Smart Energy最新文献

The 4th generation of district heating systems face a potential problem where lowered water temperatures lead to higher flow rates, which requires higher hydraulic capacity in terms of pipe and pump sizes. This increases the effect of the already existing issue of hydraulic bottlenecks, causing peripheral units (customers) to experience reduced flow rates. A coordinating control strategy is presented in this work aimed at reducing the effect of such bottlenecks on the comfort of customers. This is done by distributing the flow deficit over many units rather than a few. Previous works mainly focus on MPC-structured controllers that depend on complex system models and online optimization techniques. This work proposes a method that requires little information about models for individual units and minimal IT communication between control systems. The proposed method is compared with a traditional control strategy and an optimal baseline in a simulation study. This shows that the proposed method can decrease the worst case indoor temperature deviations.

District heating has an important role in the shift to carbon-neutral energy systems through enabling the use of heat sources that would otherwise be wasted to cover buildings’ heating demands. The availability of many renewable and surplus heat sources is however in opposite phase with the heating demand, creating a demand for seasonal thermal energy storage. This study performs a techno-economic assessment of the heat supply system of a residential area in Norway, where seasonal storage storing excess heat from a waste incineration plant is being planned. A heat supply solution combining seasonal storage and low-temperature district heating was compared with two more conventional alternatives: high-temperature district heating and direct electric heating.

The study shows that the seasonal storage is not cost optimal under the conditions assumed, in particular regarding the electricity market; however, the total costs were only 3% higher compared to electric heating. Seasonal storage additionally allows to reduce the use of peak heating units in the district heating system in the winter, thus reducing the costs and emissions related to heat production, and district heating alone has a significant impact in alleviating the pressure on the power grid. The peak power demand was reduced by 28% when investing to low- or high-temperature district heating, and seasonal storage was shown to enable up to 31% reduction in the peak heating demand. Moreover, it was shown that higher electricity prices in the winter and reduced grid capacity increase the economic viability of the solution and could make it competitive.

In this work, a local multi-modal energy market is introduced to couple district heating and electric systems. In the course of the ongoing decarbonization of energy systems, electric systems have to integrate more and more volatile renewable energies, whereas in thermal systems, the demand for sustainable heat generation is continuously increasing. Market-based coordination of local thermal-electric energy systems can help to alleviate these challenges. In this work, an adequate representation of conversion assets, e.g., heat pumps, is achieved by introducing novel coupling orders in the market. These enable an explicit coupling of heat and electricity, and thus cross-energy load-shifts. In addition, a new type of storage orders is introduced to offer flexibility options by energy storage systems in the local energy system. The benefits of the market scheme are demonstrated for a day ahead cycle of an exemplary local energy system in Germany. Inter alia, the results lead to the conclusion that coupling orders are able to alleviate price and volume risks of market participants with conversion assets. Moreover, storage orders can provide operational benefits to the local energy system, while respecting the physical characteristics of energy storage systems. For the specified day ahead cycle, the peak load to the transmission grid can be decreased by up to 18.34%, and, thus improving the self-sufficiency of the local energy system.

With the rapid urbanization in China, energy consumption and corresponding carbon emissions in residential buildings are growing. Because occupant behaviour plays a significant role in building energy performance and occupant comfort, understanding the crucial links between occupant lifestyles and energy use is the key. Most studies on the relationship between occupancy behaviour and energy usage have been conducted in public or commercial buildings. Because data is difficult to obtain and key information about existing houses is required, research on high rise residential buildings is limited. To acquire information on thermal satisfaction, residential equipment ownership and usage habits and attitude to smart meters, this paper conducted a comprehensive survey of 112 metropolitan families living in a typical booming city. A case study high-rise residential building is modelled in a building energy simulation tool. The results are compared with the actual energy bills acquired from occupants or smart meters to better understand the energy usage in this area. The results showed that a large variation in energy use could exist in different households, which is influenced by several factors such as occupancy patterns and habits, as observed in the survey. At the same time, it shows the challenge of predicting the energy use of such a building with varying internal heat gains, set points and window opening behaviours across the different households.

Existing district heating networks (DHNs) are often designed for relatively high temperatures, typically 80–120 °C supply and 40–60 °C return. The transformation of such high-temperature DHNs (HTDHNs) into more efficient low-temperature DHNs (LTDHN) and towards the 4th generation DHNs is associated with great complexity and effort. This paper discusses the integration of sub-LTDHNs into the return flow of existing HTDHNs, thereby creating an energy cascade and thus lowering the overall system temperatures of the HTDHN. The technical barriers and drivers of such sub-LTDHNs were analysed through literature research, expert interviews, and a questionnaire. Their technical design was investigated, and a techno-economic analysis was conducted for several configurations in terms of the supply and return temperatures in the sub-LTDHN, various temperatures of the HTDHN and potential connecting points. This analysis was also conducted for a planned residential area in a Nordic city. In addition, their operating dynamics resulting from different HTDHN load conditions were analysed in terms of the effects on the sub-LTDHN. It was found that, on the one hand, the connection point with its prevailing conditions (mass flow and temperature) is the key parameter to ensure that the heat demand is met. On the other hand, the savings in the HTDHN due to lower return system temperatures resulting from the sub-LTDHN integration in the return pipeline are significantly higher if the use of combustion technologies is minimized.

