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Conventional wireless sensors have difficulty solving the problem of energy limitation, especially in sensor networks in hard-to-reach extreme areas. In order to solve the problem that it is difficult to charge wireless sensors in the field using conventional energy sources, the energy harvesting wireless senor is designed to use renewable energy sources for power supply. Considering the uncertainty and unknown nature of renewable energy generation, and the need for effective energy management of the sensor. In this paper, an Node Power Control Optimization (NPCO) power allocation algorithm is proposed to adjust the power allocation problem of wireless sensor nodes within each time slot. In addition, to address the unknown and random nature of energy arrival, this paper proposes a CLSTM model based on deep learning to predict the energy arrival. The continuous autonomous energy management of wireless sensor nodes is achieved by combining the CLSTM prediction results using the NPCO algorithm. The algorithm is applicable to continuous states and is able to show good performance in the verification of real solar data. The algorithm achieves better performance in terms of long-term average net bit rate compared to the current DDPG algorithm, AC algorithm, and Lyapunov optimization algorithm. [ABSTRACT FROM AUTHOR]

Advancements in new technologies, a reduction in CO2 emissions, and the rising demand for energy are causing a growth in the share of renewable energy sources. In distribution networks, an increasing number of distributed generators (DGs) makes the utility grid's protection complex and demanding. Vector surge and rate-of-change-of-frequency are the established anti-islanding protection methods, recognizing that the standard paradigm for protection, involving distributed generation, cannot be set only once but has to be continuously updated following the requirements and changes in the system. One of the requirements is active participation in the preservation of system frequency and voltage, which can be interrupted if the DG trips and disconnects from the utility grid. Anti-islanding protection and spurious tripping can be avoided by implementing new algorithms and techniques. This paper presents a novel protection scheme based on a voltage frequency differential. The proposed algorithm employs remote and local frequency measurements in such a manner that, for the occurrence of a frequency difference, it is assumed that the DG is in an islanding state. In this article, we demonstrate the feasibility of the algorithm through numerical analysis of grid events and laboratory testing emulating real grid-measured values. The test results show that the algorithm is resilient to false tripping for non-islanding events and more reliable than conventional methods in islanding detection. The algorithm can be set to low-frequency differential values, drastically reducing the non-detection zone in any DG type, regardless of its size and voltage level at the point of common coupling. Unlike standard anti-islanding methods, the algorithm supports the ability of the DG to fault-ride through demand. [ABSTRACT FROM AUTHOR]

The overwhelming growth of cloud computing has introduced new challenges for the cloud infrastructure. Many cloud service providers (CSPs) have been adopting renewable energy (RE) sources to increase profitability and reduce carbon emissions. Therefore, recent literature focuses on managing cloud infrastructure with RE sources in addition to traditional non-renewable energy (NRE) sources whenever required. However, these works aim to maximize the usage of RE or minimize the cost without considering the uncertainty (UN). This paper presents three user request-based scheduling algorithms, namely UN-based future-aware best fit, UN-based round-robin, and UN-based highest available renewable first, by managing the UN of RE and NRE. These algorithms consider two types of UN, namely UN-RE and UN-NRE, concerning the user request. The proposed algorithms undergo a rigorous simulation process with 200 to 2000 user requests and 20 to 200 datacenters and are compared using overall cost, UN cost and time, and the number of used RE resources. [ABSTRACT FROM AUTHOR]

This paper deals with the problems related to power stability issues in the isolated wind-solar based renewable energy system. The stability issues in such system are produced mainly due to unbalanced loading, distortion of load current and intermittent nature of renewable energy sources. To overcome these problems, A voltage source converter (VSC) is used in proposed literature. The VSC is driven using a time domain-based signal decomposition algorithm consisting a 3-phase dual reduced order generalized integrators frequency Locked loop (DROGI-FLL). The 3 phase DROGI FLL has the inherent abilities of noise rejection and error tracking, which improves the system stability and maintains the voltage and frequency of the system constant under transient conditions. The transient conditions are simulated by varying the loading condition, solar irradiance and wind speed in the system. Apart from stability issues this system also deals with the issues related to reactive composition, natural current compensation and operation of SPV system at maximum power point (MPP). A perturb and observe (P&O) maximum power point tracking (MPPT) is employed for the working of solar photovoltaic systems (SPV) system at MPP and to maximize the utilization of renewable energy. The neutral current compensation is achieved by using a star-double delta transformer. This entire system is developed and tested in the MATLAB/SIMULINK environment.. [ABSTRACT FROM AUTHOR]

The need for environmentally-friendly energy generation and massive exploitation of fossil fuels are leading to the increased demand for renewable energy sources like photovoltaic (PV) and wind energy, etc. The fluctuating behaviour of PV causes various disadvantages, of which the generation of Inter-harmonics is the major problem. The production of Inter-harmonics by the load-interfacing inverters is primarily due to the Maximum Power Point Tracking (MPPT) techniques employed in PV system. The requirement of multiple perturbations in tracking peak points is the primary cause behind the inter-harmonic generation. Since solar irradiation varies throughout a specific period, there can be situations, where the operating irradiance changes abruptly from one state to the other. These abrupt changes generate enormous fluctuations in the MPPT power output, resulting in an inter-harmonics generation. This paper proposes an evolved INter-HARmonic-Elimination (INHARE) algorithm for minimizing the PV fluctuating behaviour, leading to lower voltage ripple and settling time. The proposed INHARE algorithm is examined for a 0.52 kW PV system, where the Inter-harmonics content is reduced considerably in load current for the case of varying irradiances at a constant temperature. [ABSTRACT FROM AUTHOR]

