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The increased integration of renewable energies (REs) raised the uncertainties of power systems and has changed the approach to dealing with power system challenges. Hence, the uncertain nature of all the power system variables needs to be considered while dealing with the optimal planning and operation of modern power systems. This paper presents a probabilistic optimal active and reactive power dispatch (POARPD) based on the point estimate method (PEM), considering the uncertainties associated with load variation and wind power generation. In the POARPD, the deterministic optimal active and reactive power dispatch (OARPD) is performed in two stages, which gives a deterministic two-stage OARPD (TSOARPD). The objectives of TSOARPD are the operating cost (OC) minimization in stage 1 and voltage stability (VS) maximization in stage 2, whereas the VS is improved by maximizing the system's reactive power reserve (RPR). In this paper, instead of using multiobjective optimal power flow, this TSOARPD is used to give more importance to VS when the system is substantially loaded. The POARPD problem is solved using PEM for modified IEEE-9 bus and standard IEEE-30 bus test systems by considering the correlation between the loads. The results are compared with Monte Carlo simulation (MCS). While solving POARPD, the voltage-dependent load model is used to account for the real-time voltage dependency of power system loads. This paper discusses the detailed procedure of solving POARPD by considering correlation and the increased nonlinearities by giving more importance to VS when the system is heavily loaded. [ABSTRACT FROM AUTHOR]

Changes are emerging that will significantly alter structure and operation of this century's distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation. [ABSTRACT FROM AUTHOR]

Available transfer capability (ATC) is an important measurement index to evaluate the security margin of interconnected power grids and serve as a reference for the transmission right transaction. In modern power systems, ATC is affected by the transmission network topology, renewable power output uncertainty, and load demand uncertainty. Traditional works usually model the power source-load uncertainty by using robust optimization, interval optimization, or chance-constraint optimization, which cannot fully reflect the probabilistic distribution of the daily source-load uncertainty. This paper proposes an ATC assessment methodology based on the typical stochastic scenarios of renewable output and load demand of multiarea power systems. Furthermore, the conditional generative adversarial network (CGAN) algorithm is adopted to generate and select representative scenario sets based on historical raw data, which can fully reflect the usual operating condition of a system with high renewable energy penetration. The scenario set that is fed into the ATC assessment model can fully characterize the impact of source-load uncertainty on daily ATC. Finally, the proposed method is verified by a modified three-area IEEE 9-bus system and a real-world provincial power system. [ABSTRACT FROM AUTHOR]

With the construction of a new-type power system under the China "double carbon" target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time. [ABSTRACT FROM AUTHOR]

Accurate device parameters play critical roles in calculation and analysis of power distribution network (PDN). However, device parameters are always affected by the operating status and influenced by manual entry; besides, the distribution area of PDN is very wide with many points, which brings more challenges to PDN parameter identification. Most of the proposed algorithms recently assume that the parameters of PDN contribute in a nonlinear probability space and optimize parameters by the power flow model with a loss function. Although these algorithms can achieve satisfying results in PDN analysis, the relationship between the power flow model and loss functions remains unclear. In this paper, the outputs of the power flow model have been analyzed firstly by experimental data, which includes the head and end voltages, as well as active and reactive power on the low-voltage side. It is revealed that the loss functions used by current algorithms are not suitable and reasonable for power flow model in PDN calculation, which constitutes one of the main findings of this work. Subsequently, this work proposes four novel loss functions combined with genetic algorithm (GA) and Markov Chain Monte Carlo (MCMC) to identify PDN parameters. Compared with the published algorithms, our experimental results show that the loss function defined in this paper can achieve better and more stable performance with about two times lower in MAE, RMSE, and RMPE evaluation functions to identify PDN parameters. [ABSTRACT FROM AUTHOR]

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which can result in power grid security issues such as voltage violations. To address these challenges, this paper proposes a data-driven interval-based reactive power optimization method (IRPOM). The IRPOM represents the uncertainties associated with renewable power generation and load demands as intervals within the RPO problem formulation. The proposed method uses an improved particle swarm optimization algorithm to solve the RPO problem. In each iteration, the uncertain power flow is solved using the optimizing-scenarios method- (OSM-) based interval power flow (IPF) algorithm. This approach calculates the real power losses and checks whether state quantities, including voltage, power flow, and generator output, exceed their limits. Furthermore, a data-driven modeling approach is introduced to reduce the conservativeness of the IRPOM solutions. The effectiveness of the proposed method is demonstrated through detailed computational analysis on a modified IEEE 30-bus system. The results show that the proposed approach ensures economic efficiency while maintaining a low bus voltage threshold crossing probability close to zero. [ABSTRACT FROM AUTHOR]

Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods. [ABSTRACT FROM AUTHOR]

This paper presents a newly designed modified multiport interleaved flyback converter (MMPIFC) to transfer the photovoltaic (PV) power to the DC bus of the parallel-connected multilevel inverter-based shunt active power filter (SHAPF). The proposed modified multiport interleaved flyback converter (MMPIFC) is designed to interconnect N number of input power sources into the DC bus of the SHAPF to fix the problems with the partial shadow and improve the SHAPF's ability to compensate. Compared to the commonly used interleaved flyback converter (IFC), the proposed MMPIFC has a larger boost factor toward the DC voltage input. The flow of power between the different power sources and SHAPF is controlled by varying the duty ratio of the main switches. The working principles of the proposed MMPIFC-operated PV-SHAPF are explored in this paper, and essential design concerns are highlighted. A prototype model with 1 kVA, 400 V, and 50 HZ is built and tested under various conditions to validate the theoretical analysis, design, and control method. [ABSTRACT FROM AUTHOR]

