Sizing Of System Research Articles

Multi-Objective Decision-Making for an Island Microgrid in the Gulf of Maine

Microgrid implementation often lacks economic and environmental efficiencies due to sub-optimal configuration and operation. The current study aims to explore the optimal configuration and operational strategies for a microgrid system with maximum life cycle economic and environmental co-benefits. This study was inspired by a real microgrid optimization need for Shoals Marine Laboratory, a seasonal marine teaching and research field station on Appledore Island, Maine. A system dynamic model was developed to simulate the microgrid operation, and a multi-criteria analysis was performed based on diesel electricity generation, equivalent annual costs, and carbon footprint under various system sizing and operation scenarios. This study found that an effective battery capacity of 120–165 kWh (400–550 kWh actual with 30% depth of discharge) and a solar capacity of 85–105 kW can effectively minimize all three objectives under an average daily demand of 265 kWh during the study period. Additionally, implementing an alternative system operation strategy can lead to a 12% reduction in diesel electricity generation.

Optimal sizing of battery-supercapacitor energy storage systems for trams using improved PSO algorithm

A hybrid energy storage system (HESS) of tram composed of different energy storage elements (ESEs) is gradually being adopted, leveraging the advantages of each ESE. The optimal sizing of HESS with a reasonable combination of different ESEs has become an important issue in improving energy management efficiency. Therefore, the optimal sizing method of battery-supercapacitor energy storage systems for trams is developed to investigate the optimal configuration of ESEs based on a constant power threshold. Firstly, the optimal sizing model of HESS taking size, mass, and cost of ESEs as a comprehensive objective function is established. Then, an improved particle swarm optimization (PSO) algorithm with a competition mechanism is developed for obtaining the optimal configurations of ESEs. Finally, Guangzhou Haizhu tram is taken as an example to verify the developed method. The results show that the optimal HESS with different ESEs has advantages in reducing the size, mass, and cost, and exerting the charging and discharging capacity of ESEs.

Optimum sizing of stand-alone microgrids: Wind turbine, solar photovoltaic, and energy storage system

The growing energy demand and rising fossil fuel expenses in isolated and remote regions have increased interest in renewable energy sources (RESs). However, RESs such as photovoltaics (PVs) and wind turbines (WTs) are intermittent and fluctuating; hence reliability is a major concern. The hybridization of RESs with energy storage systems can effectively address their fluctuations and intermittency and enhance overall efficiency; however, the corresponding system cost is a primary concern. Thus, determining the optimal sizing of a hybrid system is the major challenge. Previous studies have suggested metaheuristic algorithms that rely on specific parameters to find an optimal solution. To address this issue, a hybrid algorithm, namely JGWO, which combines JAYA and grey wolf optimizer (GWO), has been proposed. The aim is to solve the unit sizing problem of a hybrid system (PV-WT-battery) that can meet consumers’ demands at the lowest total annual cost (TAC) while considering the maximum allowable probability of power supply loss (LPSPmax) to ensure system reliability. The JGWO is compared to JAYA, GWO, genetic optimization algorithm (GOA), and teaching-learning based optimization (TLBO) in reliability and TAC. Findings reveal that JGWO outperforms existing algorithms at LPSPmax values of 0%, 1%, 3%, 2%, and 5%, yielding TAC of 66542, 61102, 50247, 43046, and 34464 USD, respectively, for a hybrid system (PV-WT-battery). The results also show that the proposed hybrid PV-WT-battery system is more cost-efficient than PV-battery and WT-battery systems, regardless of the LPSPmax value.

