Multi criterion decision making based on techno-economical optimization of stand-alone hybrid energy systems

Hybrid Energy System (HES)s areincreasingly becoming popular for standalone electrification due to global concerns on GHG emissions and higher depletion of fossil fuel resources. Simultaneously research work on optimal design of HESs has also made much progess based on progress with numerous optimization techniques while giving special focus to Pareto optimization considering conflicting objectives. This study comes up with a novel evolutionary algorithm to optimize HESs based on ε- dominance technique. Mathematical modeling of energy flows, cash flows, GHG emissions were carried out in order to support the optimization. Pareto analysis was conducted for two different cases where former analyzes a novel design of a HES and latter analyzes a conversion of existing Internal Combustion Generator (ICG) into a HES in the expansion process. The Levelized Energy Cost (LEC), annual fuel consumption and Initial Capital Cost (ICC) were considered to be objective functions in the first analysis. A sensitivity analysis was followed the mathematical optimization in order to evaluate the impact of power supply reliability on the Pareto front. Furthermore, sensitivity of fuel cost and renewable energy component cost on Pareto front was also investigated considering the present dynamic condition of energy market. LEC, power supply reliability and added renewable energy capacity were taken as objectives to be optimized in the second case. Sensitivity of ICG capacity on the Pareto front was also taken into discussion. Pareto analysis clearly elements such as LEC, power supply reliability and fuel consumption are conflicting to each other. Therefore it is essential to perform multi criterion analysis in order to assist decision making. In order to assist decision making, Fuzzy-TOPSIS (a multi criterion decision making technique) was combined with Pareto optimization. For that, multi objective optimization was carried out considering Levelized Energy Cost (LEC), unmet load fraction, Wasted Renewable Energy (WRE) and fuel consumption as elements in the objective functions to generate non-dominant set of alternative solutions. Pareto front obtained from the optimization was ranked using Fuzzy-TOPSIS technique and Level Diagrams were used to support this proces

The energy hub concept applied to a case study of mixed residential and administrative buildings in Switzerland

The concept of Energy Hub (EH) is getting popular as a method to integrate non-dispatchable energy sources at building and neighbourhood scale with the support of energy storage and grid. It is interesting to study the effectiveness of EH concept to integrate solar energy and wind energy at both building and neighbourhood scale considering the real-time price and curtailments in the grid. This paper presents a case study conducted to evaluate the effectiveness of EH in integrating solar energy in the SwissTech Convention Centre (STCC) and Quartier Nord on the EPFL campus in Lausanne considering both building and neighbourhood scale. The results depict that EH is more effective when both compared to standalone operation and grid integrated mode (considering grid curtailments and RTP) in the process of integration of renewable energy sources. Interacting with the grid seems to be more economical when compared to storage. Grid curtailments cause the storage to operate more frequently in both charging and discharging cycles

Evaluating the need for energy storage to enhance autonomy of neighborhoods

Energy storage is generally considered as a means to bridge a period between when/where energy is available and when/where it is in demand. Storage plays an important role by providing flexibility to energy systems, increasing the potential to accommodate variable renewables generation and improving management of electricity networks. However, currently it remains unclear when and under which conditions energy storage can be profitably operated at a district level. The present study aims to quantify the level of integration of solar energy and storage in the Junction district of Geneva. A simulation tool is developed to investigate the techno-economical and environmental assessment under different scenarios. For a given investment over 20 years, the model calculates the levelized cost of electricity (LCOE), the autonomy level as well as the CO2 emissions. Given the assumptions of the model, four scenarios are analysed based on the combination of solar PV, storage, solar thermal and heat pump to find out an economically optimal configuration in terms of system size. A comparison with the Homer software is performed to test the robustness of the solar PV and battery model. The economic profitability of solar PV and battery system is in very good agreement with Homer and the autonomy level is validated by using a simulation tool created by SI-REN (Services Industriels des Energies Renouvelables de Lausanne). However, combining solar PV with battery system doesn’t bring additional autonomy to the model for Geneva study case. Under the assumptions of the model, to foster investments in solar PV and battery installations, falling investments costs seem necessary for the future. A reduction gap between buying and selling price in grid for solar panel is recommended to increase solar installations. A validated simulation tool has been developed in this work and provide a reliable based that will be extended in the future to include the thermal demand and production. The availability of thermal storage at a large scale as well as the production over a district should further increase the autonomy of the district

Achieving energy sustainability in future neighborhoods through building refurbishment and energy hub concept: a case study in Hemberg-Switzerland

This study aims to investigate the role of distributed generation through the energy hub concept and refurbishment of existing buildings. More specifically, a computational platform combining (i) the urban energy modelling tool CitySimPro and (ii) the microgrid simulation tool Homer is developed. The energy flow on hourly basis is assessed for buildings in Hemberg, a small village in Switzerland, considering occupancy, lighting and appliances profiles, as defined by the Swiss normative. System design of the energy hub is optimized considering three energy system configurations: (i) present scenario, (ii) energy hub catering electrical demand, (iii) energy hub catering both electrical and thermal demand including heat pumps. Results for the integration of an energy hub on site show that the current use of renewables can be increased from 0.15% to over 60% by integrating heat pumps in the city energy network

