Optimization of a hybrid energy plant by integrating the cumulative energy demand

Bahlawan, Hilal; Morini, Mirko; Pinelli, Michele; Poganietz, Witold‐Roger; Spina, Pier Ruggero; Venturini, Mauro

doi:10.1016/j.apenergy.2019.113484

Cited by 35 publications

(10 citation statements)

References 49 publications

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“…The sizing optimization problem is solved by using a GA. More details about the GA used in this paper can be found in Bahlawan et al [14]. As shown from Fig.…”

Section: Methodsmentioning

confidence: 99%

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Optimal design and energy management of a renewable energy plant with seasonal energy storage

et al. 2021

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The exploitation of fossil fuels is undoubtedly responsible of environmental problems such as global warming and sea level rise. Unlike energy plants based on fossil fuels, energy plants based on renewable energy sources may be sustainable and reduce greenhouse gas emissions. However, they are unpredictable because of the intermittent nature of environmental conditions. For this reason, energy storage technologies are needed to meet peak energy demands thanks to the stored energy. Moreover, the renewable energy systems composing the plant must be optimally designed and operated. Therefore, this paper investigates the challenge of the optimal design and energy management of a grid connected renewable energy plant composed of a solar thermal collector, photovoltaic system, ground source heat pump, battery, one short-term thermal energy storage and one seasonal thermal energy storage. To this aim, this paper develops a methodology based on a genetic algorithm that optimally designs a 100% renewable energy plant with the aim of minimizing the electrical energy taken from the grid. The load profiles of thermal, cooling and electrical energy during a whole year are taken into account for the case study of the Campus of the University of Parma (Italy).

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“…The sizing optimization problem is solved by using a GA. More details about the GA used in this paper can be found in Bahlawan et al [14]. As shown from Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The optimal design and the energy management of the considered REP are identified by means of a Genetic Algorithm (GA) with the aim of minimizing the electrical energy taken from the grid. This optimization tool has been selected thanks to its ability to handle discrete space and nonlinear problems [14].…”

Section: Introductionmentioning

confidence: 99%

Optimal design and energy management of a renewable energy plant with seasonal energy storage

et al. 2021

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“…Overall operating cost Overall income FC rate capacity Start-stop cycles for FC Minimum UP/Down time Ramp rate [109] Optimization of three different types of hybrid CHP energy systems considering cumulative energy demand.…”

Section: Tpuc Taprmentioning

confidence: 99%

Classification and Analysis of Optimization Techniques for Integrated Energy Systems Utilizing Renewable Energy Sources: A Review for CHP and CCHP Systems

et al. 2021

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Energy generation and its utilization is bound to increase in the following years resulting in accelerating depletion of fossil fuels, and consequently, undeniable damages to our environment. Over the past decade, despite significant efforts in renewable energy realization and developments for electricity generation, carbon dioxide emissions have been increasing rapidly. This is due to the fact that there is a need to go beyond the power sector and target energy generation in an integrated manner. In this regard, energy systems integration is a concept that looks into how different energy systems, or forms, can connect together in order to provide value for consumers and producers. Cogeneration and trigeneration are the two most well established technologies that are capable of producing two or three different forms of energy simultaneously within a single system. Integrated energy systems make for a very strong proposition since it results in energy saving, fuel diversification, and supply of cleaner energy. Optimization of such systems can be carried out using several techniques with regards to different objective functions. In this study, a variety of optimization methods that provides the possibility of performance improvements, with or without presence of constraints, are demonstrated, pinpointing the characteristics of each method along with detailed statistical reports. In this context, optimization techniques are classified into two primary groups including unconstrained optimization and constrained optimization techniques. Further, the potential applications of evolutionary computing in optimization of Integrated Energy Systems (IESs), particularly Combined Heat and Power (CHP) and Combined Cooling, Heating, and Power (CCHP), utilizing renewable energy sources are grasped and reviewed thoroughly. It was illustrated that the employment of classical optimization methods is fading out, replacing with evolutionary computing techniques. Amongst modern heuristic algorithms, each method has contributed more to a certain application; while the Genetic Algorithm (GA) was favored for thermoeconomic optimization, Particle Swarm Optimization (PSO) was mostly applied for economic improvements. Given the mathematical nature and constraint satisfaction property of Mixed-Integer Linear Programming (MILP), this method is gaining prominence for scheduling applications in energy systems.

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“…This might not be the case for the LCI of electricity production, which might not scale linearly with the functional unit (Caduff et al, 2011;Bahlawan et al, 2019). Indeed, the technology mix that produces the electricity depends on the demand level (Lund et al, 2010;Brondi et al, 2019), it changes continuously, depending on the availability of wind and solar resources, and varies from region to region (Laurent and Espinosa, 2015;Cornago et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Quantification of Non-linearities in the Consequential Life Cycle Assessment of the Use Phase of Battery Electric Vehicles

et al. 2021

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The diffusion of Battery Electric Vehicles (BEVs) is projected to influence the electricity grid operation, potentially offering opportunities for load-shifting policies aimed at higher integration of renewable energy technologies in the electricity system. Moreover, the examined literature emphasizes electricity as a relevant driver of BEVs Life Cycle Assessment (LCA) results. To evaluate LCA impacts associated to future BEVs diffusion scenarios in Italy, we adopt the Consequential Life Cycle Assessment (CLCA) methodology. LCA conventionally assumes a proportional relation between environmental impact indicators and the functional unit. However, such relation may not be representative if the electricity system is significantly affected by the large-scale diffusion of BEVs. Our study couples the conventional CLCA methodology with the EnergyPLAN model through three different approaches, which progressively include BEV-specific dynamics, to capture correlations between additional BEVs fleets and the electricity grid operation, that affectthe mix of electricity consumed in the use phase by BEVs, in Italy in 2030. Here we show that if renewables capacity is not additionally installed in response to additional BEVs electricity demand, the marginal Climate change total indicator of BEVs may increase up to ~40%, with respect to a business-as-usual scenario. Moreover, we quantitatively support the literature indications on how to properly estimate BEVs LCA impacts. Indeed, we weight electricity LCA impacts on hourly BEV charge profiles, finding that this approach best captures BEVs interdependence with the electricity system. At low BEVs diffusion, this approach clearly shows the potential BEVs capability to increase exploitation of renewable energy, whereas at high BEVs diffusion, it fully highlights potential responses of fossil fuel power plants to additional electricity demand. Due to these dynamics, we find that linearly scaling the business-as-usual scenario results would lead to an underestimation of 12.45 Mton CO2-eq of the total impacts of an additional BEVs fleet, under a 100% BEV diffusion scenario. Our methodology could be replicated with different energy system models, or at various geographical scales. Our framework could be coupled with comprehensive assessments of transport systems, to further provide robustness to policymakers by including non-linearities in the mix of electricity consumed during the use phase of BEVs.

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Optimization of a hybrid energy plant by integrating the cumulative energy demand

Cited by 35 publications

References 49 publications

Optimal design and energy management of a renewable energy plant with seasonal energy storage

Optimal design and energy management of a renewable energy plant with seasonal energy storage

Classification and Analysis of Optimization Techniques for Integrated Energy Systems Utilizing Renewable Energy Sources: A Review for CHP and CCHP Systems

Quantification of Non-linearities in the Consequential Life Cycle Assessment of the Use Phase of Battery Electric Vehicles

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