Optimization of a hybrid solar/wind/storage system with bio-generator for a household by emerging metaheuristic optimization algorithm

The ever-increasing need for electricity in off-grid areas requires a safe and effective energy supply system. Considering the development of a sustainable energy system and the reduction of environmental pollution and energy cost per unit, this study focuses on the techno-economic study and optimal sizing of the solar, wind, bio-diesel generator, and energy storage structure. The emerging metaheuristic optimization method is used to estimate the techno-economic benefits of the suggested structure to provide the required electricity for a residential household. The optimal design is done with the harmony search (HS) method to validate the results obtained with the suggested algorithm. Minimizing the total annual cost (TAC) is a main objective for the design optimization of the system while satisfying the reliability and techno-enviro-economic constraints. The impact of the investment cost of biodiesel, biofuel cost, and reliability is investigated on the system sizing. The results show that the proposed algorithm has low simulation time (by 4.1 %) and the operating cost of the proposed algorithm is 1.12 % less than the existing HS algorithm based on the best TAC value. The TAC is received as $24,259 with the suggested method, which is the best optimal result compared to the result given by the HS algorithm. The proposed off-grid renewable energy system based on a bio-diesel generator is a viable and scalable solution to provide the required electricity for a residential household in all periods. The sensitivity analysis show that if the biofuel cost is reduced by 50.8 %, TAC will decrease by about 36.6 %. On the other hand, if the bio-diesel generator cost is reduced by 17 %, TAC will be decreased by 2.7 %. Finally, if the limit value of reliability is reduced from 2 to 0 %, TAC will increase by 13.2 %.

Techno-economic feasibility analysis of grid configuration sizing for hybrid renewable energy system in Turkey using different optimization techniques

Today, with a human population of 7.5 billion, increasing power consumption, and widespread use of high-technology equipment, the need for energy in the world is increasing day by day. The damage to nature caused by non-renewable energy resources and the economic and political disadvantages they cause in countries that must import these resources have led the world to search for alternative energy sources. One of the most important benefits of renewable energy sources is that they can create hybrid energy systems with other energy sources. Hybrid energy systems are structures in that more than one energy generation unit works together to feed the electrical load. In this paper, a hybrid system will be designed by using Homer PRO, to supply the energy consumed in the residential areas of Karaburun and Urla districts of Izmir province in Turkey with hybrid renewable energy systems. Systems of different sizes are designed for 50%, 75%, and 100% penetration rates, and sales to the grid are constrained to 0%, 25%, 50%, and no constraint on sales. Sensitivity analyses have been performed for 10% and 20% more power required by the load. Techno-economic analyses were done for the systems and the optimal system has been identified. The power flow of the optimal system is inspected by defining the network in Python and Fire Hawk Optimizer (FHO), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) are used for optimal sizing of the system. The optimal system has a Net Present Cost (NPC) of $52.3 M, 34 wind turbines, and 954 kW PV with conditions of 100% penetration and no restrictions on sales to the grid. FHO proposed 26 wind turbines and 780 kW PVs for Urla and 8 wind turbines, and 174 kW PVs for Karaburun. GWO proposed 25 wind turbines and 747 kW PVs for Urla, 9 wind turbines, and 165 PVs for Karaburun. PSO offered 24 wind turbines and 708 kW PVs for Urla, 10 wind turbines, and 246 kW PVs for Karaburun. When there is no sale to and 25% sales to the grid, it is seen that the optimal scenario is the system with a 50% penetration rate. In fully sale-constrained systems, NPCs are higher than the base system and they can never pay for themselves compared to the base system. It is concluded that smaller-sized systems are more suitable for systems with high constraints. The proposed FHO algorithm and GWO algorithm react to increasing penetration ratios by enlarging the system size in the regions with high load demand. PSO sizes the system like predictions by load demand. FHO reacts quicker to increasing penetrations than PSO and GWO. FHO algorithm is found to be usable to solve optimal power flow problems and optimal system sizing.

Bi-layer sizing and design optimization method of propulsion system for electric vertical takeoff and landing aircraft

Electric vertical takeoff and landing (eVTOL) aircraft is a promising solution for future urban air mobility, but it suffers from insufficient energy and power density as opposed to fossil fuel-based aircraft. This paper proposes a bi-layer design optimization method for electric propulsion systems. To tackle the issue of high-dimension design parameters and multiple performance constraints, the design process is divided into two parameter optimization problems. In the upper layer, only scalar parameters of components are considered to determine a feasible sizing result subject to energy-flow limits. The outcome of this layer further serves as the initial guess of the lower design layer. The constraints of power conservation and conversion among components are taken into account. The optimization is scheduled in a cascaded paradigm that maximizes the total energy efficiency with minimized total weight. The blade-element momentum method is employed to estimate propeller thrust and torque coefficients. The proposed method is applied to a tilt-wing eVTOL. The sizing results shows that the eVTOL mass is reduced by 11.66% as opposed to the benchmark method. The proposed method provides a generic framework for sizing and design optimization independent of configuration and mission profiles.