Towards climate resilient urban energy systems:a review

Climate change and increased urban population are two major concerns for society. Moving towards more sustainable energy solutions in the urban context by integrating renewable energy technologies supports decarbonizing the energy sector and climate change mitigation. A successful transition also needs adequate consideration of climate change including extreme events to ensure the reliable performance of energy systems in the long run. This review provides an overview of and insight into the progress achieved in the energy sector to adapt to climate change, focusing on the climate resilience of urban energy systems. The state-of-the-art methodology to assess impacts of climate change including extreme events and uncertainties on the design and performance of energy systems is described and discussed. Climate resilience is an emerging concept that is increasingly used to represent the durability and stable performance of energy systems against extreme climate events. However, it has not yet been adequately explored and widely used, as its definition has not been clearly articulated and assessment is mostly based on qualitative aspects. This study reveals that a major limitation in the state-of-the-art is the inadequacy of climate change adaptation approaches in designing and preparing urban energy systems to satisfactorily address plausible extreme climate events. Furthermore, the complexity of the climate and energy models and the mismatch between their temporal and spatial resolutions are the major limitations in linking these models. Therefore, few studies have focused on the design and operation of urban energy infrastructure in terms of climate resilience. Considering the occurrence of extreme climate events and increasing demand for implementing climate adaptation strategies, the study highlights the importance of improving energy system models to consider future climate variations including extreme events to identify climate resilient energy transition pathways

Techno-economical optimization of a solid waste management system using evolutionary algorithms

Renewable energy technologies are becomming popular due to higher depletion rate of fossil fuel resources. In such circumstances conversion of municipal solid waste into energy is helpful in many ways. However, it is difficult to come up with an optimum conversion technique which depends on number of techno-economical factors. There are number of difficulties in using classical optimization to optimize solid waste management systems. This research paper introduces a novel optimization algorithm based on evolutionary algorithms to conduct the optimization. The novel optimization algorithm is having the capability to conduct Pareto multi objective optimization considering constraints in both objective and decision spaces. Life cycle cost, net energy produced and landfilling capacity were taken as objective functions in the multi objective optimization. Finally, a brief discussion is presented based on the results obtained

Climate resilient interconnected infrastructure: Co-optimization of energy systems and urban morphology

Co-optimization of urban morphology and distributed energy systems is key to curb energy consumption and optimally exploit renewable energy in cities. Currently available optimization techniques focus on either buildings or energy systems, mostly neglecting the impact of their interactions, which limits the renewable energy integration and robustness of the energy infrastructure; particularly in extreme weather conditions. To move beyond the current state-of-the-art, this study proposes a novel methodology to optimize urban energy systems as interconnected urban infrastructures affected by urban morphology. A set of urban morphologies representing twenty distinct neighborhoods is generated based on fifteen influencing parameters. The energy performance of each urban morphology is assessed and optimized for typical and extreme warm and cold weather datasets in three time periods from 2010 to 2039, 2040 to 2069, and 2070 to 2099 for Athens, Greece. Pareto optimization is conducted to generate an optimal energy system and urban morphology. The results show that a thus optimized urban morphology can reduce the levelized cost for energy infrastructure by up to 30%. The study reveals further that the current building form and urban density of the modelled neighborhoods will lead to an increase in the energy demand by 10% and 27% respectively. Furthermore, extreme climate conditions will increase energy demand by 20%, which will lead to an increment in the levelized cost of energy infrastructure by 40%. Finally, it is shown that co-optimization of both urban morphology and energy system will guarantee climate resilience of urban energy systems with a minimum investment

Optimum dispatch of a multi-storage and multi-energy hub with demand response and restricted grid interactions

Integrated energy systems such as multi energy hubs which combines different energy conversion technologies and storage is recently getting popular. These systems possess the potential to integrate distributed renewable energy sources with a minimum impact to the grid. Hence, a number of recent studies have focused on optimizing the operation of energy hubs with simple energy systems. This study focuses on optimizing the dispatch of a multi-energy hub model which includes solar PV (SPV) panels, wind turbines, boiler, Internal Combustion Generator (ICG), Cogeneration plant (CHP), energy storage (heat and electricity). The energy hub is considered to maintain limited interactions with the grid. Interactions with e-mobility is considered as a flexible demand. A detailed energy hub model is developed considering energy interactions among all the aforementioned components. Dispatch strategy is optimized using evolutionary algorithm. Results obtained from the evolutionary algorithm is compared with Particle Swarm Optimization (PSO) algorithm. A detailed analysis is conducted in order to assess the energy interactions within the system. Sensitivity of system components such as storage size, renewable energy capacity etc., grid interactions, and time horizon considered for optimization is subsequently assessed and reported concisely. Results obtained clearly shows choice of dispatch strategy is considerably volatile which makes the operation of the energy hub challenging. This can be mitigated up to a certain level by increasing the capacity of battery bank