensive investigations that become infeasible to carry out. Surrogate models can be vital in addressing computational inefficiencies by emulating physics-based models and predicting building performance. However, there is a limited focus on investigating feature engineering and selection methods and their effect on the model's performance and optimization. Feature selection methods are considered effective with interpretable models such as multi-variate linear regression (MVLR) and multiple adaptive regression splines (MARS) for achieving stable prediction stability. This study proposes a modelling framework to create, optimize, and improve the performance of surrogate predictive models for energy consumption, carbon emissions, and the associated cost of building energy retrofit processes. The investigated feature selection methods are wrapper and embedded methods such as backward-stepwise feature selection (BSFS), recursive feature elimination (RFE), and Elastic Net embedded regularization in order to provide insights into the model's behavior and optimize the model's performance. The most accurate surrogate models developed achieved a mean absolute percentage error (MAPE) of 0.2–1.8% compared to the used test data. In addition, when calculated for a million samples, all developed surrogate models reduced the computational time by one-thousand-fold compared to physics-based models. The study's findings pave the way towards low-computational accurate models that can comprehensively predict building performance in near real-time, ultimately leading to identifying decarbonization measures at scale. [ABSTRACT FROM AUTHOR]

Urban wastewater could be converted into energy if microbial electrochemical technologies (METs) like microbial dual-chamber electrolysis cells (MDEC) or microbial fuel cells (MFC) are applied as a treatment method. Mathematical modelling of MFC and MDEC for wastewater treatment and energy recovery has been developed in this study. The Radaue method has been used to solve ordinary differential equations (ODEs), and the model outputs were successfully validated with previous experimental and modelling data. A case study in Montreal, Canada, has also been considered for testing the application of METs on an urban scale with a total daily wastewater flow of 75,000 L/day. The results show that from 1 m3 of wastewater, MDEC and MFC can generate 0.077 kg H2 and 0.033 kWh, respectively. [ABSTRACT FROM AUTHOR]

Transit agencies are increasingly embracing electric buses (EB) as an energy-efficient and emission-free alternative to the conventional bus fleets. They are rapidly replacing conventional buses with electric ones. As a result, emerging challenges of electrifying public transportation bus networks in cities should be addressed. Introducing electric buses to the bus transit system would affect the public transit operation planning steps. The steps are network design, timetabling, bus scheduling, and crew scheduling. Regarding the functional and operational differences between conventional buses and electric buses, such stages should be changed and optimized to enhance the level of service for the users while reducing operating costs for service providers. Many mathematical optimization models have been developed for conventional buses. However, such models would not fit the electric buses due to EBs' limited traveling range and long charging time. Therefore, new mathematical models should be developed to consider the unique features of electric buses. We present a comprehensive literature review to critically review and classify the work done on these topics. This paper compares the studies that have been done in this field and highlight the missing links and gaps in the considered papers, and the potential future studies that could be done. The considered papers cover the integration of timetabling and vehicle scheduling, recharging scheduling planning, and fast charging infrastructure location planning and its impacts on the grid. The main goal of this research is to highlight the research gaps and potential directions for future studies in this domain to encourage more realistic and applicable models and solution approaches for fully electric bus transit systems. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) can be used as a solution to ensure resilience against power supply failures in electricity grids caused by extreme weather conditions, unavailability of generation capacities, and problems with transmission components. The literature is rich in research focusing on strengthening the planning of microgrids based on overall load demand. In this study, a critical load demand indicator will be calculated and used to identify optimum operation strategies of microgrids in a power failure mode. An urban microgrid with a large educational building is selected for the case study. Operation dispatch scenarios are developed to reinforce the system's resiliency in severe conditions. A mixed-integer linear programming (MILP) approach is employed to identify global optimum dispatch solutions based on a next 48 h plan for different seasons to formulate a whole-year operational model. The results show that the loss of power supply probability (LPSP), as an indicator of resiliency, could be lowered to near zero while minimizing operational cost. [ABSTRACT FROM AUTHOR]