Energy Management and Charging Portfolio Analysis for Future Battery Powered Harbor Vessels

This paper intends to provide key insights to the marine designers and port authorities for adapting battery-operated zero emission electric harbor vessels (ZEE-HVs) and plan the resources for charging infrastructure. The present study is a cumulatively coordinated analysis of various significant aspects concerning the adaptation of ZEE-HVs for short-endurance commercial maritime operations. The sizing of onboard battery energy systems (OBES) is discussed for different short-endurance ZEE-HVs by considering the speed-resistance characteristics of the hull and open-water characteristics of the propeller without limiting their operations for specific load profiles/operating cycles. Charging portfolio selection is critical for a marine vessel as the OBES size is several times higher than a regular electric vehicle's battery. Hence charging portfolio selection criteria are presented to select a suitable battery charging protocol for each ZEE-HV as per the OBES ratings and available idle times on a typical day. Since the port is a commercial spot, uncertainty in resource planning leads to penalties and loss of goodwill. Therefore, Monte Carlo-based iterative analytical method is adopted to forecast the ZEE-HVs' maximum possible charging power demand on a typical day. By the load forecast data, optimal sizing of the load peak shaving battery (LPSB) was performed to limit load fluctuations at the port grid. The synergy of ZEE-HVs with LPSB and bi-directional charge-discharge controller is pivotal for effectively administering the seaport shore charging facility, which is demonstrated for a typical energy management cycle.

Motor Sizing of a Ship-Mounted Two-DoF Manipulator System Considering Variations of Ocean Wave Direction

Motor Sizing of a Ship-Mounted Two-DoF Manipulator System Considering Variations of Ocean Wave Direction

Optimum Design, Socioenvironmental Impact, and Exergy Analysis of a Solar and Rice Husk-Based Off-Grid Hybrid Renewable Energy System

This study examines the optimal sizing of an off-grid hybrid system comprising solar photovoltaic (PV), rice husk-based biomass, and lead-acid battery for meeting the electric demand of a rural community. Considering a selected remote village in Bangladesh as a case study, the proposed optimized system is primarily compared with the diesel generator and the micro gas turbine (MGT)-based options in techno-economic and environmental terms. The potential social benefits, such as the employment creation and the improvement in the human development index in the locality, have been investigated in this study. Moreover, the impacts of operational greenhouse gas emissions on the human health damage and the surrounding ecosystem have been examined. Additionally, an exergy analysis of the hybrid system and the components has been carried out. Results indicate that in addition to being the environmentally preferable option, the proposed PV/biomass/battery system offers a lower cost of energy of 0.314 $/kWh compared to the MGT-based system (0.377 $/kWh). Although the diesel-based system offers a marginally better economy (9.55% less energy cost), it comes with the expense of probable damages to human health and the ecosystem worth of $15,211 and $6,608, respectively, making biomass the best option with no such damages. Exergy analysis reveals higher loss from PV than biomass and 13.09% system exergy efficiency. The assessment of the social indicators testifies to the potential of promoting the human development index from its current value and the formation of 1.41 jobs to as high as 15.15 full-time permanent jobs with the installation of hybrid systems in the community.