The paper is proposed an improved equilibrium optimizer (IEO) algorithm to solve the optimal power flow (OPF) problem with the participation of a renewable energy source (RES). In the proposed IEO method, the "exponential term" is replaced by a function that does not dependent on the number of iterations. This modification of the IEO algorithm increases exploration ability compared to EO algorithm. In addition, the exploration of the proposed IEO algorithm will not decrease according to the number of iterations which avoids to get stuck at the local optimal solution. The IEO algorithm is tested on two IEEE 30-bus and IEEE 118-bus systems with three different objective functions. The performance of the proposed IEO method is compared with equilibrium optimizer (EO), artificial ecosystem optimization (AEO), cuckoo search algorithm (CSA), teaching-learning-based optimization (TLBO), artificial bee colony (ABC), and many other existing methods. Besides, a simple probabilistic formula for calculating RES output power based on the Monte-Carlo simulation model is proposed in this paper to reduce the computation time for the OPF problem with RES. The simulation results obtained show that the proposed IEO algorithm has better quality of the solution as well as stability level compared to the original EO algorithm and other algorithm. Thus, the proposed IEO algorithm is also one of effective and reliable algorithms for handling OPF problem with RES. [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]

Available transmission capacity (ATC) is a top priority and lucrative solution for electricity market players. Fast and accurate ATC solutions enable participants to gain a competitive advantage in the marketplace. In order to procure a competitive solution, we propose the differential power flow equation-based static ATC. Using the proposed method, the entire time-domain trajectory of a dynamic system can be solved along with a differential equation based on a fictional time. In this paper, a variable frequency transformer (VFT) was used to control and increase the transmission power, which made the calculations more complex. In addition, the proposed method was used in seven different systems and 50% of the time was saved on small systems. It also enjoyed a much better performance (up to 90%) on large systems. It can be concluded that the proposed static ATC introduces promising results and is suitable for online applications. [ABSTRACT FROM AUTHOR]

Summary: Congestion management (CM) is one of the most crucial tasks in power system operation and planning, which has become more challenging in recent years due to the growth of renewable energy sources (RESs) and flexible loads. This paper presents an integrated framework that simultaneously employs different methods, including re‐scheduling, transmission expansion, and demand response programs (DRPs), to manage the AC/DC transmission congestion. The uncertainty associated with remote wind/solar farms and load demand is taken into account and is modelled using the probabilistic point estimate method. To provide a comprehensive analysis, three different management approaches taking both planning and operation phases into account are considered in this study. In the context of CM, the first management approach considered is to minimize the overall system cost including both investment and operational costs (cost‐efficient approach). The second approach is to minimize the overall active power losses (energy‐efficient approach). The last one is to make a trade‐off between these two approaches (cost‐/energy‐efficient approach) by simultaneously minimizing system investment cost and operational loss as a multi‐objective optimization problem. The effectiveness of the proposed framework is evaluated on IEEE two‐area RTS‐96 (MRTS) network using an AC/DC power flow tool. [ABSTRACT FROM AUTHOR]

With the addition of new elements such as distributed generation, demand response, and price-sensitive loads, as well as grid smartness, the operation and management of distribution networks have faced additional complexities. Hence, the electricity price has a vital role for all market participants and strongly affects the demand, generation, and subsequently, the load flow analysis in the network. In this situation, there is a need for a load flow algorithm in which price is integrated into load flow equations. Therefore, this paper seeks to present a suitable algorithm called price-based load flow which considers the electricity price as a state variable similar to technical variables. In this situation, the price is added to the load flow equations as a new state variable and the effect of location on the price is considered. Therefore, it is possible to use the radial structure of the distribution network and communication between adjacent buses in order to provide an effective and appropriate method to find the price simultaneously with technical variables. The numerical study on the IEEE 33-bus distribution network shows the performance of the proposed method. Unlike the centralized market structure, the method does not need all network information to calculate distribution locational marginal price using optimal power flow. In this method, each bus can perform its computation only by information exchange with adjacent buses. [ABSTRACT FROM AUTHOR]

Due to the strong uncertainties of renewable energy and load demands, the new type of power systems is facing severe challenges in terms of generation control and load dispatch. Considering the uncertainties of the penetrated renewable energy and diversified load demands, this paper proposes a random fuzzy power flow analysis method (RFPF) to exactly depict the impact on the various flows of power systems. In the RFPF, the random fuzzy models of wind turbine generators, photovoltaic generators, and load demands are established utilizing the stochastic probability functions and fuzzy interval to reveal the uncertainties with high precision. Afterwards, the random fuzzy mean value is developed as an index of accuracy, and the fuzzy number of the output variables is extracted under the 95% confidence level. Furthermore, the proposed RFPF model is executed by applying a three-point estimate method (3PE) to figure out the corresponding power flow of the power system, costing less computation burden compared with the Monte Carlo simulation. Simulation studies conducted on the IEEE-33 system verifies the accuracy of the RFPF and the efficiency of the 3PE. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed energy resources (DER) has imposed several challenges in the analysis and operation distribution networks. In the last decade, the implementation of battery energy storage systems (BESS) in electric networks has caught the interest in research since the results have shown multiple positive effects when deployed optimally. However, a complex formulation of the optimization problem regarding DER implementations can easly become nonconvex, thus limiting the scope of the results (quality) and affecting the computational efficiency. In this paper, convex formulations of the optimal power flow (OPF) problem for DER (PV and BESS) installations in San Andres distribution network were implemented to minimize power losses. Four main simulation cases were stablished covering convex power flow analysis, convex optimal power flow to locate and size dispatchable and nondispatchable DER (PV) with demand and irradiation profiles, and the inclusion of aggregated and distributed PV and BESS systems. The computational realization of those formulations was made with three different software packages: CVX in python, CPLEX, and MATLAB (MATPOWER/PSO). The results show that installing DER capacity (both PV and/or BESS) can substantially improve the power losses in distribution networks, especially if considered distributed instead of aggregated, achieving reductions up to 75% for power losses and up to 57% for conventional generation utilization. Besides power losses, it was observed that optimal DER (PV and BESS) implementations behaved similarly as demand response signals do, potentially increasing economic benefits for DISCOs. In terms of computational efficiency, convex formulations help substantially to reduce computation times, especially if scalability is to be considered when distributed DER installations were studied. [ABSTRACT FROM AUTHOR]