The demand for a reliable, cheap and environmentally friendly source of energy makes the integration of renewable energy into power networks a global challenge. Furthermore, reliability, as one of the core elements of efficient and cost-effective energy management options, is still among the dominant factors/techniques that receive more attention for realistic penetrations of renewable energy into the electricity grid. This paper proposes an efficient way of energy management for a grid-connected microgrid. The grid-connected microgrid used in the analysis consists of solar photovoltaic (P.V.) and battery. In this microgrid configuration, oftentimes, the output power might not be equal to the system demand; in this regard, it is expected that the mismatch between these output powers is not zero. However, to reduce the mismatch between demand and supply to be close to zero, this paper proposes strategies of increasing the rated power of solar, battery and grid separately and combining them with a view of finding the cheapest option among these strategies. The results have shown that the cost increment for different options is USD 280.792, 84.48 and 48.204 for storage, P.V. and grid, respectively. These have shown that the storage option is the most expansive option for improving P.V. grid-connected microgrids. This is followed immediately by the P.V. option, which is weather dependent. On the other hand, the grid option is the cheapest option for system reliability improvement. This paper is expected to be useful to both new researchers and experts who are working in energy management with an emphasis on the reliability aspect. [ABSTRACT FROM AUTHOR]

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance. [ABSTRACT FROM AUTHOR]

In recent years, a pronounced transition to the exploitation of renewable energy sources has be observed worldwide, driven by current climate concerns and the scarcity of conventional fuels. However, this paradigm shift is accompanied by new challenges for existing power systems. Therefore, the hosting capacity must be exhaustively assessed in order to maximize the penetration of distributed generation while mitigating any adverse impact on the electrical grid in terms of voltage and the operational boundaries of the equipment. In this regard, multiple aspects must be addressed in order to maintain the proper functioning of the system following the new installations' capacities. This paper introduces a sequential methodology designed to determine the maximum hosting capacity of a power system through the optimal allocation of both active and reactive power. To achieve this goal, an Improved Salp Swarm Algorithm is proposed, aiming to establish the appropriate operational planning of the power grid considering extensive distributed generation integration, while still ensuring a safe operation. The case study validates the relevance of the proposed model, demonstrating a successful enhancement of hosting capacity by 14.5% relative to standard models. [ABSTRACT FROM AUTHOR]

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids. [ABSTRACT FROM AUTHOR]

The integration of demand response programs (DRPs) into the energy management (EM) system of microgrids (MGs) helps in improving the load characteristics by allowing consumers to interoperate for achieving techno-economic advantages. In this paper, an improved algorithm is called LINFO is proposed for modifying search ability of the original weIghted meaN oF vectOrs (INFO) algorithm as well as avoiding its weaknesses like trapping in a local optima. The improved algorithm's efficiency is confirmed by comparing its results with those obtained by the original INFO and other optimization techniques using different standard benchmark test functions. Moreover, this improved algorithm and the original version are applied for solving the EM problem with the aim of optimizing the operation cost of the MGs in the presence DRPs. They are used to solve day-ahead EM problem for optimal operation of renewable energy resources, the optimal generation from a conventional diesel engines (DEs); taking into account the participation of customers in DRP for minimizing MG operating cost, which includes the cost of DEs fuel and the power transactions cost with the main grid. To demonstrate the efficacy of the proposed LINFO, simulation results are compared with the results of well-known and newly developed optimization techniques. [ABSTRACT FROM AUTHOR]

Backward/forward load flow algorithm is commonly used in the power system, particularly in radial distribution networks. However, the rapid development in the power electronics technologies makes power systems more flexible with the integration of Renewable Energy Resources (RESs) and compensation devices. Consequently, this paper presents simple models for wind turbine generators based on Squirrel Cage Induction Generator (SCIG) and Distribution Static Synchronous Compensator (DSTATCOM) into a backward/forward load flow algorithm. In the wind generator model, the mechanical powers are collected from the manufactures data where the electrical power is calculated through the SCIG equivalent circuit and injected as negative loads at the connected buses. The second model in this paper is the DSTATCOM devices which are utilized to recover the SCIG reactive power. In the proposed model, the connected DSTATCOM bus is converted to a voltage-controlled bus (PV-bus type) with zero active power. Moreover, the reactive power of the DSTATCOM is updated during the iterative process until the load flow convergence. The simplified models for both wind generators and DSTATCOM are validated based on standard IEEE 33-bus and 85-bus radial distribution systems. The obtained results show the feasibility and effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

In order to actively respond to the "14th Five-Year Plan," the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure's low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China's carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm's path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm's path optimization is 15.72% less expensive than the genetic algorithm's. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the "14th Five-Year" energy plan. [ABSTRACT FROM AUTHOR]

Due to the increase in environmental concerns, it becomes mandatory now to focus on energy management and the planning of resources. An integration of distributed generation resources with conventional sources is gaining popularity. The hybrid modeling of the power system poses a complex and challenging optimization problem without getting trapped in a local optimum. A novel Hybrid Statistical Multiswarm Particle Swarm Optimizer-Sine Cosine Algorithm (HSMPSO-SCA) is developed in this paper, and its performance is evaluated on various test benchmark functions. The real operating conditions such as valve point loading, prohibited zones, ramp rate limits and uncertainties in solar and wind power generation for the IEEE 30 bus system are considered for combined emission economic load dispatch (CEELD). The performance of the HSMPSO-SCA is also analyzed for the solution of CEELD for various standard test systems with interaction of solar and wind plants. The efficacy of the proposed HSMPSO-SCA is found to be much better than various metaheuristics, hybrid and multi-agent algorithms in minimizing cost along with emissions optimally and achieves better accuracy and a faster convergence rate. [ABSTRACT FROM AUTHOR]

