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This paper presents a systematic approach to evaluate the time interval of optimal maintenance strategy for the subsea process system influenced by Microbiological Influenced Corrosion (MIC) within multiple defects. The proposed method incorporates the non-homogeneous Poisson, homogeneous gamma, and non-homogeneous Markov processes for modeling the generation of multiple defects, the average pit depth growth, and maximum pit depth, respectively. The maintenance strategy comprises industrial procedure, probability of failure detection, errors sizing in-line inspection tools, management actions costs, and failure cost. The developed framework simulates maintenance strategies considering time interval, cost, probability of detection, average pit depth, and maximum pit depth and identifies the optimal strategy. The practical application is demonstrated in a North Sea subsea pipeline system under MIC’s influence. This work assists decision-makers in selecting the optimal conditioned-based maintenance strategy for the processing system. While the application is demonstrated to subsea process systems under MIC influence, the developed approach is equally applicable to other process systems.

This paper proposes a novel modeling approach for optimal sizing of the components of an islanded micro-grid subject to satisfying a reliability index for meeting the loads. The proposed micro-grid incorporates photovoltaic arrays, wind turbines, a battery bank, an inverter, and an electric vehicle (EV) charging station. A demand-side management mechanism based on a deferrable load program is implemented and a model reduction technique is also utilized to mitigate the computational cost. Three different optimization algorithms, namely the whale optimization algorithm (WOA), particle swarm optimization (PSO), and the genetic algorithm (GA) are considered in this study to minimize the total cost of the system. The simulation studies have shown that although the WOA reduces the computational burden and requires much lower iterations compared with PSO and GA, it converges to sub-optimal solutions; therefore, it is not a good option for micro-grid planning purposes. Moreover, the results demonstrate that by charging coordination of EVs and deferring a pre-determined portion of the residential loads, overloading can be avoided and available components can be utilized better, which in turn reduces the sizes of the components and total cost of the system.

International audience; Psychrometric chart is a basic tool for analysis of processes in airconditioning systems. While psychrometric calculators and numerical psychrometric charts are widely available, there is a lack of numerical algorithms for solving the sizing problem by using psychrometric analysis. After introducing a classification of modelling problems in direct and inverse (based on the relation between physical and computational causality), this paper defines the design problem as a set of inverse problems of control and parameter optimization. A non-linear model is obtained by assembling the models of the elementary processes. The paper proposes to solve the direct and control problems by using a method similar to Newton-Raphson's, and the parameter optimization problem by using least-squares. Open-source implementation, published on Zenodo repository, and interactive, reproducible environment, available on Binder web service, accompany this paper (

In this paper, an economic analysis is conducted on standalone conventional solid oxide fuel cells (SOFCs) to determine their optimal economic operating conditions and to contribute to the commerci...

In this study, a new optimal allocation and sizing have been proposed for an Electric Vehicle Charging Station (EVCS) on a Distribution System in Allahabad, India. The main idea is to optimize the EVCS configuration by considering Voltage Profile Improvement Index (VPII), Reactive Power Loss Reduction Index (QLRI), Real Power Loss Reduction Index (PLRI), and the preliminary development cost to get the minimum value of the installation cost and to provide higher quality of parameters for the power grid. For solving the studied nonlinear mixed-integer optimization problem, a new improved metaheuristic, called Balanced Mayfly Algorithm (BMA) is proposed. The modification is established to improve the accuracy and to resolve the exploration issue of the algorithm. The BMA used two modifications including elite mayfly couples and chaos mechanism to resolve these issues as it is possible. After validating the algorithm, it is applied to 30-bus distribution system in Allahabad, India and its results are compared with GAIPSO and basic MA. The results indicated that the voltage shape is smoothened and a reasonable balance between voltage profile and network losses is obtained. The results also show that the suggested method with 18.358 MW active power loss, 73.826 MVar reactive power loss, 10961 s computational burden, and 415 number of charging ports gives superior performance with lesser power losses. The number of CS allocated through GAIPSO and MA does not satisfy the demand of the city’s consumers.

One feasible solution to reduce the cost of concentrated solar power is to employ a higher temperature and efficiency supercritical carbon dioxide Brayton power cycle. An alternative heat transfer medium and thermal energy storage (TES) system therefore needs to be explored. This work considered a variety of phase change materials (PCMs) and graphite as the storage media in four indirect shell-and-tube storage configurations, including a 3-PCM and 5-PCM cascade storage, a PCM-graphite-PCM hybrid storage and a single graphite storage. The sizing and design of the TES systems were performed by using a dynamic cycling methodology based on a transient 2D numerical model. The cost of those designs were then determined by using an economic model developed inhouse. This work also investigates the impact of some geometric parameters and cost assumptions on the techno-economic performance of the TES system. The analysis suggests a scenario exists whereby a low thermal efficiency storage system which therefore utilises less tube or storage material could be more cost-effective. Overall, the cost of hybrid TES is the lowest among all studied systems, at $26.96/kWht and $21.49/kWht for charging temperature of 720 °C and 750 °C, respectively, followed by the 5-PCM storage at $28.06/kWht and $21.82/kWht, respectively Refereed/Peer-reviewed

The optimal placement and the sizing of various distribution system components have gained extra significance in the recent past as the concepts of smarter distribution systems, distributed generation, and microgrids blossomed. This paper presents two novel techniques for sizing the Distributed Generators (DGs) in a large-sized radial distribution network. The primary strategy is proposed by considering the bi-directional progression of current in the parts of the circuit that occurs because of the presence of newly installed DGs. The subsequent strategy utilizes the active power sensitivity calculations for sizing the DGs. These strategies are applied on the chosen radial distribution systems to size numerous DGs all at once, with the main objective being the improvement of efficiency, by accomplishing minimization of losses and improvement in nodal voltages. Then, the maximum number of self-adequate and highly reliable microgrids is formed in the distribution network utilizing the “Reverse Current Flow” procedure cited in the literature, for the proposed sizing methods. Finally, a sensitivity analysis is performed by varying the rates of infiltration of the DGs to throw light on the suitability of the newly introduced methods of sizing. This analysis gives a set of operational rules for the controller of the smart grid.

