Optimal Power Management Research Articles

Optimizing Power Management in Distribution Networks: A Mathematical Modeling Approach for Coordinated Directional Over-Current Relay Control

Optimizing Power Management in Distribution Networks: A Mathematical Modeling Approach for Coordinated Directional Over-Current Relay Control

Comparative Analysis between Proportional-Integral and Artificial Neural Network Control of a Grid-Connected PV System

This article presents a comprehensive analysis of the modeling and control techniques applied to a photovoltaic (PV) system that is connected to a three-phase grid. To successfully integrate the PV system with the electrical grid, an innovative and reliable controller has been designed and put into practice. The utilization of an artificial neural network (ANN) enables the system to optimize power extraction from the PV panels, benefiting from the ANN's resilience and swift response to varying conditions. Moreover, a robust proportional-integral (PI) control strategy is introduced to govern the grid-side operations. This strategy of action focuses on managing the injection of both active and reactive electricity into the grid as well as controlling the voltage of the DC bus. A series of detailed simulations were carried out evaluating the efficiency of the suggested control strategy in the MATLAB/SIMULINK. The results obtained from these simulations share insightful information on the effectiveness and efficiency of the control system in ensuring optimal operation and power management of the PV system within the grid-connected setup.

Optimal power management of DGS and DSTATCOM using improved Ali Baba and the forty thieves optimizer

In this study an improved Ali Baba and the forty thieves Optimizer (IAFT) is proposed and successfully adapted and applied to enhance the technical performances of radial distribution network (RDN). The standard AFT governed by two sensible parameters to balance the exploration and the exploitation stages. In the proposed variant a modification is introduced using sine and cosine functions to create flexible balance between Intensification and diversification during search process. The proposed variant namely IAFT applied to solve various single and combined objective functions such as the improvement of total power losses (TPL), the minimization of total voltage deviation and the maximization of the loading capacity (LC) under fixed load and considering the random aspect of loads. The exchange of active powers is elaborated by integration of multi distribution generation based photovoltaic systems (PV), otherwise the optimal management of reactive power is achieved by the installation of multi DSTATCOM. The efficiency and robustness of the proposed variant validated on two RDN, the 33-Bus and the 69-Bus. The qualities of objective functions achieved and the statistical analysis elaborated compared to results achieved using several recent metaheuristic methods demonstrate the competitive aspect of the proposed IAFT in solving with accuracy various practical problems related to optimal power management of RDN.

Scrutiny of Hybrid Renewable Energy Systems for Control, Power Management, Optimization and Sizing: Challenges and Future Possibilities

To fulfill fast-growing energy needs, all energy sources should be utilized. Renewable energy is infinite and clean. However, its main disadvantage is that renewable energy sources are intermittent. A Hybrid Renewable Energy System (HRES) is built by integrating several distinct energy sources to deal with this problem. In regards to energy economy, economics, dependability, and flexibility, these hybrid systems can surpass the limits of individual energy producing technologies. The power capacity of HRESs increased from 700 GW to 3100 GW globally over the period 2000–2021. This study aimed to offer and analyze a comprehensive literature review of recently published works by several researchers in the area of HRESs. The HRES contains different Hybrid Energy Systems (HESs), which are categorized into three parts, namely, PV_Other, Wind_Other and PV_Wind_Other. These systems, based on different optimization techniques/software with techno-economic objective functions and constraints, are reviewed in this paper. The optimal sizing, control, and power management strategies of the HRES are elaborately discussed to harness its potential. It has been determined that Metaheuristic (MH) methods and HOMER software are mostly employed in the fields of HRES sizing, control, power management, and optimization. The review provides a critical analysis of the shortcomings of the existing HRES systems, while choosing optimization parameters, and control and power management schemes. Moreover, the study encapsulates the various challenges/barriers in adopting HRESs. Finally, this review highlights possible future opportunities for PV, Wind, and other HESs in the area of control, power management, optimization, and optimal sizing.