Absrtact: Background: Nowadays, numerical relays configured through power swing blocking function are vulnerable to faults with high impedance owing to nonlinearities present in the faulty path. Identification of symmetrical high impedance faults incepting during the power swing scenario is of prime importance for protection engineers to avoid cascade tripping. It is analyzed that the classical approaches for symmetrical fault detection during power swing can mal‐operate owing to high level of uncertainty arise during validation of high impedance fault, symmetrical faults and cross country faults during power swing with varied level of compensation which has been explored in this research. Aims: This paper presents an innovative scheme for series compensated line by utilizing the capability of Relevance Vector Machine (RVM) as a disturbance classifier taking into consideration of cross country faults, CT saturation; power swings prone to symmetrical faults along with high impedance faults in comparison with prevailing methods. Materials & Methods: In order to validate the proposed RVM based disturbance classifier algorithm, different kinds of faults and power swing situations have been applied in IEEE 9 bus test system using PSCAD/EMTDC software followed by validation in MATLAB. The features of sequence components of currents are estimated using Modified Full Cycle Discrete Fourier Transform (MFCDFT). The extracted features are used as control inputs to RVM for discernment among power swing and fault under the influence of wide range of system and fault dynamics. Results: The developed RVM based disturbance classifier scheme generates a tripping signal in the event of high impedance fault during power swing and remains stable during swing conditions. It can be used in conjunction with an adaptive numerical distance relaying scheme for realizing complete protection of the power grid for all zones of protection. A comparative analysis with an existing disturbance classifier algorithm indicates relative superiority with much improved fault classification accuracy. Discussion: In order to validate the proposed algorithm, different kinds of fault and power swing scenario are realized in IEEE 9 bus test system using PSCAD and current samples are acquired at sampling frequency of 4 kHz. The phasor estimation of sampled signals has been performed using MFCDFT which is used to derive feature vector extraction from symmetrical components. The extracted features are applied to RVM based fault classifier algorithm for classification of disturbance. The obtained results are also compared with existing schemes of protections. It has been proved that developed algorithm outperforms with much improved fault classification accuracy. Conclusion: This paper discovers relevance vector machine based adaptive learning approach for dynamic disturbance classification in complex power grid network. The developed algorithm has been validated on IEEE 9 bus test system modelled in PSCAD. The fault and system dynamics are varied over a wider range which includes variation in fault type, fault impedance, fault inception angle, power flow angle, fault location and degree of line compensation level. Moreover, the validation of scripted algorithm has been performed for very complex power grid scenarios like cross‐country faults, high impedance faults with varying degree of compensation, power swing and saturation of instrument transformers etc. The feature vectors are extracted using MFCDFT and applied to supervised RVM based disturbance classifier and outperforms with classification accuracy of 99.74% which indicates it's superiority over existing classifier approaches based on SVM & ANN. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a novel method is presented to study the impact of the most congested line on Locational Marginal Price (LMP), and Transmission Congestion Cost (TCC). In most congested condition, the abrupt difference in LMP between two nodes result in enormous TCC and create a huge loss for participants. The method developed in this paper is to find the most congested line on the basis of TCC value, and its sensitivity on the overall network is thoroughly studied. The LMPs computed at each node help in grouping the nodes or buses into two parts, namely, zone 1 or congested zone and zone 2 or non‐congested zone. This method helps market operators to analyze the impact of the most congested line. In addition, the method is handled by obtaining the exact location and sizing of Distributed Generation (DG). Moreover, to formulate the problem accurately, the proposed method is based on AC optimal power flow (ACOPF), which includes network losses in contrast with the lossless DCOPF. The MATLAB interior point method is used to solve the proposed method, and it is tested on IEEE Reliability Test System (IEEE‐RTS) 24 bus and IEEE 30 bus system network in different operating conditions. The results thus obtained shows the betterment of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: The power transportation industry such as electric vehicles is developing rapidly. It effectively uses green energy by reducing the use of fossil energy, and can realize alternative energy processing units, thereby bringing feasibility to future smart‐grid power systems. In addition, wireless power transfer (WPT) is the main direction of the future electric power transportation charging technology. In view of this, this paper proposes a new power processing method for wireless electric transportation systems. It has the advantages of not requiring an additional input transformer and realizing bidirectional power flow. In addition, new parameter optimization methods and control strategies of the electric transportation charging system based on finite control set model predictive control method are proposed. Simulations and experimental results show that the proposed method has higher efficiency and better performance. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an unsupervised clustering algorithm, called modified Iterative Self‐Organizing Data Analysis Technique Algorithm (m‐ISODATA), to capture representative nonchronological scenarios for representing short‐term uncertainties in power system models. The proposed approach is suitable to automatically obtain the number of scenarios required to fully capture the variability of historical series, avoiding the need of adjusting the number of clusters as in techniques commonly used in the literature. The performance of the m‐ISODATA is discussed and compared with Monte Carlo simulation, the well‐known k‐means, and hierarchical agglomerate clustering algorithms. In addition, the obtained scenarios are applied to a wind‐solar‐thermal power system generation expansion planning and to a probabilistic optimal power flow, considering uncertainties over wind and load demand. Finally, the source codes are provided with the best parameters as default. [ABSTRACT FROM AUTHOR]

