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This study aims at analyzing dynamic behavior for the full operation cycle considering various alternatives and constraints related to small-scale and seaborne environment for a BOG re-liquefaction process in LNG-fueled ships, leading to significant enhancement of its operability. The design framework includes steady-state modeling, dynamic modeling and control system, with which system-wide dynamic characteristics are readily investigated and operating guidelines for achieving optimal process operations are obtained. Equipment sizing with the appropriate choice of design parameters is made such that the dynamic model developed in this study simulates non-steady-state characteristics in a realistic manner. 8 different process control schemes are proposed, and their technical gains as well as dynamic characteristics are investigated in details. The control algorithms are assessed through controllability analysis, including open loop test, open loop disturbance test and dynamic response against disturbances. For improving process efficiency, operation procedure for shut-down and start-up is designed with constraints imposed such as rotating speed variation and inert material filling for the effective and practically-adequate control for a refrigeration cycle, with which feasible heat transfer can be readily maintained. The case study presented fully demonstrates the validity and applicability of the control system proposed in this study.

The latest advances in the field of supercapacitors (SCs) have made them a feasible alternative to be used in hybrid, energy storage systems. Sizing SCs in such systems is not obvious, especially in the case of banks comprising several series-parallel configurations. In this paper, a new algorithm for sizing a SC bank in high fluctuating power demand applications is proposed. The algorithm utilizes the expected discharge power profile, usually obtained from measurement campaigns, and a set of resistance ( R ) and capacitance ( C ) values of the equivalent circuit of the SC, which are provided by the manufacturer. Instead of yielding specific values for R and C , the method determines the RC limit curve which identifies the area of the RC-plane where the range of acceptable solutions can be found for a particular application. Thus, the internal resistance can be easily included in the design process. The validity of different cell combinations, including those not regarding each series-connected element as a sole cell, can be directly checked with this method, enabling the selection of the most suitable solution from an economic point of view.

When optimizing the design of multi-energy systems, the operation strategy and the part-load behavior of the units must be considered in the optimization model, which therefore must be formulated as a two-stage problem. In order to guarantee computational tractability, the operation problem is solved for a limited set of typical and extreme periods. The selection of these periods is an important aspect of the design methodology, as the selection and sizing of the units is carried out on the basis of their optimal operation in the selected periods. This work proposes a novel Mixed Integer Linear Program clustering model, named k-MILP, devised to find at the same time the most representative days of the year and the extreme days. k-MILP allows controlling the features of the selected typical and extreme days and setting a maximum deviation tolerance on the integral of the load duration curves. The novel approach is tested on the design of two different multi-energy systems (a multiple-site university Campus and a single building) and compared with the two well-known clustering techniques k-means and k-medoids. Results show that k-MILP leads to a better representation of both typical and extreme operating conditions guiding towards more efficient and reliable designs.

The exploitation of renewable energy sources and the use of primary energy saving techniques have been recognized as key solutions to face climate changes. The consequent energy policies are pushing the transition from a centralized power generation system to a distributed polygeneration system able to meet simultaneous heating, cooling and electricity demand. However, small scale polygeneration plants do not ensure any primary energy and cost saving without a proper sizing and operation of the plant. Furthermore, a flexible configuration of the waste heat recovery system (WHRS) adopted for polygeneration purposes can be equally important. Therefore, starting from the experimental data concerning a 15 kW micro-CHP plant previously designed and prototyped, the paper addresses the performance assessment of a CHCP plant configuration based on the same basic engine-electric generator system through the 1D thermo-fluid dynamic characterization of an alternative double water circuit WHRS. This configuration, delivering thermal power at different temperature level, could be useful to meet thermal and cooling demand from different user or when seasonal energy demand occurs. This paper also provides an effective approach for the design of WHRS which are capable to ensure a reasonable matching between the temperature level required by the user and that provided by the plant. In this way, being the energy saving dependent on the thermal power recovered and actually exploited, and so on the temperature level which characterizes the user's heat demand, primary energy savings are more easily achievable even when small scale polygeneration applications are considered. Results shows the possibility of supplying an absorption chiller and obtaining a coolling capacity of about 10.5 kW from the resulting CHCP plant configuration.