GNSS denied navigation system for the manoeuvring flying objects

PurposeBefore designing a navigation system, it is necessary to analyse possible approaches in terms of expected accuracy, existing limitations and economic justification to select the most advantageous solution. This paper aims to collect possible navigation methods that can provide correction for inertial navigation and to evaluate their suitability for use on a manoeuvring tactical missile.Design/methodology/approachThe review of existing munitions was based on data collected from the literature and online databases. The data collected included dimensions, performance, applied navigation and guidance methods and their achievable accuracy. The requirements and limitations identified were confronted with the range of sensor parameters available on the market. Based on recent literature, navigation methods were reviewed and evaluated for applicability to inertial navigation system (INS) correction in global navigation satellite system-denied space.FindingsThe performance analysis of existing munition shows that small and relatively inexpensive micro-electro-mechanical system-type inertial sensors are required. A review of the parameters of existing devices of this type has shown that they are subject to measurement errors that do not allow them to achieve the delivery accuracy expected of precision missiles. The most promising navigation correction methods for manoeuvring flying objects have been identified.Originality/valueThe information presented in this paper is the result of the first phase of a project and presents the results of the requirements selection, initial sizing and preliminary design of the navigation system. This paper combines a review of the current state of the art in missile systems and an analysis of INS accuracy including the selection of sensor parameters.

Predicting the Heat Generation Rate in Large Format Pouch-Type Lithium-Ion Battery Cells for Automotive Applications

Accurate prediction of the heat generation rate of lithium ion batteries is critical for driving towards a cost effective and efficient design of a thermal management system for various applications.[1, 2] because of the increase in battery cell dimensions associated with an increase in battery cell capacity to drive towards a reduction in range anxiety for automotive applications, as well as more aggressive operating conditions that are typically seen in the automotive industry, such as direct current fast charge and sustained driving under load, heat generation at the cell level must be considered as spatially nonuniform during operation. Proper design of the thermal management system requires prediction[3] of the heat generation of the battery cell in both a lumped fashion for system sizing, as well as a distributed mode in order to focus cooling appropriately to decrease local temperature extremes. Local temperature extremes can adversely affect the aging of the battery cell due to the non-uniformities in electrochemical utilization of the active material, increased local reaction rates, and increased mechanical stresses in active materials.In this work, we detail our efforts to characterize the heat generation[4] of a battery cell under a variety of operating conditions, quantify the impact of temperature non-uniformities[5] on the aging of the battery cell, and experimentally[6, 7] evaluate the localized aging in the battery electrodes based on local temperature conditions. Here, we will describe the modeling methods developed to link operating conditions, aging, and heat generation with the electrochemical performance of the battery cell. A discussion of the model performance for relevant automotive applications will also be included.References M. Song, Y. Hu, S.-Y. Choe and T. R. Garrick, J Electrochem Soc, 167, 120503 (2020).Y. Hu, S.-Y. Choe and T. R. Garrick, Electrochim Acta, 362, 137124 (2020).M. Song, Y. Hu, S.-Y. Choe and T. R. Garrick, J Electrochem Soc, 169, 070502 (2022).Y. Hu, S.-Y. Choe and T. R. Garrick, Applied Thermal Engineering, 189, 116709 (2021).Y. Hu, S.-Y. Choe and T. R. Garrick, J Power Sources, 532, 231350 (2022).U. Janakiraman, T. R. Garrick and M. E. Fortier, J Electrochem Soc, 167, 160552 (2020).T. R. Garrick, J. Gao, X. Yang and B. J. Koch, J Electrochem Soc, 168, 010530 (2021).

Optimal Sizing and Management of Hybrid Renewable Energy System for DC-Powered Commercial Building

DC power may be more efficient than AC power in certain applications, especially when it comes to local generation and storage. This is because AC power requires extra equipment to convert it to DC power, which can lead to energy losses. Using DC power, on the other hand, makes it easier for devices to use it directly, resulting in higher energy efficiency. Additionally, using DC power can reduce equipment capital costs as it eliminates the need for additional AC–DC conversion equipment. Finally, DC power systems can offer new communication capabilities, including plug-and-play for generation and storage devices, making it simpler to integrate these systems into existing infrastructure. This paper analyzes the optimal size of a photovoltaic/PEM fuel cell system to supply a certain DC commercial load in NEOM city. To identify the best size of the PV/PEMFC, minimizing the cost of energy (COE) and minimizing the net present cost (NPC) are considered. The paper studies three sizes of PEMFCs: 15 kW, 20 kW, and 25 kW. In addition, five different PV modules are selected: Axitec 450 Watt, Jinko 415 Watt, REC Solar 410 Watt, Seraphim 310 Watt, and Tongwei 415 Watt. The results of the study confirmed that the best size of the hybrid system comprises a 15 kW PEMFC, a 267 kW Tongwei PV array, a 60 kg electrolyzer, and a 20 kg hydrogen tank. Under these conditions, the COE and NPC are 0.293 USD/kWh and 498,984 USD, respectively.