Summary The coupling between the power system and natural gas system becomes more and more intensive, so it is necessary to study the security of integrated power and natural gas systems. This paper proposes the concept of total load energy supply capability (TLESC) for integrated power and natural gas systems. Then, a nonlinear optimization model is presented to calculate this TLESC. Normal operation of the power system and natural gas system, and the N‐1 contingency of the power system are considered. Piecewise linearization is used to linearize nonlinear models to obtain a uniquely effective solution. Then, the impact of contingencies on the TLESC is measured by the load change rate and the contingency load rate. A method for dividing the security level of the integrated power and natural gas systems is proposed. The N‐1 check is used to judge whether the integrated power and natural gas systems are secure to operate. If the N‐1 check is not satisfied, load loss rate, threshold crossing, hazard index, and so on are used to measure the insecure operation state; If the N‐1 check is satisfied, security margin index, power flow‐gas flow index, comprehensive margin index are used to measure the secure operation state of the system. An integrated 6‐bus power system and 7‐node natural gas system, an integrated 39‐bus power system and 20‐bus natural gas system and the modified IEEE 118‐bus power system and a 40‐node natural gas system are carried out to validate the effectiveness of the proposed model and security level classification method are verified by simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, an improved slime mould algorithm (ISMA) is proposed for finding the optimal location and sizing of photovoltaic distributed generation units (PVDGUs) in unbalanced distribution systems (UDSs). The proposed method is developed by changing the control variable update mechanism of the original slime mould algorithm (SMA). Total power losses on distribution lines and voltage deviation index of all buses are minimized under the consideration of various constraints. The location and sizing of PVDGUs found by ISMA are added into the cosimulation between MATLAB and OpenDSS for reaching other remaining parameters of UDSs. In addition, sunflower optimization (SFO), social-ski drive (SSD), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), bonobo optimizer (BO), and SMA are also run for finding PVDGUs placement solution on the unbalanced three-phase IEEE 123-bus test feeder. As a result, the proposed ISMA can reduce the power loss up to 78.88% and cut the voltage deviation up to 1.4779 pu while that of others is from 69.10% to 78.87% and from 1.5759 to 1.4996 pu. Thus, ISMA should be used to place PVDGUs in UDSs meanwhile the cosimulation between MATLAB and OpenDSS should be applied as a power flow calculation tool for UDSs. [ABSTRACT FROM AUTHOR]

Rural and remote area electrification is of grave concern around the globe. Therefore, well-planned and cost-effective microgrids integrating renewable energy sources are emerging as effective solutions. However, the microgrid's stable operation and its future deployment is affected by the perturbations caused due to uncertainity in renewable sources, dependency on the battery state of charge, and load variation. So, considering the possible concerns affecting the planning and development of a microgrid for any given region, this paper proposes a comprehensive performance assessment of the hybrid residential microgrid based on a load model of Kibber village in Himachal Pradesh, India. The proposed approach is divided into three parts for the best planning of microgrids. Firstly, the MATLAB–Simulink software technically analyzes the system performance under perturbations considering the available renewable sources. Secondly, an economic analysis using HOMER Pro software is done to examine the cost-effectiveness of the proposed microgrid model through the simulation of electrical loads for Kibber village, considering the available renewable sources. Lastly, a real-time analysis of the proposed prototype of programmable logic controller-based hardware test bench has been developed, aiming for future regional microgrid deployment. System voltage, frequency, power shared, percentage of load met, energy cost, available renewable energy resource, etc. have been considered for validating the proposed controller. The proposed comprehensive assessment of the microgrid model is reproducible with necessary modifications for any geographical location. It will be helpful for its future deployment aiming at rural and remote electrification. [ABSTRACT FROM AUTHOR]

Nowadays, the use of demand response programs (DRPs) in a variety of long-term and short-term planning problems has been explored. In this paper, a generation and transmission expansion planning (GTEP) model along with FACTS device allocation is presented. Furthermore, demand response programs are taken into account for more load flexibility. The proposed model is presented as a multi-objective minimizing problem considering emission, cost, and voltage security index. Furthermore, the conventional Pareto optimization is adopted using fuzzy weighted sum method (FWSM) to achieve a single-objective model. The final problem is constrained by equations of alternative current power flow, operation and voltage security limits, planning model of shunt FACTS devices, and operation of the DRP. Adaptive robust optimization (ARO) is used to reach suitable models for the active power of renewable resources and power consumption. As a main search algorithm, a hybrid combination of water cycle algorithm (WCA) and ant lion optimization (ALO) is proposed to find the optimum solution with a small standard deviation. The problem is tested on different standard IEEE systems, and the results validate the operation and network security improvement due to optimal location of FACTS devices. According to the results, the economic and environmental status of the network has also improved. [ABSTRACT FROM AUTHOR]

With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs. [ABSTRACT FROM AUTHOR]

This paper presents a flexible-reliable operation strategy for clean microgrids (MGs) consisting of power sources, energy storage systems (ESSs), and responsive loads. The proposed strategy attempts to provide the minimum expected operating cost for the MG, power sources, predicted pollutant emission, and expected energy not-supplied (EENS) because of an N − 1 event considering different applications. The objective function of the problem is subject to several constraints including AC optimal power flow equations, flexibility and reliability boundaries, and model of power sources and active loads. The Pareto optimization based on the weighted sum method is then incorporated to obtain a single-objective model. Probabilistic programming is also used to model the uncertainties of load, renewable energy, electrical energy price, and MG equipment accessibility. The ant-lion and crow search algorithms are merged to solve the problem and find a reliable optimal solution. A standard MG is employed to test the proposed strategy and indicate its capability in enhancing technical and economic indicators of the MG while ensuring that the MG is clean. [ABSTRACT FROM AUTHOR]

Due to the growing use of Plug-in Electric vehicles (PEVs) in transportation networks, the charge/discharge scheduling of PEVs in Electric Vehicles Parking Lots (EVPLs) can be effective on the distribution network's (DN) resiliency. This paper presents a bilevel optimization model to improve the resiliency of the DN taking into account the interaction between the DN islanding problem and the charge/discharge scheduling of PEVs in the energized EVPLs. In the Upper-Level (UL) problem, regarding the electrical loads and managing the charge/discharge of PEVs, the islands' boundaries are determined with the aim of maximizing the amount of restored load. Knowing the islands' boundaries and the energized EVPLs from the UL problem, the changes in travels characteristic including destination EVPLs are determined in the Lower-Level (LL) problem to identify the nearest energized EVPL to the out-of-service destination EVPL. The number of PEV drivers that change their deenergized destination depends on the distance between the nearest energized EVPL to the destination. A combination of mathematical programming and evolutionary algorithm is applied to reach the final solution. The proposed model is implemented by applying several concurrent faults to the 118-bus active DN, which is coupled with a 25-node traffic network. The results confirm the efficiency of the proposed model for improving the resiliency of DNs with managing the charge/discharge of PEVs in the restored EVPLs. [ABSTRACT FROM AUTHOR]