Standalone photovoltaic system is promising sustainable energy source. Accurate modeling and sizing of these systems strongly affect the system's feasibility. Thus, in this paper, optimal sizing of standalone photovoltaic system is conducted based on multi-objective differential evolution algorithm integrated with hybrid multi-criteria decision making methods. Multi-objective differential evolution algorithm is used to optimize set of configurations of a system by minimizing technical and cost objective functions simultaneously. After that, an analytical hierarchy process integrated with a technique for order preference by similarity to ideal solution are used to order preference of configurations based on the loss of load probability and life cycle cost of system. The results of the proposed sizing method are validated by a numerical method to explain the superiority of the proposed method. According to results, the proposed sizing method is faster than numerical method by around 27 times. This is because the multi-objective differential evolution algorithm requires roughly 0.23 of simulations that is required by numerical method. Furthermore, the performance of multi-objective differential evolution algorithm is evaluated by various metrics. As a result, for the adapted load demand, the optimal configuration is 63 PV modules and 66 battery unit with maximum capacity of 500 Ah.

Distributed generation expansion planning (DGEP) is one of the most important types of planning in power distribution networks. Most of the previous studies associated with the DGEP problem have been performed from the viewpoint of network owner, while the investment, sizing and siting of Distributed Generations (DGs) are mostly taken by the private sector. Therefore, DGEP problem in the viewpoint of private investors should be taken into special consideration. In this paper, attempts are made to propose a model for DGEP problem based on the private investor's viewpoint to meet their objectives and also, take the technical constraints of the distribution network into account, at the same time. In this light, the uncertainties of the renewable DGs have also been considered in the problem and the Hong's Two Point Estimate Method (HTPEM) is used to provide a solution for probabilistic load flow problem. The method presented in this paper has been applied to the modified 18-bus distribution network which has been derived from the 30-bus IEEE system.

In this paper, a novel configuration of microgrid with incorporation of turbo-expander (TE) has been presented. The cooling and electrical energy recovered by TE are used to supply the microgrid demand. The purification and preheating processes are imbedded on the inlet gas, which, according to the need, provide and control the recovery of electrical energy and cooling. Due to the unique features and limitations of TE, determination of type and optimal size of energy resources in this microgrid is special; so an innovative optimal capacity sizing (OCS) method was presented. For determination of optimal size of TE, the constraints and needs of the local natural gas (NG) network should also be considered, which, differentiates it from other energy sources. Another key feature of the proposed OCS method is optimization of inlet NG temperature passing through TE. Performance and economic evaluation of the TE-based microgrid was compared with the traditional microgrid without TE. Analysis showed that O&M and emission costs of the TE-based microgrid were reduced significantly (about 27% and 30%, respectively) culminating in low prices for the generated energies. In addition, net present value and internal rate of return of the TE-based microgrid were higher (38.5% and 6%, respectively).

International audience; The efficiency of a District Heating (DH) network depends among other things on the quality of its design and more precisely on the sizing of the distribution network pipes. Providing DH designers with relevant methodology to select appropriate pipes is crucial to maximize the utility of those systems. This paper presents a methodology focusing on the optimal sizing of pipe diameters in DH networks: the methodology uses a genetic algorithm to generate a set of Pareto-optimal sizing choices. An implementation of the methodology is presented, and is validated on three test cases, each of them corresponding to an elementary configuration, which can be encountered in real-world DH network. As a result, the performance of the results provided by the optimization for each test case are as good or better than the best reference solutions we could provide. The last part of this paper addresses the scalability of the proposed sizing methodology. The interest of the presented methodology is lies in the typology of optimal solutions provided for the test cases that could not be devised with traditional sizing methods and by the scalability of the method that makes it suitable for optimal sizing at city scale.

The paper proposes a novel planning approach for optimal sizing of standalone photovoltaic-wind-diesel-battery power supply for mobile telephony base stations. The approach is based on integration of a comprehensive probabilistic sequential Monte Carlo simulator and a black-box optimizer using DIRECT (DIviding RECTangles) method. The main property of the simulator is that input variables are modeled as correlated random processes rather than independent random variables without a time index. By taking into account autocorrelation and mutual correlation, temporal properties of all input variables are kept realistic, such as sunrise and sunset times in irradiance model, daily and seasonal changes in temperature model, and consumption of cooling devices and electronic equipment which depends on the ambient temperature and setting of the parameters of the control devices. The optimization target is to select rated capacities of major system components and to tune the main control parameters for achieving minimum total annual costs without compromising system reliability. The proposed algorithm is tested on planning a typical 2 kW potential base station located on a windy and sunny hill in the Mediterranean region.