The grid-connected inverter is the key component for the reliable and safe operation of grid-interconnected renewable energy systems. In order to ensure that the inverter output current is in-phase synchronized with the grid voltage, a highly efficient and fast control strategy is required. This paper proposes an algorithmically driven approach for designing the adaptive controller for a grid-connected DC/AC inverter. The controller consists of two adaptive IIR filters based on the LMS algorithm and has two modes of operation. During the learning mode, the controller uses the filters to estimate the coefficients of the inverse transfer function of the PWM-driven inverter at several grid frequencies within a specific range. During the online mode, the controller generates the driving signal for the inverter block using a set of learned coefficients corresponding to the current grid frequency. By constantly monitoring the grid voltage and reconfiguring the controller accordingly, a fast adaptation of the inverter output current to grid frequency variations is achieved. For grid frequency variations in the range of 48 to 52 Hz, the MATLAB simulation results show that the phase difference between the grid voltage and inverter output current waveforms becomes less than 1 degree after at most four periods. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate. [ABSTRACT FROM AUTHOR]

The lack of partnerships between biomass generating producers is one of the main barriers to efficient biogas production. Partnerships can help producers properly dispose of waste, assist in the supply of biodigesters, allow the union of properties with different potential biomass generators into clusters, and optimize operating costs. In this sense, new measures must be adopted with regard to the establishment of strategic partnerships between producers. Therefore, in this paper, we propose a new clustering algorithm, BiogasCluster , to decide the optimal number of clusters for generating a renewable energy community. To test the proposed algorithm's applicability, we implemented the algorithm in R language. Experiments with real databases showed that the BiogasCluster proved to be efficient in generating stable clusters for all considered scenarios. It was also found that the filters and criteria used in the proposed algorithm have not been used in previous studies. This study provides important guidance to creating strategic partnerships within bioenergy generation in addition to obtaining a preliminary assessment of the possibility of establishing clusters of rural properties with the aim of achieving energy security and adding value to their businesses. [ABSTRACT FROM AUTHOR]

The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of an MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes an MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework. To enhance the generalization ability of dealing with various uncertainties, we also propose an improved multi-agent soft actor-critic (MASAC) algorithm, which facilitates energy transactions between multi-agents in MMG, and employs automated machine learning (AutoML) to optimize the MASAC hyperparameters to further improve the generalization of deep reinforcement learning (DRL). The test results demonstrate that the proposed method successfully achieves power complementarity between different entities and reduces the MMG system's operating cost. Additionally, the proposal significantly outperforms other state-of-the-art reinforcement learning algorithms with better economy and higher calculation efficiency. [ABSTRACT FROM AUTHOR]

The frequency diversion in hybrid power systems is a major challenge due to the unpredictable power generation of renewable energies. An automatic generation controller (AGC) system is utilised in a hybrid power system to correct the frequency when the power generation of renewable energies and consumers' load demand are changing rapidly. While a neural network (NN) model based on a back-propagation (BP) training algorithm is commonly used to design AGCs, it requires a complicated training methodology and a longer processing time. In this paper, a bacterial foraging algorithm (BF) was employed to enhance the learning of the NN model for AGCs based on adequately identifying the initial weights of the model. Hence, the training error of the NN model was addressed quickly when it was compared with the traditional NN model, resulting in an accurate signal prediction. To assess the proposed AGC, a power system with a photovoltaic (PV) generation test model was designed using MATLAB/Simulink. The outcomes of this research demonstrate that the AGC of the BF-NN-based model was effective in correcting the frequency of the hybrid power system and minimising its overshoot under various conditions. The BP-NN was compared to a PID, showing that the former achieved the lowest standard transit time of 5.20 s under the mismatching power conditions of load disturbance and PV power generation fluctuation. [ABSTRACT FROM AUTHOR]

The integration of the variable renewable energies makes the operation conditions of the power system ever-changeable. Consequently, the power system operational reliability evaluation is increasingly important. This paper introduces the concept of decision-dependent uncertainty (DDU) in the operational reliability evaluation. Unlike the exogenous uncertainties, DDU reveals that the decisions of the system operation could significantly affect the resolution of the uncertainties which influence the reliability metrics. In this paper, the proposed DDU modeling method links the device reliability indices, i.e., the forced outage rate, and the operational-decision variables. The impacts of DDU on operational reliability are analyzed based on a reliability-constrained stochastic unit commitment (UC) model. An adaptive reliability improvement UC (ARIUC) algorithm is proposed to efficiently solve the problem. Case studies underline the necessity of considering DDU in power system operational reliability evaluations. [ABSTRACT FROM AUTHOR]

The renewable energy sources provide various benefits including low operating costs and environmentally friendly compared to traditional power sources. The integration of renewable energy sources in traditional power systems has increased the difficulty for operation of the power system because of the discontinuous nature and uncertain of these sources. Thus, solving the optimal power flow (OPF) problem incorporation renewable energy sources is one of the challenges for system operators. This paper uses the AHA algorithm to solve the problem of OPF with renewable energy sources (RES) for five other objective functions including total cost, emission, active power loss, voltage deviation and L-Index. To demonstrate the effective of AHA for OPF with RES problem, the Salp Swarm Algorithm (SSA), Pratical Swarm Optimization (PSO), Teaching Learning based Optimization (TLBO) technique, Differential Evolution (DE) are implemented to contrast with AHA algorithm. The simulation result on the IEEE 30 bus system shows the AHA can obtain the better solution with the higher success rate than SSA, PSO, DE, and TLBO methods. In addition, the result comparisons with other exiting algorithms show that, AHA algorithm is an effective approach for the OPF with RES problem. [ABSTRACT FROM AUTHOR]