In order to increase energy efficiency, the energy hub is considered as a form of aggregator and coordinator of various resources and storage. With the optimal performance of resources and storage generators based on a proper energy management system, it is expected that hubs can gain financial benefits from energy markets and ancillary services. So, the paper presents the participation of networked energy hubs in day-ahead (DA) reserve regulation and energy markets, where the hub operator incorporates a coordinated energy management (CEM) strategy to manage power sources and energy storage devices within the hub. Hence, this problem maximizes the total profit of hubs in the DA energy and up and down reserve markets. Also, the problem is constrained by optimal power flow (OPF) constraints in gas, electricity, and thermal networks, reserve limits, and hub constraints, including the model of the combined heat and power (CHP), renewable energy source (RES), electrical/thermal storage, parking lots of electric vehicles (EVs), and boiler. Following that, a linear format is obtained for the nonlinear equation using traditional linearization methods so that an optimal solution is found in less time considering less computational error. Eventually, a standard case system is used to test the strategy, and thus, the capabilities of the approach are investigated. The obtained findings validate the potential of the proposed design in enhancing the economic situation of power sources and storage in hub form, which can enhance operation indices by optimal management of the hub so that the energy management of resources and storage in the form of a hub based on CEM compared to their independent management plan has been able to increase the profit of these elements in energy and up and down reserve markets by about 17%, 28%, and 15%, respectively. Regarding technical indices of energy networks, the proposed scheme by creating low energy losses in the gas network and providing pressure drop, overvoltage, and overtemperature within their permissible limits succeeded in reducing the energy losses in electricity and heat networks by about 83% and 38%, respectively, compared to power flow studies. Also, in these conditions, it has reduced the maximum voltage and temperature drop by 45% and 39%, respectively. [ABSTRACT FROM AUTHOR]

In recent years, diverse energy has been integrated into the power system, which constitutes a regional integrated energy system (IES). However, the coupling and complementation of multiple energy sources make load forecasting more difficult. For the time‐sequence and non‐linear characteristics of electric load and the complementarity of different energy in IES, this paper proposed an attention‐based convolutional neural network (CNN) combined with long short‐term memory (LSTM) and bidirectional long short‐term memory (BiLSTM) model for short‐term load forecasting in IES. The historical load, temperature, cooling load, and gas consumption of the past 5 days are used as the input features. CNN integrated with attention block is utilized to extract effective features of the load impact factors. Then the load of the next hour is forecasted by the LSTM combined with BiLSTM layers. Finally, the model is verified by the data from an integrated energy park in North China. The results show that the proposed method has better forecasting performance than CNN‐BiLSTM, CNN‐LSTM, BiLSTM, LSTM, backpropagation neural network (BPNN), random forest regression (RFR), and support vector machine regression (SVR). [ABSTRACT FROM AUTHOR]

Recent technological advances in power electronics has led to a substantial increase in nonlinear loads. This has led to deterioration in quality of power in distribution networks and sparked interest in power quality issues and its solutions. This paper discusses the issue of harmonics, which is a power quality problem: their estimation and subsequent control and mitigation using a shunt compensator. The control of shunt compensator using proportional-resonant (PR) controllers has been designed and implemented. Literature review presents a wide range of control techniques and proportional-integral (PI) controllers are the simplest and most commonly used in industries. Recently introduced PR controllers have been presented as an alternative to PI controllers. These controllers are believed to overcome the shortcomings of the conventional PI controller, which include the presence of steady-state errors and the need for transformation in three-phase systems using the synchronous dq frame. The added advantages of PR controllers include the possibility of implementation for simultaneous detection and extraction of harmonic components without requiring excessive computational efforts. This paper highlights the application of PR controller for harmonic estimation and their reduction in the utility grid using shunt compensator. Closed loop control using PR controllers as filters has been demonstrated using simulation as well as experimental results. Extensive results are presented for different loading conditions, and they demonstrate the application of PR controllers in grid-interfaced converter for power quality improvement. [ABSTRACT FROM AUTHOR]

Remote area electrification is a crucial need in sub-Saharan Africa's drive to attain universal electrification. In Sierra Leone, with a rural population of over 5 million, the electrification rate accounts for less than 10% of the total inhabitants. This paper presents a comparative techno-economic analysis carried out to determine the most feasible of four individual options for off-grid mini-grid power generation system utilizing sources that include: Solar Photo Voltaic (SPV), Diesel Generator (DG), and Battery Storage (BS) system, to provide electricity for a rural and remote village located in the northwestern part of Sierra Leone (longitude 9.1°W and latitude 12.6°N), with an average daily solar irradiance between 4.6 and 6 kWh/m2/day. An assessment of the total electrical load estimated an expected daily consumption of 178 kWh. Simulation, optimization, and sensitivity analyses of each one of the individual power generation systems were carried out using HOMER software. Economic parameters such as Cost of Capital (CC), Net Present Cost (NPC), Levelized Cost of Electricity (LCOE)), technical parameters (energy production characteristics), and greenhouse gases emissions were compared and analyzed. Key findings from the simulation result indicate that systems consisting of DG only ($29,750) and SPV/BS ($110,131) obtained the least and highest CC, respectively. Similarly, in a respective manner, the highest and least NPC were obtained for systems with DG only ($496,336) and PV/DG/BS ($152,491) over a 25-year project lifetime. Furthermore, the least LCOE was obtained for the system comprising PV/DG/BS ($0.336/kWh). With an expected annual generation of (75,121 kWh), PV/DG/BS was obtained to be the most optimal solution. The sensitivity analysis observed that a reduction in the discount rate consequently reduces the LCOE of such a system. Furthermore, the model accounts for a 90% renewable energy fraction, with a significant reduction in the amount of annual GHG emissions, when compared with a generation system using diesel generator only. [ABSTRACT FROM AUTHOR]