The United Nations estimates that about 25% of the earth’s population will live in countries where access to water will be a recurrent problem by 2025. This article proposes a methodology and open-access software tool for rural off-grid communities and users with little knowledge about solar photovoltaic water pumping systems (SPVWPS) to provide access to safe water for consumption. The proposed methodology assists in all stages of the project: conducting a prefeasibility study, sizing and optimizing, maintenance and financial analysis. It includes components selection, optimal dimensioning of the SPVWPS, behavior prediction, yearly distribution of the water shortage probability (WSP) and a basic financial analysis. The tool suggests a water tariff to help ensure the project’s financial viability. The most interesting findings of this research is that the size – hence the feasabilty – of a project depends mostly on the population tolerance to water shortage. The concept of water shortage probability (WSP) is introduced to help in defining the appropriate size and cost of the systems. This WSP is calculated for each day of the year. A comprehensive literature review is presented and two case studies were used to validate its potential. It is found that only about 10% of the total energy produced by the PV modules is effectively used to fulfill the water needs.

Layout-dependent effects (LDEs) have become increasingly more important in the synthesis of analog integrated circuits. In this article, a two-phase hybrid sizing method for high-performance analog circuits is proposed. It consists of $g_{m}$ / $I_{D}$ -based device characterization, circuit modeling, sensitivity-based constraints for LDEs, mixed-integer nonlinear programming (MINLP) in the first phase, and many-objective evolutionary algorithm (many-OEA)-based sizing in the second phase. In the first phase, accurate device characterization is handled with little modeling effort thanks to the $g_{m}$ / $I_{D}$ design methodology. Then, the LDE parameters that are linked to the normalized dc current are further optimized with the aid of sensitivity analysis. Thus, a variety of electrical, geometrical, and LDE-related constraints can be conveniently integrated into modeling of the sizing problem. In the second phase, the many-OEA-based sizing refiner can further optimize the LDE parameters by using more detailed layout information via our proposed model. A new floorplan variation scheme is also applied to improve computation efficiency and enhance optimization effectiveness. The experimental results demonstrate high efficacy of our proposed methodology in LDE-aware analog sizing optimization.

Hybrid ground-coupled heat pump systems can efficiently heat and cool buildings by exchanging heat with geological materials. Their sizing is, however, complex and their financial profitability is hard to establish during the design phase due to uncertainties that taint important design parameters. Historically, impacts of uncertainties are assessed after sizing completion using sensitivity analyses. Unfortunately, these analyses cannot mitigate design risks, should the consequences of uncertainties be significant. Here, we show how the net present value-at-risk, a stochastic financial indicator inspired by a metric used in the financial sector, can weave the impacts of uncertainties throughout the design phase. The net present value-at-risk is compared to the net present value in a case study that considers uncertainty of construction costs, building’s heat load, energy tariffs and ground thermal conductivity. Results show that this financial indicator, although it comes at a higher computational price than traditional net present value indicator, leads to shorter payback periods, greatly reduces the risks of unforeseen financial losses and does not require further sensitivity analysis. By describing uncertain parameters with statistical distributions during sizing, the proposed designs are more conservative, still efficient and financially viable while being shielded from worst case scenarios.

Geographical Islands have been considered a challenge environment to test renewable energy integration strategies as well as cutting-edge technologies due to the alternation between actual grid-connected and island mode. This is due to the weak connection to the mainland and, subsequently, the Power Grid as well as the strong changes in seasonal energy demand leading to congestion and stability issues. Hydrogen technologies could be the pathway to mitigate the afore-mentioned issues providing bi-directional energy exchange as in the case of reversible solid oxide cells or interacting with other sectors such as transport by fuelling fuel cell vehicles by means of H2 produced by electrolysers. The scale of renewables locally installed plays a key role and this is why the amount of surface dedicated to solar energy exploitation as well as the number of buildings involved in the design are crucial. Therefore, in this paper a sensitivity analysis is carried out between 1 to 3 buildings, i.e. 20 to 100 kW of committed electrical power located in the Island as well as the surface dedicated to PV array for local production. The hydrogen energy system layouts of the simulated scenarios are optimized by a Mixed Integer Quadratic Programming software. The scale affects the choice of the best hydrogen technology to integrate in order to increase the self-sufficiency of the compound and, consequently, the energy security of its supply. Furthermore, when coupled with CO2 emissions the performance vary with the final use of the stored hydrogen.

This paper presents the optimal placement and sizing (OPAS) of active power filter (APF) considering photovoltaic distributed generation (PVDG) and nonlinear load to control the harmonics as per IEEE-519 standard limits. For the varying nature of PVDG output, the injection of harmonics into the distribution system is thoroughly investigated by incorporating hourly solar irradiance data. In this paper, a novel extended nonlinear load position based APF current injection (ENLPCI) technique is proposed to find the number of APFs and optimal buses for placement of APFs. The optimal placement for APF is calculated here for three distinct cases: a) only one state for minimum number of APFs, b) more than one state with different total harmonic distortion in voltage (THDv) with the same number of APFs and c) more than one state with the same THDv with the same number of APFs. The size and cost of APF are calculated and compared using grey wolf optimization (GWO) and its adaptive version (AGWO) algorithms for each hour. Further, the ENLPCI is also validated by GWO and AGWO algorithms for the optimal results. The proposed algorithm is tested on the IEEE-69 bus test system. The results show that the variation of solar irradiance remarkably affects the OPAS of APF. The AGWO performed better compared to GWO to find the optimal cost of APF.