Optimal sizing and feasibility analysis of second-life battery energy storage systems for community microgrids considering carbon reduction

The rapid development of distributed renewable energy has made energy storage essential for efficient operation. However, energy storage is challenging for prosumers due to high investment costs and long payback periods. The second use of batteries has been evaluated as an alternative energy storage after the first service in electric vehicles with the remaining 80% capacity. This study investigates the feasibility of using secondary-use batteries as shared storage for prosumers. Financial and technical break-even points regarding the gradually increasing carbon limit on the way to 100% renewable have been determined. The proposed method's performance has been verified by conducting sensitivity analyses regarding various environmental and economic characteristics. The self-consumption rates have been achieved by 65.9% in Spain (Madrid) and 49.9% in England (London), and 48.5% in Türkiye (Antalya) without incentive. Increasing the carbon tax by 1 $/t for high and low energy tariffs can reduce carbon emissions by 181.3 and 1607 kg/yr. The optimum sizes remain until the carbon tax threshold is 60 $/tCO2eq for high energy tariffs. After 60 $/tCO2eq, it is tended to enlarge the hybrid power system. However, severe carbon emission restriction for zero carbon targets prevents the optimum system design since the carbon reduction cost increases to 232.7–304.8 $/kg. This study encourages investors and prosumers to plan the shared energy storage system sensibly and guides governments to develop realistic incentive mechanisms to implement second-life batteries effectively.

Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning

Reducing peak demand and enhancing self-sufficiency have made the battery energy storage system (BESS) essential in microgrids when combined with photovoltaic (PV) systems. Previous studies have shown that BESS usage remains expensive, and it is more practical to share it within a community rather than installing individual systems. Therefore, this study aimed to propose a framework for BESS sizing and scheduling in an energy-sharing community based on reinforcement learning. To validate the proposed framework, a case study was conducted based on the BESS ownership scenarios (i.e., individual-owned BESS (IOB) and community-shared BESS (CSB)) considering the following purposes of potential stakeholders: (i) enhancing SSR; (ii) reducing peak demand; and (iii) increasing economic profit. The maximum SSR of CSB was 12.4% higher than IOB, while the peak load of CSB was 0.07% lower than IOB. As the BESS size increased, the total cost of BESS increased, and the total profits on electricity decreased. The economic profit increased by about 38% when CSB was used compared to IOBs. The proposed framework offers more promise for a broader range of building loads and PV generation within the energy-sharing community. Ultimately, this study would allow prosumers, community practitioners, and policymakers to better understand the sharing mechanisms in the community and provide decision guidelines for ownership types, operational strategies, and sizing of BESS and PV systems.

Effect of differential control and sizing on multi-FCS architectures for heavy-duty fuel cell vehicles

The current trend towards a zero-emission transport sector has increased the interest of the scientific community and the industry in fuel cell (FC) technologies in the past few years. Previous studies have focused on passenger car analyses to differentiate them from the current battery electric vehicle (BEV) alternative. However, deploying these technologies may be even more critical for the transportation-produced global emissions if they are used in different applications, such as heavy-duty commercial vehicles. This study uses a differential control strategy to find the best fuel-cell performance for a heavy-duty vehicle application. In addition, and as a differentiation point from other studies in the literature, this article exploits the modularity of the heavy-duty truck sector to implement a design with optimal fuel cell system (FCS) sizing and control dynamics distribution in terms of durability and H2 consumption. Low dynamics could increase 471% in durability just for a 3.8% increase in H2 consumption. When using a multi-FCS with non-equal power FCS, a high dynamics behavior of the small FCS significantly improves the durability for a small consumption penalty (less than 0.7%). The obtained data has proven that the combination of these two design strategies shows an improved vehicle performance that could lead to environmental impact and cost reduction, which is significant in the current development stage of fuel cell vehicle (FCV) technologies.