Renewable Energy Communities (RECs) are emerging as an effective concept and model to empower the active participation of citizens in the energy transition, not only as energy consumers but also as promoters of environmentally friendly energy generation solutions, particularly through the use of photovoltaic panels. This paper aims to contribute to the management and optimization of individual and community Distributed Energy Resources (DER). The solution follows a price and source-based REC management program, in which consumers' day-ahead flexible loads (Flex Offers) are shifted according to electricity generation availability, prices, and personal preferences, to balance the grid and incentivize user participation. The heuristic approach used in the proposed algorithms allows for the optimization of energy resources in a distributed edge-and-fog approach with a low computational overhead. The simulations performed using real-world energy consumption and flexibility data of a REC with 50 dwellings show an average cost reduction, taking into consideration all the seasons of the year, of 6.5%, with a peak of 12.2% reduction in the summer, and an average increase of 32.6% in individual self-consumption. In addition, the case study demonstrates promising results regarding grid load balancing and the introduction of intra-community energy trading. [ABSTRACT FROM AUTHOR]

Fossil fuels cause many negative impacts on the environment. Thus, the integration of renewable energy sources into traditional systems is increasingly prioritized for development. In this paper, Improved Coyote Optimization Algorithm (ICOA) is proposed for finding the most suitable location and the best capacity of photovoltaic distributed generators (PDGs) in radial distribution systems (RDSs). This new algorithm is an upgraded version of Coyote Optimization Algorithm (COA) and it has a better performance, a faster convergence speed and a more stable feature than its original form. The study focuses on the main objective function of minimizing both total active power losses on conductors and the impact of harmonics on node voltage and current flowing in distribution lines. In addition to currents with base frequency, harmonic flows also cause active power loss in distribution lines and the negative impact is also considered in the paper as a novelty. ICOA together with COA and Salp Swarm Algorithm (SSA) are applied for the IEEE 69-node radial distribution system for comparison with other methods in the literature. The comparative analysis sees that ICOA is a powerful method in optimally placing PDGs for reducing power loss and harmonic distortions. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable energy, new challenges, such as power fluctuation suppression and inertial support capability, have arisen in the power sector. Battery energy storage systems play an essential role in renewable energy integration. In this paper, a distributed virtual synchronous generator (VSG) control method for a battery energy storage system (BESS) with a cascaded H-bridge converter in a grid-connected mode is proposed. The VSG is developed without communication dependence, and state-of-charge (SOC) balancing control is achieved using the distributed average algorithm. Owing to the low varying speed of SOC, the bandwidth of the distributed communication networks is extremely slow, which decreases the cost. Therefore, the proposed method can simultaneously provide inertial support and accurate SOC balancing. The stability is also proved using root locus analysis. Finally, simulations under different conditions are carried out to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow problem, called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution operator in a single algorithmic framework. Additionally, an adaptive method is proposed to update the number of solutions evolved by each DE operator based on both the diversity of the population and the quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%. • Development of an adaptive method (AM) for optimizing diversity and solution quality. • Innovative constraint handling approach, progressively adding constraints for improved performance. • Incorporation of intermittent renewable energy models for realistic problem solving. • Extensive validation on IEEE 30-bus and IEEE 118-bus networks, outperforming state-of-the-art algorithms in cost, loss, and environmental impact reduction. [ABSTRACT FROM AUTHOR]

Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system ( DEED IWEv ) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the DEED IWEv model. [ABSTRACT FROM AUTHOR]

The vigorous development and the increasing popularity of cloud computing highlight the necessity of reducing data center energy consumption and the environmental impact of carbon dioxide emissions. For geographically distributed data centers, cloud servers are connected to the conventional power grid and in addition they are supported by an attached renewable energy source. Since the carbon footprint rate of energy consumption has dynamic differences in space, reducing energy consumption does not mean decrease carbon emission, which indicates that energy consumption and carbon footprint need to be synergistically optimized. In this paper, an energy and carbon-aware algorithm for virtual machine placement is proposed. The goal is to obtain a virtual machine allocation scheme that aims to achieve the trade-off between energy consumption and carbon emissions by improving renewable energy utilization. The experimental results show that the proposed approach is more energy-efficient and greener, which can also maximize the renewable energy utilization with 73.11% while ensuring the SLA violation with 0.2% in comparison to the baseline algorithms. • Propose a novel energy-aware VM placement approach in cloud data centers. • Introduce renewable energy for managing energy consumption and carbon footprint. • Design renewable energy generation prediction method based on EEMD and TCN. • Demonstrate the superiority of the proposed algorithm with five baseline algorithms. [ABSTRACT FROM AUTHOR]

The integration of renewable energy generation and nonlinear power electronic equipment into the grid brings about complex harmonics and interharmonics problems. The amplitude and frequency of harmonics and interharmonics should be detected by high time-frequency (T-F) resolution methods owing to their time-varying transient features. In this paper, a synchrosqueezing adaptive S-transform (SAST) method is proposed to detect the parameters of harmonics. Firstly, the time-frequency spectrum (TFS) of the harmonic signals is acquired by an adaptive S-transform (AST) algorithm. The TFS results are then subjected to synchronous compression, so as to achieve higher time-frequency representation precision. The detection results of the simulation signals show that SAST can achieve a better time-frequency resolution than the S-transform (ST) and synchrosqueezing short-time Fourier transform (SSTFT). In addition, the application of SAST to the analysis of experimental signals also suggests its superiority in the parameter detection of harmonics, especially for the time-varying interharmonics. [ABSTRACT FROM AUTHOR]

This document presents in detail the mathematical formulation of the second renewable energy auction in Colombia, used for the allocation of long-term energy contracts, which was carried out through a double-sided auction, defined in Resolutions 4-0590 and 4-0591 of 2019 from the UPME. The mixed integer linear programming model developed in this paper solves an optimization problem that seeks the combination of offers that maximizes the benefit of the consumer, subject to operational and economic constraints proposed by regulatory mechanisms. The effectiveness of the algorithm developed in Python was tested by validating it with real data, obtaining the same results published by UPME. [ABSTRACT FROM AUTHOR]