Many installed solar thermally driven cooling systems suffer from high auxiliary electric energy consumption which makes them not more efficient than conventional compression cooling systems. A main reason for this is the use of non-efficient controls with constant set points that do not allow a chiller power modulation at partial-load and therefore lead to unnecessary high power consumption of the parasitics. The aims of this paper are to present a method to control efficiently solar thermally driven chillers, to demonstrate experimentally its applicability and to quantify the benefits. It has been shown that the cooling capacity of a diffusion absorption chiller can be modulated very effectively by adjusting both the temperature and the flow rate of the cooling water. With the developed approach and the use of optimization algorithms, both the temperature and the flow rate can be controlled simultaneously in a way that the cooling load is matched and the electricity consumption is minimized. Depending on the weather and operating conditions, electricity savings between 20% and 60% can be achieved compared to other tested control approaches. The highest savings are obtained when the chiller is operated at partial load. The presented method is not restricted to solar cooling systems and can also be applied to other conventional heating ventilation and air conditioning (HVAC) systems. [ABSTRACT FROM AUTHOR]

The results in this work show the influence of long-term operation on the decomposition of working fluids in eight different organic rankine cycle (ORC) power plants (both heat-led and electricity-led) in a range of 900 kWel to 2MWel. All case study plants are using octamethyltrisiloxane (MDM) as a working fluid; the facilities are between six to 12 years old. Detailed analyses, including the fluid distribution throughout the cycle, are conducted on one system. All presented fluid samples are analyzed via head space gas chromatography mass spectrometry (HS-GC-MS). Besides the siloxane composition, the influence of contaminants, such as mineral oil-based lubricants (and their components), is examined. In most cases, the original working fluid degrades to fractions of siloxanes with a lower boiling point (low boilers) and fractions with a higher boiling point (high boilers). As a consequence of the analyses, a new fluid recycling and management system was designed and tested in one case study plant (Case Study #8). Pre-post comparisons of fluid samples prove the effectiveness of the applied methods. The results show that the recovery of used working fluid offers an alternative to the purchase of fresh fluid, since operating costs can be significantly reduced. For large facilities, the prices for new fluid range from €15 per liter (in 2006) to €22 per liter (in 2013), which is a large reinvestment, especially in light of filling volumes of 4000 liters to 7000 liters per unit. Using the aforementioned method, a price of €8 per liter of recovered MDM can be achieved. [ABSTRACT FROM AUTHOR]

Electrification is an efficient way to decarbonize by replacing fossil fuels with low-emission power. In addition, energy efficiency measures can reduce consumption, making it easier to shift to a zero-carbon society. In Québec, upgrades to aging buildings that employ electric resistance heating offer a unique opportunity to free up large amounts of hydroelectricity that can serve to decarbonize heating in other buildings. However, another source of energy would be needed to electrify mobility because efficiency measures free up small amounts of electricity in summer compared to winter. This study reveals how building efficiency measures combined with solar electricity generation provide an energy profile that matches the requirements for decarbonizing both mobility and heating. The TRNSYS software was used to simulate the annual energy performance of an existing house and retrofitted/rebuilt low-energy houses equipped with a photovoltaic (PV) roof in Montreal, Québec, Canada (45.5° N). The electricity that is made available by upgrading the houses is mainly considered for powering battery and fuel cell electric vehicles (BEVs and FCEVs) and electrifying heating in greenhouses. The results indicate that retrofitting 16% or rebuilding 12% of single-detached homes in Québec can provide enough electricity to decarbonize heating energy use in existing greenhouses and to operate the new greenhouses required for growing all fresh vegetables locally. If all the single-detached houses that employ electric resistance heating are upgraded, 33.4 and 21.8 TWh year−1 of electricity would be available for decarbonization, equivalent to a 19% and 12% increase of the province's electricity supply for the retrofitted or rebuilt houses, respectively. This is enough energy to convert 83–100% of personal vehicles to BEVs or 35–56% to FCEVs. Decarbonization using the electricity that is made available by upgrading to low-energy solar houses could reduce the province's greenhouse gas (GHG) emissions by approximately 32% (26.5 MtCO2eq). The time required for the initial embodied GHG emissions to surpass the emissions avoided by electrification ranges from 3.4 to 11.2 years. Building energy efficiency retrofits/rebuilds combined with photovoltaics is a promising approach for Québec to maximize the decarbonization potential of its existing energy resources while providing local energy and food security. [ABSTRACT FROM AUTHOR]