Interactive artificial ecosystem algorithm for solving power management optimizations

Introduction. Power planning and management of practical power systems considering the integration and coordination of various FACTS devices is a vital research area. Recently, several metaheuristic methods have been developed and applied to solve various optimization problems. Among these methods, an artificial ecosystem based optimization has been successfully proposed and applied to solve various industrial and planning problems. The novelty of the work consists in creating an interactive process search between diversification and intensification within the standard artificial ecosystem based optimization. The concept of the introduced variant is based on creating dynamic interaction between production operator and consumer operator during search process. Purpose. This paper introduces an interactive artificial ecosystem based optimization to solve with accuracy the multi objective power management optimization problems. Methods. The solution of the problem was carried out using MATLAB program and the developed package is based on combining the proposed metaheuristic method and the power flow tool based Newton-Raphson algorithm. Results. Obtained results confirmed that the proposed optimizer tool may be suitable to solve individually and simultaneously various objective functions such as the total fuel cost, the power losses and the voltage deviation. Practical value. The efficiency of the proposed variant in terms of solution quality and convergence behavior has been validated on two practical electric test systems: the IEEE-30-bus, and the IEEE-57-bus. A statistical comparative study with critical review is elaborated and intensively compared to various recent metaheuristic techniques confirm the competitive aspect and particularity of the proposed optimizer tool in solving with accuracy the power management considering various objective functions.

Hybrid Micro-Grid: a Review on Operational Stability Aspects

With the significantly rising demand for alternate energy resources like renewable sources, the power demands are fulfilled at reduced expenses, and increased efficiency. Despite these alternate generation techniques, there are still considerable challenges in controlling and working Microgrids (MG’s) configured in DC and AC. Hybrid Micro-Grids (HMGs) are developed particularly for overcoming the effective power regulation difficulties encountered in standalone MGs by combining the advantages of individual AC and DC configurations. Since microgrids are equipped with power electronic interfaces, unlike the conventional system, maintaining voltage stability, frequency stability, and optimal power management is challenging and requires a different approach. This paper attempts to analyze various stability issues in existing systems and the control methods employed in prior works for a better understanding and management of HMG systems. It also presents a comparative study of related works in terms of their control techniques and achieved performance. The paper further discusses various research challenges prevailing in this field thereby providing an insight into the development of better control methods and techniques for resolving various stability issues in HMG for better management and supporting future applications.

Energy management for solar-hydrogen microgrids with vehicle-to-grid and power-to-gas transactions

This study addresses the energy management of an integrated solar-hydrogen microgrid. The microgrid includes zero-emission vehicles, renewable energy sources, an electrolyzer, bidirectional charging stations, and a hydrogen refueling station with hydrogen storage. Vehicle-to-grid (V2G) charging stations can alleviate renewable electricity variability by discharging the energy of vehicle batteries back to the grid. The electrolyzer can absorb excess solar generation to balance supply and demand with power-to-gas (P2G) transactions. Given the uncertainties from intermittent renewable generation and energy demand, a two-stage stochastic optimization framework derives the optimal power management strategy for the component subsystems, aiming to minimize operating costs. The first stage of the model determines the day-ahead charging price and power supply for electric vehicle charging and hydrogen production, and the second stage performs real-time energy scheduling under different scenarios. The performance of the proposed optimization strategy is examined through a case study using real-world data, and the results demonstrate that daily operating costs can be reduced by up to 27.5%. Moreover, inclusion of the P2G process with hydrogen storage yields better performance to support peak shaving and reducing costs compared to using V2G or dynamic pricing-based demand response policies only.

Power management optimization of electric vehicles for grid frequency regulation: Comparative study

Electric vehicles (EVs) have shown promise in providing ancillary services, e.g., frequency regulation. This is mainly due to their capacities and fast response. On the contrary, the rapid integration of EVs in the grid poses challenges, such as frequency and voltage stability. In order to mitigate the above-mentioned issues, several dispatching strategies have been introduced in the recent literature to optimize the charging/discharging rates of EVs. In this paper, a comparative study of power management strategies for secondary frequency regulation (SFR) employing a fleet of EVs is presented. A hierarchical control scheme is employed to compare two cases, namely control at the charging station (CS) level and novel control at the EVs level. Under both cases, a multi-objective optimization approach is utilized to define the optimal charging and discharging rates of EVs using a pattern search algorithm. Furthermore, the performance of the two models is experimented under contingency cases, a notable contribution of this study. Finally, simulations are carried out using OPAL-RT real time simulator to validate the performance of the two models based on real-time traces obtained from Pennsylvania, New Jersey, and Maryland (PJM) interconnection and California independent system operator (CAISO). To further validate the proposed model, a comparison with a mixed-integer linear programming (MILP) based model is presented.