Due to the increasing concerns about the environmental and economic issues of traditional ships, all-electric ships with energy storage and renewable energy integration have become more and more appealing for the forthcoming future. In this paper, an optimal energy storage system (ESS) capacity determination method for a marine ferry ship is proposed; this ship has diesel generators and PV panels. ESSs sizing optimization and power system scheduling optimization are simultaneously conducted and it is converted to a mixed-integer quadratic programming (MIQP) model with special modeling techniques. The case study shows that the proposed method is flexible and effective, and the relationships between the ESSs size and the discharge rate, life cycle times, or initial investment cost are investigated.

This paper introduces a new framework for optimum design and operation of hybrid renewable energy plants (HREP) augmented with battery energy storage systems (BESS). A new renewable energy management system (REMS) is developed comprising three components: 1) Enhanced joint forecasting of wind and solar outputs based on deep neural networks and also multiplicative weights update (MWU); 2) an advanced optimization model for sizing the HREP-BESS components and the policy of BESS operation; and 3) Augmenting the rolling hourly dispatch for HREP-BESS with a novel dynamic ramping limit and a criterion for reduction of deviations from the hour-ahead dispatch schedule. The proposed REMS tool enables maintaining the inter-hourly ramping of the HREP-BESS output within a threshold. In this context, a novel dynamic ramp limit is proposed to minimize the energy curtailment during operation and maximize energy sales to the power grid. The advantage of the proposed REMS tool over the classical renewable energy systems operation scheme is the mitigation of the volatility of renewable energy sources (RES) by suppressing extreme ramping events with minimum curtailment. Moreover, the costs and revenues of the HREP-BESS design and operation are assessed over a 25 years period. The design problem is solved for different scenarios, and the optimal solution always encloses a hybrid mix of renewables where the share of the PV plant can reach up to 37.1% of the total plant size. With the proposed REMS, the curtailment of RES never exceeds 12.9% even when the HREP is operated without a reserve margin. For the selected design, the optimum BESS capacity is 12.9% of the HREP capacity. The number of hours which observe a ramping violation event is 2.4% of the season's length (2184 hours). 99% of all ramping events fall within the defined ramping limits. The use of the MWU method increases the total profit by 2.53% compared with adopting the average forecast.

Analog integrated circuit design has as integral parts both analytical reasoning and numerical validation in the process from topology construction to sizing. Given a circuit topology, different circuit sizing results can be obtained from different processes of sizing inference. Sizing methods by simulation-based numerical searching have been a continuously studied subject. However, almost all approaches in this category require an overwhelming number of circuit simulations to arrive at an optimized sizing result. On the other hand, many published manual sizing methods by using the conventional device equations also require repeated SPICE simulations to correct the equation-based sizing results. This paper proposes a systematic gm/ID-based initial sizing method specifically customized for designing multiple-stage operational amplifiers (Op Amps). A main feature of the proposal is to use circuit-level design equations as constraints on the gm/ID table lookup method to substantially reduce the uncertainty in the sizing calculations. As a result, a significant amount of SPICE based correction work can be reduced to complete an initial sizing. The proposed sizing procedure includes a few regular sizing rules customized to the configuration of multi-stage Op Amps. We validate the proposed sizing method by application to several multi-stage Op Amp examples with a capacitive load or Miller compensation. Simulations have justified that the produced initial sizing results can achieve most of the prespecified design targets.

In this paper, a fuel-based distributed generator (DG) allocation strategy is proposed to enhance the distribution system resilience against extreme weather. The long-term planning problem is formulated as a two-stage stochastic mixed-integer programming (SMIP). The first stage is to make decisions of DG siting and sizing under the given budget constraint. In the second stage, a post-extreme-event-restoration (PEER) is employed to minimize the operating cost in an uncertain fault scenario. In particular, this study proposes a method to select the most representative scenarios for the SMIP. First, a Monte Carlo Simulation (MCS) is introduced to generate sufficient scenarios considering random fault locations and load profiles. Then, the number of scenarios is reduced by the K-means clustering algorithm. The advantage of scenario reduction is to make a trade-off between accuracy and computational efficiency. Finally, the SMIP is solved by the progressive hedging algorithm. The case studies of the IEEE 33-bus and 123-bus test systems demonstrate the effectiveness of the proposed algorithm in reducing the expected energy not served (EENS), which is a critical criterion of resilience.

Widespread deployment of residential solar photovoltaic (PV) systems have been encouraged with decreasing system costs. Recently, cutbacks in government incentives such as Feed-in-tariffs (FiT) and feed-in power limits enforced by grid operators have questioned economic feasibility of rooftop PV systems. Residential battery energy storage system (BESS) is not only a solution to the above issues but also helps to overcome problems related to intermittent PV power. However, high investment cost of the BESS remains the key barrier in many markets around the world for the wide implementation of the BESS. This study investigates avenues of improving the economic sense of the BESS considering the demand side management (DSM) of residential loads. Results show that the proposed method has achieved 63.71 ± 18.24% capacity reduction compared to the optimal size of BESS without considering the DSM. Further, the impacts of DSM on optimal sizing of BESS are analyzed in terms of battery degradation, individual BESS value propositions as well as return on investment (ROI). More importantly, houses which use DSM must size the BESS considering DSM which increases the ROI of the BESS by 13.59 ± 4.11% in comparison to the ROI of BESS sized without DSM.

Providing electricity access to remote rural areas plays a crucial role in the socio-economic development of the rural community. The use of renewable energy sources (RES) for electrification of re...

This paper propounds a new metaheuristic optimization algorithm to obtain the optimum site and size of distributed generation (DG) in radial distribution system (RDS). This problem is concomitant with reducing the active power loss, the total voltage deviation and the voltage stability index of the RDS considering different types of load models (such as constant power (CP), industrial (IL), residential (RES) as well as commercial (COM)). To solve this weighting factor-based multi-objective DG allocation problem, a novel metaheuristic optimization algorithm, student psychology-based optimization (SPBO), is suggested in this article. A multi-criteria approach (such as the analytic hierarchy process) is employed to optimize the weighting factors involved. To the best of the authors’ knowledge, this is the first time that this novel SPBO algorithm is being used for optimal siting and sizing of DGs in the RDS for different types of load models (like CP, IL, RES and COM) for IEEE 33-bus, IEEE 69-bus and practical Brazil 136-bus RDS. The simulation results attained by the proposed SPBO algorithm are compared with the results provided by recently surfaced Harris hawks optimization (HHO) algorithm and other state-of-the-art algorithms. The outcomes prove that the suggested SPBO algorithm is more efficient to solve the optimal multiple DG allocation problem with minimum real power loss, less computational time and prominent convergence rate.

