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Fouling is an important factor affecting the heat transfer performance of condensers in nuclear power plants. In response to the lack of experience in the current cleaning of condensers in nuclear power plants, this paper proposes a particle swarm optimization based LSTM method for predicting fouling thermal resistance and a dynamic optimization theory for condenser cleaning cycles. On the basis of LSTM and the PSO algorithm, compared with the prediction results of LSTM, the MAE, RMSE and MAPE values of PSO-LSTM decreased by 43.3%, 47.3% and 42.1%, respectively, while R2 increased by 26.4%. Considering the load loss and maintenance loss of the unit, as well as the economic loss caused by condenser scaling, the optimal cleaning time of the condenser glue ball system under different fouling rate and circulating water temperature was analyzed, and a calculation theory based on the unit output, the running loss cost of the glue ball and the minimum optimal cleaning cycle of the condenser was proposed. Results show that at rated flow, optimizing the cleaning cycle, with an annual operating time of 300 days, can save $0.45 million in operating costs per year. • Particle swarm optimization based LSTM method for predicting fouling thermal resistance of condenser of nuclear power plant are proposed. • Condenser health index of nuclear power plant based on dynamic power loss is established. • Condenser cleaning cycles of a practical power unit is optimized to show the trade-off. [ABSTRACT FROM AUTHOR]

More accurate multivariate load forecasting is conducive to integrated energy system planning optimization and economic operation. In this paper, based on the consideration of exogenous factors such as weather, endogenous factors are added into the prediction, and a short-term prediction model for integrated energy multivariate load based on endo-exogenous uncertainty is established using fine-tuned transfer learning. The LSTM pre-training model is first initially established using genetic algorithm and exogenous factors such as weather. Then the exogenous uncertainty of the pre-training model and the knowledge of endogenous uncertainty in the target model are combined using fine-tuned transfer learning to continuously optimize the basic parameters of the prediction model to obtain the final multivariate load prediction model. The performance of the four models is comprehensively evaluated after the historical load validation of the integrated energy system in Beijing, China. Compared with the traditional LSTM multivariate load forecasting model, the MAPE of the proposed model in this paper is reduced by 21.03 %, 13.00 %, and 24.39 % for electricity, heat, and cooling load forecasting, respectively. • The coupling mechanism between multiple energy sources of the integrated energy system is analyzed. • An endogenous-exogenous uncertainty method is proposed to extract the dynamic coupling relationship between multiple loads. • An endogenous-exogenous uncertainty-based GA-LSTM multivariate load prediction model is developed. • Optimizing model parameters using fine-tuned migration learning to deepen the impact of endo-exogenous uncertainty. [ABSTRACT FROM AUTHOR]

Zero-energy buildings usually achieve annual net-zero energy balance by generating electricity using on-site renewable energies and interconnecting with the power grid. However, the impact of energy mismatch problem in zero-energy buildings design on the grid is often overlooked, which potentially compromises grid's stability. To address this issue, this paper presents an optimization design method based on "grid-friendly interaction" which optimizes the energy system of zero-energy buildings to ensure better alignment between the building's electricity purchase/sale and grid demand. In this study, an indicator of "grid-friendly interaction", which represents the ratio of the net building friendly interaction power to the total annual interaction power, is firstly established to guide buildings in improving their interaction with the grid. Next, the energy supply, demand, and storage schemes of renewable energy systems in the zero-energy building are iteratively adjusted to achieve the best match with grid's expectations. This optimization design method was applied to a practical case in Hong Kong. Results show that, compared to the benchmark building, the optimal system design scheme increases grid friendliness by 83 %, reduces carbon dioxide emissions by 77 %, but also incurs a total cost increase of 180 %. The optimization design method proposed in this paper can effectively mitigate the impact of zero-energy buildings on the power grid, fostering enhanced interplay between the building structures and the grid. The study significantly contributes to the advancement of exploring the potential for energy flexibility in zero-energy buildings, which is of great significance for the safe and stable operation of future grids. The purpose of this study is to explore the energy flexibility potential of zero-energy buildings, which is of great significance for the safe and stable operation of future grids. • A novel indicator that reflects the degree of friendly interaction is proposed. • An optimization design method based on "grid-friendly interaction" is presented. • Single-objective and multi-objective optimization are applied respectively to ZCB. • It is of great significance for the safe and stable operation of future grids. [ABSTRACT FROM AUTHOR]

Fuel cell distributed drive electric tractors (FCDET) are one of the necessary means to achieve truly green agriculture. However, low traction efficiency, poor control coordination, and excessive energy consumption are the main reasons hindering the industrialization of FCDET. This paper proposed a parametric collaborative optimization design method and innovative verification system that considers the drive system and energy system. Based on the tractor plowing operating conditions, a 7-DOF coupled dynamics model of the distributed drive system and a tire-soil interaction model were established, and the key component selection and parameter matching were completed. On the drive system optimization level, the front and rear wheelside transmission ratios are optimized based on a multi-island genetic algorithm (MIGA). On the energy system optimization level, the globally optimal power distribution ratio is found based on the dynamic programming algorithm (DP). A complete experimental prototype verification system is constructed based on the MATLAB/Simulink-NI PXI joint simulation platform, physical prototype test bench, and real vehicle multi-index test platform. The results show that the average efficiencies of the optimized drive and energy system are increased by 0.38 % and 3.82 %, the total energy consumption (total hydrogen consumption) is reduced by 25.40 % and 15.39 %, respectively. The maximum fault-free operating time is 5.5h, the average traction power is 21.07 kW, and the average traction force is 10610 N in the all-wheel drive mode. The experimental results fully demonstrate that the electric tractor with the fuel cell distributed drive system as the core can meet the verification of multiple indicators. This study can provide a new theoretical basis and technical verification method for the optimal design and system control of fuel cell distributed drive electric tractors. • Parameter collaborative optimization of drive system and energy system. • Tractor multi-body dynamics model and tire-soil interaction model. • Multi-island genetic algorithm and dynamic programming algorithm. • MATLAB/Simulink-NI PXI joint simulation test, physical prototype test and real vehicle multi-indicator test. • Innovative verification method combining theoretical and experimental. [ABSTRACT FROM AUTHOR]

This paper proposes a novel mathematical optimization model aimed at incorporating demand response strategies in the operational scheduling problem of Electric Vehicle (EV) Battery Swapping Stations (BSS). The primary goal of the model is to minimize the operational costs while accounting for intricacies arising from diverse charging modes, variations in battery capacities, charger availability, different states of charges (SOC) of incoming discharged batteries, time-of-use power prices, power constraints and fluctuating customer demand patterns. Furthermore, the model takes into consideration the effect of faster charging modes on battery health to ensure a sustainable operation. The proposed model is further extended to incorporate the effect of swapping demand uncertainty on the operational decision-making of the BSS, and a two-stage stochastic optimization model with a recourse approach is proposed. The proposed stochastic optimization model is tested with relevant case studies, and the results demonstrate the efficacy of the model in providing a risk-neutral solution by determining the optimal demand satisfaction pattern and calculating the number of additional batteries needed to minimize demand shortfalls across all scenarios. The proposed scheduling framework holds the promise of improving both the efficiency of the BSS operations and customer satisfaction, paving the way for accelerated EV adoption. • An optimization model for scheduling an EV battery swapping station is proposed. • We integrate demand response strategies into an operational scheduling framework. • The degradation effect of faster charging modes on battery health is considered. • Uncertainties related to battery swapping demands are addressed. • A two-stage stochastic optimization model is proposed to account for uncertainties. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore the applications of quantum computing to energy systems optimization problems and discuss some of the challenges faced by quantum computers with techniques to overcome them. The basic concepts underlying quantum computation and their distinctive characteristics in comparison to their classical counterparts are also discussed. Along with different hardware architecture description of two commercially available quantum systems, an example making use of open-source software tools is provided as a first step for diving into the new realm of programming quantum computers for solving systems optimization problems. The trade-off between qualities of these two quantum architectures is also discussed. Complex nature of energy systems due to their structure and large number of design and operational constraints make energy systems optimization a hard problem for most available algorithms. Problems like facility location allocation for energy systems infrastructure development, unit commitment of electric power systems operations, and heat exchanger network synthesis which fall under the category of energy systems optimization are solved using both classical algorithms implemented on conventional CPU based computer and quantum algorithm realized on quantum computing hardware. Their designs, implementation and results are stated. Additionally, this paper describes the limitations of state-of-the-art quantum computers and their great potential to impact the field of energy systems optimization. Image 1096313 • Applications of quantum computing for energy systems optimization are demonstrated. • Instances for unit commitment, facility-location, and heat exchanger networks solved. • Some small problems perform better on quantum computers than CPU-based computers. • Limitations of quantum systems negatively impact solution quality of large problems. [ABSTRACT FROM AUTHOR]

