Multi-Energy Systems

The Teaching Learning-Based Algorithm (TLBA) is a powerful and effective optimization approach. TLBA mimics the teaching-learning process in a classroom, where TLBA’s iterative computing process is separated into two phases, unlike standard evolutionary algorithms and swarm intelligence algorithms, and each phase conducts an iterative learning operation. Advanced technologies of Voltage Source Converters (VSCs) enable greater active and reactive power regulation in these networks. Various objectives are addressed for optimal energy management, with the goal of attaining economic and technical advantages by decreasing overall production fuel costs and transmission power losses in AC-DC transmission networks. In this paper, the TLBA is applied for various sorts of nonlinear and multimodal functioning of hybrid alternating current (AC) and multi-terminal direct current (DC) power grids. The proposed TLBA is evaluated on modified IEEE 30-bus and IEEE 57-bus AC-DC networks and compared to other published methods in the literature. Numerical results demonstrate that the proposed TLBA has great effectiveness and robustness indices over the others. Economically, the reduction percentages of 13.84 and 21.94% are achieved for the IEEE 30-bus and IEEE 57-bus test systems when the fuel costs are minimized. Technically, significant improvement in the transmission power losses with reduction 28.01% and 69.83%, are found for the IEEE 30-bus and IEEE 57-bus test system compared to the initial case. Nevertheless, TLBA has faster convergence, higher quality for the final optimal solution, and more power for escaping from convergence to local optima compared to other published methods in the literature. Full article

The heating, ventilation, and air conditioning (HVAC) system is a major energy consumer in office buildings, and its operation is critical for indoor thermal comfort. While previous studies have indicated that reinforcement learning control can improve HVAC energy efficiency, they did not provide enough information about end-to-end control (i.e., from raw observations to ready-to-implement control signals) for centralized HVAC systems in multizone buildings due to the limitations of reinforcement learning methods or the test buildings being single zones with independent HVAC systems. This study developed a model-free end-to-end dynamic HVAC control method based on a recently proposed deep reinforcement learning framework to control the centralized HVAC system of a multizone office building. By using the deep neural network, the proposed control method could directly take measurable parameters, including weather and indoor environment conditions, as inputs and control indoor temperature setpoints at a supervisory level. In some test cases, the proposed control method could successfully learn a dynamic control policy to reduce HVAC energy consumption by 12.8% compared with the baseline case using conventional control methods, without compromising thermal comfort. However, an over-fitting problem was noted, indicating that future work should first focus on the generalization of deep reinforcement learning. Full article

This paper proposes a distributed robust multi-energy dynamic optimal power flow (DR-DOPF) model to overcome the uncertainty of new energy outputs and to reduce water spillage in hydropower plants. The proposed model uses an ambiguity set based on the Wasserstein metric to address the uncertainty of wind and solar power forecasting errors, rendering the model data-driven. With increasing sample size, the conservativeness of the ambiguity set was found to decrease. By deducing the worst-case expectation in the objective function and the distributed robust chance constraints, the exact equivalent form of the worst-case expectation and approximate equivalent form of the distributed robust chance constraints were obtained. The test results of the IEEE-118 and IEEE-300 node systems indicate that the proposed model could reduce water spillage by more than 85% and comprehensive operation cost by approximately 12%. With an increasing number of samples, the model could reduce conservativeness on the premise of satisfying the reliability of safety constraints. Full article

The solution of the complex neutron diffusion equations system of equations in a spherical nuclear reactor is presented using the homotopy perturbation method (HPM); the HPM is a remarkable approximation method that successfully solves different systems of diffusion equations, and in this work, the system is solved for the first time using the approximation method. The considered system of neutron diffusion equations consists of two consistent subsystems, where the first studies the reactor and the multi-group subsystem of equations in the reactor core, and the other studies the multi-group subsystem of equations in the reactor reflector; each subsystem can deal with any finite number of neutron energy groups. The system is simplified numerically to a one-group bare and reflected reactor, which is compared with the modified differential transform method; a two-group bare reactor, which is compared with the residual power series method; a two-group reflected reactor, which is compared with the classical method; and a four-group bare reactor compared with the residual power series. Full article