Coupled thermo-electrical dispatch strategy with AI forecasting for optimal sizing of grid-connected hybrid renewable energy systems

In multi-energy systems the full utilisation of the generated energy is a challenge. Integrating heat and electricity supply at the system level design could provide an opportunity to address this challenge. In this paper we introduce and examine two coupled thermal-electrical dispatch strategies for grid-connected hybrid multi-energy systems supplying electrical and thermal demand loads. The dispatch strategy employs forecasting of energy resources and demand loads to prioritise supplying the thermal load in times of renewable surplus. Four forecasting algorithms, namely, baseline forecast, Facebook Prophet (FBP), Neural Prophet (NP), and Long Short-Term Memory model (LSTM) are implemented and used to generate annual forecast data for solar irradiance, wind speed, and thermal and electrical demand loads. To integrate forecast data within the dispatch strategy, new parameters are proposed to quantify the expected available energy within the forecast time horizon. A building complex for the Department of Education in the UK is used for conducting a system design case study. A genetic algorithm-based multi-objective optimisation with the levelised costs of electricity and heat as two objectives is conducted. The results show that the proposed dispatch algorithm produces systems with reduced levelised costs compared to the base case of using utility gas and electricity. Forecasting is particularly useful in reducing cost of heat, as it can prioritise supplying the thermal load in times of renewable surplus. LSTM proved to be the most accurate forecasting algorithm for this case, where the data has strong seasonality and trends. The main contribution of this work is to propose and demonstrate the effectiveness of tightly coupling thermo-electrical dispatch algorithms of HRES from the design stage, and how to effectively integrate forecast data within such algorithms.

Effects of photovoltaic panel type on optimum sizing of an electrical energy storage system using a stochastic optimization approach

This article proposes a stochastic optimization approach for an off-grid solar-wind with an electrical energy storage structure. The proposed optimization approach is a probabilistic algorithm based on simulated annealing for approximating the global optimum of the objective function. The optimal size of the electrical energy storage system for the solar-wind energy system as the final aim is calculated considering the reliability and annualized cost of the system. The objective function of this study is to reduce the unavailability and cost of the system with different types of solar panels (monocrystalline, polycrystalline, and thin film). The optimization process of the system size is accomplished using a stochastic optimization approach by considering the battery capacity, photovoltaic (PV) panel, and wind turbine area as decision variables. The harmony search algorithm is used to confirm the optimality of the system. The effects of photovoltaic panel type on the optimum sizing of electrical energy storage system are calculated in a case study over a year. So, a sensitivity study is performed to analyze the effect of the initial costs of components and the efficiency of photovoltaic panels on the annualized cost of the system. The results show that polycrystalline-PV solar/battery systems are more suitable for the proposed location than other systems, due to economic, technical, and environmental conditions. Also, the stochastic optimization approach is effective for the sizing process and can achieve the goals of the system. In three solar cell types, there is a slight increase (about 3.6 %) in TCOS (total cost of the system) value by increasing PV panel unit cost; and, there is a significant decrease in TCOS value (about 82 %) by decreasing battery unit cost; also, there is a significant decrease in TCOS value (about 28 %) by increasing PV efficiency.