Installations of an Energy Storage System (ESS) with various functions such as power stabilization of renewable energy, demand management, and frequency adjustment are increasing. In particular, ESS for demand management is being established for high-voltage customers (300 KVA–1000 KVA) who have placed an Auto Section Switch (ASS) at the connection point within the distribution system. However, a power outage may occur in the Power Receiving System (PRS) when a short-circuit fault due to insulation breakdown occurs at the ESS DC side. The reason for this breakdown is that the fault current is reduced by transformer impedance, and the ASS is opened before the DC power fuse. Therefore, using the Graphic Solution Method (GSM), this paper presents an operation algorithm for protection coordination that isolates the fault section by first operating the DC power fuse with a small fault current. Furthermore, fault analysis modeling for a PRS composed of a switchgear section, a main distribution panel, a Power Conditioning System (PCS), a power fuse, and a battery is performed through PSCAD/EMTDC. From the simulation results, it is confirmed that the fault section is quickly isolated, and power outages for high-voltage customers are prevented because the DC power fuse selected by the proposed operation algorithm of protection coordination is opened before the ASS. [ABSTRACT FROM AUTHOR]

It is now more then a choice to use small-distributed generators (DGs) based on renewable energy resources (RES) due to their benefits in minimizing environmental problems and simplifying power system planning and operation. The integration of DGs in the main grid or in forming a standalone microgrid (MG) is increasing day by day and became as an alternative solution to large conventional central power stations and as a key for the electrification of rural areas. This paper presents an overall comparison of controlling the MG system combining between different DGs in islanded mode. Energy management system (EMS) applied is mainly an essential task to maintain an ideal flow of energy in-between generation and consumption, many are strategies to do so, the artificial intelligence (AI) (highlighted) is one of the most advantages methods to help sizing, optimizing, and power energy managing. To solve this problem in this paper we present a Fuzzy Logic Control of isolated MG in comparison with a classic PI control system to see the different influences in maintaining stability in voltage and frequency output especially in the standalone application. The considered HRES combines a wind turbine (WT) and photovoltaic (PV) and an energy storage system (ESS). Simulation results obtained from MATLAB/Simulink environment demonstrates the effectiveness of the proposed intelligent artificial algorithm. [ABSTRACT FROM AUTHOR]

The intermittency of single renewable energy sources affects significantly their reliability, and hence, hybrid renewable energy systems (HRESs) are introduced. HRESs are notably used to meet the needs of residential loads. Nevertheless, power surplus and shortages still represent issues that must be handled by energy management systems. In this paper, a novel method for optimal energy management of a grid connected micro-grid composed of five smart Moroccan prosumers is presented. The proposed energy management strategy is organized using three levels, namely, HRES level, load-scheduling level, and communication-sharing level between HRESs. The first two levels of energy management are processed at the local level, i.e., inside each smart home. For the third level, our paper demonstrates the feasibility of interactions and communication between the five different houses interconnected using an optimized infrastructure topology as a smart neighborhood grid. Each of these houses is empowered by their own hybrid renewable energy system made of possible system hybridization based on PV (Photovoltaic), wind turbine, and battery storage system. This helps each house to manage its surplus and to deal with energy shortage through the proposed multi-level system's energy management algorithm. The application of information and communication technologies enables the set of smart homes connected in a micro-grid framework to contribute to the emergence of the concept of the internet of sustainable green things. Simulations conducted by the MATLAB platform validate the proposed methodology, which relies on micro-grid surplus injection and neighborhood storage during extreme surplus, else load dynamics thereafter micro-grid-injected energy recovering during hybrid energy deficit. The proposed system manages the energy produced by HRESs and reduces the energy demanded by the utility grid using the proposed topology that connects houses and ensures interactions between the micro-grid and the utility. The results show that the frequency of micro-grid deficiency was significantly reduced due to the established micro-grid with the five houses according to the proposed topology and the developed multi-level management system. [ABSTRACT FROM AUTHOR]

Coal-fired power plants are widely used to achieve a power balance in grids with renewable energy, which leads to new requirements for speediness in load dispatch. This paper presents a nondominated-sorting grey wolf optimizer algorithm (NSGWO) for the multiobjective load dispatch of coal-fired power plants that employed efficient nondominated sorting, a reference-point selection strategy, and a simulated binary crossover operator. The optimization results of the benchmark functions indicated that the NSGWO algorithm had a better accuracy and a better distribution than the traditional multiobjective grey wolf optimizer algorithm. Regarding the load dispatch of economy, environmental protection, and speediness strategies, the NSGWO had the best performance of all the simulated algorithms. The optimal-compromise solutions of the economy and speediness strategies of the NSGWO algorithm had a good distribution, which elucidated that this novel algorithm was favorable to allowing coal-fired power plants to accommodate renewable energy. [ABSTRACT FROM AUTHOR]