The penetration of distributed generation (DG) in the distribution network has become a necessity and a significant solution to improve power grid quality, and solve power losses issue. To reach these targets, integrating these DGs in an optimal placement with an optimal sizing should be investigated and taken into consideration. This paper focuses on obtaining the optimal allocation and size of a photovoltaic (PV) distributed generation (PVDG) in order to reduce the total power losses and enhance voltage and frequency profiles of a modified IEEE 14 node distribution network (Hooshmand in J Appl Sci 8(16):2788–2800, 2008, https://doi.org/10.3923/jas.2008.2788.2800 ). An objective function is used in this paper aims to reduce grid power losses, and two optimization algorithms are applied to solve this function which are the particle swarm optimization (PSO) and the genetic algorithm (GA). Added to that, two scenarios are discussed in this paper in order to analyze the effects of variable PV penetration level, hourly load consumption profile variation and atmospheric condition change on the sizing optimization resolution. The obtained simulation results prove that the PSO algorithm has better performance compared to the GA in terms of speed convergence, power loss reduction and grid quality improvement (voltage and frequency profiles). Then, it shows that variable load consumption curve and weather condition change can affect not only the determination of the PVDG optimal position and capacity but also grid security.

With increasing environmental sensitivity and developing technology, Distributed Generations (DGs), Electric Vehicles Charging Stations (EVCSs), and Energy Storage Systems (ESSs) have been significant part of a distribution grid. However, inappropriate sitting and sizing of these components may negatively affect the performance of distribution grid. Besides, efficient operation of distribution grid can be achieved by only choosing the proper sizes of DGs, EVCSs, ESSs and placing them at the appropriate locations in the grid. In this paper, a weighted sum multi-objective function is formulated for reducing power loss, increasing voltage level and integration capacity of DGs, EVCSs, ESSs, simultaneously. Also, hybrid Soccer League Competition–Pattern Search (hSLC-PS) optimization algorithm is proposed to improve fine-tune performance of optimization. Simulations are carried out considering time-varying and stochastic nature of Photovoltaics (PVs) and Wind Turbines (WTs) generations and EVCSs load demand. The performance of the proposed method has been investigated on 33-bus and 85-bus test systems under different conditions and the statistical results are compared to SLC and Grey Wolf Optimization (GWO) algorithms to validate the effectiveness of the proposed algorithm. Statistical results clearly reveal the superiority of hSLC-PS in simultaneous sitting and sizing of DGs, EVCSs and ESSs.

–This paper deals with the optimal sizing of a hybrid photovoltaic-battery storage system for home energy management considering reliability against grid outages and demand response. To that end, a novel optimization framework is developed which aims at minimizing the electricity bill while the reliability of the system is ensured for certain common outages. In order to ensure the accuracy of the results, a large amount of characteristic outages along with demand, solar irradiance and temperature profiles are generated from real data. Clustering techniques are used for reducing this data to those most characteristics profiles and manage with the unpredictable behaviour of the outage events. Demand response is incorporated via different incentives like tariffs based on time of use and real time pricing, along with the optimal scheduling of different typical deferrable appliances. A case study on a smart-prosumer environment serves to illustrate the capabilities of the developed approach as providing sufficient guidelines for its universal applicability. Different cases studies are simulated considering different battery technologies and electricity tariffs for comparison. Various aspects related with the reliability against grid outages are also analysed like its impact on the project cost or the influence of demand response strategies.

The electric grid’s available capacity to accommodate solar photovoltaic on national scales is currently uncertain. This makes decisions about grid capacity expansion, which can be very costly for local grid operators, difficult to make. Yet, knowledge of national solar photovoltaic grid capacity is central in order to formulate realistic solar PV targets and strategies. We present a methodology based on publicly available data to estimate the grid’s hosting capacity of residential solar photovoltaic at both the national and local scale. The model is applied to Sweden, Germany and the UK and shows that low-voltage grid capacity for residential solar photovoltaic is very large, 33 (+5/-7) GW (Sweden), 248 (+5/-24) GW (Germany) and 63 (+1/-14) GW UK, and similar to current total generation capacity. Based on our estimations, we find that with the capacity of the present grid Sweden can supply 24%, Germany 60% and UK 21% of their current annual net electricity consumption from residential solar photovoltaic. In addition, we find that the grid-supported individual solar PV system sizes increase as population density decreases. Finally, our work highlights the importance of implementing sizing incentives for customers when installing their solar PV systems.

A dual-temperature open display cabinet is a new product that can provide simultaneous heating and cooling stored products, in response to the rising demand from consumers and retailers. The unique property of the studied dual-temperature open display cabinet is an uneven air duct to separate the heated air and the refrigerated air. However, such characteristic leads to an uneven distribution of the air curtain. Since 70% of the load is from the air curtain, this paper focuses on characterizing the non-uniform air curtain that is inevitably introduced by the dual-temperature open display cabinet. Two dimensionless parameters that describe the profile of the air curtain are proposed based on 3-dimensional CFD simulation. Using the experimentally validated model and these two parameters, the relation between the discharge air and the flow from back panel is revealed in details to guide the design for the height and positions of the back panel perforations and proper sizing of the fan. These results will promote the development of more efficient new products for supermarket and convenient store applications.