This study starts by modeling and analyzing a smart combined energy system that includes a concentrated solar power plant, steam Rankine, Brayton, organic Rankine cycles, reverse osmosis unit, and a thermoelectric generator. The system is then subjected to bi-criteria optimization, using non-dominated sorting genetic algorithm II (NSGA-II) and minimizing annual costs and maximizing exergy efficiency. The system is located in Isfahan (central Iran) and intended to produce electricity and freshwater. The thermodynamic results indicated the most critical parameters affecting system performance: direct normal irradiance, number of heliostats, turbine efficiency and inlet temperature, compressor pressure ratio, and steam Rankine cycle pump inlet temperature. A Pareto frontier was charted, producing a set of optimal points, where a decrease in costs was achieved if the exergy efficiency was slightly compromised, leading to the identification of an optimal location within the Pareto frontier.

Transitioning from heavy carbon fuels such as coal and oil to lighter carbon fuels and renewable energy is necessary to reduce greenhouse gas emissions to keep global temperatures less than 2 °C above pre-industrial levels. This study considers the transition from full natural gas power generation to include renewables via utility-scale photovoltaic (PV) facilities in the Caribbean small island state of Trinidad and Tobago. By using the EnergyPLAN software and hourly solar radiation and electricity data, the electric power generation and quantities of natural gas avoided for several hundred-megawatt PV facilities were estimated. The direct and opportunity savings that could be derived from the avoided natural gas is substantial for a small island state. Additionally, payback periods, avoided carbon dioxide emissions from the power generation sector and levelized costs of electricity make a strong economic case for utility-scale PV. As solar PV is intermittent, a smart energy system is suggested to provide affordable and efficient electricity generation and to include other renewable energy sources such as wind power and electric vehicles.

The transport sector contributes to approximately one third of Danish greenhouse gas (GHG) emissions and almost half of emissions from the energy sector. A unified Danish parliament agreed to reduce total emissions with 70% compared to 1990 levels by 2030. This paper estimates the potential for reducing the national transport sector GHG emissions in 2030 and proposes a pathway towards full decarbonisation in 2045 using a complex set of measures.

Towards 2030, the major focus is on an extensive electrification for passenger cars, alongside the implementation of significant measures to achieve lower growth rates for kilometers travelled by car and aircraft. From 2030 onwards, a decisive focus is set on sector integration. Production of electrofuels proves to be a key measure to decarbonize aviation, shipping and long-distance road freight transport.

The results show a reduction of GHG emissions of 41% in 2030 and full decarbonisation in 2045. The reduction is achieved without a significant increase of socio-economic costs. From 2030 to 2045, a substantial electrification of road transport and a focus of moving the need for mobility from roads towards rail and bicycles drives the full-decarbonisation together with the replacement of fossil fuels with electrofuels for aviation, shipping and heavy-duty road transport.

Combined Heat and Power (CHP) generation from Sewage Sludge (SS) offers two simultaneous advantages: greenhouse gas emission reduction and increase of renewable energy generation as promoted by the European Union Green Deal 2021. In this work, a numerical model has been developed via Aspen Plus for the evaluation of CHP generation potentiality from SS through gasification integrated with an internal combustion engine system. The model is applied to the case of Italy and eight other European countries for the first time. The gasification model has been developed based on the experimental data on syngas generation from SS in a bench-scale rotary kiln reactor under laboratory conditions available in the literature. Sensitivity analysis revealed optimal operating temperature and equivalence ratios for gasification were 900 ^°C and 0.2 respectively. The CHP generation potentiality of SS resulted to be 2.73 kWh per kg SS as dry solid.

According to the statistical analysis used in the present study, SS generation will reach 680 kt per year as dry solid by 2030 based on the current sludge generation rate as well as improvement in the wastewater collection and treatment expected for the future in Italy. Within this time, the projected electrical and thermal energy generation rate per year can reach 714 GWh and 1142 GWh respectively. Electrical and thermal energy generation rates from sewage sludge have been estimated for eight EU countries in 2015 and compared with the Italian scenario, founding the highest one in Spain and the lowest in Luxembourg.

Due to an ongoing energy transition, electricity networks are increasingly challenged by situations where local electrical power demands are high but local generation is low and vice versa. This finally leads to a growing number of technical problems. To solve these problems in the short-term, the electrical power of load and generation must be adjusted as available flexibility. In zonal electricity systems, one often discussed concept to utilize flexibility is local flexibility markets. Based on auction theory, we provide a comprehensible framework for the use of network-supportive flexibility in general. In this context, we discuss the problem of matching supply and demand. We introduce three matching approaches that can be applied and adapted for different network situations. In addition to a qualitative description of the three approaches, we present a case study of an exemplary distribution network and explore different scenarios to demonstrate the utility of the algorithms. We compare the three approaches on a qualitative level with quantitative inputs from the case study. The comparison considers the specific cost, flexible energy, ensured demand coverage, data minimization, computational effort and the transferability of the three approaches.