High penetration levels of Plug-in Electric Vehicles (PEVs) could cause stress on the network and might violate the limits and constraints under extreme conditions, such as exceeding power and voltage limits on transformers and power lines. This paper defines extreme conditions as the state of a load or network that breaks the limits of the constraints in an optimization model. Once these constraints are violated, the optimization algorithm might not work correctly and might not converge to a feasible solution, especially when the complexity of the system increases and includes nonlinearities. Hence, the algorithm may not help in mitigating the impact of penetrating PEVs under extreme conditions. To solve this problem, an original algorithm is suggested that is able to adapt the constraints' limits according to the energy demand and the energy needed to charge the PEVs. Different case scenarios are studied for validation purposes, such as charging PEVs under different state of charge levels, different energy demands at home, and different pricing mechanisms. Results show that our original algorithm improved the profiles of the voltage and power under extreme conditions. Hence, the algorithm is able to improve the integration of a high number of PEVs on the distribution system under extreme conditions while preserving its stability. [ABSTRACT FROM AUTHOR]

This paper proposes an original multi-criteria decision-making optimization algorithm to select the best solar panels in an existing market and optimally size the photovoltaic (PV) system for an electric vehicle parking lot (EVPL). Our proposed algorithm is called rank-weigh-rank (RWR), and it is compared to the well-known technique for order of preference by similarity to ideal solution (TOPSIS) optimization algorithm under the same conditions for validation purposes. Results show that the speed of our proposed algorithm (RWR) in finding the best solution increases exponentially compared to TOPSIS when the numbers of alternatives and criteria increase. Moreover, 77% is the probability of obtaining results with more than 80% accuracy compared to TOPSIS, which validates the efficiency of our algorithm. In addition, we were able to design an EVPL with a power self-sufficiency ratio of 60.8%, the energy self-sufficiency ratio of 74.7%, and a payback period of 10.58 years. Moreover, the renewable energy-based EVPL was able to reduce the power losses on the network by 95.7% compared to an EVPL without a renewable energy system and improve the voltage deviation. [ABSTRACT FROM AUTHOR]

Biogas technology is an important alternative energy source worldwide. Blackwater and kitchen refuse represent ideal waste streams for bioenergy recovery through anaerobic co-digestion. Modeling of the biokinetics of anaerobic digestion on several aspects, such as microbial activity, substrate degradation, and methane production, from co-digestion of black water (BW) and kitchen refuse (KR) was the objective of this research. A mathematical model was developed towards a simulation based on mass balances on biomass, the organic substrate, and biogas. The model was implemented in INSEL and experimental data from the literature were used for model validation. The study shows that the simulation results fit well with the experimental data. The energy consumption and generation potential of anaerobic co-digestion of BW and KR were calculated to investigate if the produced biogas could supply the digester's energy demand. This study can be used to pre-design anaerobic digestion systems in eco-districts. [ABSTRACT FROM AUTHOR]