When using ”conventional” approaches for the optimal sizing of electrical machines, the application of optimization algorithms makes it possible to search for the values of their input characteristics (dimensions, power supply, materials, etc.) giving the desired performances (torque, efficiency, mass, etc.), provided that a certain number of constraints are satisfied. This (one-way) approach has several weaknesses, the main one being that it is not clearly apparent whether this sizing problem has at least one solution. If sets of solutions exist, it becomes interesting to determine the characteristics of all these valid machines. On the other hand, if no machine can be defined by exactly meeting the specifications, it becomes more useful to look for the constraints to be relaxed to reveal solutions, or to determine and then choose among all the quasi-optimal configurations those that are the most interesting. In this context, this work aims to respond to these issues by introducing a methodological procedure giving assistance in the optimal sizing of electrical machines in particular, and in optimization in general. In this procedure, a number of numerical processing tools are implemented, to offer the opportunity to understand more finely, typically through 2D graphical representations, the complexity of optimization design problems. In other words, this work is an attempt to find an intermediate configuration between the pure optimization approach and the one based solely on modeling, in the sense that these models are first represented graphically in order to understand the complexity of the corresponding optimization problem. The application of this approach is illustrated by the presentation of the main steps of the optimal design of a Permanent Magnet Synchronous Machine.

The principal motivation of the marine predators algorithm (MPA) is the common foraging technique, including Levy and Brownian motions in ocean predators coupled with optimal contact intensity policy in predator–prey biological interaction. This paper proposes an improved marine predators algorithm (IMPA), which is an extension of the original MPA. The suggested improvements lead to rapid convergence and avoid local minima stagnation for the original MPA. IMPA controls the active and reactive power injected into distribution systems to minimize the total system losses and the total voltage deviations and maximize the voltage stability and improve the distribution system's overall performance. On the one hand, the proposed IMPA determines the optimal location and active power (location and size, respectively) of distributed generation (DG). On the other hand, the IMPA controls reactive power by optimally placing and sizing the shunt capacitors (SCs) and determining the PF of DGs. Two standard test systems, 69-bus and 118-bus distribution networks, are considered to prove the proposed algorithm’s efficiency and scalability. Results of the proposed IMPA are compared with those obtained by MPA, AEO, and PSO algorithms. The findings of the simulation results demonstrate that the proposed IMPA can effectively find the optimal problem solutions and beats the other algorithms. Moreover, the framework of multi-objective IMPA outperforms based on MPA in terms of the performance measures of diversity, spacing, coverage, and hypervolume.

This paper intends to propose a power quality design model for the distributed system through nonlinear functions, and hence the prerequisite of power quality enhancements can be precisely quantified. As the model is adaptable, it needs a robust optimization algorithm for estimating the optimal location and compensation of the D-STATCOM. Hence, this paper develops a hybrid meta-heuristic optimization algorithm based on the prey targeting behavior of whales. The proposed hybrid whale optimization, Whale with Grey Wolf Optimization (WG), is used for determining the optimal placing and sizing of D-STATCOM by solving the power quality model. The solutions will be reactive power-encoded with two bound constraints to address both the localizing and sizing problems. Besides, along with the renowned literature, we determine the Mean Voltage Stability Index. The updating algorithm of the whale optimization will be hybridized with the hunting behavior of grey wolves so that the location and sizing of D-STATCOM can be estimated precisely. The proposed WG algorithm compares its performance over other conventional methods such as GA, ABC, PSO, GWO, and WOA in terms of convergence analysis, cost analysis, and total loss and determines the effectiveness of the proposed power quality model.

The IEEE 2430 measurement design fails primary school mathematics and produces numerical noise rather than a number with metrological properties required in engineering. This article presents an alternative approach to nonfunctional requirement sizing utilizing the COSMIC ISO 19761 method.

This paper presents a new numerical model of the vertical U-tube ground heat exchanger (GHE). Objective of creating the new model was to obtain more accurate, detailed and reasonably fast simulation of the GHE system, which guarantees proper sizing of such a system during design process. Different approaches for modelling GHE are described with emphasis on the new thermal resistance model (TRM) of an U-tube GHE. The 1D model of the heat transfer between working fluid of a GHE and the borehole is applied. This model was coupled with a 3D model of heat transfer in the ground surrounding the GHE. The global model of a ground heat source system is obtained by connecting segments representing volume of ground influenced by each of the GHE. The paper describes a discretization scheme, coupling method, boundary conditions and limitation of the proposed model. The simulation results and the measured in-situ data from literature are compared, it differ by 3% taking into account the heat extracted from the ground. Simulation results of heat extracted from the ground, working fluid temperature and ground temperature distributions around GHE are presented. Conclusions concerning obtained results shows possible use for simulation of the operation of GHE.

Accurate estimation of the balance of costs required to deliver a turnkey battery energy storage system (BESS) is highly important for decision making on optimal battery type and sizing for a given project. Such costs can be greater than the direct current (DC) boundary module costs for short duration systems. Despite this, very little data is publicly available for such costs at the commercial/industrial scale, and data decomposed into $/kW, $/kWh and fixed components is even scarcer. In the present work, cost models reported by PNNL and Lazard are tested against cost instances reported by EPRI. As system duration increases the Lazard model increasingly overpredicts balance of costs, whereas the PNNL model increasingly underpredicts them. This is due to the former placing more costs in the $/kWh category. It is shown that averaging the PNNL and Lazard models gives better agreement with the EPRI data than either does alone (10% discrepancy compared to 36/30%). The disagreement in cost scaling requires further attention, as it currently hinders the study of longer duration systems.

