Remote community integrated energy system optimization including building enclosure improvements and quantitative energy trilemma metrics

Quitoras, Marvin Rhey; Campana, Pietro Elia; Rowley, Paul; Crawford, Curran

doi:10.1016/j.apenergy.2020.115017

Cited by 35 publications

(14 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work extends the deterministic results from [17] by looking at the impact of temperature on BT storage performance considering the thermal properties (Table 6) of both LA and LI batteries.…”

Section: Impact Of Temperature On Battery Storage Performancementioning

confidence: 72%

“…Scenarios 3 and 4 use air-source heat pumps (ASHP) instead of the baseboard heater in the first two scenarios. Previous analysis by the authors [17] showed that the ASHP reduced spaced heating load by about 40% as compared to baseboard heaters. However, the simulation results indicated that load fluctuations caused by the variations of the heat pumps' coefficients of performance negatively impacted the operation of the energy system.…”

Section: Resultsmentioning

confidence: 94%

“…in [17] and [35]. The heat load simulation conducted for this study was elaborated by the authors in [17].…”

Section: Case Study Input Datamentioning

confidence: 99%

See 2 more Smart Citations

Towards robust investment decisions and policies in integrated energy systems planning: Evaluating trade-offs and risk hedging strategies for remote communities

Quitoras

Cabrera

Campana

et al. 2021

Energy Conversion and Management

Self Cite

View full text Add to dashboard Cite

Section: Impact Of Temperature On Battery Storage Performancementioning

confidence: 72%

Section: Resultsmentioning

confidence: 94%

See 1 more Smart Citation

Towards robust investment decisions and policies in integrated energy systems planning: Evaluating trade-offs and risk hedging strategies for remote communities

Quitoras

Cabrera

Campana

et al. 2021

Energy Conversion and Management

Self Cite

View full text Add to dashboard Cite

“…Recently, several studies on the technical-environmental-economic feasibility and the design of various combinations of HESs have been investigated as an alternative option for the conventional power system in off-grid areas. It includes wind turbine (WT)/PV/Battery (B)/DG [23][24][25][26][27][28][29][30][31], WT/PV/B [32][33][34][35], PV/B/DG [36][37][38], PV/Fuel cell (FC)/DG [39], PV/WT/FC [40][41][42], PV/FC [43], PV/WT/Biodiesel (BD)/B [44], PV/DG/ Pump Hydro Storage (PHS) [45], PV/Biomass (BM)/WT/PV/B/DG [46], and Floating PV (FPV)/FC [47]. Table 1 shows the comprehensive summary of these studies with the different objective functions and algorithms of each approach.…”

Section: Literature Review For Decentralized Hess In Remote Off-grid Areasmentioning

confidence: 99%

Balancing Energy Trilemma Using Hybrid Distributed Rooftop Solar PV (DRSP)/Battery/Diesel Microgrid: A Case Study in Gilutongan Island, Cordova, Cebu, Philippines

2021

View full text Add to dashboard Cite

Design strategies for achieving reliable, affordable, and clean electricity are crucial for energy sustainability. Attaining it requires managing the three core factors (TCF) of the energy trilemma (ET) to increase reliability (energy equity), minimize the levelized cost of electricity (LCOE) (energy equity), and avoid potential CO2 emission (environmental sustainability) simultaneously. This paper aims to present a design strategy for the hybrid energy system microgrid (HESM) model, consisting of a distributed rooftop solar PV (DRSP), battery, and diesel-generator to meet the increasing demand while balancing the TCF of the ET. The design strategy was applied in a cluster of 11 households in Gilutongan Island, Cebu, Philippines, where there is no open land space for a solar PV microgrid system. This study used PVSyst and HOMER Pro software to perform the techno-enviro-economic (TEE) analysis to select all feasible system configurations (FSCs). To identify the optimal FSC, a scoring mechanism that considers the LCOE based on the 5% household electricity expense limit, the 5% unmet load fraction, and the renewable penetration fraction was used. Results show that the optimal system requires an average of 32.2% excess energy from DRSP to balance the TCF of the ET based on the energy demand considered. Thus, planning when energy demand increases is vital to map the next appropriate steps toward sustainable energy transition. Overall, the obtained results can support project developers and policymakers to make informed decisions in balancing the ET from various trade-offs of energy systems.

show abstract

“…In addition, remote areas in Russia, Alaska, and Canada are heavily dependent on diesel generators for electricity supply, which imposes an economic, environmental, and social burden on the local populations as diesel generators create noise pollution, aggravate the local air quality and increase the emissions of greenhouse gases [27]. These examples confirm the importance of transitioning towards new energy systems to improve economic, social, and environmental conditions, in addition to ensuring a reliable power supply [27][28][29].…”

Section: Introductionmentioning

confidence: 97%

Predicting Energy Demand in Semi-Remote Arctic Locations

et al. 2021

View full text Add to dashboard Cite

Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at the end of the radial distribution network without alternative energy supply. Therefore, it is crucial to develop an accurate forecasting model to manage and optimize the limited energy resources available. We first compare the accuracy of several models that perform short-and medium-term load forecasts in rural areas, where a single industrial customer dominates the electricity consumption. We consider both statistical methods and machine learning models to predict energy demand. Then, we evaluate the transferability of each method to a geographical rural area different from the one considered for training. Our results indicate that statistical models achieve higher accuracy on longer forecast horizons relative to neural networks, while the machine-learning approaches perform better in predicting load at shorter time intervals. The machine learning models also exhibit good transferability, as they manage to predict well the load at new locations that were not accounted for during training. Our work will serve as a guide for selecting the appropriate prediction model and apply it to perform energy load forecasting in rural areas and in locations where historical consumption data may be limited or even not available.

show abstract

Remote community integrated energy system optimization including building enclosure improvements and quantitative energy trilemma metrics

Cited by 35 publications

References 22 publications

Towards robust investment decisions and policies in integrated energy systems planning: Evaluating trade-offs and risk hedging strategies for remote communities

Towards robust investment decisions and policies in integrated energy systems planning: Evaluating trade-offs and risk hedging strategies for remote communities

Balancing Energy Trilemma Using Hybrid Distributed Rooftop Solar PV (DRSP)/Battery/Diesel Microgrid: A Case Study in Gilutongan Island, Cordova, Cebu, Philippines

Predicting Energy Demand in Semi-Remote Arctic Locations

Contact Info

Product

Resources

About