Designing district-scale energy systems with renewable energy sources is still a challenge, as it involves modeling of multiple loads and many options to combine energy system components. In the current study, two different energy system scenarios for a district in Montreal/Canada are compared to choose the most cost-effective and energy-efficient energy system scenario for the studied area. In the first scenario, a decentral energy system comprised of ground-source heat pumps provides heating and cooling for each building, while, in the second scenario, a district heating and cooling system with a central heat pump is designed. Firstly, heating and cooling demand are calculated in a completely automated process using an Automatic Urban Building Energy Modeling System approach (AUBEM). Then, the Integrated Simulation Environment Language (INSEL) is used to prepare a model for the energy system. The proposed model provides heat pump capacity and the number of required heat pumps (HP), the number of photovoltaic (PV) panels, and AC electricity generation potential using PV. After designing the energy systems, the piping system, heat losses, and temperature distribution of the centralized scenario are calculated using a MATLAB code. Finally, two scenarios are assessed economically using the Levelized Cost of Energy (LCOE) method. The results show that the central scenario's total HP electricity consumption is 17% lower than that of the decentral systems and requires less heat pump capacity than the decentral scenario. The LCOE of both scenarios varies from 0.04 to 0.07 CAD/kWh, which is cheaper than the electricity cost in Quebec (0.08 CAD/kWh). A comparison between both scenarios shows that the centralized energy system is cost-beneficial for all buildings and, after applying the discounts, the LCOE of this scenario decreases to 0.04 CAD/kWh. [ABSTRACT FROM AUTHOR]

Landfilling of organic waste is still the predominant waste management method in Canada. Data collection and analysis of the waste were done for the case study city of Montréal in Canada. A life cycle assessment was carried out for the current and proposed waste management system using the IWM-2 software. Using life cycle assessment results, a non-dominated sorting genetic algorithm was used to optimize the waste flows. The optimization showed that the current recovery ratio of organic waste of 23% in 2017 could be increased to 100% recovery of food waste. Also, recycling could be doubled, and landfilling halved. The objective functions were minimizing the total energy consumption and CO2eq emissions as well as the total cost in the waste management system. By using a three-objective optimization algorithm, the optimized waste flow for Montréal results in 2% of waste (14.7 kt) to anaerobic digestion (AD), 7% (66.3 kt) to compost, 32% (295 kt) to recycling, 1% (8.5 kt) to incineration, and 58% (543 kt) to landfill. [ABSTRACT FROM AUTHOR]

The increased integration of Electric Vehicles (EVs) into the distribution network can create severe issues—especially when demand response programs and time-varying electricity prices are applied, EVs tend to charge during the off-peak time to minimize the electricity cost. Hence, another peak demand might be created, and other solutions are required. Many researchers tried to solve the problem; however, limitations exist because of the decentralized topology of the network. The system operator is not allowed to control the end-users' load due to security and privacy issues. To overcome this situation, we propose a novel data-energy management algorithm on the transformer's level that controls the power demand profiles of the householders and exchange energy between them without violating their privacy and security. Our method is compared to an existing one in the literature based on a decentralized control strategy. Simulations show that our approach has reduced the electricity cost of the end-users by 3%, increased the revenue of the system operator, and reduced techno-economic losses by 50% and 42%, respectively. Our strategy shows better performance even with a 100% penetration level of EVs on the network, in which it respects the network's constraints and maintains the voltage within the recommended limits. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to develop a reliable and accurate model of the wastewater biochemical treatment process and to explore the behaviour through a general dynamic simulation environment, namely the INtegrated Simulation Environment Language (INSEL), for the analysis of the energy demand of the whole wastewater treatment plant. In particular, the presented model pays special attention to the chemical kinetics involved in the activated sludge process for the reduction of nitrogen and carbon compounds. According to the best practices, the plant configuration considered in this work includes the denitrification-nitrification process, performed by completely mixed reactors. In particular, the process analysed in this paper is based on the Ludzak-Ettinger process. The biological process is simulated according to the well-known method widely used in the literature, namely the Activated Sludge Model No 1 (ASM1). The model includes a set of equations for the calculation of aerobic growth of heterotrophs, anoxic growth of heterotrophs, aerobic growth of autotrophs, decay of autotrophs, ammonification of soluble nitrogen, hydrolysis of entrapped organics, and hydrolysis of entrapped organic nitrogen. All these equations, along with energy and mass balances, are solved by the explicit Euler method. The developed model is validated using literature data, showing a great accuracy (deviation below 1%). As for the temperature, results show that, between 15 and 25 °C, in the initial part of the process, transport effects dominate the consumption ones. When the temperature is higher than 30 °C, nitrate consumption is so fast that biomass growth is limited by this effect. Conversely, in case of low temperatures (5–10 °C), biomass growth is not limited by nitrate availability. Finally, results also showed that temperature significantly affects the denitrification process, whereas the effect on the oxygen is lower. [ABSTRACT FROM AUTHOR]