Coordinated voltage control CVC is necessary in microgrid with high penetration of distributed renewable energy resources, to keep the voltage in required range and to optimize the grid performance. The SCADA-as-a-service (SCADA-aaS) approach allows the secured deployment of CVC and provides the interoperability framework for the network operators to distribute their CVC algorithm to remote microgrid or to implement a third-party CVC. This paper presents the first real-world implementation of CVC algorithm deployment via SCADA-aaS. This real-world implementation demonstrates the feasibility of the SCADA-aaS approach. Experiences and lessons learned from the implementation are discussed. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) plays a vital role in electrical power networks. However, power loss reduction, voltage profile improvement, friendly environment, and reliability are all benefits of DG units. In this research work, a worthwhile methodology is recommended for optimal allocation of traditional (gas turbine) and renewable energy sources that are based on distributed generators which include solar and wind in the distribution system. The major objective of the research paper is the minimization of real, reactive power losses and emissions produced during the application of these conventional sources. Originally, the best locations to place this DG are identified using the concept of water, energy, and food algorithm (WEFA). The number and sizes of these renewable energy sources selected (wind and solar) are determined by applying the concepts of the Dragonfly Algorithm. The Weibull and beta distribution functions are modeled to extract the exact position to fix our DGs to minimize losses within the distribution network. To assess the performance of WEF five different cases scenario considered are DG capacity, Location of Bus, voltage profile, maximum power loss as well as utilization rate. The proposed WEF Algorithm is tested on the IEEE standard 33-bus system. The simulated results were compared with others found in literature and found to be better in terms of power loss reductions. [ABSTRACT FROM AUTHOR]

The ability of an algorithm to extract power quality parameters on a low-end digital controller with constrained resources and computational capacity can promote comprehensive adoption of power quality control and monitoring infrastructure because of reduced cost and thus improve operational ability in a microgrid or distribution grid and support more in-depth integration of renewable energy. Since conventional harmonic extraction for single-phase grid mostly operate on Fourier transform, lock-in amplifier and their derivative algorithms, they demand large memory and computational power which are available with high-end application-specific processors, digital signal processors (DSPs) and field programmable gate arrays (FPGAs). In this paper, a computationally efficient harmonic extraction algorithm is proposed, which can be implemented on commercially available microcontrollers with ease, without compromising accuracy. A detailed theoretical analysis of the proposed method, along with various simulation and the experimental results are presented. Workarounds for minor implementation issues on digital controllers are discussed as well. [ABSTRACT FROM AUTHOR]

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (d FDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the d FDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (d FDBARO) algorithm. Moreover, the practicability of the proposed d FDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed d FDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the d FDBARO algorithm is available at https://www.mathworks.com/matlabcentral/fileexchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm. [ABSTRACT FROM AUTHOR]

The concept of a microgrid (MG) has been introduced to integrate the conventional generators, different renewable energy resources and energy storage systems (ESS) to meet the specific load demand. However, the intermittent nature of renewable energy resources produces a variable output, which drives an imbalance between power generation and demand in MG. The ESS is utilized to makes a balance between power generation and demand. When several renewable energy resources and ESS are available in MG as energy resources, then an energy management system (EMS) is required that can handle the stochastic nature of renewable energy resources, schedule the power of renewable energy resources and ESS for managing the power flow among MG resources and main grid while ensuing cost-effective operation. Therefore, this paper proposed an optimum EMS that aims to minimize the overall operating cost of grid-connected MG along with the short-term forecasting of PV power and load demand. The proposed EMS consists of four modules: forecasting, scheduling, data acquisition (DAQ), and human–machine interface (HMI) modules. An improved hybrid forecasting approach that combines a 3-level stationary wavelet transform (SWT) and grey wolf optimization-based least-square support vector machine (GWO-LSSVM) is proposed in the forecasting module to achieve day-ahead forecasting of PV power and load demand. In the scheduling module, the weighted salp swarm algorithm-based scheduling is applied to achieve the optimum power flow of grid-connected MG. Then, the DAQ and HMI module is used to monitor, analyze, and modified the input variables of the forecasting and scheduling module. The MATLAB/Simulink environment is then used to simulate the proposed EMS for grid-connected MG. Finally, numerical results demonstrate the efficiency of the proposed EMS for grid-connected MG with commercial load demand over the existing competitive approaches. [ABSTRACT FROM AUTHOR]

Renewable energy resources based hybrid microgrid has emerged as a significant component of the power system. Nevertheless, the control and management of power, voltage and frequency under transition between grid-connected mode (GCM) and islanded mode (IM) is a major concern. This paper proposes a self-sustained and flexible novel cooperative control strategy (CCS) for smart hybrid microgrid (SHMG) under GCM and IM mode operations. In islanded operation of SHMG, conventional droop offers ease of control but its execution with seamless transition between operating modes poses poor voltage regulation and frequency deviation. For this purpose, an intelligent cooperative controller is proposed. It consists of a modified P/f-Q/V droop control (MDC) algorithm along with soft computing based VF-controller unit (VFCU). The proposed control scheme is implemented to regulate voltage and frequency. It also enhances the power quality with unbalanced non-linear load and source intermittencies. The absence of a communication link reduces cost and also eliminates the risk of single point breakdown. The SHMG has been simulated in MATLAB and implemented in real-time. The obtained experimental results demonstrate the competency and high robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Solar energy is the most techno‐economically viable renewable source of energy and can be effectively converted into electrical power by Photovoltaic (PV) systems. As partial shading (PS) may reduce the harvested energy of PV systems, this paper proposes a novel tunicate swarm algorithm (TSA) based MPPT (maximum power point tracking) strategy to tackle the PS issue. More specifically, the simple and effective modeling of TSA with a search and skipping (SAS) scheme is utilized to minimize the tracking time and search area. The SAS scheme can discard the voltage range lacking global maximum power point (GMPP) and significantly reduce computation time. Consequently, power tracking, tracking time, and robustness can be greatly enhanced. The performance of the proposed TSA strategy is comprehensively analyzed against state‐of‐the‐art techniques, including incremental conductance (InC), improved particle swarm optimization (IPSO), grey wolf optimization (GWO), and cuckoo search algorithm (CSA), through detailed case studies, which include standard array configurations, PS conditions, varying irradiance patterns, fast‐changing temperature, and the field atmospheric data. TSA is further validated on a low‐cost hardware setup, confirming its superior performance. The results provide insightful validation of the practicality of the proposed TSA strategy in the real‐world applications. [ABSTRACT FROM AUTHOR]

