The sizing of microgrids depends on the type of load and its operational hours. The significance of understanding the load operational characteristics in special purpose islanded microgrids is much needed for economic system sizing. The load operation of special-purpose microgrids often consumes high power for a short duration and remains idle most of the time, thus reducing the load factor. The inclusion of such loads in microgrid sizing causes huge capital costs making islanded microgrids an unfeasible solution. The islanded microgrid under study is an agricultural microgrid in a village having a small Crab Processing Plant (CPP) and a Domestic Sector (DS). The CPP constitutes the major power consumption. The community has a unique load consumption trend that is dependent on the highly uncertain parameter of availability of the crabs. Interestingly, crab availability is an independent parameter and cannot be accurately scheduled. The existing system sizing of the microgrid is performed based on the conventional methods that consider the CPP for full-day operation. However, the microgrid sources, especially the storage system may be reflected as oversized if the crab processing plants do not operate for several days due to the uncertain behavior of CPP causing enormous power wastage. In this paper, an integrated fixed and operational mode strategy for uncertain heavy loads is formulated. The proposed algorithm is based on the optimal sizing methodology aided by the load scheduling control strategy. The Particle Swarm Optimization technique is used for the optimal sizing integrated with the fuzzy logic controller to manage the available load. The membership functions are available excess power and the state of the charge of storage that defines the operational conditions for CPP. Based on input membership functions, the fuzzy controller decides on power dispatch in DS or CPP, keeping considerable SoC available for night hours. The simulation result shows that the time-dependent fuzzy controller approach manages to provide power to both sectors under optimal sizing while reducing the overall cost by 24% less than the existing microgrid.

Water canal networks that are widely used for irrigation are an equally good source of micropower generation to be fed to the nearby areas. A practical example of such a system is the micro-hydro generation at Renala Khurd Pakistan integrated with the national grid known as hydro–grid configuration. Apart from the rare Renala Khurd hydro generation example, solar photovoltaic generation integrated with a mainstream network, i.e., solar PV-Grid configuration, is widely used. The integrated operation of combinations of primary distributed generation sources has different operational attributes in terms of economics and reliability that are needed to be quantified before installation. So far, various combinations of primary distributed generation sources have been simulated and their accumulative impact on project economics and reliability have been reported. A detailed economic and reliability assessment of various configurations is needed for sustainable and cost-effective configuration selection. This study proposes a trigeneration combination of solar–hydro–grid with an optimal sizing scheme to reduce the solar system sizing and grid operational cost. A genetic algorithm based optimal sizing formulation is developed using fixed hydro and variable solar and grid systems with a number of pre-defined constraints. The hydro–grid, solar–grid, and grid–hydro–solar configurations are simulated in HOMER Pro software to analyze the economic impact, and to undertake reliability assessments under various configurations of the project. Finally, optimal values of the genetic algorithm are provided to the HOMER Pro software search space for simulating the grid–hydro–solar configuration. It was revealed that the net present cost (NPC) of hydro-to-grid configuration was 23% lower than the grid–hydro–solar configuration, whereas the NPC of grid–hydro–solar without optimal sizing was 40% lower than the solar–grid configuration, and the NPC of grid–solar–hydro with the genetic algorithm was 36% lower than the hydro–grid configuration, 50.90% lower than solar–grid–hydro without the genetic algorithm, and 17.1% lower than the grid–solar configuration, thus proving utilization of trigeneration sources integration to be a feasible solution for areas where canal hydropower is available.

Residential buildings are turning out to be huge power-consumption units that accommodate multiple families. To limit energy consumption and environmental impacts, conventional buildings are being retrofitted as green buildings. However, due to the lack of awareness among residents and construction agencies, the adoption pace of green building retrofitting is slow. Under current retrofitting procedures, the building envelopes have been given great attention, which is an expensive and complex process. However, the sizing of lighting and cooling loads has not been considered in the retrofitting process, and careful attention is needed as they are major building loads. In this research, an appropriate sizing of lighting and cooling loads, along with optimized water consumption, is performed. The sizing is obtained using the parametric variations, i.e., varying the coefficient of utilization (CU) factor of lighting and the wall-to-window ratio (WWR) of different rooms to quantify their impact on power consumption. The optimization of water consumption is performed using particle swarm optimization. A case study of a residential building was taken, and a residential unit was considered in the study. It was found that parametric variations can reduce sizing, which has a significant impact on the overall power consumption, whereas optimizing the water consumption can significantly reduce the utilization of water in the building. Finally, the comparison is made in terms of power consumption for the current practices and the optimal analysis. It was found that with the proposed sizing and subsequent retrofitting, the lighting consumption was reduced to 34%, whereas the space cooling requirements were reduced to 19%.

In large buildings, effective load shedding and shifting and providing the maximum power through solar renewable sources remain challenges because of users’ unpredictable load consumption. Conventionally, load shifting, load shedding, and load covering are majorly dependent on user inputs. The lack of user interest in participating in demand responses for effective load shifting and covering remains a problem. Effective load covering through renewables and user-friendly load shedding and shifting with maximized user participation are challenging and demand high-resolution user load consumption information, which are not possible without sophisticated communication and digital twins. In this research work, a novel fuzzy-logic-based cascaded decentralized load-controlling mechanism has been developed that manages the residential building load through load-shifting, load-covering, and load-shedding schemes without any communication protocols and digitization between residential units. The decentralized controller aims to effectively utilize the centralized resources of power generation with the effective automated participation of users. The quantification of the load shifting, covering, and shedding performed during peak hours was well covered under the load-covering scheme, and the results showed that flexibility capacities of 1617 kW were achieved for load covering, 294 kW for load shedding, and 166.34 kW through shifting. A total load of 60 kW, which was reduced during shedding and shifting, was well covered during load covering through renewables.

Due to global environmental impacts, the electric vehicle (EV) adoption rate is increasing. However, unlike conventional petrol vehicles, EVs take a considerable time to charge. EVs on the road with different battery charging statuses and driving demographics may cause uncertain peak time arrivals at charging stations. Battery-swappable charging stations are a quick and easier way to replace uncharged batteries with charged ones. However, charging due to uncertain EV arrival causes higher charging profiles posing load to the grid, management of charged and discharged batteries, and peak time charging tariffs. These challenges hinder the wide operation of battery-swappable charging stations. Nevertheless, a pre-assessment of peak hours using EV demographics can reduce congestion. In recent literature surveys for battery-swappable charging stations, spot congestion has not been given much attention, which has a direct influence on the sizing and operation of battery-swappable charging stations. This research study is focused on estimating peak time events using a novel integrated techno-economic assessment framework. A fuzzy-based parametric assessment tool is developed that identifies the factors that influence higher congestion events. Based on the peak event assessment, grid, and solar PV-based generation is optimized using mixed integer linear programming. In the final step, an environment analysis of a swappable charging station is performed. Furthermore, the results achieved using the proposed framework for battery-swappable charging stations (BSCSs) were compared with fast-charging (FC) stations. FC can economically perform well if integrated with solar PV systems; however, the capital cost is 80% greater than the BSCSs designed under the proposed framework. The operational cost of BSCSs is 39% higher than FC stations as they use 29% higher grid units than FC stations due to night operations under congestion.