The development of emerging technologies has enhanced the demand response (DR) capability of conventional loads. To study the effect of DR on the reduction in carbon emissions in an integrated energy system (IES), a two-stage low-carbon economic dispatch model based on the carbon emission flow (CEF) theory was proposed in this study. In the first stage, the energy supply cost was taken as the objective function for economic dispatch, and the actual carbon emissions of each energy hub (EH) were calculated based on the CEF theory. In the second stage, a low-carbon DR optimization was performed with the objective function of the load-side carbon trading cost. Then, based on the modified IEEE 39-bus power system/Belgian 20-node natural gas system, MATLAB/Gurobi was used for the simulation analysis in three scenarios. The results showed that the proposed model could effectively promote the system to reduce the load peak-to-valley difference, enhance the ability to consume wind power, and reduce the carbon emissions and carbon trading cost. Furthermore, as the wind power penetration rate increased from 20% to 80%, the carbon reduction effect basically remained stable. Therefore, with the growth of renewable energy, the proposed model can still effectively reduce carbon emissions. Full article

The European Union and the Austrian government have set ambitious plans to expand renewable energy sources and lower carbon dioxide emissions. However, the expansion of volatile renewable energy sources may affect today’s energy system. To investigate future challenges in Austria’s energy system, a suitable simulation methodology, temporal and spatially resolved generation and consumption data and energy grid depiction, is necessary. In this paper, we introduce a flexible multi-energy simulation framework with optimization capabilities that can be applied to a broad range of use cases. Furthermore, it is shown how a spatially and temporally resolved multi-energy system model can be set up on a national scale. To consider actual infrastructure properties, a detailed energy grid depiction is considered. Three scenarios assess the potential future energy system of Austria, focusing on the power grid, based on the government’s renewable energy sources expansion targets in the year 2030. Results show that the overwhelming majority of line overloads accrue in Austria’s power distribution grid. Furthermore, the mode of operation of flexible consumer and generation also affects the number of line overloads as well. Full article

Oil tanker disasters have been a cause of major environmental disasters, with multi-generational impacts. One of the greatest hazards is damage to the propulsion system that causes the ship to turn sideways to a wave and lose stability, which in storm conditions usually leads to capsizing and sinking Despite the perceived consequences of maritime disasters in the current solutions for the propulsion of oil tankers, there are no legal or real solutions for independent emergency main propulsion in this type of ship. Stressing that the reliability of the propulsion system has a significant impact on the ship’s safety at sea, the authors presented a new solution in the form of a power take-off/power take-in (PTO/PTI) system. This is the emergency use of a shaft generator as the main electric motor, operating in parallel in a situation when the main engine (ME), (the main engine of the ship’s direct high-power propulsion system that is slow-speed) loses the operational capability to propel the ship. Since one cause of wear, or failure, of the main engines is improper operational decisions, the paper shows the wear mechanism in relation to the accuracy of operational decisions. Using classical reliability theory, it also shows that the use of the proposed system results in an increase in the reliability of the propulsion system. The main topic of the paper was the use of an electrical system called PTO/PTI as an emergency propulsion system on the largest commercial vessels, such as bulk carriers and crude oil tankers, which has not been used before in maritime technical solutions. Semi-Markov processes, continuous in time, discrete in states, and which are used in technology, were also proposed as a tool describing the process of the operation of such a ship propulsion system, and they are useful to support operational decisions affecting the state of the technical condition of the engine. There are two ship operation strategies that can be adopted: the four-state model, for normal operation, and the three-state model, which operates with the occurrence of failure. For these types of models, their limiting distributions were defined in the form of probabilities. It was also demonstrated that faster than expected engine wear and the occurrence of inoperability of the main engine can be caused by wrong operational decisions made by the shipowner or crew. Using this type of main engine operating methodology, it is possible to support the decision of the engineer to stop the main engine and to subject it to the process of restoration to an acceptable state of technical condition (before the failure or during the failure in severe storm conditions), with the parallel use of the proposed electric propulsion (PTO/PTI) as an emergency propulsion, giving the crew a chance to maintain the steering necessary to maintain safe lateral stability. Full article

Energy system integration enables raising operational synergies by coupling the energy infrastructures for electricity, methane, and hydrogen. However, this coupling reinforces the infrastructure interdependencies, increasing the need for integrated modeling of these infrastructures. To analyze the cost-efficient, sustainable, and secure dispatch of applied, large-scale energy infrastructures, an extensive and non-linear optimization problem needs to be solved. This paper introduces a nested decomposition approach with three stages. The method enables an integrated and full-year consideration of large-scale multi-energy systems in hourly resolution, taking into account physical laws of power flows in electricity and gas transmission systems as boundary conditions. For this purpose, a zooming technique successively reduces the temporal scope while first increasing the spatial and last the technical resolution. A use case proves the applicability of the presented approach to large-scale energy systems. To this end, the model is applied to an integrated European energy system model with a detailed focus on Germany in a challenging transport situation. The use case demonstrates the temporal, regional, and cross-sectoral interdependencies in the dispatch of integrated energy infrastructures and thus the benefits of the introduced approach. Full article