Abstract Real-time electricity pricing could promote the adaptation of demand response programs in the presence of price volatility in smart grids. This paper proposes a real-time price-based demand response management model for heat and power consumers. In the proposed demand response program (DRP) the responsive electrical load can vary in different time intervals. In addition, total power and heat demands of the consumer are met, without any curtailment. The price uncertainty is envisaged through robust optimization for minimizing the worst-case electricity and heat demand procurement cost while flexibly adjusting the solution robustness. The proposed model can be easily embedded in the energy management system of the customer equipped with combined heat and power systems (CHPs), a power-only unit, a boiler unit and a heat buffer tank (HBT) and makes it possible to achieve a minimum cost. In this paper, the dual dependency characteristic of the heat and power in different types of CHPs has been taken into account. Furthermore, technical constraints, i.e. minimizing number of start-ups and shut-downs, ramp rate limits and minimum up/down-time limits of generation facilities are satisfied. Numerical simulations confirming the applicability and effectiveness of the proposed model are provided. According to the simulation results, there is a significant increase in the daily cost of the consumer without smart grid technology in comparison with the consumer employing the proposed real-time model. Highlights • A real-time price-based DRP is presented for heat and power consumers. • In the DRP, the responsive electrical load can vary in different time intervals. • The proposed model can be embedded in the energy management system of a customer. • The price uncertainty is considered through a robust optimization model. [ABSTRACT FROM AUTHOR]

Abstract The paper deals with the issue of fluctuations in heat demand and how they are dealt with when planning investments in the field of energy recovery of waste. The lifetime of Waste to Energy plants (WtEP) is typically 20–30 years. Their construction is also a time and investment-intensive business and requires a robust design with low sensitivity to future changes in key parameters. The paper analyses the effect of fluctuations in heat demand on the accuracy of techno-economic models and their outputs. The aim is to determine the sensitivity of optimisation models to the applied time step. The sensitivity of two different models is compared – a simple model of a WtEP cooperating with a gas boiler and a complex model of a WtEP integrated with a combined heat and power plant. Optimisation models, commonly used to design these facilities, perform calculations in certain time steps, typically on an annual or monthly basis. The simplification from hour to month time step may cause inaccuracies. The paper offers alternative solutions to modelling methods of WtEP, using so-called correction coefficients. They help to increase the accuracy of the models while maintaining acceptable calculation time. Highlights • Tracking short-term changes to important parameters for the WtE plant economy. • Modelling the operation of an existing and newly considered thermal facility. • Creating and applying correction coefficients for fluctuation parameters. [ABSTRACT FROM AUTHOR]

Although the advancements in automotive diesel engines in the last two decades have resulted in the possibility of achieving better performance with lower pollutant emissions and fuel consumption, the increased complexity of the system and the high number of control parameters require the solution of optimization problems of high dimensionality. It is of crucial importance to identify suitable methodologies, which allow achieving the full exploitation of the potential of these powertrains. In this paper, an original methodology for optimizing the latest generation of common rail automotive diesel engines has been presented. Random optimization methods along with surrogate models were firstly used to generate a population of engine calibrations, which then served as an initial population to a specifically conceived Genetic Algorithm (GA) based optimizer, which was finally applied on a real data set for a particular engine operating point. The results were compared with a calibration optimized using a traditional local approach method. A simultaneous reduction of about 20% in NO X and 1% in Brake Specific Fuel Consumption was achieved, with no significant increase in other emissions. The methodology described in the paper has the potential to reduce the calibration time and effort by half, while obtaining better calibrations. [ABSTRACT FROM AUTHOR]

The performance of regional distributed energy systems is closely related to operation strategies and is also affected by the renewable energy penetration rate of the power system. According to the load characteristics of a public building in Changsha, a regional distributed energy system of a combined heating and power unit coupled with a photovoltaic, ground source heat pump, air-source heat pump, and energy storage is proposed to explore the impact of the power system's renewable energy penetration rate on its optimization and operation. Based on the following electric load, following thermal load, and following hybrid electric-heating load strategies, this paper conducts a multi-objective optimization design for the proposed system according to two scenarios, where Scenario 1 considers the change of renewable energy penetration in the power system, and Scenario 2 does not. According to the optimization results, the impact of the change in the power system's renewable energy penetration rate on the system performance is comparatively analyzed. The results show that the electricity consumption share of the system from Scenario 1 is greater than that of the system from Scenario 2, regardless of the design and operation conditions. With the increase of renewable energy penetration, the primary energy saving rate of the system from Scenario 1 is higher than that of the system from Scenario 2, with increasing rates of 37.60%, 7.92%, and 36.87% under three strategies, respectively. Furthermore, for Scenario 1, the system operation performance under the following thermal load strategy is better than that under the other two strategies. The results of this paper provide some guidance for the optimal design of grid-connected distributed energy systems. • A RDES is proposed based on building load characteristics. • A multi-objective optimization model considering the power system's renewable energy penetration rate is developed. • The impact of the power system's renewable energy penetration on the design and operation of the RDES is explored. [ABSTRACT FROM AUTHOR]

The rapid increasing of data centers calls for an efficient method to reduce high operation costs and carbon emissions. This paper proposes a cooperative online schedule framework for local interconnected data centers considering shared energy storage. A time-average optimization problem is built to reduce the overall operation cost with essential operational constraints. Multiple randomness and the temporal coupling constraints of energy system levels make it challenging to solve the stochastic problem. Adapting the Lyapunov optimization theory with relaxation and perturbation technique, an online algorithm is proposed to find an acceptable solution to the problem to achieve both good service coordination and efficient energy coordination. The algorithm does not require prediction of random parameters, and has a fast convergence rate. Especially, the theoretical conditions for convergence and the distance between the acceptable and real optimal solution are derived in a closed form. Simulation studies show that the algorithm can effectively reduce operation cost of data centers, carbon emissions, and dependence on the main grid. • A new microgrid consisting of local data centers and shared ESS is investigated. • A cooperative framework to realize service and energy coordination is provided. • The proposed online algorithm finds an acceptable solution without predictions. • The algorithm is compared with forward-looking algorithm and rolling algorithm. [ABSTRACT FROM AUTHOR]

Most computer models used in energy systems optimization modeling studies are formulated using linear equations. However, since linear formulations do not always well reflect real-world conditions, they may not always be adequate as policy and support tools. This is particularly the case for local system studies attempting to represent technologies at the individual scale, as in the case for local heating system modeling. Thus, the aim of this paper is to investigate differences in the resulting heating solutions and model solution times for a local expanding heating system. Three different investment cost structures for individual and district heating solutions for the heating of new housing are investigated using linear and mixed integer linear programming. The results show that the use of district heating is higher for the cost structures that use mixed integer linear programming than it is for the linear cost structures. This result is attributed mainly to the fact that individual air-to-water heat pumps benefit from the linear equation formulation due to its high coefficient of performance during summertime. This finding is important to consider when modeling local energy systems. The solution time is, however, significantly shorter for the linear formulations than for the mixed integer linear formulations. • Linear and mixed integer linear programming methods used for cost structure analyses. • Three different investment cost structures for new heat technology installations. • Integration of air-to-water heat pumps in heating system modeling. • Higher usage of district heating in mixed integer linear programming. • Significantly longer solution time for mixed integer linear programming. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative multi-generation hybrid renewable energy system (HRES). The proposed HRES is optimized using the recently developed equilibrium optimizer algorithm and the efficacy of this algorithm is investigated by comparing the optimized sizing with results obtained from more traditional optimization algorithms, i.e., gray wolf optimizer, lightning search algorithm, and artificial bee colony. The results indicate that using the equilibrium optimizer algorithm outperforms conventional optimization algorithms in minimizing the levelized cost of electricity to $0.83 per kWh. This proves applicability of this modern optimization algorithm in improving HRES, which is a novelty of this research. Moreover, machine learning is implemented to predict the exergy efficiency of the proposed HRES, which provides insight into the performance and sustainability of the system. A thermodynamic database is established to train the machine learning model using secondary data. Using the multi-layer perceptron class, the exergy efficiency of the system is predicted; the trained model is able to deliver a prediction with an R-Squared value of 0.98. Thus, the appropriateness of machine learning algorithms in predicting the performance of HRES is verified. This research will, therefore, pave the way towards expanding real-world application of HRES's. This study provides insights into the optimization and economic and environmental benefits of hybrid renewable energy systems and inform policymakers on how to incentivize their implementation. As the study is performed for a specific location, exploring the feasibility of implementing the proposed system in other locations is recommended for future research. • An innovative hybrid renewable-based multigeneration system is proposed. • Various optimization algorithms are studied and compared for optimization of the system. • The performance of the system is predicted using machine learning. [ABSTRACT FROM AUTHOR]