Real‐time resilient microgrid power management based on multi‐agent systems with price forecast

AbstractMicrogrids have emerged to diversify conventional electric generation using small‐scale distributed generation. Large efforts have been put into designing control strategies to optimise the power schedules of microgrids, however, verification that such control systems also are reliable in terms of stability during normal operation and fault conditions is needed. This study presents a hierarchical distributed control system that fulfils these conditions for an AC microgrid. The stability maintained by proposed controller, considering the large signal model, is analysed with the use of Lyapunov's direct method. Resilient control distribution is achieved by the implementation of suitable forecast models and fault‐tolerance mechanisms to avoid single points of failure. The resilience of the control system is verified with the use of graph theory. The stable and resilient operation of the proposed control system is tested by a real‐time microgrid model implemented with an OPAL‐RT real‐time simulator, combined with a communication network built with Raspberry Pis, testing the control system presented under normal and faulty conditions. Simulation results show a stable operation in terms of voltage and frequency in both conditions, resilient operation is shown for the faulty condition case. Additionally, cost minimisation performance is included to validate optimal power management capabilities.

Comparative feasibility analysis of an integrated renewable energy system (IRES) for an urban area

To identify cost-effective outcomes of IRES, this paper considers attributes such as Net Present Cost (NPC) and Cost of Energy (COE) for metropolitan areas. Mathematical model have been used and a simplified operational strategy have been presented for different configurations in MATLAB software and HOMER software.The designated urban area has periodic average solar radiation of 5.12 W/m2/day, annual biomass collection of 135.7 tons, wind speed of 4.35 m/s, and average river inflow velocity of 1.7 m/s. This study estimates the energy that possible to extract from each IRES element and optimal power management of a Integrated energy system is achieved by controlling the charging and discharging in MATLAB software. HOMER Software discovered a possible potential of PV array 15,900 (MWh/yr), total wind turbine output 1238.5 (MWh/yr), biogas plant output 423.8 (MWh/yr), hydrokinetic turbine 150.1 (MWh/Yr), and fuel cell output 1320.6 (MWh/Yr) to meet the selected urban area's 14398.84 (MWh/Yr) energy need.

Design Methodology and Mission Assessment of Parallel Hybrid Electric Propulsion Systems

Abstract An integrated engine cycle design methodology and mission assessment for parallel hybrid electric propulsion architectures are presented in this paper. The aircraft case study considered is inspired by Fokker 100, boosted by an electric motor on the low-pressure shaft of the gas turbine. The fuel burn benefits arising from boosting the low-pressure shaft are discussed for two different baseline engine technologies. A three-point engine cycle design method is developed to redesign the engine cycle according to the degree of hybridization. The integrated cycle design and power management optimization method is employed to identify potential fuel burn benefits from hybridization for multiple mission ranges. The sensitivity of these mission results has also been analyzed for different assumptions on the electric powertrain. With 1 MW motor power and a battery pack of 2307 kg, a 3% fuel burn benefit can be obtained by retrofitting the gas turbine for 400 nm range. Optimizing the power management strategy improves this fuel burn benefit by 0.2-0.3%. Redesigning the gas turbine and optimizing the power management strategy, provides a 4.2% fuel benefit on 400 nm. The results suggest that a high hybridization by power, low hybridization by energy, and ranges below 700 nm are the only cases where the redesigned hybrid electric aircraft has benefits in fuel burn and energy consumption relative to the baseline aircraft. Finally, it is found that the percentage of fuel burn benefits from the hybrid electric configuration increases with the improvement in engine technology.