Active power filters (APFs) are used to mitigate the harmonics generated by nonlinear loads in distribution networks. Therefore, due to the increase of nonlinear loads in power systems, it is necessary to reduce current harmonics. One typical method is utilizing Shunt Active Power Filters (SAPFs). This paper proposes an outstanding controller to improve the performance of the three-phase 25-kVA SAPF. This controller can reduce the current total harmonic distortion (THD), and is called fractional order PI-fractional order PD (FOPI-FOPD) cascade controller. In this study, another qualified controller was applied, called multistage fractional order PID controller, to show the superiority of the FOPI-FOPD cascade controller to the multistage FOPID controller. Both controllers were designed based on a non-dominated sorting genetic algorithm (NSGA-II). The obtained results demonstrate that the steady-state response and transient characteristics achieved by the FO (PI + PD) cascade controller are superior to the ones obtained by the multistage FOPID controller. The proposed controller was able to significantly reduce the source current THD to less than 2%, which is about a 52% reduction compared to the previous work in the introduction. Finally, the studied SAPF system with the proposed cascade controller was developed in the hardware-In-the Loop (HiL) simulation for real-time examinations. Full article

Escalation fuel consumption occurs in various regions of the world. However, world oil reserves decline from year to year so that it becomes scarce and causes oil prices to surge up. This problem can be solved by saving fuel consumption. One method of saving fuel is adding bio-additives from citronella oil as a sustainable resource to diesel fuels. Citronellal, citronellol and geraniol are the main components of citronella oil which can be used as fuel additives. This study aimed to evaluate the effect of citronella oil fractions as bio-additives to the performance of diesel engine. The research stages include: extraction of citronella oil, vacuum fractionation of citronella oil, physical chemical characterization of citronella oil and its fractions, formulation of bio-additive -fuel blending, characterization of blending, and evaluation of fuel efficiency. The effect of concentration of the bio-additives was examined towards three diesel fuels; dexlite, pertamina-dex, and biosolar. The results showed two main fractions of citronella oil; citronellal dominant component (FA) and citronellol-geraniol dominant components (FB). The concentration variation of bio-additives was 0.1–0.5%. Fuel consumption efficiency was tested using diesel engine at an engine speed of 2000 rpm and a load increment of 1000, 2000 and 3000 psi with 7 min running time. The fractions represented the different tendencies to enhance the fuel efficiency up to 46%, influenced by the mixture’s concentration. Generally, citronella oil and the fractions showed the potency as bio-additive to diesel fuels. Full article

Future low-carbon systems with very high shares of variable renewable generation require complex models to optimise investments and operations, which must capture high degrees of sector coupling, contain high levels of operational and temporal detail, and when considering seasonal storage, be able to optimise both investments and operations over long durations. Standard energy system models often do not adequately address all these issues, which are of great importance when considering investments in emerging energy carriers such as Hydrogen. An advanced energy system model of the Irish power system is built in SpineOpt, which considers a number of future scenarios and explores different pathways to the wide-scale adoption of Hydrogen as a low-carbon energy carrier. The model contains a high degree of both temporal and operational detail, sector coupling, via Hydrogen, is captured and the optimisation of both investments in and operation of large-scale underground Hydrogen storage is demonstrated. The results highlight the importance of model detail and demonstrate how over-investment in renewables occur when the flexibility needs of the system are not adequately captured. The case study shows that in 2030, investments in Hydrogen technologies are limited to scenarios with high fuel and carbon costs, high levels of Hydrogen demand (in this case driven by heating demand facilitated by large Hydrogen networks) or when a breakthrough in electrolyser capital costs and efficiencies occurs. However high levels of investments in Hydrogen technologies occur by 2040 across all considered scenarios. As with the 2030 results, the highest level of investments occur when demand for Hydrogen is high, albeit at a significantly higher level than 2030 with increases in investments of large-scale electrolysers of 538%. Hydrogen fuelled compressed air energy storage emerges as a strong investment candidate across all scenarios, facilitating cost effective power-to-Hydrogen-to-power conversions. Full article