The relatively low power coefficient restricts the wide application of vertical axis wind turbines (VAWTs). An effective solution to this problem is to design specific airfoil profiles which directly influence the capture ratio of wind power. The main aim of the present study is to develop an automatic airfoil profile optimization system to improve the power performance of a VAWT. A three-bladed high-solidity VAWT is adopted as the research object with its chord length, blade span and rotor diameter being 0.2 m, 0.8 m and 0.8 m, respectively. The optimization is conducted at a moderate tip speed ratio (TSR) with a value of 1.0 and the method of coupled CFD simulations with genetic algorithms is employed. The following points make this paper different from previous studies: (a) introducing Multi-Island Genetic Algorithm to optimize airfoils for VAWTs; (b) investigating the airfoil as part of the VAWT rather than as a single isolated body with 3D simulations. The results show that the power coefficient of the VAWT equipped with the optimized blades sensibly improves at all TSRs from 0.4 to 1.5 and the maximum growth rate of it occurs at TSR = 0.9 with a value of 26.82%. The integrated optimization system used in this paper provides an effective way to generate suitable airfoil profiles for given VAWTs with the goal to achieve higher power efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a quantitative maintenance optimization problem for developing reliability centred maintenance for a power system with renewable energy sources. Reliability and cost are two important interlinked aspects considered by system operators in many deregulated power systems. Reliability centred maintenance is an effective method to consider both of these aspects when performing the maintenance optimization. Nevertheless, this method has not adequately studied for a power system with renewable energy sources included. According to the maintenance optimization problem proposed in this paper, first, the most critical components of the system are selected. Then, a set of maintenance strategies are proposed for all critical components. After that, the total cost of each maintenance strategy for all critical components are calculated as the summation of operation, maintenance, environmental, and interruption costs. Finally, the best maintenance strategy for each critical component is selected by identifying the lowest total cost of different maintenance strategies. The proposed method is tested on IEEE 14-bus system. The results show that the proposed maintenance optimization method provides a useful way for deciding the most proper maintenance strategies for the studied system. [ABSTRACT FROM AUTHOR]

Adjoint sensitivity based optimization methodology for a supercritical CO 2 (S-CO 2 ) cycle is developed in this paper. The adjoint sensitivity analysis method (adjoint method) is a way to analyze sensitivity very quickly. By using the developed methodology, the optimal state variation due to the change of cycle parameters is analyzed. The S-CO 2 recompression Brayton cycle is used for an example case for the demonstration of the proposed method. The independence of time consumption for the developed adjoint sensitivity analysis method to the number of optimized variables is demonstrated. For the test case, the developed algorithm shows the ability to make the design parameters converge with a precision of 10 −6 by more than ten times faster for calculating the sensitivity than conventional optimization methods. Validation of the obtained optimal point is also included in the paper. For the validation, a response surface analysis is performed to visualize the pathway during the iteration for optimization. It is very challenging to carry out the optimization having the same precision using a brute force algorithm or a probability-based optimization algorithm since the number of variables to be optimized is substantial for this type of a problem. The smoothness of an optimal cycle efficiency variation is observed in every case. [ABSTRACT FROM AUTHOR]

The electricity mix in Tunisia mainly relied on conventional energy sources for over 50 years. Recently, due to fossil fuel prices oscillations and national reserves shortage, the need arose for restructuring the energy supply system. Targeting the integration of renewable energies could be a plan for satisfying the increasing demand and the supply independence. However, several macroeconomic conditions and policies present barriers for the integration of Renewable Energy Sources (RES), despite their abundance, availability and environmental benefits. This paper presents a long-term model of Tunisia electricity system, based on OSeMOSYS (Open Source energy MOdelling SYStem), aimed at unveiling potential benefits of increasing RES in electricity production. The paper first investigates peculiarities of Tunisia electricity system, arguing the necessity to include them in the electricity system model. Then, it explains the choice of OSeMOSYS and brought modifications, including peculiar system characteristics. Finally, the model is applied to two scenarios, a Business As Usual case and a 30% RES target in electricity production case, for time horizon 2010–2030. Results demonstrate the importance of system features detailed modelling. Specifically, they show that targeting RES state-invested integration in the electricity mix may allow higher energy independence to be reached, without increasing significantly system costs. [ABSTRACT FROM AUTHOR]

This paper addresses optimal day-ahead scheduling of Combined Heat and Power (CHP) units of an Active Distribution Network (ADN) with Electric Storage Systems (ESS) and Thermal Storage Systems (TSS) considering Industrial Customers (ICs) inter-zonal power exchanges. The ADN operator may use CHP units to supply its ICs and based on smart grid conceptual model, it can transact electricity with upward wholesale electricity market and its downward ICs' systems; meanwhile its ICs can transact energy with each other through the ADN main grid. Basically, the optimal scheduling of CHP units problem is a Mixed Integer Non Linear Programing (MINLP) problem with many stochastic and deterministic variables. However, the electricity transactions between the ADN and its ICs in normal and contingency scenarios may highly complicate this problem. In this paper, linearization techniques are adopted to linearize equations and a two-stage Stochastic Mixed-Integer Linear Programming (SMILP) model is utilized to solve the problem. The first stage models behavior of operation parameters and minimizes the operation costs; check the feasibility of the ICs' requested firm and non-firm power exchanges, and the second stage considers the system's stochastic contingency scenarios. The competitiveness of ADN in the deregulated market can be improved by adjustment of the proposed decision variables in the two stage optimization procedure. The proposed method is applied to 18-bus, 33-bus IEEE test systems. The effectiveness of the proposed algorithm has been investigated. [ABSTRACT FROM AUTHOR]

An electric aircraft propulsion system utilizes a high efficiency motor to generate thrust for electric aircraft. However, it can be challenging to maintain high efficiency for the fixed pitch propeller, which is commonly used in electric aircraft, through all phases of the flight. This paper investigates the power demands of the electric aircraft during the climb and the cruise phases according to the flight profile, and presents a model to estimate the energy consumption per flight. Simulation results suggest that the efficiency in the climb and the cruise phases can affect the energy consumption of an electric aircraft. Efficiency during the climb phase can have a significant effect on the capacity of the electric aircraft propulsion system. Based on the aforementioned results, this paper proposes a novel approach to achieve high efficiency, in which the optimal efficiency points of the electric aircraft propulsion system in the climb and the cruise phases can be obtained by solving an optimization problem with minimizing the energy consumption per flight as the object function and under the constraints that the climb and cruise phases must be successful. For testing purposes, the proposed method is adopted to the design of the electric aircraft propulsion system on a specific two-seater electric aircraft. Experimental results obtained from real flights suggest that the proposed method can reduce the energy consumption of the electric aircraft by over 10%, while reducing the capacity demand for the electric aircraft propulsion system. [ABSTRACT FROM AUTHOR]