During the on-site operation, the PV water pumping system can be faced different unexpected troubles, although the accurate sizing and the fit erection. Recently, different studies showed that the lack of enough information about the geospatial characteristics of the area, required during the sizing of a Photovoltaic Water Pumping System (PVWPS) is the main cause. In this attempt, an investigation study on geospatial characteristics has been conducted, in the Mansoura desert region; about 70 km south of Ghardaia headquarter. The main goal was to spotlight on different suitable locations for implementing PVWPS. The study has been carried out on six zones; namely: Ain Losseik, Old Mansoura, New Mansoura, Oued Ghazalat, Khanget-fedj and Zawiat Lacheikh. The evaluation consists on census and classifying the wells or boreholes according to the geographical location, the water source behavior, the soil specifics, agriculture and ranching activity, type of the crops, etc … It has been averred that the renewable aquifer levels vary between the averages of 20 and 45 m. However, the Albian borehole static levels can be reached between the averages of 3 and 8 m. The aquifer hydraulic behavior has been achieved by calculation of hydrogeology properties. Thus, the obtained data were compared and classified, whereas the suitable DC pumps were selected, accordingly. This method can be used to detect the different local geospatial effects influencing the system operation. Furthermore, it can be considered as a key point subject that could be extended, in future works.

This paper proposes a methodology that is based on mixed-integer linear programming (MILP) to calculate the optimal sizing of a hybrid wind-photovoltaic power plant in an industrial area. The proposed methodology considers the: i) load requirements; ii) physical and geometric constraints for the renewable plants installation; iii) operating and maintenance costs of both wind and PV power plants; and the iv) electric energy absorbed by the public network. The power demand variation associated with the production cycles is considered by using a stochastic simulation tool. To consider both the load and seasonality variability, and to adapt the methodology to the actual operational use of the power plant, the optimization is performed separately for each month of the year. Then, an integrated economic analysis is discussed. The methodology is adopted to analyze an industrial plant in the Rome area used as a train depot and for maintenance purposes. The results, which combine the needs of the plant activity with the availability of renewable energy, enabled the determination of optimal solutions and the relevant savings achievable.

This work presents a two-stage optimization procedure for the conceptual design and operation of A-frame dry cooling systems for concentrated solar power facilities. First, the optimal geometry of the A-frame including sizing, number of fans and blade geometry, and unit parameters such as pipe length, configuration and number is determined. Finally, the operation of the system over a year for minimum energy consumption is computed. The geometry problem is formulated as a mixed-integer non linear programming (MINLP) problem. A tailor-made branch and bound algorithm is used to solve the complex non-linear programming sub-problems. The second problem consists of a multi-period MINLP. A fixed geometry is used to evaluate the usage of fans over time. The solution suggests an apex angle of 63°, one row of 75 pipes of 13.5 m long with a diameter of 3.3 mm, and 4 fans are used but they only operate at full capacity during summer. This design allows reducing the energy required by 20% by using the appropriate pipe configuration and number. The unit consumes around 4% of the energy produced by the CSP plant that serves. It is a promising result that can be affected by plant layout and ground availability.