This paper presents a new Fuel Cell Fuel Consumption Minimization Strategy (FCFCMS) for Hybrid Electric Vehicles (HEVs) powered by a fuel cell and an energy storage system, in order to minimize as much as possible the consumption of hydrogen while maintaining the State Of Charge (SOC) of the battery. Compared to existing Energy Management Strategies (EMSs) (such as the well-known State Machine Strategy (SMC), Fuzzy Logic Control (FLC), Frequency Decoupling and FLC (FDFLC), and the Equivalent Consumption Minimization Strategy (ECMS)), the proposed strategy increases the overall vehicle energy efficiency and, therefore, minimizes the total hydrogen consumption while respecting the constraints of each energy and power element. A model of a hybrid vehicle has been built using the TruckMaker/MATLAB software. Using the Urban Dynamometer Driving Schedule (UDDS), which includes several stops and accelerations, the performance of the proposed strategy has been compared with these different approaches (SMC, FLC, FDFLC, and ECMS) through several simulations. Full article

The building sector is responsible for 50% of worldwide energy consumption and 40% of CO₂ emissions. Consequently, a lot of research on Building Energy Efficiency has been carried out over recent years, covering the most varied topics. While many of these themes are no longer of interest to the scientific community, others flourish. Thus, reading trends within a field of knowledge is wise since it allows resources to be directed towards the most promising topics. However, there is a paucity of research on trend analysis in this field. Therefore, this article aims to analyse the evolution of the Building Energy Efficiency field of knowledge, identifying the recurrent themes and pointing out their trends, supported by statistical methods. Such an analysis relied on more than 9000 authors’ keywords collected from 2000 articles from the Scopus database and classified into 30 topics/themes. A frequency distribution of these themes enabled us to distinguish those most published as well as those whose academic interest has cooled down. This field of knowledge has evolved over three distinct phases, throughout which, eight themes presented an upward trend. These findings can assist researchers in optimising time and resources, investigating the topics with growing interest, and possibilities for new contributions. Full article

The interdependency of electric and natural gas systems is becoming stronger. The challenge of how to meet various energy demands in an integrated energy system (IES) with minimal cost has drawn considerable attention. The optimal scheduling of IESs is an ideal method to solve this problem. In this study, a day-ahead optimal scheduling model for IES that included an electrical system, a natural gas system, and an energy hub (EH), was established. The proposed EH contained detailed models of the fuel cell (FC) and power to gas (P2G) system. Considering that the optimal scheduling of an IES is a non-convex complex optimal problem, a piecewise self-adaptive particle swarm optimization (PCAPSO) algorithm based on multistage chaotic mapping was proposed to solve it. The objective was to minimize the operating cost of the IES. Three operation scenarios were designed to analyze the operation characteristics of the system under different coupling conditions. The simulation results showed that the PCAPSO algorithm improved the convergence rate and stability compared to the original PSO. An analysis of the results demonstrated the economics of an IES with the proposed EHs and the advantage of cooperation between the FC and P2G system. Full article

Scientists have been paying more attention to the shortage of water and energy sources all over the world, especially in the Middle East and North Africa (MENA). In this article, a microgrid configuration of a photovoltaic (PV) plant with fuel cell (FC) and battery storage systems has been optimally designed. A real case study in Egypt in Dobaa region of supplying safety loads at a nuclear power plant during emergency cases is considered, where the load characteristics and the location data have been taken into consideration. Recently, many optimization algorithms have been developed by researchers, and these algorithms differ from one another in their performance and effectiveness. On the other hand, there are recent optimization algorithms that were not used to solve the problem of microgrids design in order to evaluate their performance and effectiveness. Optimization algorithms of equilibrium optimizer (EQ), bat optimization (BAT), and black-hole-based optimization (BHB) algorithms have been applied and compared in this paper. The optimization algorithms are individually used to optimize and size the energy systems to minimize the cost. The energy systems have been modeled and evaluated using MATLAB. Full article

As renewable energy increasingly penetrates into electricity-heat integrated energy system (IES), the severe challenges arise for system reliability under uncertain generations. A two-stage approach consisting of pre-scheduling and re-dispatching coordination is introduced for IES under wind power uncertainty. In pre-scheduling coordination framework, with the forecasted wind power, the robust and economic generations and reserves are optimized. In re-dispatching, the coordination of electric generators and combined heat and power (CHP) unit, constrained by the pre-scheduled results, are implemented to absorb the uncertain wind power prediction error. The dynamics of building and heat network is modeled to characterize their inherent thermal storage capability, being utilized in enhancing the flexibility and improving the economics of IES operation; accordingly, the multi-timescale of heating and electric networks is considered in pre-scheduling and re-dispatching coordination. In simulations, it is shown that the approach could improve the economics and robustness of IES under wind power uncertainty by taking advantage of thermal storage properties of building and heat network, and the reserves of electricity and heat are discussed when generators have different inertia constants and ramping rates. Full article