This research work proposes the hybrid meta-heuristic algorithm to solve the multi-area economic dispatch problem to operate the power systems economically. The proposed hybrid algorithm combines the exchange market and grasshopper optimization algorithms. The grasshopper optimization algorithm sometimes suffers from premature convergence. To overcome this drawback, a hybrid algorithm for the better balance between exploration and exploitation and thus finding optimal solutions with high quality and robustness, is proposed in this paper. The main target of this paper is to investigate the application of the hybrid algorithm for optimizing the multi-area economic dispatch problem, satisfying different constraints of the valve point loading effect, multiple fuels, prohibited operation zones, ramp-rate limit, the tie-line constraints, and transmission loses. In this regard, after validation of the proposed hybrid algorithm by applying it on some benchmark functions of diverse nature, the effectiveness and robustness of the proposed algorithm are evaluated in three case studies of the multi-area economic dispatch problem. The comparative results confirmed the effectiveness of the proposed method in terms of robustness and quality solutions. Also, the applied method reduces the fuel cost in the range of 0.0229%–1.1622%. • Introducing a new meta-heuristic algorithm based on the combination of EMA and GOA. • Using the EMA-GOA to optimize some well-known benchmark functions. • Applying the algorithm for the first time to different kinds of multi-area economic dispatch problems. • Investigating the proposed method performance on different types of the mentioned problems. [ABSTRACT FROM AUTHOR]

At present, the performance analysis of volumetric expanders by semi-empirical model is a very popular method. How to improve the universality without reducing the calculation accuracy is the key issue. For an atypical volumetric expander, the irreversibility analysis of the working process for single screw expanders is still lacking. In this paper, we explore the possibility of using a semi-empirical model considering geometric constraints to improve the universality. Firstly, a basic model of SSEs is proposed, in which the model parameters related to the structure are directly calculated by the geometry instead of being calculated by the optimization algorithm. So, the number of the model parameters is reduced to 5. And then, the model is solved by our previous experimental results. However, the deviation between the calculation results and the experimental data is significantly large. So, the sub-process assumptions and calculation formulas are adjusted, including mechanical loss, leakage loss and pressure resistance loss. Then, the modified model is solved and compared with the result of the basic model. From the calculation results, the deviation of the modified model is significantly lower than that of the basic model. Meanwhile, the calculation results are in good agreement with the experimental data. Moreover, due to considering the exhaust resistance loss, not only improves the calculation accuracy of the modified model, but also makes the physical meaning of the model clearer. The work of this paper indicates that adding geometric constraints and modifying sub-process assumptions at the same time is an effective method to improve the accuracy and universality of the semi-empirical model simultaneously. • A semi-empirical model of SSEs considering geometry was proposed. • The number of the model parameters was reduced to 5. • The sub-process of adiabatic exhaust pressure drop was added. • The formulas of mechanical loss, leakage loss and flow loss in the basic model are modified. • The modified model improve the accuracy and universality simultaneously. [ABSTRACT FROM AUTHOR]

Recently, phase change materials are emerging in thermal drying technology. This study offers a new approach in thermal drying of a moist paper sheet deploying solid-solid phase change material, tentatively as an alternative to solid-liquid micro-encapsulated PCMs uniformly dispersed inside the void part of porous sheet. In contrast to SL-MEPCM scenario, SS-PCM is fabricated in solid matrix of porous medium, remaining the sheet porosity intact. Additionally, comparing to its SL-MEPCM counterpart, any costly PCM encapsulation process or change in the design of headbox of commercial paper machine is avoided. As well, volume change during phase transition is negligible. This configuration is particularly ideal for drying applications where a low porosity, initially highly moist sheet is to be dried. A numerical experiment is carried out to investigate the dewatering behavior of SS-PCM fabricated paper in a drying process. A real shape-stabilized polymeric SS-PCM is used in this study. A pattern search optimization technique is employed to minimize the target moisture content. This study shows SS-PCM primarily becomes effective in last stage of drying where it internally releases the heat gained in previous stages. The numerical results indicate the moisture removal is enhanced by approximately 29% utilizing SS-PCM. The effect of SS-PCM volume percentage and particularly infrared emitters are studied, as well. It is observed that SS-PCM equipped paper does not perform effectively in drying sectors where IR emitters are retrofitted. Eventually, a comparative study is performed to fairly compare the thermal performance of SL-MEPCM and SS-PCM in paper drying. • A new thermal drying approach utilizing solid-solid PCM is introduced. • In contrast to SL-PCM scenario sheet porosity remains intact in this configuration. • Drying rate is significantly improved employing a real polymeric solid-solid PCM. • Low porosity initially highly moist material is ideal for this drying application. • Solid-solid PCM performance alternates amid heat source and heat sink repeatedly. [ABSTRACT FROM AUTHOR]

In this paper, a tramway's system model is established to evaluate its longitudinal dynamics performance, where the model includes multiple masses point connecting together powered by the catenary and a hybrid energy-storage system which consists of a battery pack and ultracapacitor pack. To explore an optimal or appropriate real-time power distribution among these energy sources, a dual fuzzy logic control-based energy management strategy combined with wavelet transform and multi-objective optimization is proposed. The control parameters of the dual fuzzy logic controller are adjusted using a multi-objective particle swarm optimization algorithm while considering the daily operating cost of the tramway. To verify the effectiveness and validity of the proposed model and energy management strategy, its simulation results are further compared and verified with the field experimental results of a real tramway and a real driving cycle in China. • A improved simulation model is presented for a multi-sources hybrid tramway. • A optimal strategy combined with fuzzy logic, wavelet transform and PSO is proposed. • The dual fuzzy logic controller is optimized by multi-objective PSO algorithm. • The strategy shows lower tramway daily operating cost and better energy efficiency. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) models are multivariate with different nonlinear elements which should be identified accurately to assure dependable modeling. Metaheuristic algorithms are perfect candidates for this purpose since they do an informed search for finding the parameters. This paper utilizes three algorithms, namely shuffled frog-leaping algorithm (SFLA), firefly optimization algorithm (FOA), and imperialist competitive algorithm (ICA) for the PEMFC model calibration. In this regard, firstly, the algorithms are employed to find the parameters of a benchmark PEMFC model by minimizing the sum of squared errors (SSE) between the measured and estimated voltage for two available case studies in the literature. After conducting 100 independent runs, the algorithms are compared in terms of the best and the worst SSEs, the variance, and standard deviation. This comparison indicates that SFLA marginally outperforms ICA and FOA regarding the best SSE in both cases while it performs 20% and twofold better than other algorithms concerning the worst SSE. Furthermore, the obtained variance and standard deviation by SFLA are much less than the other algorithms showing the precision and repeatability of this method. Finally, SFLA is used to calibrate the model for a new case study (Horizon 500-W PEMFC) with variable temperature. Image 10882 • New metaheuristic algorithms are used for PEMFC parameters extraction problem. • Robustness of the algorithms is assessed over 100 independent runs. • A new 500-W open cathode PEMFC case study is introduced. • SFLA has been selected for the parameters identification of the new case study. [ABSTRACT FROM AUTHOR]

Advancing methods for modeling combustion systems and optimizing their operations is beneficial to improve the combustion performance. This paper develops a deep data-driven framework for the optimization of combustion system operations. First, a deep belief network based method is developed to model both of the combustion efficiency and the NOx emission. Next, a multi-objective optimization model is developed by integrating the deep belief network based models, the considered operational constraints, and the control variable constraints. Two objectives, maximizing the combustion efficiency and minimizing the NOx emission, are considered in the optimization. Due to the nonlinearity and complexity of the optimization model, traditional exact solution methods are not applicable to solve it. A recently presented swarm intelligence method, the JAYA algorithm, is applied to obtain the optimal solutions of the developed optimization model. Advantages of using JAYA are proved by benchmarking against well-known computational intelligence methods. The feasibility and effectiveness of the developed framework for optimizing the combustion process using industrial data is validated by computational experiments. Results demonstrate the potential of further improving both of the combustion efficiency and NOx emission by optimizing control settings of the combustion system. • The multi-objective optimization of combustion system operations using deep data-driven models is studied. • A deep belief network based method is developed to model the combustion efficiency and NOx emission. • A data-driven multi-objective optimization model of combustion process operations is developed. • A JAYA algorithm is firstly applied to solve the developed optimization model. • Computational results show the potential of further improving the combustion performance based on the developed model. [ABSTRACT FROM AUTHOR]