Although renewable technologies are progressing fast, there are still challenges such as the reliability and availability of renewable energy sources and their cost issues due to capital intensity that hinder their broad adoption. This research aims at developing a configuration-sizing approach to enhance the cost efficiency and sourcing reliability of renewable energies integrated in microgrids. To achieve this goal, various technologies were considered, such as solar PV, wind turbines, converters, and batteries for system configuration with minimization of net present cost (NPC) as the objective. Grid connection scenarios with up to 100% renewable contribution were analyzed. The results show that the integration of renewable technologies with some grid backup could reduce the levelized cost of energy (LCOE) to about half of the price of the electricity that the university purchases from the grid. Also, different kinds of solar tracker systems were studied. The outcome shows that by using a vertical axis solar tracker, the LCOE of the system could be reduced by more than 50 percent. This research can help the decision-maker to opt for the best scenarios for generating reliable and cost-efficient electricity. [ABSTRACT FROM AUTHOR]

The present paper introduces an iterative methodology to progressively reduce building simulation model complexity with the aim of identifying potential trade-offs between computational requirements (i.e., model complexity) and energy estimation accuracy. Different levels of model complexity are analysed, from commercial building energy simulation tools to low order calibrated thermal networks models. Experimental data from a residential building in Germany were collected and used to validate two detailed white-box models and a simplified white-box model. The validation process was performed in terms of internal temperature profiles and building thermal energy demand predictions. Synthetic profiles were generated from the validated models and used for calibrating high order models. A reduction (trimming) procedure was applied to reduce the model complexity using an energy performance criterion prior to model trimming. The proposed methodology has the advantage of keeping the physical structure of the original RC model, thus enabling the use of the trimmed lumped parameter building model for other applications. The analysis showed that it is possible to reduce the model complexity by half, while keeping the accuracy above 90% for the targeted building. [ABSTRACT FROM AUTHOR]

In the paper, a method was developed to automatically dimensionalize and calculate central energy generation and supply scenarios with a district heating system for cities based on 3D building models in the CityGML format and their simulated heat demand. In addition, the roof geometry of every individual building is used to model photovoltaic energy generation potential. Two types of supply systems, namely a central heat pump (HP) system and a large co-generation (combined heat and power-CHP) system (both with a central storage and district distribution system), are modeled to supply the heat demand of the area under investigation. Both energy generation models are applied to a case study town of 1610 buildings. For the HP scenario, it can be shown that the case study town's heat demand can be covered by a monovalent, low-temperature system with storage, but that the PV only contributes 15% to the HP electricity requirement. For the CHP scenario, only 61% of the heat demand can be covered by the CHP, as it was designed for a minimum of 4000 operating hours. Both the PV and the CHP excess electricity are fully injected into the grid. As a result, the primary energy comparison of both systems strongly depends on the chosen primary energy factors (PEF): with given German regulations the CHP system performs better than the HP system, as the grid-injected electricity has a PEF of 2.8. In the future, with increasingly lower PEFs for electricity, the situation reverses, and HPs perform better, especially if the CHP continues to use natural gas. Even when renewable gas from a power to gas (P2G) process is used for the CHP, the primary energy balance of the HP system is better, because of high conversion losses in the P2G process. [ABSTRACT FROM AUTHOR]