Energy management is a critical issue for the economical and optimum operation of grid integrated renewable energy systems. In conventional grid integrated systems, the control methods are operated according to the design criteria for grid connection modes and cause surplus energy for low-loading conditions under grid-failure situations. For this purpose, in this paper, a flexible control strategy is improved basis on perturb & observe MPPT, and it is purposed to improve the system efficiency under grid-connection and grid-failure modes of renewable energy systems. In this way, it is aimed to ensure optimized power delivering capability with optimum load voltages/currents at desired values, and it also prevents overloading under grid-failure mode. To show the proposed method's validity, the algorithm of control method is embedded in a function block and performed for grid connection & grid-failure modes under different load conditions. The performance results show that the flexible control strategy prevents surplus energy under the grid-failure mode and reduces the power losses compared to the conventional method. Also, the proposed method increases the load-side power quality. The results show that THD values are significantly reduced to fewer than the conventional perturb & observe method. • A flexible control strategy for grid-connection and grid-failure modes. • Optimum energy generation for local loads under grid-failure mode. • Comparison with conventional perturb & observe method based system. • Enhancement of electrical energy quality at load-side. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the allocation of a hybrid system that includes PV‐DG and DSTATCOM. The planning problem considers the variations of load demand and solar irradiance under deterministic and probabilistic conditions. An efficient optimization algorithm called MPA is implemented to assign the optimal placement and ratings of the hybrid PV‐DG and DSTATCOM. The considered objective function is a multi‐objective function that includes the annual cost reduction, improvement of voltage profiles, and system stability improvement. The assessment is accomplished with the inclusion of a single and two‐hybrid system on a large 94‐bus system. For validating the effectiveness of the MPA, the yielded results are compared with the PSO, which is considered a commonly used algorithm. In deterministic conditions, the hourly variations of the load demand and solar radiation are considered in four yearly seasons, while in probabilistic conditions, 3 years of hourly historical data of solar irradiance and load demand are utilized to describe the uncertainties of the load demand and solar irradiance. The simulation results demonstrate that the optimal inclusion of a single‐hybrid PV‐DG and DSTATCOM system can enhance the system's technical performance (the voltage profile, the voltage stability) and enhance the economic scheme (total annual cost reduction). In addition, the inclusion of two‐hybrid systems is superior compared with the inclusion of a single‐hybrid system in terms of the considered objective functions, as well as the proposed technique is more efficient for solving the allocation problem of the hybrid system. [ABSTRACT FROM AUTHOR]

Summary: Quasi Z‐source inverter (qZSI) is a single‐stage high gain buck‐boost converter suitable for various applications like renewable energy sources, power electronic drives, and power supply systems. Controller design for qZSI should ensure simultaneous control of multiple objectives such as the DC capacitor voltage, source inductor current, and load current. While controllers dealing with multiple objectives, obtaining a good steady‐state and transient performance with low control complexity is essential. Fortunately, Model Predictive Control (MPC) is emerging as an effective alternative that can address all the above‐said issues simultaneously. However, some of the severe problems to be addressed with the conventional MPC algorithms developed for qZSI are applying a single vector per sampling duration, higher computational requirements, and lack of analytic procedure to handle weight factors. This paper presents a control algorithm with features of weight factor elimination and AC load current ripple reduction by using a modified double vector predictive control approach with fewer computational requirements. The principle of inclusion of zero vector, weight factor elimination, and a detailed calculation for duty ratio are included. As the converter operates at a fixed frequency, significant ripple reduction for DC current, link voltage, and load current ripple have been achieved. The proposed MPC requires only ≈81% of computations needed for the conventional MPC. Methodological comparison has been provided to present the superiority of the proposed MPC compared to appropriate conventional MPCs available in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm, known as Refraction based Whale Optimization Algorithm (RWOA), for providing optimal solutions to the Combined Economic Emission Dispatch problem based on the production of an optimum solar energy system. It validates the robustness of the proposed algorithm in Hybrid Renewable Energy Systems (HRES). To control the emission and economic cost, it is necessary to connect the wind turbine with the thermal power plant. Finally, the performance of the proposed approach is compared with the other conventional approaches concerning the economic cost, emission cost transmission loss. Accordingly, the results, shows that the proposed RWOA method is 92.81%, 99.2%, 92.8%, 80%, 37.8%, 47.34%, and 45.83% superior to GA, GWO, WOA, DA, GWSO, LGSO and WWO at 40th iteration. [ABSTRACT FROM AUTHOR]

The optimal operation analysis plays an important role in estimating the expected return of investment for power systems. However, this work could be highly challenging under the context of active distribution network, where a variety of renewable energy sources and emerging active loads (such as Internet data centers) could be penetrated that introduce a high level of nonlinearity and nonconvexity characteristics into the system modeling. To overcome such challenge, this paper presents a new methodological framework based on the improvements of symbiotic organisms search (SOS) algorithm to address the optimal operation problem of active distribution systems containing renewable energy sources and datacenter resources, aiming to provide a practical tool for system analysis, particularly subject to nonconvexity nature of system components. For this purpose, a generic model for the distribution system including wind power, PV, and datacenter resources is first developed, which not only captures the uncertain nature of system components but also accounts for their spatiotemporal flexibility during operation. On this basis, in order to improve the computation efficiency of the problem, the SOS algorithm is improved by designing the selection strategy of random parameters. By using the penalty function method, the concerned problem is expressed as a nonlinear unconstrained optimization problem. The performance of the proposed model and algorithm is examined through comparative studies. It is shown that the proposed method is able to schedule the renewable energy resources and flexible demand of datacenters coordinately to reduce the operation cost of the system significantly in the case study. In addition, the proposed algorithm demonstrates a higher level of accuracy as well as better convergence efficiency compared to other conventional techniques. [ABSTRACT FROM AUTHOR]