Made available in DSpace on 2021-06-25T10:18:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-05-01 The deployment of battery energy storage systems (BESS) is rapidly increasing as a prominent option to support future renewable-based energy systems. However, despite its benefits from a technical perspective, there are still challenges related to its economic viability. On the other hand, sizing BESS considering only their economic viability can be impractical because financial objectives could be in conflict with other aspects, such as battery degradation and grid impact. This article proposes a multi-objective approach to determine the optimal size of BESS providing stackable services, such as frequency regulation and peak shaving. The proposed optimization method comprises financial and technical aspects represented by the payback period, battery life span, and grid impact. Given a set of market rules, a cost-benefit function, a regulation signal, consumption profiles and grid data, an enumerative approach is adopted to provide a set of Pareto optimal solutions. The performance of the proposed method is validated using the regulation market structure from PJM interconnection. Furthermore, a real 240-node distribution grid is used to assess the grid impact via OpenDSS. Simulations demonstrate that the proposed approach is a flexible and practical decision-making tool that investors can exploit when designing new BESS. Department of Energy Systems School of Electrical and Computing of Engineering University of Campinas Department of Electrical Engineering Center for Electric and Energy Technical University of Denmark School of Energy Engineering São Paulo State University (UNESP) School of Energy Engineering São Paulo State University (UNESP)

Some applications have the property of being resilient, meaning that they are robust to noise (e.g. due to error) in the data. This characteristic is very useful in situations where an approximate computation allows to perform the task in less time or to deploy the algorithm on embedded hardware. Deep learning is one of the fields that can benefit from approximate computing to reduce the high number of involved parameters thanks to its impressive generalization ability. A common approach is to prune some neurons and perform an iterative re-training with the aim of both reducing the required memory and to speed-up the inference stage. In this work we propose to face CNN size reduction from a different perspective: instead of reducing the network weights or look for an approximated network very close to the Pareto frontier, we investigate whether it is possible to remove some neurons only from the fully connected layers before the network training without substantially affecting the network performance. As a case study, we will focus on “fine-tuning”, a branch of transfer learning that has shown its effectiveness especially in domains lacking effective expert-designed features. To further compact the network, we apply weight quantization to the convolutional kernels. Results show that it is possible to tailor some layers to reduce the network size, both in terms of the number of parameters to learn and required memory, without statistically affecting the performance and without the need for any additional training. Finally, we investigate to what extent the sizing operation affects the network robustness against adversarial perturbations, a set of approaches aimed at misleading deep neural networks.

This paper presents the Stud krill herd algorithm (SKHA) implementation to find the optimal location and sizing of DG units in a radial distribution system. In recent years, distributed generation (DG) plays an important role in the electric power industry. DG units are connected to the system mainly for improving voltage stability, real power loss reduction, reliability, grid strengthening, and reduction of SO2, CO2 gas emission. The problem formulation is based on the multi-objective function. This work focuses on real power loss reduction, cost minimization, and improvement of voltage stability and formulates a single objective function with suitable weightage preference subject to various constraints like voltage limit, DG real power limit, power balance constraint, and DG location limit. The algorithm has been tested on IEEE 33 bus, 69 bus, and 94 bus radial systems at different load levels with DG placement. The obtained simulation results are compared with other methods. The comparison reveals that the proposed algorithm offers a better solution for the optimal placement of DG in the radial distribution system with faster convergence.

The design and development of Stratospheric Airships for High-Altitude Long-Endurance missions (HALESAs) has generated interest worldwide. Conventional airships usually have a single-lobed axisymmetrical envelope shape. In contrast, several non-axisymmetric envelope configurations have been proposed for the HALESAs, such as flattened single lobed and multi lobed. This paper describes a methodology for carrying out a comparative analysis of a conventional HALESA and the multi-lobed HALESA designed for the same design mission. A sizing methodology which enables the estimation of its design parameters to meet some user-specified requirements has been developed for airships with envelopes of both these shapes. A Multidisciplinary Design Optimisation (MDO) approach has been followed in this methodology, which includes considerations from the disciplines of aerodynamics, energy, environment and structures. The study indicates that the envelope volume, solar array area and total mass of the single-lobed conventional airship are better than those of the tri-lobed HALESA. While the multi-lobed HALESA has the advantage of a flatter upper surface resulting in higher efficiency of the solar panels, the conventional airship has lower drag, which results in superior mission performance.

The high penetration of volatile renewable energy challenges power system operation. Energy storage units (ESUs) can shift the demand over time and compensate real-time discrepancy between generation and demand, and thus improve system operation flexibility and reduce renewable energy curtailment. This paper proposes two parametric optimization models to quantify how the power (MW) and energy (MWh) capacity of ESU would impact renewable energy utilization from two aspects: renewable energy curtailment and system flexibility for uncertainty mitigation. The two indicators are characterized as multivariate functions in the capacity parameters of ESUs. A severity ranking algorithm is suggested to pick up critical scenarios of fluctuation patterns from the uncertainty set; consequently, the proposed models come down to multi-parametric mixed-integer linear programs (mp-MILPs) which can be solved by a decomposition algorithm. The proposed method provides analytical expressions of the two indicators as functions in MW and MWh capacity. Such a characterization delivers abundant sensitivity information on the impact of ESU capacity parameters, and provides a powerful tool for visualization and useful reference for storage sizing. Case studies verify the effectiveness of the proposed method and demonstrate how to use the geometric information.

The paper investigates the energy performances of several configurations of solar-assisted heat pumps equipped with photovoltaic (PV) and photovoltaic-thermal (PVT) panels as well as solar thermal collectors for domestic hot water production. The systems are sized to serve small aggregates of users assumed as micro energy communities in low housing density context in the South of Italy. Electricity storages are also considered to smooth the interaction with the electric grid. Several indices are defined and calculated to guide toward a correct sizing, to check users’ satisfaction and to assess primary energy savings. The results show how the energy consumption from the analysed systems is mainly influenced by the amount and structure of domestic hot water demand per day. Also, according to the chosen plant layouts, it is shown that thermal contribution of PVT slightly influences the system performances. Better results in terms of less non-renewable primary energy consumption are recorded for the PV-based plant. Solar thermal collectors, even if sized for high solar fractions, do not properly support the daily heat pump operation, especially in winter, and present a certain electricity consumption for auxiliaries which results to be penalizing. Additional calculations were performed for the case of energy communities in a context with higher uses of non-renewable energy for electricity generation. Finally, opportunities to support demand-response actions for grid electricity management were investigated.