A study on the application of oxygenated turpentine oil as a bio-additive in diesel fuel was conducted. The purpose of this research was to investigate the effect of oxygenated turpentine oil additive in diesel fuel on the performance and emission characteristics in diesel engines. Oxygenated turpentine oil is obtained from the oxidation process of turpentine oil. In this experimental study, the influences of oxygenated turpentine oil-diesel blended fuel OT0.2 (0.2% vol oxygenated turpentine oil and 99.8% vol diesel) were compared with pure diesel on engine performance, and emission characteristics were examined in a one-cylinder four-stroke CI engine. The test was performed at two engine loads (25% and 50%) and seven engine speeds (from 1200–2400 rpm with intervals of 200 rpm). The physiochemical characteristics of test fuels were acquired. The engine indicated power, indicated torque, fuel flow rate, and emissions (carbon dioxide, CO₂; carbon monoxide, CO; and nitrogen oxide, NO_X) were examined. The results revealed that the engine power shows slight increments of 0.7–1.1%, whereas the engine torque slightly decreased with oxygenated turpentine usage compared to pure diesel in most conditions. Furthermore, a reduction in NO_X emission decreased by about 0.3–66% with the addition of oxygenated turpentine in diesel compared to diesel. However, usage of OT0.2 decreased fuel flow rate in most speeds at low load but gave a similar value to diesel at 50% load. CO emissions slightly increased with an average of 1.2% compared to diesel while CO₂ emissions increased up to 37.5% than diesel. The high-water content, low cetane number, and low heating value of oxygenated turpentine oil were the reasons for the inverse effect found in the engine performances. Full article

The continued use of fossil fuels is contributing to severe environmental pollution and the establishment of an abnormal climate; consequently, alternative renewable energy sources are being actively investigated worldwide. Further, following global trends, numerous countermeasures aimed at improving carbon neutrality, promoting sustainable agriculture, and reducing fossil fuel dependence are being implemented in the Republic of Korea. Therefore, this study was conducted to investigate the application of renewable energies for greenhouse heating in the Republic of Korea. Three hybrid systems, numbered 1–3, were constructed using a pellet boiler, hydrothermal heat pump, and solar heat collection system, respectively. Thereafter, the heating performance, combined heat efficiency, energy consumption per temperature lifting, and energy cost per temperature lifting of the systems were compared. The combined thermal efficiency results showed no significant differences. However, in terms of energy consumption and cost, hybrid system 1 demonstrated 25.7 and 24.1% savings, respectively, compared with the other systems. Moreover, based on economic analysis via the net present value and life cycle cost analysis methods, the system reduced costs by 29.2 and 27.7%, respectively, compared with conventional fossil fuel boilers. Thus, hybrid system 1 was identified as the most economical system. Full article

The operational challenge of a photovoltaic (PV) integrated system is the uncertainty (irregularity) of the future power output. The integration and correct operation can be carried out with accurate forecasting of the PV output power. A distinct artificial intelligence method was employed in the present study to forecast the PV output power and investigate the accuracy using endogenous data. Discrete wavelet transforms were used to decompose PV output power into approximate and detailed components. The decomposed PV output was fed into an adaptive neuro-fuzzy inference system (ANFIS) input model to forecast the short-term PV power output. Various wavelet mother functions were also investigated, including Haar, Daubechies, Coiflets, and Symlets. The proposed model performance was highly correlated to the input set and wavelet mother function. The statistical performance of the wavelet-ANFIS was found to have better efficiency compared with the ANFIS and ANN models. In addition, wavelet-ANFIS coif2 and sym4 offer the best precision among all the studied models. The result highlights that the combination of wavelet decomposition and the ANFIS model can be a helpful tool for accurate short-term PV output forecasting and yield better efficiency and performance than the conventional model. Full article