Multi-energy systems are reported to have a better environmental and economic performance relative to the conventional, single-carrier, energy systems. Electrification of district heating networks using heat pumps and combined heat and power technologies is one such example. Due to lack of suitable modelling tools, however, the sizing and optimal placement of heat pumps is always done only from the heating network point of view which sometimes compromises the electricity network. This paper proposes an integrated optimization algorithm to overcome such limitation. A load flow model based on an extended energy hub approach is combined with a nested particle swarm optimization algorithm. A waste to energy combined heat and power plant, heat pumps (HPs), heat only boiler (HOB), solar photo-voltaic, wind turbines and imports from the neighborhood grids are considered in the case studies. The results show that optimal placement and sizing of HPs and a HOB using the proposed methodology avoids an unacceptable voltage profiles and overloading of the electricity distribution network, which could arise while optimizing only from the heating network point of view. It also shows that up to 41.2% of the electric loss and 5% of the overall operating cost could be saved. • A nested PSO is integrated with an extended energy hub load flow model. • Operational parameters, losses and constraints in both networks are considered. • Up to 41.2% of the electricity loss can be saved. • Up to 5% saving in the operation cost is achieved. • Better voltage profile and transmission line loading are observed. [ABSTRACT FROM AUTHOR]

Seasonal climate variations affect electricity demand, which in turn affects month-to-month electricity planning and operations. Electricity system planning at the monthly timescale can be improved by adapting climate forecasts to estimate electricity demand and utilizing energy models to estimate monthly electricity generation and associated operational costs. The objective of this paper is to develop and test a computationally efficient model that can support seasonal planning while preserving key aspects of system operation over hourly and daily timeframes. To do so, an energy system optimization model is repurposed for seasonal planning using features drawn from a unit commitment model. Different scenarios utilizing a well-known test system are used to evaluate the errors associated with both the repurposed energy system model and an imperfect load forecast. The results show that the energy system optimization model using an imperfect load forecast produces differences in monthly cost and generation levels that are less than 2% compared with a unit commitment model using a perfect load forecast. The enhanced energy system optimization model can be solved approximately 100 times faster than the unit commitment model, making it a suitable tool for future work aimed at evaluating seasonal electricity generation and demand under uncertainty. • An energy system optimization model is repurposed for seasonal planning. • Results are compared with a unit commitment model under two demand forecasts. • The enhanced energy system model can generate results nearly 100 times faster. • This model performance makes it possible to do seasonal planning under uncertainty. [ABSTRACT FROM AUTHOR]

Achieving the economical and stable operation of Multi-microgrids (MMG) systems is vital. However, there are still some challenging problems to be solved. Firstly, from the perspective of stable operation, it is necessary to minimize the energy fluctuation of the main grid. Secondly, the characteristics of energy conversion equipment need to be considered. Finally, privacy protection while reducing the operating cost of an MMG system is crucial. To address these challenges, a Data-driven strategy for MMG systems with Shared Energy Storage (SES) is proposed. In this paper, the Mixed-Attention is applied to fit the conditions of the equipment, and Multi-Agent Soft Actor-Critic(MA-SAC) , Multi-Agent Win or Learn Fast Policy Hill-Climbing (MA-WoLF-PHC) are proposed to solve the partially observable dynamic stochastic game problem. By testing the operation data of the MMG system in Northwest China, following conclusions are drawn: the R-Square (R2) values of results reach 0.999, indicating the neural network effectively models the nonlinear conditions. The proposed MMG system framework can reduce energy fluctuations in the main grid by 1746.5 kW in 24 h and achieve a cost reduction of 16.21% in the test. Finally, the superiority of the proposed algorithms is verified through their fast convergence speed and excellent optimization performance. • A data-driven method to describe the nonlinear conditions of energy conversion equipment was proposed. • A novel energy management-energy auction strategy based on MMG system framework with SES was designed. • SAC algorithm and WoLF-PHC algorithm were improved. • Privacy protection of MGs by nonlinear and non-convex constraints is realized. • The effect of different hyperparameters on the MA-SAC algorithm was analyzed. [ABSTRACT FROM AUTHOR]

Optimal reactive power dispatch (ORPD) in electrical networks is essential for the secure and stable operation of the entire power system; it also significantly impacts the economic situation. However, the solution of ORPD is highly complex while considering the multiple variables, time-varying characteristics, dynamic loads such as electric vehicles, and operational constraints. The ORPD problem is classified as a non-linear, non-convex complex problem. Thus, a robust optimization technique should be utilized to solve the ORPD. This paper presents a novel Enhanced Transient Search Optimization (ETSO) technique for the optimal solution of the ORPD problem by integrating electric vehicles (EVs). The proposed ETSO implemented for the optimal solution of ORPD consequently minimize the active power loss and voltage deviation at the load buses. The proposed method is tested and verified on the IEEE 30-bus, the IEEE-57 bus, and IEEE 118-bus systems. The simulation results show the robustness and efficiency of the proposed ETSO optimization in solving the ORPD problem by evaluating and comparing it with other well-established metaheuristic optimization methods under the same system data, control variables, and constraints. Statistical features of the proposed ETSO algorithm and the results obtained led to an improvement in the performance of the power systems. • Optimal energy management and reactive power dispatch is presented. • The model is solved using a novel enhanced TSO algorithm. • The impact of electric vehicles on the system performance is studied. • Real data of electric vehicles are implemented for obtaining realistic responses. • The results of the proposed method are compared with other models. [ABSTRACT FROM AUTHOR]

Collaborative optimization of system design and operation is a valid way to boost the integrated performance (economic, energetic, and environmental) improvement of a distributed energy system. However, when developing operation strategies, only the operational performance of the system, such as fuel consumption savings, is usually considered, which makes it challenging to explore the potential benefits of integration. Therefore, this paper analyzes the limitations of conventional operation strategy in terms of integrated performance and accordingly investigates the load adjustment decisions that can improve multi-objective benefits under different scenarios. On this basis, two novel operating strategies (denoted as "theoretical" and "realistic") are developed to manage load flexibility. The difference between the two is the idealized requirement in the former to accurately predict solar irradiation and user loads. An off-grid solar-based distributed energy system for a residential building is used as a case study. The obtained simulation results show that the conventional strategy performs well in operation but poorly in terms of matching with system configuration, which results in low equipment utilization and high initial investment. With load flexibility management, the theoretical strategy improves equipment utilization and reduces the SOFC size, resulting in a better match between system design and operation. The results of the proposed theoretical strategy with multiple-objective optimization indicate that the total annual cost and CO2 emissions are reduced by 35.62% and 8.93%, respectively, and the primary energy savings is improved by 3.73%. Considering a more practical case, the proposed realistic strategy has higher reliability in flexible load management, compared to the theoretical strategy at the cost of 5.74%, 4.56%, and 1.73% degradation of those three indicators, respectively. In general, the original results of this research can provide an important reference for the multi-objective collaborative optimization of design and operation in distributed energy systems. [Display omitted] • A new body of knowledge regarding operation strategies in distributed energy systems. • Novel operation strategies are developed considering matching the design with operation. • Load flexibility management in peak scenarios helps reduce required installed capacity. • The theoretical strategy reduces annual total cost by 39.94% over the conventional one. • The realistic strategy achieves higher reliability with slight performance degradation. [ABSTRACT FROM AUTHOR]

Energy system optimization models are widely used to aid long-term investment decision-making for energy systems. From a socio-technical system viewpoint, existing models focus on the cost modeling of the technical subsystem, while the indirect costs of the social subsystem are not often modeled. This paper incorporates indirect costs into such a model, including those associated with generation capacity, energy production, and bilateral trades, respectively. As a proof-of-concept, the model has been applied to a case study for the Dutch power system, reflecting the Dutch national program Regional Energy Strategies, where regions collectively plan wind and solar energy capacities. We conclude that incorporating indirect costs significantly changed the optimal investment capacities and the associated costs for the regions compared to benchmark results from the conventional models. Furthermore, in this case study, a potential free-rider problem with regard to the national climate target occurs. Our model is used as a negotiation simulator to inform the regions about the hypothetical free-riding behaviors and thus helps to achieve a socially acceptable investment plan. The proposed energy system optimization model with indirect costs goes beyond the prevalent cost-minimization paradigm, and can be used to study transaction costs, trading barriers, and willingness to pay. • Existing energy system optimization models largely ignore the social subsystem. • We propose a model incorporating several indirect costs. • This unique model goes beyond the prevalent cost-minimization paradigm. • A proof-of-concept case study for the Dutch power system is conducted. • The model is used as a negotiation simulator for a hypothetical free-rider problem. [ABSTRACT FROM AUTHOR]