In recent years, due to increase in load demand, distribution networks have got substantial attention to get optimized. It is significant to optimally place the distributed generators (DGs) in distribution networks to minimize power losses and voltage drops. However, the DGs incur certain investment and operational costs, and their placement is only viable if these costs overcome energy losses. Therefore, current paper investigates the optimal sizing and placement of DGs in distribution networks with a novel concept to simultaneously minimize total energy cost along with total power loss and average voltage drop. Artificial bee colony (ABC) algorithm is proposed to solve the considered multi-objective problem. The performance of the proposed ABC algorithm is tested with standard algorithms. Newton Raphson load flow (NRLF) analysis is conducted on IEEE 33 and 69-bus radial networks and on CIGRE medium voltage (MV) benchmark grid. Two test cases have been formed and investigated. The results prove that proposed ABC algorithm mostly outperforms other algorithms.

This article presents a simple analytical model for the sizing of synchronous reluctance (SynRel) machines. The accuracy of the method is achieved by modeling a simple rotor geometry that presents all the characteristics of a real machine. The analytical equations proposed are able to guarantee accurate and fast results during the preliminary design of the machine. A generalized sizing approach, based on the saliency ratio, is presented in detail. The method is flexible and can be adapted for any SynRel machine. The accuracy of the proposed model is validated for a range of operating conditions, comparing the results with both finite element simulations and experimental measurement carried out from an existing four poles SynRel 15-kW prototype.

The increasing number of weak-grid-connected renewable energy resources in power systems has created various challenges in recent years. Some examples include un-damped voltage oscillations in the ERCOT and sub-synchronous resonance in the North-China power grid. Several solutions for these challenges have been proposed, such as Static Compensators and Synchronous Condensers (SynCons). SynCons, being synchronous machines without a prime mover, provide several benefits in weak power systems, such as frequency support, system strength, and voltage regulation. Although SynCons are widely-utilized to mitigate the weak grid integration challenges, their installation /operation costs make them a costly solution. Additionally, their lead-time can be more than a year, which means their initial sizing and allocation must be optimal. In this paper, a method for the optimal allocation and sizing of SynCons is proposed. The main objective of this method is maintaining Short Circuit Ratio (SCR) in the system greater than pre-defined values while the investment and operation costs of SynCons, and voltage deviation in the system are minimized. Three meta-heuristic optimization algorithms are used to implement the proposed method, and its performance is evaluated via Electromagnetic Transient (EMT) time-domain simulation in a modified IEEE 39-bus system. The PSCAD/EMTDC software is used for the time-domain simulation. The simulation results confirm that with optimized allocation and sizing, the SCR of the network in all areas is more than pre-defined values, and also, all renewable energy resources can ride through disturbances and comply with given grid codes.

In this paper, we study the stochastic lot sizing problem with probabilistic carbon emission constraints. To the best of our knowledge, this is the first study that considers this problem. We study the problem under static uncertainty strategy and α service level constraints. We consider both the periodic and global carbon emission constraints and use chance constraints to ensure that the carbon emission constraints are satisfied with the desired probabilities. We formulate the problems as mixed integer program and solve them by a commercial solver. Our computational experiments show that the formulations can be solved for quite large problem instances in reasonable times. We compare the probabilistic global and periodic emission constraints according to their effects on the total cost and carbon emissions.

Integration of Distributed Generations (DGs) into radial distribution network (RDN) is an emerging need to explore the benefits of renewable energy sources (RES). Increasing penetration of RES based DGs in RDN without proper planning leads to several operational problems such as excessive energy losses, poor voltage quality and load balancing. Hence, in this work, multi-objective optimization (MOO) problem is formulated by carefully chosen three conflicting objectives such as power loss minimization, enhancement of load balancing index (LBI) and aggregate voltage deviation index (AVDI). Teaching-Learning-Based-Optimization (TLBO) is used to optimize MOO problem considering placement of DGs at multiple locations in RDN satisfying the constraints on bus voltage magnitude, branch flows and DG size. Comprehensive simulation studies have been carried out to obtain optimal performance for 69-nodes RDN with the increasing penetration of DGs at multiple locations. It is shown that determination of optimal sizing of DGs at multiple locations in RDN with MOO results in lesser power losses, improved voltage profiles and better load balancing as compared to placement of single DG in RDN. Performance measures such as spacing and spread indicators are used for characterizing Pareto solutions for MOO problem. Such set of non-dominated solutions obtained from Pareto front during multi-objective TLBO gives proper guidelines to the utility operator about sizing and placement of DGs based on the assigned priorities to the objectives.

The endless problem of energy supplies are always floating on the surface. As a result, there are a daily improvement to optimize power generators, networks and system configuration. Renewable distributed generators (RDG) are in the heart of these developments. The size of RDG is increasing daily so, it must be optimized to maximize benefits and eliminate drawbacks. Optimization algorithms are one of the fast growing techniques. In this study the Manta Ray Foraging optimization algorithm (MRFO) is applied to minimize power losses through sizing and allocation of DG type I integrated into radial distribution network (RDN). The proposed technique was tested on three different networks, IEEE 33, 69 and 85 test systems. Also, three cases were assumed to evaluate the effectiveness of MRFO algorithm. The results were compared to recent applied techniques.

We examine the behavior of a strategic firm that invests-in and operates wind, energy storage, and transmission. The capacity of the energy storage and transmission are co-optimized with the firm's wind-supply and energy-storage offers into a centrally dispatched electricity market. We employ a bi-level stochastic optimization model. The upper level determines the capacities and offering strategies to maximize the firm's expected profits. Multiple lower-level problems represent market clearing under different operating conditions, which capture uncertainties. The resulting large-scale optimization model is solved using multi-cut Benders's decomposition. The model is applied to a case study that is based on Alberta's electricity market.