Optimal design and power management control of hybrid biofuel–electric powertrain

This paper presents a multi-objective optimization procedure for a hybrid biofuel–electric vehicle (HBEV) powertrain design and fuzzy logic control. The vehicle concept is based on an electric drivetrain, while the battery is recharged by an onboard generator coupled to an ethanol-powered engine. The optimization procedure is done by the interactive adaptive-weight genetic algorithm method, aiming for the minimization of the fuel consumption, tailpipe emissions, powertrain size, and charging time, while the battery lifespan is improved. The powertrain parameters are defined as design variables, along with the membership functions, rules, and weights of the fuzzy logic controllers, responsible for the power split among the four in-wheel electric motors, the engine and generator operation. The optimization is done under the combination of four standard driving cycles combined in a 4 times loop, resulting in a 281.8 km path. As compared to a conventional vehicle with the same engine data, the best HBEV solution was able to save 17.78% fuel, while decreasing the emissions by 52.38% HC, 22.85% CO, and 28.57% NOx, considering a battery life expectation of 23,907 h. Moreover, this optimum HBEV was also evaluated under two combinations of real-world driving cycles, reaching fuel savings up to 30% and lower emissions as compared to the conventional vehicle in all analyzed scenarios. Finally, a cost analysis is performed to compare the proposed HBEV powertrain concept with other hybrid vehicle configurations presented in the literature.

Design optimization and optimal power management of standalone solar-hydrogen system using a new metaheuristic algorithm

Optimal configuration models and efficient energy management strategies are developed based on a standalone energy system, including hydrogen fuel cell and photovoltaic (PV) in green buildings. To optimize a hybrid scheme, integer decision variables and continuous decision variables are considered as the number of scheme components. To the optimal size of the system, the objective function is defined based on the system annual cost and loss of power supply probability (LPSP) concept for reliability. To define the optimal solution to the optimization problem, the use of three modified harmony search (MHS) algorithms are proposed, namely, MHSLD, MHSWF, and MHSLWF. These methods are compared with modified and original HS algorithms. Furthermore, three loss of power supply probability values are introduced (1 %. 2 %, and 3 %) for the optimal power management. In this regard, the effect of the PV price and reservoir tank cost variation on the justification of the systems is investigated. The simulation results reveal that the MHSWF performs with high accuracy and robustness as it obtains the minimal values of the investigated indexes for different desired reliability, βL, (1 %, 2 %, and 3 %) in all simulation runs obtained. Therefore, the ranking order of the search power of the algorithms is MHSWF > MHSLD > MHSLWF > MHS > HS. For the desired βL with 1 %, MHSWF obtained the minimal SAC values and achieved cost savings of 0.8 %, 6.1 %, 11 %, and 20.2 % as compared with four other methods, and the saving reached up to 13.2 % in the costs for other βL values. The sensitivity analyses have been performed on the results of the modeling which reveals that by the increase of the desired βL (from 1 % to 3 %), the values of PV areas, and the number of hydrogen tanks decrease by 5.4 % and 31 %, respectively, while the value of SAC decreases by 31 %.In different desired βL, by decreasing the PV panel area cost, the value of SAC based on LPSP decreases, and by reducing the hydrogen tank cost of the system by 46 %, the SAC of the system based on LPSP decreased by 39 %.

Optimal Eco-Routing for Hybrid Vehicles With Powertrain Model Embedded

Exploiting the full potential of hybrid electric vehicles (HEVs) requires suitable (i) route selection and (ii) power management. Due to coupling of the two subproblems, an integrated optimization problem is desired, i.e., optimizing simultaneously the route selection and the split between combustion engine and electric motor over the entire route selection. The resulting optimal route and vehicle operation can be used as a basis for a subordinate vehicle controller. We present an eco-routing approach that embeds a hybrid (mechanistic/data-driven) model of the HEV powertrain in an integrated routing and power management optimization problem. Formulating the integrated routing problem with the hybrid model yields a mixed-integer bilinear program which we reformulate and solve a mixed-integer linear program using a state-of-the-art solver. The results show the validity of the developed hybrid powertrain model and demonstrate that the eco routing approach with the powertrain model embedded can be applied to large-scale problems. We consider optimization for minimal travel time and minimum fuel consumption. The latter results in fuel demand reductions up to 70 %. Alternatively, we minimize the fuel consumption while constraining the travel time to a maximum value resulting in up to 50 % fuel demand reductions. The highest fuel demand reductions are achieved in urban environments. The entire framework is written in python and provided as an open-source version (MIT License) under <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://git.rwth-aachen.de/avt-svt/public/optimal-routing</uri> that can readily be applied.