This paper explores the use of an elitist-Jaya algorithm for economic optimization of shell-and-tube heat exchanger (STHE) design. Three different optimization problems of STHE are considered in this work. The same problems were earlier attempted by other researchers using genetic algorithm (GA), simulated annealing (SA), non-dominated sorting algorithm (NSGA-II) and design approaches motivated by constructional theory. Jaya algorithm is a newly developed algorithm and it does not require any specific parameters to be tuned except the common control parameters. Elitist version of the algorithm is proposed in this paper to optimize the setup cost and operational cost of STHE simultaneously. Discrete variable optimization is also carried out in order to take the advantage of the standard design available for heat exchanger parts. The effect of common controlling parameters e.g. population size, number of iterations and elite size, is tested by considering different combinations of the same. Furthermore, two other case studies of STHE design are considered from the literature and the performance of the elitist-Jaya algorithm is compared with the recently published results. The results of computational experiments proved the superiority of the proposed algorithm over the latest reported methods used for the optimization of the same problems. [ABSTRACT FROM AUTHOR]

This paper presents a detailed numerical analysis of a trigeneration system serving a hospital, aiming at determining the best economic operating strategy. In this paper, a novel approach is presented aiming at developing a detailed tool to be used to predict the real time performance of the trigeneration system and to optimize its operation designing efficient control strategy. To this scope, a detailed dynamic simulation model was developed in TRNSYS environment. The simulated system provides electrical, thermal and cooling energy. It includes: a natural gas fired reciprocating engine, heat exchangers for waste-heat recovery, a single-stage LiBr-H 2 O Absorption Chiller (ACH), a cooling tower, pumps, a backup boiler, a backup vapor-compression electric chiller, storage tanks, valves, mixers. For such components, suitable control strategies and detailed algorithms were implemented in order to develop a model as much realistic as possible. To this scope, cooling and thermal loads were estimated by implementing detailed building dynamic simulations strictly related to the trigeneration system model. Three different operating strategies were evaluated in order to minimize the plant cost and maximize the performance of the system, namely: Thermal Load Tracking mode (TLT), Maximum Power Thermal Load Tracking mode (MPTLT) and Electricity Load Tracking mode (ELT). In the first one (MPTLT), the thermal power provided by the engine is always lower or equal to the thermal demand; in the second one (TLT), the engine operates according to an On/Off strategy; in the third one (ELT), the electrical power provided by the engine is always lower or equal to the electrical demand. The results of the case study were presented on different time bases (days, weeks, years). Such results show that the ELT control strategy can achieve a better profitability, with a simple payback period, SPB, equal to 4 years. For this strategy, 256 simulations were also performed by varying the main model parameters, in order to determine the combination showing the lowest SPB value and the highest Primary Energy Saving (PES) value. The optimum and the analysis of the optimum response surface was obtained by using Design of Experiments (DoE) method, showing that the best SPB value, equal to 3.9 years, comes out choosing an engine capacity ratio equal to 1, an ACH capacity ratio equal to 1, a hot tank volume equal to 5 m 3 and a cold tank volume equal to 2 m 3 . The best PES value, instead, is equal to 20.6% and comes out selecting an engine capacity ratio equal to 0.5, an ACH capacity ratio equal to 1, a hot tank volume equal to 6 m 3 and a cold tank volume equal to 2 m 3 . [ABSTRACT FROM AUTHOR]

A new class of wind turbine termed the external axis wind turbine (EAWT) has been recently developed in response to the anticipated increase in global demand for renewable energy. The EAWT combines the high power output of the HAWT with the low installation and maintenance cost of the VAWT. This paper optimizes this EAWT to simultaneously maximize the power while minimizing power fluctuation and the time required to reach the optimal operating point. The multi-objective optimization on the EAWT using Genetic Algorithm is performed on a response surface that is generated using CFD simulations in OpenFOAM. The turbulence model and mesh-sizing for the CFD simulations are validated against previously published experimental results on a bluff body. The paper first optimizes the EAWT using steady state CFD simulations which are found to be not valid. The paper then optimizes the EAWT using transient CFD simulation with moving mesh, first for a single Reynolds number then for a wide range of Reynolds numbers. From the analysis, a blade count of 10 at an operational tip speed ratio of 0.32 is recommended. This paper is the first study on EAWT. To the best of authors' knowledge, this paper is also the first study on wind-turbines to optimize changing blade count and operating point to simultaneously maximize power, while minimizing power fluctuation and the time needed to reach peak operating point. [ABSTRACT FROM AUTHOR]

Forecasting of electricity energy consumption (EEC) has been always playing a vital role in China's power system management, and requires promising prediction techniques. This paper proposed an optimized hybrid GM(1,1) model to improve prediction accuracy of EEC in short term. GM(1,1) model, in spite of successful employing in various fields, sometimes gives rise to inaccurate solution in practical applications. Time response function (TRF) is an important factor deeply influencing modeling precision. Aiming to enhance forecasting performance, this paper proposed a novel grey model with optimal time response function, referred to as IRGM(1,1) model. As of unknown variables in TRF, a nonlinear optimization method, based on particle swarm algorithm, is constructed to obtain optimal values, for shrinking simulation errors and improving adaptability to characteristics of raw data. The forecasting performance has been confirmed by electricity consumption data of China, comparing with three alternative grey models. Application demonstrates that the proposed method can significantly promote modeling accuracy. [ABSTRACT FROM AUTHOR]

The low-order modeling of wind turbine dynamic wake is a challenging problem, which requires approximating high-dimensional, nonlinear dynamic systems with a limited number of states and linear structures. The Koopman-linear flow estimator is regarded as a promoting method in this area to approximate the dynamic wake field from a few physical-measured states. This paper presents optimization methods for the Koopman-linear flow estimator to improve its generalization applicability and modeling accuracy in specific applications. Firstly, the flow estimator's wake field prediction process is organized using Koopman modes and amplitudes; both are identified initially. Then, a optimization method is proposed to optimize the Koopman amplitudes for a given application scenario, which maintains a consistent form for uncontrolled and controlled systems based on the error-source analysis. After this, a sequential particle swarm optimization algorithm is adopted, which improves the computationally-intensive problem during sensor configuration optimization. The proposed algorithm quickly solves a feasible and optimized sensor configuration plan instead of the unavailable global-optimal one. The verification results show two conclusions: On the one hand, the nonlinearity during the yaw-induced wake deflection process is evident, which poses a significant challenge to the linear low-order approximation of the dynamic wake field. On the other hand, the proposed optimization methods highly improve the dynamic wake modeling accuracy under free-wake and yaw-controlled scenarios. Sparse sampling is necessary for dynamic wake behavior study in industrial. This paper solves the incomplete measurements and wake deflection nonlinearity problem caused by sparse sampling and promotes the generalization applicability for specific applications. • The Koopman amplitudes is defined and optimized for the Koopman-linear flow estimator. • Organize the uncontrolled and controlled systems with similar form and solving method. • Sensor configuration optimization problem is improved with low computational cost. • Error-accumulation of free-developed and yaw-controlled wake prediction is reduced. [ABSTRACT FROM AUTHOR]

Swarm-based algorithms are widely accepted in different fields of engineering as they have proven themselves effective in solving real-world optimization problems. According to the "No-Free-Lunch" theorem, there is no such algorithm that can perform well in all optimization problems. This paper proposes a novel swarm-based optimization algorithm, influencer buddy optimization (IBO), that mimics human behavior in social environments. This algorithm exploits the fact that humans are influenced by a group of individuals rather than a single person. However, every individual can select a buddy from this group based on their preferences. The mathematical representation of this social human behavior is shown in this paper. The algorithm is tested on twenty-one standard benchmark functions, and twenty-four noiseless black-box optimization benchmarking (BBOB) functions to validate its effectiveness over fourteen optimization algorithms. Further, two hybrid models are proposed by combining IBO with an artificial neural network (ANN) and recurrent neural network (RNN) to solve electricity load and price forecasting problems. The results of hybrid models are compared with nine recent and popular optimization algorithms. The simulation result of hybrid models shows that ANN-IBO and RNN-IBO generate the least forecasting error in their respective classes of network architectures. • Proposed a novel swarm-based optimization algorithm named IBO. • IBO is tested with multiple benchmark functions in different dimensions. • IBO is combined with ANN and RNN for solving electricity prediction problems. • Two electricity datasets are used to test the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