The discrete topology and sizing optimization of frame structures with compliance constraints is studied using a novel approach, which is capable of finding the theoretical lower bounds and high-quality discrete solutions in an efficient manner. The proposed approach works by reformulating the discrete problem as a relaxed semidefinite programming (SDP) problem. This reformulation is made possible by a linear relaxation of the original discrete space and the elimination of the nonconvex equilibrium equation using a semidefinite constraint. A continuous global optimum is first derived using existing solvers and then the discrete solution is discovered by the neighborhood search. Numerical examples are presented, including the sizing optimization of 2-Bay 6-Story frame and 3-Bay 10-Story frame, the topology and sizing optimization of 2-Bay 6-Story braced frame. A topology and sizing example with multiple load cases is also provided. The proposed approach and three other metaheuristic algorithms are used to solve these examples. Theoretical lower bounds for these examples can be efficiently discovered by the proposed approach. For the sizing problems, the discrete solutions by the proposed approach are all better than the other algorithms. For the topology and sizing problems, the proposed approach achieves discrete solutions better than genetic algorithm, but worse than the other metaheuristics. The computational superiority of the proposed approach is validated in all the examples.

In this paper, the sizing of an electrical power system for the Dhofar region in the southern of Oman is proposed and discussed through an optimization of the net project cost of different configur...

Hydrokinetic turbines (HKT’s) are used to produce power from the kinetic energy of the river current. A hydrokinetic turbine, when combined with a photovoltaic (PV)-battery system is an excellent o...

We consider a solar microgrid design and dispatch problem using an adaptive stochastic optimization framework. First, we propose a two-stage mixed-integer model for optimal placement and planning of distributed generation (DG) units and energy storage system units. We incorporate time series modeling into stochastic optimization approach to characterize the solar irradiance uncertainty. In the first stage, design decisions (e.g., location and sizing of DGs) are made and in the second stage, dispatch decisions (e.g., how much to generate, how much to store) are made such that electricity demand is met in a reliable and cost effective way. Chance constraints are employed to control the real load shedding within a predefined probability level. Then, we propose a combined sample average approximation (SAA) and linearization technique to solve this problem more efficiently. The advantage of this approach is that no additional binary variables are introduced while reformulating the chance constraints. Computational time, quality of solution, and load shedding percentage are compared with the traditional SAA. Moreover, we carry out a comprehensive out-of-sample simulation on a real-world case study in the state of Arizona assessing the effectiveness of our approach. Numerical experiments demonstrate that the chance constraints are effective tools for control of load shedding in distributed generation and the proposed approach outperforms traditional methods both in terms of true load shedding percentage and computational time.

This article presents a two-stage adaptive robust optimization (ARO) for optimal sizing and operation of residential solar photovoltaic (PV) systems coupled with battery units. Uncertainties of PV generation and load are modeled by user-defined bounded intervals through polyhedral uncertainty sets. The proposed model determines the optimal size of PV-battery system while minimizing operating costs under the worst-case realization of uncertainties. The ARO model is proposed as a trilevel min-max-min optimization problem. The outer min problem characterizes sizing variables as 'here-and-now' decisions to be obtained prior to uncertainty realization. The inner max-min problem, however, determines the operation variables in place of 'wait-and-see' decisions to be obtained after uncertainty realization. An iterative decomposition methodology is developed by means of the column-and-constraint technique to recast the trilevel problem into a single-level master problem (the outer min problem) and a bilevel subproblem (the inner max-min problem). The duality theory and the Big-M linearization technique are used to transform the bilevel subproblem into a solvable single-level max problem. The immunization of the model against uncertainties is justified by testing the obtained solutions against 36 500 trial uncertainty scenarios in a postevent analysis. The proposed postevent analysis also determines the optimum robustness level of the ARO model to avoid over/under conservative solutions. Refereed/Peer-reviewed

The economic development of any country is closely linked with the consumption of energy. Therefore, international policies encourage increasing penetration of renewable energy sources (RES) into the electrical grid in order to reduce CO2 emissions and cover ever-increasing demands. However, high variance of RES complicates their integration into power systems and complicates their transition from central to distributed energy sources. On the other hand, increasing the penetration of RES in electrical networks stimulates the demand for large capacity for energy storage. This paper presents a new approach to optimize the size of on-grid renewable energy systems integrated to pumped storage system using Salp Swarm Algorithm (SSA). This approach allows the examination of various energy sources and their combination to handle the optimal configuration of the hybrid system. The simulation and optimization process of the studied system have been carried out by MATLAB programming. The impact of the system under study on the grid is examined according to the power exchange values between the system and the grid. Moreover, different scenarios have been introduced for optimal operation. The simulation results indicate that these hybrid systems can reduce power exchange with the grid and ensure that the proposed system is economically and environmentally feasible. Furthermore, the results indicate the technical feasibility of seawater hydroelectric power plants in increasing the capacity of the electric grid to allow for high penetration of RES. Finally, the results showed that the best minimum value of the objective function is 3.9113 and showed that CO2 emission can be reduced about 29.65% per year compared to the conventional power plants.

This article presents an optimal placement and sizing of distribution static compensator (DSTATCOM) in radial distribution networks as multi-objective optimization with the objective of power loss reduction, voltage profile improvement and network reliability considering different variable demands using whale optimization algorithm (WOA). The WOA is inspired by the hunting behavior of humpback whales. The optimal location and size of DSTATCOM are considered as decision variables that are determined using the WOA. Network reliability is defined as energy not supplied (ENS) of the customers caused by line outages. The proposed method is implemented on IEEE 33-bus and Ahvaz 59-bus distribution networks. The optimal placement and sizing of DSTATCOM are investigated considering different residential, commercial and industrial loads, and also the effect of incorporating reliability is evaluated on power loss, minimum voltage and ENS. The simulation results show that the power loss and ENS are declined and also voltage profile is improved by determining the location and size of DSTATCOM in the networks in different loading conditions using the WOA. The obtained results demonstrate that considering reliability further reduces the ENS and more customer demand is met. The power loss decreases 21.60%, 30.71%, and 22.03% for residential, commercial and industrial loads in the 33-bus network and 26.86%, 31.94%, and 24.39% for the 69-bus network, respectively compared to base networks. Moreover, the performance of the WOA is compared with previous studies and the results indicate the superiority of the proposed method.