Nearly zero energy buildings (nZEBs) are considered as a promising solution to mitigate the energy and environmental problems. A proper sizing of the nZEB systems (e.g. HVAC systems, PV panels, wind turbines and batteries) is essential for achieving the desirable level of thermal comfort, energy balance and grid dependence. Parameter uncertainty, component degradation and maintenance are three crucial factors affecting the nZEB system performances and should be systematically considered in system sizing. Until now, there are some uncertainty-based design methods been developed, but most of the existing studies neglect component degradation and maintenance. Due to the complex impacts of degradation and maintenance, proper sizing of nZEB systems considering multiple criteria (i.e. thermal comfort, energy balance and grid dependence) is still a great challenge. This paper, therefore, proposes a robust design method of nZEB systems using genetic algorithm (GA) which takes into account the parameter uncertainty, component degradation and maintenance. The nZEB life-cycle cost is used as the fitness function, and the user’ performance requirements on thermal comfort, energy balance and grid dependence are defined as three constraints. This study can help improve the designers’ understanding of the impacts of uncertainty, degradation, and maintenance on the nZEB life-cycle performances. The proposed method is effective in minimizing the nZEB life-cycle cost through designing the robust optimal nZEB systems sizes and planning the optimal maintenance scheme, meanwhile satisfying the user specified constraints on thermal comfort, energy balance, and grid dependence during the whole service life.

Demand-side management has the potential to reduce the cost of solar based community micro-grids and solar home systems for electricity access. This paper presents a methodology for optimal least-cost sizing of generation assets while meeting explicit reliability constraints in micro-grids that are capable of active demand management. The battery management model considers kinetic constraints on battery operation and represents dispatch in the field to regulate the depth of discharge. The model allows consideration of the trade-off between depth of discharge, cycle life, and calendar lifetime in lead-acid batteries. Separate reliability targets for disaggregated, residential load profiles at hourly timesteps are considered to evaluate the performance and cost reduction potential of demand-side management capabilities — with economic results and sensitivity analyses around key input assumptions subsequently presented. We find that demand-side management can reduce the number and cost of requisite solar panels and batteries with the integration of real-time management and controls – a key result for justifying next generation micro-grids for electricity access. Keywords: Micro-grids, Electricity access, Demand-side management, Reliability, Asset selection, Off-grid, Techno-economic model, Cost, Massachusetts Institute of Technology. Tata Center for Technology and Design

This paper proposes an approach to optimize sizing and placement of photovoltaic solar panels (PV) and batteries in systems which employ diesel generators (DGs) as their only source of electrical energy, forming a PV hybrid system. Three objectives have been defined: reduction of the levelized cost of electricity (economic objective); levelized CO2 equivalent life cycle emissions (environmental objective); and accumulated grid voltage deviation (grid quality objective). As this is a multi-objective optimization problem, the current state-of-the-art meta-heuristic algorithm is applied, namely non-dominated sorting genetic algorithm III. Furthermore, a robust design has been developed by considering the worst case scenarios regarding weather conditions. Subsequently, its outcome is compared with non-robust designs. Optimal pareto fronts composed of many combinations are generated with a lot of freedom to choose for a desired design. The results show the advantages of PV hybrid system in remote locations ranging from being cost effective, reducing the accumulated emissions and even to improving the grid quality.

This paper proposes a Hybrid Nelder-Mead and Cuckoo Search (HNMCS) algorithm to minimize the power loss in hybrid AC/DC microgrid systems by optimizing the output power of Renewable Energy Distributed Generators (REDG). The non-linear power loss minimization problem is solved by the proposed HNMCS to optimize the size of REDG. So far, the REDG sizing is determined by considering generator output as variable whereas in the proposed technique, the area required for the operation of REDG in hybrid AC/DC microgrid is taken as variable. The microgrids are developed by categorizing the existing distribution system to multiple zones. A hybrid AC/DC microgrid is developed with AC grids supported by substation and DC grids operated by their individual REDG units. The suitable location for REDG units including the combination of solar-photovoltaic modules and fuel cells in DC grid is identified by Loss Reduction Sensitivity Factor (LRSF). A standard 33-bus and 69-bus radial distribution system is modeled as a hybrid AC/DC microgrid system. The system is analyzed for its performance in stand-alone system and extended to zone cataloging as residential, industrial and commercial zones. The proposed HNMCS algorithm identifies the optimal solution for REDG sizing with improved convergence rate and reduced simulation time.