Active and passive methods for cooling load reduction in a tropical building: A case study

In the present study, energy saving potential and economical viability of implementing various active and passive cooling methods to a hostel building was accessed for tropical climate. The transmitted load for the building envelope was reduced by proposed Energy conservation measures (ECMs) including Gypsum-based cow dung plaster at the walls, double glazing glass for the windows and cool paint in conjunction with Photovoltaic panel for the roof. The experimentally calculated U-values for proposed plastered wall, double glazing window and cool painted with Photovoltaic shaded roof were 1.85 W/m2 K, 3.45 W/m2 K and 1.1 W/m2 K respectively. The ventilation load for the proposed centralized air conditioning system was reduced by connecting an Earth Air Heat Exchanger (EAHE) to the outlet vent port. The sizing of EAHE system was done using a MATLAB code validated with own experimental data. By parametric analysis, EAHE system of 16 pipe configuration with each of length 100 m and diameter 0.4 m was selected as a good compromise between effectiveness of heat transfer and pressure drops. Using eQuest simulation software, the total peak cooling load calculated for base and proposed case was 285 TR and 186 TR respectively. For the proposed case, the ventilation load was reduced by 18 TR and transmission load was reduced by 81 TR. The energy saving, payback time, and Greenhouse house gas mitigation for the proposed case with ECMs and EAHE was 344 MWh/year, 3.2 year, and 378 t CO2/year respectively.

Comparative assessment of multi-objective optimization of hybrid energy storage system considering grid balancing

With the accelerated transition towards affordable and clean energy sources, the energy sector is undergoing a structural transformation that has resulted in a further increase in the complexity of energy system planning with rapid changes in techno-economic, environmental, reliability and social constraints. This signifies the consideration of purpose-driven multi-objective functions depending upon the functionality and applicability of the model. However, most of the studies adopt conventional bi-objective optimization either involving techno-economic, reliability and grid balancing parameters but there is a lack of comparative assessment of multi-objective optimization sizing for grid-interactive hybrid renewable energy system (HRES) consisting of short and long-term, battery and pumped hydro storage (PHS), energy storage systems (ESS). This study presents a comparative multi-objective framework to assess bi- and tri-objective function sizing techniques under grid balancing and non-balancing modes, to understand the scope and adaptivity of the modeling process for large-scale grid-interactive HRES. The analysis of results shows that the non-balancing mode underestimates the cost of energy (COE) by 18–30% compared to the grid balancing mode due to smaller decision variable space while long-term ESS dominance is vital for the reduction of grid burden compared to short-term ESS. In terms of configuration, a hybrid ESS system, 0.22 MWh battery, 18.1 MWh PHS, and 5.4 MW PV capacity, is the best optimal configuration in grid balancing mode with the COE, EEI and EII equal to 0.09 $/kWh, 7.5% and 10.5% respectively, whereas higher grid energy mismatch is induced by non-balancing mode with the overestimation of EEI and EII indexes up to 30% and 33% respectively. The environmental analysis shows that the carbon emissions avoided (CEA) are underestimated by 59.1% with the non-consideration of grid balancing. This signifies that the adaptive optimization model improves the design and planning process of grid-interactive HRES by capturing larger uncertainties related to COE, grid balancing, and CEA with changes in the system and ESS sizing. Overall, this analysis provides a purpose-driven perspective to energy modelers and policymakers for the energy system modeling process of grid-interactive HRES.

Optimal Allocation and Sizing of Decentralized Solar Photovoltaic Generators Using Unit Financial Impact Indicator

A novel financial metric denominated unit financial impact indicator (UFII) is proposed to minimize the payback period for solar photovoltaic (PV) systems investments and quantify the financial efficiency of allocation and sizing strategies. However, uncontrollable environmental conditions and operational uncertainties, such as variable power demands, component failures, and weather conditions, can threaten the robustness of the investment, and their effect needs to be accounted for. Therefore, a new probabilistic framework is proposed for the robust and optimal positioning and sizing of utility-scale PV systems in a transmission network. The probabilistic framework includes a new cloud intensity simulator to model solar photovoltaic power production based on historical data and quantified using an efficient Monte Carlo method. The optimized solution obtained using weighted sums of expected UFII and its variance is compared against those obtained by using well-established economic metrics from literature. The efficiency and usefulness of the proposed approach are tested on the 14-bus IEEE power grid case study. The results prove the applicability and efficacy of the new probabilistic metric to quantify the financial effectiveness of solar photovoltaic investments on different nodes and geographical regions in a power grid, considering the unavoidable conditional and operational uncertainty.