Electrical vehicles (EVs) are among the fastest-growing electrical loads that change both temporally and spatially on distribution networks. The large-scale integration of EVs equipped with power electronic-based chargers into distribution networks, to meet new electrical load demands, can cause instability and power quality issues. Moreover, the absence of control strategies for the smart charging and discharging of EVs at their plug-in intervals poses serious challenges to them. Accordingly, the implementation of controlled charging/discharging scheduling of EV batteries along with the use of charger capabilities, such as reactive power support, is a must. Against this background, this paper introduces an integrated model to solve the problem of simultaneous active and reactive power management in distribution networks subject to network operation constraints imposed by EV batteries and chargers. To this end, this problem is modeled as an optimization problem. In this respect, minimization of the costs associated with power generation and losses and improvement of the total harmonic distortion of voltage (THDv) on network buses are two terms of the objective function. The problem is solved by a hybrid technique named the " PSO-GA algorithm" that takes advantage of both the genetic algorithm (GA) and the particle swarm optimization (PSO) method. Accordingly, the effectiveness of the proposed model is examined in a standard IEEE 33-bus distribution network populated with EVs and nonlinear devices (NLDs). The results obtained show that the maximum possible penetration rate of EVs into the network is facilitated, while technical and financial goals of the network and parking lots are ensured. [ABSTRACT FROM AUTHOR]

In this paper, the optimal scheduling of energy grids and networked energy hubs based on their participation in the day-ahead energy wholesale and retail markets is presented. The problem is formulated as a bilevel model. Its upper level minimizes the expected energy cost of electricity, gas, and heating grids, especially in the form of private distribution companies in the mentioned markets, in the first objective function, and it minimizes the expected energy loss of these networks in the second objective function. This problem is constrained by linearized optimal power flow equations. The lower-level formulation minimizes the expected energy cost of hubs (equal to the difference between sell and purchase of energy) as an objective function in the retail market. Constraints of this model are the operation formulation of sources and active loads and the flexibility limit of hubs. The unscented transformation approach models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage. Then, the Karush–Kuhn–Tucker approach and Pareto optimization technique based on ε-constraint are adopted to extract the single-level single-objective formulation. Finally, obtained results verify the capability of the present method in improving the economic status of hubs and the economic and operation situation of the mentioned networks simultaneously so that the proposed scheme by managing the power of energy hubs compared with power flow studies has been able to reduce operating costs by 8%, reduce energy losses by 10%, and improve voltage profile and temperature by 36% and 30%. [ABSTRACT FROM AUTHOR]

This paper proposes a novel holomorphic embedding approach for solving the nonlinear power flow equation for meshed electric distribution networks with ZIP load model. In the proposed approach, bus voltages are modelled as holomorphic functions in the constant power injections and then expanded using Maclurin series. The Z-bus matrix is implicitly used to calculate Maclurin series coefficients for the expanded voltage functions in a recursive manner. The necessary and sufficient conditions for the convergence of expanded voltage functions are found. Performance evaluations show that the proposed approach solves the nonlinear power flow equations faster than the existing approaches when applied to 18-, 33-, 69-, 141-, 3239-, 5701-, and 6921-bus distribution network test cases. [ABSTRACT FROM AUTHOR]

Linking AC and DC microgrids via a bidirectional AC-DC interfacing converter has emerged the hybrid AC-DC microgrid. The DC bus is connected to the grid by a series converter to mitigate the grid voltage's power quality problems. To enhance the reliability and to increase transferring active and nonactive powers between DC and AC main buses, the AC-DC bidirectional interfacing converters are connected in parallel. In this paper, the optimal number (n) of parallel-connected bidirectional interfacing converters is obtained to minimize the annual cost of investment and the reliability cost. A two-step deciding algorithm is proposed to find n. First, active and nonactive powers between DC and AC buses are obtained with power quality and power flow considerations. Based on two types of powers, two reliability indices are calculated, including the expected energy shortage (EES) based on active power and the expected Volt-Amps shortage (EVAS) based on nonactive power. The sum of two reliability index costs is considered as the reliability cost. Next, a decision-making strategy is executed to determine the optimal n by making a trade-off between minimization of two objectives: the annual cost of investment and the reliability cost. The simulations of a grid-tied hybrid AC-DC microgrid are done with the experimental measurement data of DC and AC loads and distributed generations. Simulation results verify the performance of the represented approach. [ABSTRACT FROM AUTHOR]

Summary: Dynamic voltage restorer (DVR) can preserve a sensitive load against electrical distribution system interruption. After connecting DVR to the system, voltage sag is quickly resolved and no voltage oscillation caused by malfunction of sensitive loads occur. When there is a high voltage sag in distribution or transmission, in order to compensate voltage to the value before fault, both reactive compensation and active power compensation should be performed which requires a DC source in DC link of power quality compensators; if there is only one capacitor in DC link, only reactive power compensation can be performed, as a result significant voltage drops cannot be modified. Distributed generation sources like photovoltaic (PV) can be used as a battery instead of DC link supply of D‐FACTS devices due to DC nature of their output voltage. Performance of DVR in modifying power quality indices depends on performance of its control system. In this paper, a fuzzy adaptive controller is used instead of the conventional PI controller in the control system of the DVR‐PV hybrid compensator to improve its performance in compensating voltage interruption and different types of voltage sag. DVR based on fuzzy controller performs better than DVR based on other controllers like PI. Simulation of electrical distribution system is done in the presence of DVR based on fuzzy‐adaptive control scheme and PV using Matlab/Simulink. [ABSTRACT FROM AUTHOR]

Small-scale islanded microgrid technology has the potential to utilize renewable sources for electrification in extreme areas where conventional transmission of power is difficult. But due to the absence of a utility grid in the islanded operation, voltage and frequency control becomes the major concern. Instead of using a centralized large battery storage system, electric vehicle- (EV-) based distributed energy storage may provide a dynamic and much cheaper energy storage solution for small-scale systems in the long run. The only issue is to effectively manage the dynamics of EV penetration for achieving the overall control in the islanded system. To address the same issue, this paper proposes a novel segregation-based inverter control structure for an islanded microgrid while employing EVs as an energy storage system, thus eliminating the need for centralized storage. The designed integrated control structure simultaneously attends to the voltage and frequency regulation need of the system, overcoming the issue of control lag, along with energy storage and management aspects in the microgrid operation for controlling the power flow under various practical scenarios. Extensive experimental studies are performed in MATLAB/SIMULINK environment, which indicates that the proposed integrated control structure gives a satisfactory performance under various scenarios encompassing load variation, renewable energy uncertainties, and EV dynamics in comparison with conventional control schemes employed in the islanded microgrid. [ABSTRACT FROM AUTHOR]