DC-Distributed Power System Modeling and Hardware-in-the-Loop (HIL) Evaluation of Fuel Cell-Powered Marine Vessel

Environmentally driven regulations are significantly affecting shipping in recent years, where the shipbuilding industry is required to comply with upcoming restrictions concerning polluting emissions. The all-electric ship (AES) is one of the most promising technologies for complying with the increasingly strict environmental regulations, improving fuel efficiency, and enhancing system dynamic performance. In this study, the dc-distributed power grid of an AES integrated with fuel cells and batteries has been configured using extensive electrification technology, where the system-level shipboard power plant has been modeled with the average modeling method. The model not only incorporates the hybrid power source integration but also the primary and secondary power management as a whole. In addition, a hardware-in-the-loop (HIL) has been set up to replicate the real-time system behavior, which is essential for the verification of any optimal power management control algorithms to be developed in future work. Finally, both the mathematical and real-time models are validated against the full-scale hybrid shipboard power system.

HİBRİT GÜÇ SİSTEMİ İLE İKLİMLENDİRME YÜKLERİNİN OPTİMAL İŞLETİM MODELİ VE HASTANE İKLİMLENDİRME YÜKLERİ İÇİN TALEP CEVABI UYGULAMASI

Bu çalışmada, kamu kuruluşları, hastane, okul vb. gibi kurumların iklimlendirme cihazlarının yük talebinin karşılanmasında alternatif enerji kaynaklarından optimal olarak yararlanma stratejisi ele alınmıştır. İklimlendirme yüklerinin karşılanmasında rüzgar türbini (RT), güneş pili (GP) ve batarya (Bat) ile teşkil edilen hibrit güç sistemi önerilmiştir. Hibrit güç sistemi optimal talep cevabı uygulamasında kullanılmasına imkan verecek şekilde modellenmiştir. Ayrıca, hibrit kaynakların kesintili üretim karakteristikleri gereği hibrit güç sistemine şebeke gücü de ilave edilmiştir. Optimizasyon probleminde alternatif enerji kaynaklarının maksimum kapasitede kullanımı ve ilgili kaynakların öncelikli olarak iklimlendirme yüklerinin talebine cevap verecek şekilde kullanımı amaç olarak belirlenmiştir. Elde edilen karışık tamsayı lineer programlama modeli GAMS v.24.1.3 ortamında CPLEX v.12 çözücüsü ile test edilmiştir. Kış ve yaz koşulları dikkate alınarak optimal işletim modelinin benzetim çalışmaları yapılmıştır ve ilgili sonuçlar sunulmuştur.

Joint coordination of optimal power management and energy storage system sizing for a full-scale marine current turbine considering microgrid integration constraint

The issues of variability and uncertainty of tidal speed result in severe challenges for standalone microgrid (as island grids for examples) operators. Indeed, to maintain the system power balance, existing classical generators which are associated with tidal power units must be able to frequently change their operating point, either up or down, to respond to the fast fluctuations of tidal power and consumption and sometimes exceed their maximal ramp capability. This in turn accelerates aging process and results in serious damage. The switching to a safer and more reliable system seems to be a promising and long-term solution. This paper proposes to associate a supercapacitor-based Energy Storage System (SC-ESS) to a tidal turbine to smooth the fast power production fluctuations. The association of this SC-ESS with the tidal turbine unit aims to meet requirements on the variation of injected tidal power in an islanded microgrid. An original smart power management strategy is proposed for the use of the ESS. Taking into account this strategy, an optimal design methodology based on simulations on the more severe encountered day cases is proposed. This methodology allows to determine a minimal sizing of the ESS to fulfil the grid requirements. The paper also proposes a complete modeling of the whole system behavior. The Energetic Macroscopic Representation (EMR) tool is used to realize the system model where the inversion-based control principle is then used to deduce the control schemes. This global methodology is applied to a test tidal turbine which is based on a real 1 MW prototype setup to the islanded grid of Ushant in France. Using real measured data, it is shown that a relatively small and low cost SCs pack is sufficient to accommodate the high tidal power fluctuations. A comparison is performed between the existing configuration where a resistor bank is used to shave the excessive power variation and the considered case where SCs pack is used. For one tidal cycle, it is shown that a reduction in energy loss of 18.2% is achieved, which presents a non-negligible gain for Ushant's Island microgrid.