Hydropower is the largest renewable energy power generation source with the largest construction scale and power generation capacity. A reasonable prediction of hydropower generation is conducive to achieving the carbon peak and neutrality goals. Hydropower generation is affected by seasons and precipitation, which have significant randomness and uncertainty. To realize a reasonable prediction of hydropower generation in China, this paper constructs a novel grey combination optimization model using different parameter combination optimizations based on the three-parameter discrete grey model TDGM(1,1). Further research shows that a better model performance can not necessarily achieved by a higher number of parameter combination optimizations, mainly due to the different effects and influence of various parameters on the model. Subsequently, the TDGM(1,1, r , ξ , Csz) model is applied to forecast China's hydropower generation. The results show that China's hydropower generation can reach 1687.738 hundred million kWh in 2025, an increase of 24.5% compared with 2020. Finally, the rationality of the prediction results is analyzed, and relevant countermeasures and suggestions are proposed. • The highlights of this paper are as follows: • A new grey model is proposed to forecast the hydropower generation in China. • This paper studies the optimization algorithm and implementation process of the new model. • It solves the problem of low accuracy of traditional model in China's hydropower prediction. • Finds can help government of China formulate reasonable power policies. [ABSTRACT FROM AUTHOR]

In many urban contexts, energy systems are undergoing fundamental change towards more interconnected system layouts. Appropriate planning tools are necessary to guide this transition towards more energy efficient system designs. Thus, the aim of this paper is to present workflow automation approaches to model buildings and district energy systems for dynamic simulation and integral system analyses. For data collection and management, we use a Geographic Information System coupled with a PostgreSQL database. In this paper, we present the software tools TEASER and uesmodels which use this data to automatically generate dynamic building and district energy system models in the modeling language Modelica. To demonstrate the application of these tools for workflow automation, we analyze a university campus with 39 buildings. In one scenario, an optimization led to an improved heating curve, with which yearly primary energy demand in the model was reduced by 0.9%. In a second scenario, the retrofitting of all building envelopes in the district energy system reduced primary energy demand by 16.0%. These examples showed that the presented approach is suited to evaluate options for improving district energy system, ranging from improved operation to changes in system design, and a combination of both. [ABSTRACT FROM AUTHOR]

This paper has proposed a novel Hybrid modified Genetic – Imperialist Competitive Algorithm (HGICA) for solving thermal Unit Commitment Problem (UCP). The UCP is a mixed integer problem with many equality and inequality constraints like the minimum down and minimum up time, spinning reserve, and ramp rate so need to a complex optimization process. In this paper the constraint handling of the problem is realized without any penalizing of solutions so a wide range of feasible solutions will be available for final optimum response. The proposed modified genetic is a novel method which has better performance than its original version and helps ICA to find more optimized responses and escape for local minimum areas easily. The main advantages of HGICA are good quality of the solution and high computational speed, which make it a suitable method for solving optimization problems. This method is carried out for three case studies including 10 and 20 units systems to efficiency of it be proved. Also the obtained results is compared to other optimization methods represented in literature for different scenarios. [ABSTRACT FROM AUTHOR]

The heat transfer from the combustion gases to the cylinder walls inside a spark ignition engine is a key factor in an engine's design, due to its influence on the engine's efficiency, power and emissions. Therefore a lot of research has been conducted in order to accurately model the heat transfer, for engine design and optimization purposes. These models have been found to provide inaccurate predictions for fuels which have significantly different gas properties compared to traditional fossil fuels. This indicates that the models either do not properly include gas properties, or are missing some important properties in their formulation. In order to construct a general (“fuel-independent”) heat transfer model, new measurements need to be executed, with multiple fuels that have different properties. Designing such an experiment requires a thorough understanding of the factors influencing the heat transfer and their interactions. In this paper a literature review is presented of heat transfer measurements in spark ignition engines in order to investigate the effect of the engine factors on the heat transfer. Based on this review, a root cause analysis is conducted to identify the independent factors that affect heat transfer. These factors are then used to set up two experiments according to a Design of Experiments methodology that allows the investigation of the effect of different gas properties and engine settings on the heat transfer in a consistent way. The results of these measurements for motored operation are discussed in [1] and for fired operation in a companion paper [2]. [ABSTRACT FROM AUTHOR]

A crucial problem of designing thick airfoils is balancing structural and aerodynamic requirements. This paper documented a new idea to deal with the thick airfoil's design. Firstly, the relative thickness of the original airfoil was increased to enhance its structural property. Then the overall aerodynamic performance was improved by the optimization design method. Specifically, this paper put forward a mathematical model of the overall optimization employing airfoil's performance evaluation indicators which represent modern rotor blades' aerodynamic requirements of “high efficiency, low extreme load, wide range of operating angle of attack and stability with varying operating conditions”. Based on this model, an integrated optimization platform for thick airfoils' overall design was established. Through an optimization experiment, a new 35-percent relative thickness airfoil was obtained. The new airfoil was predicted with high design lift coefficient, acceptable maximum lift to drag ratio, moderate stall parameter, and desirable stability parameters. These characteristics contribute to a high overall performance which could be competent with commonly used thick DU airfoils. Lift characteristics of the new airfoil have been validated by tests. These results confirmed the proposed method has effectively balanced airfoil's complicated requirements and successfully improved the new airfoil's overall performance. [ABSTRACT FROM AUTHOR]

Building thermal mass control has great potentials in saving energy consumption and cost. Optimal control schemes are able to utilize passive thermal mass storage to shift the cooling load from peak hours to off-peak hours to reduce energy costs. As such, this paper explores the idea of model predictive control for building thermal mass control. Specifically, this paper presents a study of developing and evaluating a multi-objective optimization based model predictive control framework for demand response oriented building thermal mass control. This multi-objective optimization framework takes both energy cost and thermal comfort into consideration simultaneously. In this study, the developed model predictive control framework has been applied in six commercial buildings at Boston, Chicago, and Miami, under typical summer weather conditions. Time-of-use electricity prices from these three locations are used to calculate the cooling and reheating energy costs. Pareto curves for optimal temperature setpoints under different thermal comfort requirements are calculated to show the trade-off between the cost saving and thermal comfort maintaining. Comparing with a typical “night setback” operation scheme, this model predictive control schemes are able to save energy costs from 20% to 60% at these three locations under different weather and energy pricing conditions. In addition, the Pareto curves also show that the energy cost saving potentials are highly dependent on the thermal comfort requirements, weather conditions, utility rate structures, and the building constructions. [ABSTRACT FROM AUTHOR]

This paper construct a CCHP and renewable energy based hybrid energy system driven by distributed energy resources (DERs CCHP). Then, the paper constructs performance indexes from energy, economic and environment. Thirdly, the paper proposes a multi-objective optimization model for DERs CCHP system under four optimization of energy rate (ER), total operation cost (TOC), carbon dioxide emission reductions (CER) and joint optimization. Finally, Guangzhou Higher Education Mega Center (GHEMC) in China is taken as the object for comparatively analyzing the operation performance of DERs CCHP system and CCHP system driven by natural gas (NG CCHP system). Results show: Joint optimization mode could balance the results of different optimization modes. DERs CCHP system shows better operation performance. ER of ER optimization, CER optimization and joint optimization are higher than that of NG CCHP system. NPV of all optimization modes is positive, IRR are bigger than the expected yield rate. DERs CCHP system could reduce CO 2 emission by utilizing wind energy and solar energy to replace NG. The sensitivity analysis indicates the performance of the DERs CCHP system will become better with the increase of chiller COP, decrease of NG price and wind-photovoltaic equipment cost. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive framework including a multi-objective interval optimization model and evidential reasoning (ER) approach to solve the unit sizing problem of small-scale integrated energy systems, with uncertain wind and solar energies integrated. In the multi-objective interval optimization model, interval variables are introduced to tackle the uncertainties of the optimization problem. Aiming at simultaneously considering the cost and risk of a business investment, the average and deviation of life cycle cost (LCC) of the integrated energy system are formulated. In order to solve the problem, a novel multi-objective optimization algorithm, MGSOACC (multi-objective group search optimizer with adaptive covariance matrix and chaotic search), is developed, employing adaptive covariance matrix to make the search strategy adaptive and applying chaotic search to maintain the diversity of group. Furthermore, ER approach is applied to deal with multiple interests of an investor at the business decision making stage and to determine the final unit sizing solution from the Pareto-optimal solutions. This paper reports on the simulation results obtained using a small-scale direct district heating system (DH) and a small-scale district heating and cooling system (DHC) optimized by the proposed framework. The results demonstrate the superiority of the multi-objective interval optimization model and ER approach in tackling the unit sizing problem of integrated energy systems considering the integration of uncertian wind and solar energies. [ABSTRACT FROM AUTHOR]