Summary: It is known that the demand for renewable energy systems during recent years has increased significantly, especially for wind and photovoltaic systems. The continuous improvement of the efficiency and performance of these systems is important to their growth, particularly in terms of harvesting the maximum power. Many types of methods and techniques are available for tracking the maximum power point, the most important of which is the concept of artificial intelligence. Artificial intelligence (eg, neural networks and fuzzy neural networks) has great potential to handle complex systems or those that have non‐linear parameters and are influenced by environmental conditions. In this paper, artificial intelligence is used to predict the optimal reference values for wind turbines and photovoltaic panels used in maximum power point tracking (MPPT). Neural networks were used in the wind turbine system, while fuzzy neural networks were used in the photovoltaic systems. The networks were trained in real‐time using an incremental training mode based on the back‐propagation algorithm. The proposed system was designed practically based on the microcontroller and has been tested, and the results are extracted in real‐time. The results have proven their effectiveness and conformity with the required optimal values. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to explore the performance of a residential grid-tied hybrid (GTH) system which relies on economic and environmental aspects. A photovoltaic- (PV-) wind turbine- (WT-) battery storage system with maximizing self-consumption and time-of-use (ToU) pricing is conducted to examine the system efficiency. In so doing, technical optimization criteria with taking into consideration renewable energy benefits including feed-in-tariff (FIT) and greenhouse gas emission (GHG) reduction are analyzed. As the battery has a substantial effect on the operational cost of the system, the energy management strategy (EMS) will incorporate the daily operating cost of the battery and the effect of the degradation. The model can give the opportunity to the network to sell or purchase energy from the system. The simulation results demonstrate the effectiveness of the proposed approach in which the new objective function achieves the maximum cost-saving (99.81%) and income (5.16 $/day) compared to other existing strategies as well as the lowest GHG emission. Furthermore, the battery enhances the best daily self-consumption and load cover ratio. Then, as the model is nonlinear, a comparison with other existing algorithms is performed to select the feasible, robust, and reliable model for the residential application. A hybrid algorithm (HGAFMINCON) is developed to demonstrate the superiority of the algorithm over FMINCON and GA shown in terms of cost savings and income. [ABSTRACT FROM AUTHOR]

Transactive energy distribution systems (TEDS) have unleashed new economic opportunities in the distribution sector via a new local distribution operator (LDO) that enables transactions between peers and the LDO (P2LDO) and peer‐to‐peer transactions. Aiming to unleash full benefits from existing distributed energy resources (DERs), this paper introduces an optimization algorithm for the operation of small, numerous, and disparate DER aggregations. The proposed algorithm seeks to maximize aggregator profits obtained via P2LDO and peer‐to‐peer transactions for energy and demand response, while being cognizant of capacity obligations acquired during transactive energy distribution system planning phases. In addition, the concept of universal metering is introduced to upgrade the economic opportunity of DERs. Results obtained via case studies show that the proposed approach can help DER owners to increase their revenue. An aggregated case study for 300 m shows that the overall revenue can be increased by more than 100% when operating DERs in an aggregated fashion. [ABSTRACT FROM AUTHOR]

With the introduction of massive renewable energy sources and storage devices, the traditional process of grid operation must be improved in order to be safe, reliable, fast responsive and cost efficient, and in this regard power flow solvers are indispensable. In this paper, we introduce an Interior Point-based (IP) Multi-Period AC Optimal Power Flow (MPOPF) solver for the integration of Stationary Energy Storage Systems (SESS) and Electric Vehicles (EV). The primary methodology is based on: 1) analytic and exact calculation of partial differential equations of the Lagrangian sub-problem, and 2) exploiting the sparse structure and pattern of the coefficient matrix of Newton-Raphson approach in the IP algorithm. Extensive results of the application of proposed methods on several benchmark test systems are presented and elaborated, where the advantages and disadvantages of different existing algorithms for the solution of MPOPF, from the standpoint of computational efficiency, are brought forward. We compare the computational performance of the proposed Schur-Complement algorithm with a direct sparse LU solver. The comparison is performed for two different applicational purposes: SESS and EV. The results suggest the substantial computational performance of Schur-Complement algorithm in comparison with that of a direct LU solver when the number of storage devices and optimisation horizon increase for both cases of SESS and EV. Also, some situations where computational performance is inferior are discussed. [ABSTRACT FROM AUTHOR]

Summary: The application of renewable energy sources in electrical energy generation is becoming widespread due to the decrease of installation costs and the increase of environmental concerns. Hybrid power generation systems are advantageous to meet the load demand, but optimal sizing is the main concern for having a cost‐effective system based on given load demand and techno‐economic indicators. This paper proposes a deterministic algorithm and utilizes genetic and artificial bee colony (ABC) optimization algorithms for optimal sizing of PV/battery and PV/WT/battery hybrid systems with minimum levelized cost of electricity (LCOE) constraint for two locations, Nigde and Bozcaada, in Turkey. The loss of power supply probability (LPSP) is used to build a reliable system and to make sure that the system produces required energy. Experimental results showed that optimal sizing of each location is different due to different wind and solar characteristics of locations. PV/battery model is more suitable for Nigde location with 1.22% LPSP and 0.1514 [$/kWh] LCOE, while PV/WT/battery model is more cost‐efficient for Bozcaada location with 1.952% LPSP and 0.0872 [$/kWh] LCOE. Time performances of the algorithms are also investigated. It has been seen that the ABC algorithm has better performance and less execution time. This study demonstrated that heuristic algorithms are more applicable than deterministic algorithms, due to fast discovery of optimal solutions for hybrid renewable energy systems. [ABSTRACT FROM AUTHOR]