Multiple DC subgrids in close proximity can be connected to form a DC microgrid. The interconnected DC subgrids enhance reliability and efficient utilization of resources. In this paper, a novel coordinated power management strategy is proposed for interconnected subgrids of a DC microgrid. Each DC subgrid has PV and battery units. The proposed strategy is implemented on interlinking bidirectional converters (IBCs). The strategy considers the state-of-charge (SoC) and charging/discharging current of the batteries for SoC balancing-based power sharing through IBCs. A fuzzy logic controller is implemented to decide the amount of power flow through the IBCs from SoC and the charging/discharging current of battery units. Moreover, with the coordinated strategy, a high SoC battery takes more loads compared to a lower SoC battery. The proposed strategy supports fast charging and fast discharging of batteries depending on the SoC level. In addition, the coordinated strategy can seamlessly transfer the control of IBC from the power regulating mode to the voltage regulating mode without any additional control scheme during an outage of the battery unit. The feasibility of the proposed strategy is validated through simulation in MATLAB/Simulink and OPAL-RT real-time digital simulator. [ABSTRACT FROM AUTHOR]

In deregulated electricity markets, generation companies (GENCO) try to maximize their economic benefits considering the electricity demand, transmission network condition, and other participants' behaviors. The increasing penetration of renewable sources such as wind power generation with intermittent nature poses several challenges to the participation of GENCOs in the electricity market. Thus, this paper presents a stochastic bilevel optimization model to determine the coordinated bidding strategy of a wind-thermal GENCO with the aim of maximizing its profit in the day-ahead and real-time balancing market. Herein, the model aims to maximize the profit of GENCO in the day-ahead and the balancing market in the upper-level problem while minimizing the operation cost of the system in the lower-level problem. The uncertainties of wind power generation and electricity demand are modeled by defining a set of scenarios considering their mutual correlation using the copula technique. Additionally, incorporating AC power flow constraints in the proposed optimization model offers a better solution to the coordinated bidding strategy of the wind-thermal GENCO. Further, the nonlinear AC power flow equations are linearized using the piecewise approximation technique to reduce the computational complexity and enhance the accuracy of the optimal solution. In the end, the developed algorithm is implemented on the IEEE 24-bus RTS, and the simulation results are provided to validate the efficiency and applicability of the proposed coordinated bidding strategy model. The results advocate that the participation of the thermal unit along with the wind farm might mitigate the risk of uncertainties, but it causes an intense increase in the locational marginal price of the system. Importantly, the simulation results indicate the computational efficiency of the model by developing an exact AC power flow model without compromising the results. Notably, it has been found that the profit of the wind-thermal GENCO would be increased by 35.2% employing the copula technique to model the mutual correlation of uncertain parameters. [ABSTRACT FROM AUTHOR]

This paper presents a novel multi-objective energy management method for a microgrid that includes renewable energy, diesel generators, battery storage, and various loads. The fluctuation of the grid tie-line power flow is regarded as one of the optimal objectives in order to decrease the power exchange between the microgrid and the main grid caused by intermittent and variable renewable resources. The uncertainty of renewable energy and loads is also considered in this paper. Stochastic optimization method is implemented for the output of renewable resources and controllable loads. Using the ε-constrained and weighted sum method, the multi-objective operation optimization problem, which includes the minimum microgrid operation costs, fluctuations of the grid tie-line power, and the risk management, is converted to a single-objective mixed-integer linear optimization problem. The scheduling is achieved for controllable units such as diesel generators, batteries, controllable loads, and grid tie-line power under various weights. The main contribution of the study is to achieve the energy management for a microgrid with regulation of the grid tie-line power fluctuations and peak shaving. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the increasing penetration of unregulated solar energy, a load service entity (LSE) can exert an externality cost to guide the charging behaviors of electric vehicle (EV) users and level the Duck Chart. This paper introduces a dynamic Stackelberg game model between LSE and EV users to analyze consumer behavior, and a complete information static game model to help LSEs increase profits by gaining share of the EV market. These dynamic and static game models are incorporated into a novel theoretic algorithm with mixed integer programming. A case study shows that the models and the proposed algorithm can level the Duck Chart and guarantee an LSE's share of the EV market. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the integration of high proportional photovoltaics (PVs) into distribution networks, the superposition of uncertain output power of PVs and stochastic load demand fluctuations have posed a serious challenge to voltage stability. In this paper, a multitimescale decomposition-coordination-based voltage control method is proposed under the cloud-edge cooperative architecture. The distribution network is firstly decomposed into several subnetworks. In each subnetwork, the edge computing device is equipped to realize the distributed control and provide external computing resources for the cloud center. Then, a multitimescale control scheme containing the interval dispatch stage and the real-time time-window stage is proposed. In the interval dispatch stage, a cloud-edge collaborative calculation strategy considering balanced resource allocation is well designed to obtain the global optimal power flows and the corresponding reference operating points of the voltage control equipment. Meanwhile, in the real-time time-window stage, a consensus-based voltage correction mechanism under the optimal power flow boundary constraints is designed to avoid the voltage violations caused by unexcepted power fluctuations deviating from the representative scenarios. Simulation results with the improved 33-bus and IEEE 123-bus systems have demonstrated the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Electricity price risk assessment (EPRA) is essential for spot market analysis and operation. The statistical moments (i.e., the mean and standard deviation) of the price need to be assessed to support market risk control. This paper proposes a data-driven approach for EPRA based on the Gaussian process (GP) framework. Compared with the deep learning algorithms, GP has two merits: (1) the scale of training sample required is small and (2) the time-consuming hyperparameter tuning process is avoided. However, the direct application of GP for EPRA is not tractable due to the complicated discrete relationship between the system operating status and the electricity price. To deal with that, a data-driven EPRA framework is developed that contains a GP surrogate model for the direct current optimal power flow (DC-OPF) problem and a hybrid model-data-based hybrid electricity price calculation method. To guarantee the accuracy of EPRA, an adaptability criterion and a second verification process based on the Karush–Kuhn–Tucker (KKT) condition are developed to distinguish the samples with GP learning errors. Numerical results carried out on IEEE benchmark systems demonstrate that the proposed method can achieve exactly the same EPRA results as Monte Carlo (MC) simulation, which significantly improved the computational efficiency. [ABSTRACT FROM AUTHOR]