This contribution presents the optimization of parallel distribution between electricity and heat production for a geothermal plant. The geothermal fluid is split into two streams, one used for an Organic Rankine Cycle (ORC) system, and the other for a District Heating Network (DHN). The superstructure to be used for the optimization problem includes the ORC components, one of which is an optional internal heat exchanger which allows exchange between the outlet streams of the turbine and the pump. Each of the components' characteristic dimensions (used in the installation cost) is an optimization variable. The operating cost of the ORC is proportional to the installation cost. The superstructure also includes the DHN topology constituted by a definite consumer and optional consumers. A Mixed Integer Non-Linear Programming (MINLP) optimization problem is formulated and solved using the GAMS software. The strategy used to overcome the critical point of the initialization of the MINLP problem is presented. It consists in dividing the general problem into sub-problems which are solved successively. Three different academic study cases are compared to a reference case. The results validate the stability and the robustness of this optimization tool. A sensitivity analysis is performed in geothermal source conditions. All these results highlight the relevance of the simultaneous approach.

The difference in electricity pricing based on the time of power use has led to load shifting from peak to off-peak hours in hybrid power systems (HPS). Apart from optimising electricity cost, shifting of the load may also change the capacity of storage in the system. Power Pinch Analysis has been recently applied to guide load shifting aiming to minimise the cost of electricity, considering the peak and off-peak electricity pricing. The HPS was assumed to be ideal with 100% efficiency, which is not achievable in actual operational condition. This work extends the study by scrutinising the effects of peak-off-peak load shifting on the capacity of storage in HPS. The effects of energy losses due to the inefficiency during power conversion, transfer and storage in the HPS are considered in developing shifting heuristics to ensure optimal storage size is achieved. Implementation of the proposed load shifting strategy on a case study demonstrates that a reduction of up to 30% in the storage size can be achieved, which led to a minimum storage cost. The distribution of peak hours' demand to off-peak hours also successfully provide significant savings in the electricity bill.

In this paper, a hybrid genetic algorithm with particle swarm optimization (GA-PSO) is applied for the optimal sizing of an off-grid house with photovoltaic panels, wind turbines, and battery. The GA-PSO is one of the most powerful single-objective optimization algorithms. In the other hand, the multi-objective PSO (MOPSO) can solve the optimization problems considering all objectives without transforming them. Minimizing the total present cost including initial cost, operation and maintenance cost, and replacement cost with satisfying the load demand is the main goal of this study. In this optimization problem, the considered reliability factor is a loss of power supply probability, which specifies the subtraction of the load power and generated power. The wind velocity, solar irradiance, and load demand are simulated in 12 months of a year by the HOMER software for a suburbs of Tehran. Then, the optimal size of PV and WT are obtained with both GA-PSO and MOPSO methods, and compared with the HOMER results. At last, the strengths and weaknesses of each method are explained. The results show that the proposed approach with 0.502 of the levelized cost of energy for the PV/WT/BAT system has the best result through the compared methods.

The real penetration of renewable energy sources under random variability and unpredictability of weather conditions requires an optimal sizing of hybrid renewable power generation systems. This paper presents a techno-economic optimization model, to perform the optimal sizing of a stand-alone hybrid photovoltaic/wind/hydrogen/battery system. Optimum sizing has been applied to a system consisting of an induction motor coupled to a centrifugal pump, located at Sahline-Tunisia. The optimal-cost design and the new suitable power management approach are the two main objectives. The optimal sizing is assessed on the basis of technical (the Deficiency of Power Supply Probability, the Relative Excess Power Generated) and economic (the Energy Cost as well as the Total Net Present Cost) criteria. The power management strategy optimizes how the spare energy is used. The results highlight the important role of the hybridization of renewable energy sources, photovoltaic and wind turbine, in reducing the cost of the system. It is also noted that the hydrogen chain prolongs the life of the battery, preventing the massive use of the latter. The optimized sizing algorithm gives all the possible configurations of the hybrid system not only for a pumping system but also for any autonomous load located all over the world.

This work comprises the systematic study of a monolithic reactor design for steam methane reforming (SMR) to achieve optimal dimensions for the highest rate of reaction. The design is developed using an analytical model to establish the optimum length of the reactor. The result is confirmed by a computational fluid dynamics (CFD) model. Experimental work on SMR is carried out in lab scale. In the analytical models, the design equations for the square channel reactor are derived to estimate the channel length that affords the highest rate of reaction. The optimum length is determined as 41.6 mm with a reaction rate of 2.88 × 10−8 mol/s at the channel height of 1.5 mm, 873 K, and 1 atm. The respective optimum channel length from the CFD and experimental results are 80.0 mm and 90.0 mm, respectively, with respective reaction rates of 7.42 × 10−7 mol/s and 6.85 × 10−7 mol/s. The effects of channel heights ranging from 0.5 to 3.0 mm are investigated by CFD. Methane conversion per unit channel perimeter is defined as a design parameter for the sizing and rating of this platform. The highest value (50% mm−1) is afforded at a channel height of 0.50 mm.

