Input–output efficiency model of urban green-energy development from the perspective of a low-carbon economy

With the acceleration of urbanization, cities are the main targets for carbon neutrality and urban energy is the terminal of energy consumption and the integration point of various energy systems. Therefore, there is a need to promote the development of urban green energy and achieve low input and high output to achieve a low-carbon economy in cities. Previous studies have not considered the input–output efficiency of urban green-energy development. This study fills this gap. Based on the economic–energy–environmental framework, an input–output efficiency-evaluation index system for urban green-energy development was constructed. Based on improved data-envelopment analysis, a comparative evaluation of the input–output efficiency of green-energy development was carried out in 30 provinces in China in 2019. Considering the differences in regions, the development of urban green energy in different provinces was classified. From the perspective of a low-carbon economy, economic growth factors and environmental constraint factors were set. Together with the generalized Divisia index approach, the input–output efficiency optimization directions of urban green-energy development were obtained. The results showed that the input–output efficiencies of urban green-energy development in Jiangsu, Zhejiang, Fujian, Inner Mongolia, Ningxia and other provinces and cities were relatively high. Provinces with faster economic development and higher environmental carrying capacity have advantages after optimization and will become pilot areas for the development of urban green energy. This research provides a reference for the development of urban green energy in various provinces from the input and output perspective.

Techno–economic–environmental feasibility study of a photovoltaic system in northern part of Iran including a two-stage multi-string inverter with DC–DC ZETA converter and a modified P&O algorithm

Inverters play a significant role in the configuration of grid-connected photovoltaic (PV) systems. The perturb-and-observe (P&O) algorithm is a common method to derive the maximum power from grid-connected inverters; however, the possibility of losing maximum power due to sudden changes in radiation is a significant drawback of this control strategy. To overcome this barrier, the two-stage multi-string inverter using the ZETA DC–DC converter and a novel P&O algorithm has been proposed to increase the efficiency of these systems. The proposed inverter has been simulated in MATLAB/SIMULINK software. To investigate the performance of the proposed inverter, technical, environmental and economic feasibility studies have been performed for the construction of a 5-kW PV power plant in a northern city of Iran (Sari) using the RETScreen software developed by Natural Resources Canada. On the other hand, most feasibility studies for power-plant construction are based on the concept of inverter peak efficiency, which leads to non-optimal system design due to the short operation duration of the inverter at this value. However, the weighted European efficiency has been used in the feasibility study for more accurate computations. Moreover, the performance of the proposed inverter is compared to that of a two-stage multi-string inverter using a conventional P&O algorithm and the single-stage (central) inverter. The simulation results indicated that the proposed inverter injects 7.6 MW of power into the grid per year. Moreover, it prevents the emission of 88 tons of CO2 (over 20 years), which is equivalent to saving 1883.5 litres of gasoline per year.

CO2-capture research and Clean Energy Technologies Research Institute (CETRI) of University of Regina, Canada: history, current status and future development

Summary Clean Energy Technologies Research Institute (CETRI) was formerly known as the International Test Centre for CO2 Capture in the early 2000s. The original focus of the centre was to help lower the carbon intensity of the current energy sources to low-carbon ones in Canada. Currently, CETRI’s mandates have expanded and now include most of the low-carbon and near-carbon-free clean-energy research activities. Areas of research focus include carbon (CO2) capture, utilization and storage (CCUS), near-zero-emission hydrogen (H2) technologies, and waste-to-renewable fuels and chemicals. CETRI also brings together one of the most dynamic teams of researchers, industry leaders, innovators and educators in the clean and low-carbon energy fields.

Optimization of synchronized frequency and voltage control for a distributed generation system using the Black Widow Optimization algorithm

A distributed generation network could be a hybrid power system that includes wind–diesel power generation based on induction generators (IGs) and synchronous generators (SGs). The main advantage of these systems is the possibility of using renewable energy in their structures. The most important challenge is to design the voltage-control loop with the frequency-control loop to obtain optimal responses for voltage and frequency deviations. In this work, the voltage-control loop is designed by an automatic voltage regulator. A linear model of the hybrid system has also been developed with coordinated voltage and frequency control. Dynamic frequency response and voltage deviations are compared for different load disturbances and different reactive loads. The gains of the SG and the static volt-ampere reactive compensator (SVC) controllers in the IG terminal are calculated using the Black Widow Optimization (BWO) algorithm to insure low frequency and voltage deviations. The BWO optimization algorithm is one of the newest and most powerful optimization methods to have been introduced so far. The results showed that the BWO algorithm has a good speed in solving the proposed objective function. A 22% improvement in time adjustment was observed in the use of an optimal SVC. Also, an 18% improvement was observed in the transitory values.

Optimal cost and feasible design for grid-connected microgrid on campus area using the robust-intelligence method

In this paper, a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum cost. The campus microgrid needs to be optimized with further investigation, especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply. A modified combination of genetic algorithm and particle swarm optimization (MGAPSO) is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel system. Then, a sustainable energy-management system is also defined to analyse the characteristics of the microgrid. The optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional methods. The MGAPSO optimization reduces the system cost by up to 11.99% compared with the conventional methods. In the rest of the paper, the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation characteristics. Further investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization.