The use of solar energy as a source of clean energy is increasing throughout the world. Therefore, designing higher-quality photovoltaic cells has attracted researches. Several equivalent circuits have been proposed for the photovoltaic cell, but it is necessary to note that in order to achieve maximum power point (MPP), finding appropriate circuit model parameters is required. Many methods for finding the optimal parameters have been proposed. In this paper, flexible particle swarm optimization (FPSO) algorithm is proposed to estimate the parameters of PV cell model. In this algorithm, an elimination phase is added to classic PSO. At the beginning of each phase, a certain number of worst particles are deleted and some new particles are replaced in the new search space. Also, the search space of the parameters in each particle is changed based on the value of these parameters. These modifications have enhanced the proposed algorithm performance by adding the ability of global search and also searching in a reasonable space. To highlight the superiority of the FPSO algorithm, this method is used to estimate the parameters of the single diode model, double diode model, and the photovoltaic module. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known optimization methods. Furthermore, to ensure the practical use of the FPSO algorithm, it is validated by three different solar modules such as monocrystalline (SM55) and multi-crystalline (KC200GT) and polycrystalline (SW255). The simulation results show that the proposed algorithm has high performance in terms of accuracy and robustness. • A novel optimization algorithm called FPSO is presented. • Single diode model, double diode model, and the photovoltaic module are simulated. • The effectiveness of the FPSO is validated by three different solar modules. • The accuracy of the FPSO is compared with different well-known algorithms. [ABSTRACT FROM AUTHOR]

In modern power systems, the increasing penetration of renewable energy provides promising ways to operate the system at low cost and low pollution. Unfortunately, the noticeable uncertainties caused by the forecast errors of renewable generation as well as power load restrict the utilization of renewable energy (e.g. cause wind curtailment), and moreover, bring unprecedented challenges to maintain system reliability (e.g. cause load shedding which generally leads to high compensation). One prevailing solution is to prearrange sufficient spinning reserve of thermal units, which however, deviates from the ambition of cost saving. This paper analyzes the relationship between operation cost and wind curtailment amount using multi-objective unit commitment. The main contents include: First, systems with mixed generation sources including thermal units, wind farms and battery-based energy storage are investigated, whereafter Value-at-Risk-based worst-case estimations on load shedding and wind curtailment are established under hybrid uncertainties. Second, operation cost with generation, emission and load shedding concerns is established, whereafter multi-objective optimization is performed to investigate the inherent nature between operation cost and wind curtailment. Third, a multi-objective particle swarm optimization algorithm with reinforcement learning is developed to solve the complicated model. Finally, experiments on two case studies were performed to demonstrate the research effectiveness. • Worst-case estimations on load shedding and wind curtailment are established. • The relationship between operation cost and wind curtailment amount is analyzed. • The impacts of energy storage, load shedding and uncertainty sets are significant. • The algorithm is effective to solve the proposed multi-objective model. [ABSTRACT FROM AUTHOR]

Abstract Artificial neuro-fuzzy inference system known as the ANFIS tool was earlier developed by authors [1] for exergoeconomic optimization of an energy system. It was shown that the ANFIS could achieve the optimal solutions of systems with reasonable accuracy, very low computation time, and less dependency on experts' knowledge. This paper aims to test a similar methodology for retrofit and real-time optimization of a large energy system in response to the load variation. A 250 MW unit in a fossil-fueled steam power plant was considered as a case study. The profit of the power plant was estimated using an exergoeconomic analysis, and it was maximized by adjusting flow rates of extracted steams that flow from turbines into feed-water heaters. The ANFIS methodology was developed in detail to optimize the profit of the proposed power plant. The advantage of the ANFIS for this purpose was compared to other alternatives such as genetic algorithm (GA) and fuzzy-inference system (FIS). It was shown that the ANFIS is much faster than the GA and considerably easier than the FIS for real-time optimization energy systems. It was shown that using ANFIS, it is possible to achieve more profit in the proposed power plant up to 320 $.hr−1 by implication control on the flow of extracted steam; however, this figure is different at various loads of the power plant. Highlights • Real-time optimization based on adaptive neuro-fuzzy inference system, ANFIS. • Optimizing extracted steams in response to the load using the ANFIS. • Maximizing the profit of a fossil-fueled steam power plant. • A quite faster optimization methodology in comparison to conventional methods. • Automatic generation of governing fuzzy rules of optimization by the ANFIS. [ABSTRACT FROM AUTHOR]

Abstract Agriculture is responsible for 7.2% of the final energy consumption in the Netherlands; most energy is used for heating and lighting in the greenhouse sector. Currently, the greenhouse sector faces major challenges in reducing its energy demand while increasing crop quality and quantity. One route to improve the performance of industrial greenhouses could be based on using climate adaptive shells. These shells are capable of changing their thermal and optical properties on an hourly, daily, or seasonal basis to optimize performance. The climate adaptive shell concept shows considerable potential for performance improvement in the building sector. However, its potential for the greenhouse sector is yet unknown. This paper quantifies this potential by predicting the energy savings and the increase in net profit using a new framework based on numerical simulation and optimization techniques. The simulation results show that climate adaptive greenhouse shells increase net profit between 7% and 20% for tomato producing Dutch greenhouses. Monthly and hourly adaptation resulted in considerable primary energy savings of 23% and 37%, respectively. It is expected that the predicted net profit increase and energy savings will drive the attention of the greenhouse industry towards the development of climate adaptive greenhouse shells. Highlights • Outdoor climate has the greatest impact on the greenhouse performance. • The study proposes new greenhouse concept entitled Climate Adaptive Greenhouse Shell. • Greenhouse simulation shows CAGS generated an increase in net profit of 7%–20%. • Monthly and hourly adaptation showed energy saving of 23% and 37% respectively. • Optimal properties and their sensitivity inspire the designs of future greenhouse shells. [ABSTRACT FROM AUTHOR]

In this paper, the heat pump is coupled to a humidification dehumidification desalination system, with open-air configurations, to enhance the energy conversion efficiency. After establishing the energetic and entropic equations for all the thermal processes, the correlations between the desalination performance and the critical parameters, including the compression pressure ratio, pinch temperature difference of the condenser, terminal temperature difference of the evaporator, are revealed. Afterwards, the mass flow rate ratio and effectiveness during humidification and dehumidification are treated as the decision variables to optimize the energy conversion efficiency of the heat pump driven desalination system. The simulation results show that the actual top water production and gained-output-ratio of the desalination system reach 88.34 kgh−1 and 3.72 at the balance condition of the humidifier. It is also obtained that raising the compression pressure ratio and reducing the pinch temperature difference of the condenser and terminal temperature difference of the evaporator, can promote the desalination performance. Furthermore, based on the particle swarm optimization algorithm, the best desalination performance, with 151.03 kgh−1 for the water production, and 5.95 for the gained-output-ratio, is optimized within the prescribed range of the decision variables, while the corresponding cost of produced water arrives at 0.015$L−1 through the economic analysis. • An heat pump driven desalination system with open-air humidification dehumidification system is proposed. • Energetic and entropic analysis of the HPDDS is achieved. • Correlations between the critical parameters and the system performance are revealed. • Thermodynamic performance of the HPDDS is optimized based on the PSO algorithm. • Economic performance of the HPDDS is also calculated and presented. [ABSTRACT FROM AUTHOR]