In this work, a novel convex sequence framework for real-time receding horizon operation optimization of a hybrid renewable energy system integrated with optimal sizing is presented to increase the penetration rate of renewable energy in supplying the demand. The proposed framework optimizes the entire lifetime cost of a system consisting of two main steps which are 1) design & installation and 2) operation as two sequence modules. This framework is applied to a hybrid renewable energy system which includes PV, wind turbine, batteries and a diesel generator. In the operation optimization, receding horizon strategy is used to optimize the operation schedule. Mixed integer convex programming method is applied in order to achieve the optimal operation. The hybrid renewable energy system is installed to actualize the design optimization outputs and to measure the required data for real-time operation optimization. The results show the proposed framework can be applied to facilitate the reliable real-time operation using real optimal input data for taking better advantage of the renewable energy resources. The effect of length of the horizon on optimal scheduling is also investigated. The results indicate that increasing of prediction horizon length enhances the economic performance and increases the share of renewable energy in the hybrid renewable energy system (from 68.5% to 81.4%).

The paper aims at presenting a methodology to choose the proper sizing of an integrated Photovoltaic unit and a storage architecture starting from the knowledge of the load and solar irradiance time profiles. To do this, first a simple logic to manage the charging/discharging of the storage is proposed, then the function representing the overall cost during the whole life of the system is derived. The computation of the cost function for different photovoltaic and storage sizes allows finding the best sizing of the system. Finally, a thorough economic analysis is conducted to provide criteria to check whether the investment is economically advantageous. This is done considering the impact of the economic variables which are more influential on the investment key assessment parameters. The proposed methodology has been validated in different realistic test cases against the results provided by an energy management system, showing an excellent agreement between the approaches. Moreover, the economic analysis has been applied to four realistic situations in order to tailor the microgrid components ratings on the specific test case.

This study develops a mixed-integer linear program for modeling the optimal equipment capacity and dispatch of a central utility plant (CUP) in a residential neighborhood and its ability to improve rooftop solar integration. The CUP equipment includes a microturbine, battery, chiller plant, and cooling storage. The CUP model is exposed to a variety of electricity rate structures to see how they influence its operation. The model finds the optimal capacity for each piece of CUP equipment, optimizing their hourly dispatch to meet neighborhood cooling and electric demand while maximizing profit. In an Austin, TX, USA base case, the neighborhood benefits economically by including the CUP, although the CUP demonstrates limited potential to integrate high penetrations of rooftop solar resources. While peak demand and reverse power flows are reduced under all tested rate structures, the CUP worsens net demand ramp rates. A time-of-use rate with no demand charge and moderate differences between off-peak and on-peak prices balances the output parameters, reducing reverse power flows by 43%, peak demand by 51%, and annual cost by 9.1% versus the “No CUP” base case while limiting net demand ramp rate increase to 84% more than the base case.

Renewable energy installation capacity has rapidly increased in recent years. Subsequently, developing and commercializing large energy storage systems (ESSs) has become an important research objective. To evaluate development and compare between different ESSs, levelized cost of energy storage (LCOES) has been used. However, current LCOES often includes cost of electricity production in total investment, and neglects off-design characteristic of ESSs. Therefore, we proposed a new definition for the LCOES (LCOES2) to resolve these problems. An ESS using RSOFC coupled with waste steam was investigated for ESS efficiency characterization. The proposed ESSs and LCOES calculations were applied to South Korea case. The hourly average solar power data for each month was used. Hydrogen production and consumption were matched for sizing ESS and estimating electricity demand profile. The results show that, conventional LCOES, which considers charging electricity cost, overestimated the cost by 7.7% and 14.8% compared to the LCOES2, at RSOFC stack cost of $700/kW and $225/kW, respectively. The constant part-load efficiency model resulted in lower LCOES values, by up to 6.3%, due to higher overall round-trip efficiency. The new LCOES definition, considering variable part-load efficiency, was proved to be an efficiency-sensitive and reliable cost indicator.