Optimal LED Power Allocation Framework for a Location-Assisted Indoor Visible Light Communication System

The widespread deployment of white light-emitting6 diodes (LEDs) for illumination provides a unique opportunity to create a flexible, accurate, and ubiquitous indoor communication and positioning system. In this paper, we exploit the location information obtained via LEDs to improve the communication performance of an indoor visible light communication (VLC) system. Specifically, we propose an optimal LED power management framework to maximize the average data rate across the room while satisfying the bit error rate (BER) and illumination constraint across the room. The maximum allowed localization error, as a function of the number of blockages and the LED irradiance angle, have been calculated. In addition, the closed-form expression for the BER is derived for the proposed optimal LED power allocation scheme. We have also formulated an optimization problem that will maximize the power savings among the LEDs with respect to the number of blockages and permissible error in localization. It has been shown that, by employing the proposed optimal LED power allocation will results in a significant amount of power-saving, which is approximately 40% for 4 LEDs configuration and 70% 26 for 8 LEDs configuration as compared to equal power allocation. Further, the maximum allowed localization error is found out to be 28 approximately 7 cm and 18 cm with 4 and 8 LEDs, respectively, to 29 achieve the maximum achievable data rate. Finally, it is shown that 30 the dimming range of up to 70% can be achieved for the proposed system.

Optimal heat and power management of a reversible solid oxide cell based microgrid for effective technoeconomic hydrogen consumption and storage

This paper proposes and examines a highly integrated microgrid based on a reversible solid oxide cell, aimed at satisfying electrical and thermal loads of a 20-unit residential complex as well as the demands of electric and fuel cell vehicles. Such a system has been conceived as a profitable ready-made solution to be embedded into existing plants already equipped with renewable energy sources (i.e., wind farm and photovoltaic panels) by means of a reversible solid oxide cell and energy storage technologies. A dynamic programming-based routine has been suitably implemented as an algorithm for both the electrical and thermal sides of the plant for managing the power split indices. In addition, an external routine has been deployed to consider economic aspects; in particular, attention has been paid to the levelized cost of energy, allowing for comparisons with current reliable energy generation technologies. The analyses involve parametric assessments of multiple reversible solid oxide cell sizes and economic discount rates while fixing the lifetime of the plant at 30 years. In accordance with the results of the optimal microgrid design, by exploiting 100% of the rSOC working time (shared by mode as 40% fuel cell and 60% electrolyzer) a simple payback period of 5.97 years is achieved along with a levelized cost of energy index value in the 0.1 €/kWh-0.2 €/kWh range.

Stochastic Optimal Strategy for Power Management in Interconnected Multi-Microgrid Systems

A novel stochastic strategy for solving the problem of optimal power management of multi-microgrid (MMG) systems is suggested in this paper. The considered objectives are minimizing the total cost and emission of the system. The suggested algorithm is applied on a MMG consisting of four microgrids (MG), each including fossil fuel-based generator units, wind turbine (WT), photovoltaic (PV) panel, battery, and local loads. The unscented transformation (UT) method is applied to deal with the inherent uncertainties of the renewable energy sources (RES) and forecasted values of the load demand and electricity price. The proposed algorithm is applied to solve the power management of a sample MMG system in both deterministic and probabilistic scenarios. It is justified through simulation results that the suggested algorithm is an efficient approach in satisfying the minimization of the cost and the environmental objective functions. When considering uncertainties, it is observed that the maximum achievable profit is about 23% less than that of the deterministic condition, while the minimum emission level increases 22%. It can be concluded that considering uncertainties has a significant effect on the economic index. Therefore, to present more accurate and realistic results it is essential to consider uncertainties in solving the optimal power management of MMG system.