The adoption of a diversification strategy of the energy mix to include low-water consumption technologies, such as floating photovoltaics (FPV) and onshore wind turbines, would improve the resilience of the Zambian hydro-dependent power system, thereby addressing the consequences of climate change and variability. Four major droughts that were experienced in the past fifteen years in the country exacerbated the problems in load management strategies in the recent past. Against this background, a site appraisal methodology was devised for the potential of linking future and existing hydropower sites with wind and FPV. This appraisal was then applied in Zambia to all the thirteen existing hydropower sites, of which three were screened off, and the remaining ten were scored and ranked according to attribute suitability. A design-scoping methodology was then created that aimed to assess the technical parameters of the national electricity grid, hourly generation profiles of existing scenarios, and the potential of variable renewable energy generation. The results at the case study site revealed that the wind and FPV integration reduced the network’s real power losses by 5% and improved the magnitude profile of the voltage at nearby network buses. The onshore wind, along with FPV, also added 341 GWh/year to the national energy generation capacity to meet the 4.93 TWh annual energy demand, in the presence of 4.59 TWh of hydro with a virtual battery storage potential of approximately 7.4% of annual hydropower generation. This was achieved at a competitive levelized cost of electricity of GBP 0.055/kWh. Moreover, floating PV is not being presented as a competitor to ground-mounted systems, but rather as a complementary technology in specific applications (i.e., retrofitting on hydro reservoirs). This study should be extended to all viable water bodies, and grid technical studies should be conducted to provide guidelines for large-scale variable renewable energy source (VRES) integration, ultimately contributing to shaping a resilient and sustainable energy transition. Full article

This paper proposes a hybrid algorithm that combines two prominent nature-inspired meta-heuristic strategies to solve the combined heat and power (CHP) economic dispatch. In this line, an innovative hybrid heap-based and jellyfish search algorithm (HBJSA) is developed to enhance the performance of two recent algorithms: heap-based algorithm (HBA) and jellyfish search algorithm (JSA). The proposed hybrid HBJSA seeks to make use of the explorative features of HBA and the exploitative features of the JSA to overcome some of the problems found in their standard forms. The proposed hybrid HBJSA, HBA, and JSA are validated and statistically compared by attempting to solve a real-world optimization issue of the CHP economic dispatch. It aims to satisfy the power and heat demands and minimize the whole fuel cost (WFC) of the power and heat generation units. Additionally, a series of operational and electrical constraints such as non-convex feasible operating regions of CHP and valve-point effects of power-only plants, respectively, are considered in solving such a problem. The proposed hybrid HBJSA, HBA, and JSA are employed on two medium systems, which are 24-unit and 48-unit systems, and two large systems, which are 84- and 96-unit systems. The experimental results demonstrate that the proposed hybrid HBJSA outperforms the standard HBA and JSA and other reported techniques when handling the CHP economic dispatch. Otherwise, comparative analyses are carried out to demonstrate the suggested HBJSA’s strong stability and robustness in determining the lowest minimum, average, and maximum WFC values compared to the HBA and JSA. Full article

Modular and scalable distributed generation solutions as combined cooling, heating and power (CCHP) systems are currently a promising solution for the simultaneous generation of electricity and useful heating and cooling for large buildings or industries. In the present work, a solar-heated trigeneration approach based on different organic Rankine cycle (ORC) layouts and a single-effect H₂O/LiBr absorption heat pump integrated as a bottoming cycle is analysed from the thermodynamic viewpoint. The main objective of the study is to provide a comprehensive guide for selecting the most suitable CCHP configuration for a solar-heated CCHP system, following a systematic investigation approach. Six alternative CCHP configurations based on single-pressure and dual-pressure ORC layouts, such as simple, recuperated and superheated cycles, and their combinations, and seven organic fluids as working medium are proposed and compared systematically. A field of solar parabolic trough collectors (SPTCs) used as a heat source of the ORC layouts and the absorption heat pump are kept invariant. A comprehensive parametric analysis of the different proposed configurations is carried out for different design operating conditions. Several output parameters, such as energy and exergy efficiency, net electrical power and electrical to heating and cooling ratios are examined. The study reveals that the most efficient CCHP configuration is the single-pressure ORC regenerative recuperated superheated cycle with toluene as a working fluid, which is on average 25% and 8% more efficient than the variants with single-pressure simple cycle and the dual-pressure recuperated superheated cycle, respectively. At nominal design conditions, the best performing CCHP variant presents 163.7% energy efficiency and 12.3% exergy efficiency, while the electricity, cooling and heating productions are 56.2 kW, 223.0 kW and 530.1 kW, respectively. Full article

Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage. Full article