The industry is a crucial actor towards the energy transition with the possibility to adopt new energy strategies including a prosumer model. However, industries are struggling to adopt smart energy approaches, and initiatives supporting them should be improved. To enhance industrial participation in energy transition, it is required to assess the optimal energy infrastructure considering its economic advantages and associated risks. Up to date, the literature dealing with energy sizing optimisation does not consider the time evolution of parameters or the uncertainty linked to the energy framework. The objective of this paper is to fill this literature gap by proposing a novel complete methodology to optimise the design and operation of the energy infrastructure for its lifetime while assessing its uncertainty and risk through an uncertainty analysis, as well as to identify the inputs causing it by a two-stage sensitivity analysis. This framework is applied to a case study based on a real industrial manufacturing SME. The results indicate that the proposed methodology produces robust results in front of the present uncertainties, being energy price the one that causes most of it and thus the one more attention should be paid to when evaluating energy investment decisions. Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant

Establishing reliable, clean, and inexpensive solar PV systems is a complex interplay between the level of reliability (LPSP), financial constraints, and CO2 emissions. This paper investigates the impact of these factors on stand-alone (SA) and grid-supplemented (GS) solar PV systems over multiple seasons. The research uses established hardware models, detailed power management strategies as well as realistic Australian grid tariffs and Genetic Algorithms to find the minimum Cost of Energy (COE) subject to LPSP and financial constraints. The developed power management strategies are also tested experimentally on a real solar PV system. The results indicate that the grid-supplemented system yields 30% lower COE compared to the stand-alone at baseline (LPSP

More utilities, energy providers, and governments are considering the transition to 100% renewable or carbon-free generation to satisfy electricity demand. This transition requires consideration of numerous factors including cost, resource adequacy, and geographical location, among others. Therefore, models that can explore the optimality of tradeoffs between multiple factors are crucial for planning this transition. An optimization problem formulation is proposed to analyze the amount of renewable generation and energy storage required to balance 100% of a utility's electricity demand on an hourly timescale over multiple years, while minimizing a desired cost. This formulation accounts for geographical location and accommodates regional energy trading, and it enables analysis of important metrics for planning, such as firm capacity, capacity factors, land area requirements, and amount of curtailed generation. This optimization-based approach is used to explore case studies in New Mexico, which is an area with significant potential for solar and wind generation in the United States. Considering multiple years of historical meteorological data and electricity demand data, results show that the amount of renewable generation required is an order of magnitude larger than the average demand, and that most of the generation is curtailed, which motivates a regional energy trading approach.

Demand-side management and load-shifting strategies can reduce peak loads as well as temporal production/consumption mismatch, two classic issues in district energy networks that integrate solar sources. Nevertheless, the classic current sizing methods for such networks only consider the total demand, and not the possible loads after use of such techniques. The present paper aim is so to ascertain the connection between the possible demand reductions and the capacity design of generation sources. The study proposes an iterative sizing method with demand-side management as the central pillar. It retro-fits production units by assessing the network's overall performance through several criteria, both energetic and economical and with operational considerations. Exergy, which accounts for the quality of energy and is especially useful for multi-energy networks, is also considered. The method is illustrated on a mixed grid coupling a standalone heating network with a local electrical grid. Thousands of residential dwellings, with haphazard demands covered by solar-assisted technologies and a heat-pump are used in a series of ten scenarios with various management strategies, pricing policies and types of end-user contracts. In summary, the iterative method reduced the number of installed solar thermal collectors and photovoltaic panels by 13–38 % and 8–30 %, respectively. Furthermore, the method is stable: results converged after 2 iterations, in all scenarios. We also discuss the influence of low or high demand-side management penetration rate, and the final sizing selection by the decision-maker.