Multi-criteria prioritization of the renewable power plants in Australia using the fuzzy logic in decision-making method (FMCDM)

The presented study focused on developing an innovative decision-making framework to select the best renewable-power-plant technologies, considering comprehensive techno-economic and environmental variables. Due to the favourable conditions, Australia was selected as the case study. A fuzzy-logic method and analytical hierarchy process were applied to prioritize different renewable-energy power plants. The techno-economic factors included levelized cost of energy, initial cost, simple payback time, and operation and maintenance costs along with environmental factors including carbon payback time, energy payback time and greenhouse-gas emissions were used to rank the power plants. The results showed that the capital cost and simple payback time had the highest priority from an economic point of view. In comparison, greenhouse-gas emissions and carbon payback time were the dominant environmental factors. The analysis results provided economic and environmental priority tables for developing different power plants in the current state and a future scenario by 2030. The fuzzy results and pairwise composite matrix of alternatives indicated that the onshore wind, offshore wind, single-axis tracker polycrystalline photovoltaic, single-axis tracker monocrystalline photovoltaic, fix-tilted polycrystalline photovoltaic and fix-tilted monocrystalline photovoltaic scored the highest in the current state. In contrast, by 2030, the single-axis tracker photovoltaic power plants will be the best choice in the future scenario in Australia. Finally, the results were used and analysed to recommend and suggest several policy implementations and future research studies.

A microgrid for the secluded Paana Theertham Kani settlement in India

Recognizing the importance of electricity as a driver of rapid economic growth and poverty alleviation, India aims to provide access to all households by 2030. Despite the best efforts of state and federal governments to meet consumers’ electrical needs, budget constraints, inefficient operations and massive loan burdens have hampered their efforts. Aside from these concerns, rural India, which accounts for 65% of the population, is plagued by a slew of issues, including low electricity demand, a low load factor and the expectation of cheap electricity. These concerns bind the authorities’ hands, preventing them from moving forward. As a result, this project aims to model an autonomous microgrid system that integrates three potential renewable-energy systems, namely wind, sun and hydrokinetic, to provide electricity for a remote society. It starts with assessing the region’s electricity needs with its inhabitants. The HOMER Pro platform creates a cost-effective microgrid based on the demand estimate. The components of the microgrid include 6.4-kW small wind turbine (SWT) groups, 4.4-kW solar photovoltaic (PV) panels, a 5-kW hydrokinetic water turbine, battery storage and a converter. The project is unique in that it considers site-specific initial capital costs, replacement costs, and operation and maintenance costs of the renewable-energy systems, and it does not include any environmentally hazardous energy system. The successful optimization results in terms of levelized energy costs are $0.0538, $0.0614 and $0.0427/kWh for wind, solar and hydrokinetic components, respectively, without any environmental issues.

Design and simulation analysis of high-temperature heat-storage combined-circulation system

In view of the continuous increase in the proportion of renewable energy connected to the grid in China and the increasing peak-to-valley difference in electricity demand on the power grid, this paper proposes a high-temperature thermal-storage combined-cycle power-generation system. Using Thermoflex thermal simulation analysis software, a high-temperature thermal-storage combined-cycle simulation analysis system model was established, and the influence of different initial temperatures and pressure ratios on the combined-cycle system was analysed. Sensitivity analysis of factors such as electricity cost, annual operating hours, initial equipment investment, unit efficiency and other factors that affect the net income of the system was carried out. According to the current power-peak-shaving auxiliary service market in China, it is pointed out that high-temperature thermal-storage combined-cycle projects must be profitable and obtain good economic benefits. The results obtained, on the one hand, provide suggestions for the flexibility and transformation of current gas-fired steam generators for peak shaving and, on the other hand, provide references for the subsequent development of high-temperature thermal-storage combined-cycle demonstration projects.

Monitoring of lithium-ion cells using a microcontroller

Safe and efficient operation of batteries is always desired but batteries with a high energy density pose a threat to the system causing thermal breakdown, reduced performance and rapid ageing. To reduce such vulnerabilities, an optimum environment with controlled parameters is required. Four parameters have been considered for analysis, i.e. state of charge, current, voltage and temperature. The module makes a detailed analysis of the above-mentioned parameters and suggests a microcontroller-based prototype that is capable of monitoring the external factors in real time and generating relevant warnings.

A maximum power point tracking strategy applied to building integrated photovoltaics

To solve the problem of permanent-shadow shading of photovoltaic buildings, a maximum power point tracking (MPPT) strategy to determine the search range by pre-delimiting area is proposed to improve MPPT efficiency. The single correspondence between the solar-cell current–voltage (I–V) curve and the illumination conditions was proved by using the single-diode model of photovoltaic cells, thus proving that a change in the illumination conditions corresponds to a unique maximum power point (MPP) search area. According to the approximate relationship between MPP voltage, current and open-circuit voltage and short-circuit current of a photovoltaic module, the voltage region where the MPP is located is determined and the global maximum power point is determined using the power operating triangle strategy in this region. Simulation carried out in MATLAB proves the correctness and feasibility of the theoretical research. Simulation results show that the MPPT strategy proposed in this paper can improve the average efficiency by 1.125% when applied in series as building integrated photovoltaics.