Although the layout of combined cooling, heat and power (CCHP) systems is well known from a technical perspective, the optimal operating strategy and sizing of such systems are relatively complex, due to the high number of variables and constraints involved. To address this research gap, the present paper describes the formalization, implementation, and validation of an innovative multi-objective optimization model for CCHP poly-generative energy systems. As a novelty, the model uses two levels of optimization: a first level to optimize the best operating strategy of the system for different unit sizes, minimizing an economic cost function, maximizing the performance of the plant, and minimizing polluting emissions; the second level to optimize the size of the plant, through a technical-economic optimization function based on the Net Present Value. The model is highly flexible, allowing the execution of sensitivity analyses. The analyzed energy system is composed of a cogeneration unit, namely solid oxide and polymer electrolyte membrane fuel cells, and of heat pumps, electrical storage, and traditional auxiliary boilers. In order to prove the reliability of the CCHP units, the modeled performance curves for the fuel cell systems have been validated with existing commercial units, resulting in a degree of correlation mostly over 90%.

Solar combisystems supplying thermal energy for both domestic hot water and space heating can reduce primary energy consumption for residential buildings. As their overall performance depends on their design, this paper presents the development and use of a multi-objective optimization framework for the selection of configuration and equipment sizing of a residential solar combisystem in Montreal, Quebec, Canada. A generic solar combisystem model, which enables different configurations to be chosen, is first developed, and then coupled with a micro-time variant multi-objective particle swarm optimization, which was developed and used to find the non-dominated combisystem design alternatives. The life cycle cost (LCC), energy use (LCE), and exergy destroyed (LCX) of the solar combisystem are used as objective functions to find the best feasible designs. For the minimum LCC, only one flat-plate collector is required to store energy within one thermal storage tank, whereas seven evacuated-tube collectors and two thermal storage tanks are used for the minimum LCE value. The micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO) algorithm used found a non-dominated solution that reduced the LLC by 29% compared with the initial design solution, with the increase of LCE by 72%. Since the LCC and LCE objective functions are conflicting, another non-dominated solution increased the LCC by 36% and reduced the LCE by 27%. The proposed multi-objective optimization framework can therefore be used to get the most out of solar thermal combisystems given specific economic and environmental conditions.

© 2017 Elsevier Ltd Owing to a lack of power density of conventional batteries, the onboard energy storage systems of an electric vehicle has to be oversized to compensate worst-case load condition, which is sub-optimal as it induces a heavy penalty on overall system weight and cost. One solution to overcome this limitation is to hybridize it with supercapacitors in order to boost its power performance via a power converter. This paper presents a multi-objective optimization problem over the parameters of such hybrid energy storage systems, with the aims to solve two conflicting objectives – cost and total stored energy in the hybrid energy storage system, under a set of pre-defined design constraints. An algorithm is first developed to find all feasible solutions to the problem. Two popular design examples are then tested differentiating Lithium Iron Phosphate based batteries from Lithium Manganese Oxide/Nickel-Cobalt-Manganese based batteries. A Pareto frontier is recreated for each example and an ξ-constraint method is finally adopted to choose the best member for comparison. This is so far, according to the authors’ knowledge, the first reported multi-objective optimal sizing method for an active hybrid energy storage system considering the effect of the power converter to gain a clearer understanding of its impact over various design choices.

Thermal energy storage (TES) is a key technology for enabling increased utilization of industrial waste heat in district heating. The ability of TES to equalize offsets in demand and supply depends strongly on the sizing, control and integration in a heating plant. We consider the problem of sizing TES in heating plants utilizing a varying waste-heat source. To this end, we propose a combined dynamic simulation and model predictive control approach that accounts for the dynamics and optimal control of the heating plant with TES. A case study has been carried out on a district-heating plant located in Norway, with 90% of its annual heat production being heat recovered from the off-gas from a ferrosilicon plant. We evaluate the effective peak-heating reduction with different TES sizes and the energy-to-heat-flow-ratio for the TES discharging periods as performance metrics. For the case study, our results suggest that a modest TES tank volume of 1500 m3 is sufficient to achieve a half-year peak-heating reduction of 12% and comparable performance with larger volumes. The proposed methodology constitutes a numerically tractable means of incorporating the impact of model predictive control on the sizing of TES for heating plants with time-varying waste-heat supply and demand. Keywords Thermal energy storageWaste-heat utilizationDistrict heatingModel predictive control