The simultaneous investigation of false data injection cyber‐attack (FDICA) and demand response (DR) participation in smart power systems are dealt with in this paper. For this purpose, simultaneous and separate detection of FDICA and determination of the changes in the power consumption for participation in DR (PDR) are necessary. To balance between generation and consumption, PDR must be equal to the load disturbances (LDs). Active‐disturbance rejection control (ADRC) with the extended state observer (ESO) is implemented to detect FDICA and LDs. But, ESO only detects the sum of FDICA and LDs without the ability to separate them. Therefore, a nonlinear sliding mode observer (NSMO) is implemented to detach FDICA from LDs. In order to improve the NSMO estimation accuracy, the modified salp swarm algorithm (SSA) is implemented to design NSMO parameters. Moreover, to achieve better performance of ADRC, a fuzzy tuner is applied to the online tuning of controller parameters. The proposed optimized fuzzy sliding‐based modified active‐disturbance rejection control (FSADRC) is tested for load frequency control of the IEEE 9 bus standard test system and the 10 machines New England test power system considering communications time delay. The results of several simulation scenarios indicate the efficient performance of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Flexible alternating‐current transmission system (FACTS) devices as power electronic‐based technologies have been developed to improve the performance of power systems. This paper proposes a probabilistic framework for optimal allocation of unified power flow controller (UPFC) as one of the most beneficial FACTS devices to increase the reliability of the power system. For this purpose, the expected power not served (EPNS) is considered as a reliability index that is obtained based on the summation of required load shedding in all buses during single contingencies, including transmission lines and generators outage. Power demands and wind speed are considered as uncertain input variables. In addition, the correlations among these variables are included in the proposed study method. The well‐known particle swarm optimization (PSO) algorithm is used to optimize the proposed objective function. Also, the firefly algorithm (FA) is implemented to determine the total amount of load shedding for the calculation of EPNS, considering each possible generated solution by the PSO. The k‐means based data clustering method (DCM) is used for probabilistic evaluation of the problem, for the first time. Also, the correlations among uncertain input variables are modeled with the Cholesky decomposition method. Extraction of statistical information of UPFC parameters for improving power system reliability in a probabilistic framework is one of the most important achievements of the proposed method. This information is very important in deciding on the UPFC sizing. In order to evaluate the effectiveness of the proposed method, the IEEE 14‐bus and 30‐bus test systems have been used. [ABSTRACT FROM AUTHOR]

Summary: Deregulation of power system is advantageous economically, but this affects the voltage and thermal stability of the transmission lines. These effects on the transmission line are assessed by the stability analysis of the power system. Various power system security parameters/indices are proposed in the literature, but only a few are used in security assessment studies. In this work, numerous critical sensitive indices are considered in terms of transmission line loading and bus voltage magnitude deviation to carry out security assessment studies. The optimal placement of FACTS devices enhances the security of the power system. Therefore, in this work, multi‐line unified power flow controllers (UPFC, GUPFC) are employed for power system security enhancement. The present paper provides an in‐depth security assessment for the power system incorporated with multi‐line UPFCs operated with and without control modes (voltage, active, and reactive power) at different case scenarios. These cases constitute normal, transaction, and contingency conditions of a deregulated power system operating at specified transaction limits. The proposed security enhancement strategy is implemented on standard IEEE test systems such as the 30‐ and 118‐bus systems. The results obtained with UPFC and GUPFC are promising for security enhancement of a power system constricted to the specified transaction limits. Among the FACTS controllers, GUPFC has shown better performance, especially in contingency conditions. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the distributed load frequency control problems for the multi‐area interconnected power system in smart grid. Compared with current results, we develop a new model transformation for traditional interconnected systems with unknown communication time delays and uncertainties. The load frequency control problem becomes the state synchronization problem of multi‐agent system (MAS) consisting of linear double‐integrator system with uncertainties and delays. Then, the H∞ distributed controller is designed based on the new MAS model, which weakens the impact of communication delays, parametric uncertainties, and load disturbance. Meanwhile, the proposed distributed controller only needs its neighbors' information and some rough information about the communication network to achieve the frequency's synchronization. In addition, the power flow in the power system can achieve the power scheduling and peak regulation with the changes of each area's load, which can cause unacceptable frequency deviation. The switching topologies are used to express this new power flow distribution. The corresponding distributed controllers are designed to achieve the frequency's synchronization. Finally, simulation result shows the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to advance the recently progressing research on solid‐state transformers (SST) and examines the impact of uncertainty on load demand and distributed generation (DG) units while integrating SST into an active distribution network (ADN) in the presence of battery backed solar photovoltaic (BBSPV) and wind generation. The uncertainty related to solar irradiance, wind speeds, and load demands is modeled through kernel density estimation (KDE), generalized extreme value (GEV) distribution, and Gaussian distribution summed with the maximum likelihood estimation (MLE) approach respectively. The K‐medoid clustering procedure is utilized to group the load demand into multiple load levels, which helps in the intelligent scheduling of charging and discharging of battery energy storage systems (BESS). The distribution network planning process manages two conflicting criterion functions of real power loss (RPL) reduction and sum of square deviations of the expected voltage values, which are optimized utilizing the non‐dominated sorting genetic algorithm (NSGA‐II). The probabilistic power flow uses the direct load flow procedure utilizing bus injection to branch current (BIBC) matrix along with the forward sweep method to investigate each run of Monte Carlo simulation (MCS). Multiple case studies have been conducted on the IEEE 33 bus radial distribution network (RDN). The outcomes demonstrate the potential benefits of RPL reduction and voltage profile improvement (VPI) with increasing penetration levels of SST. [ABSTRACT FROM AUTHOR]

Summary: The cost allotment of the transmission system is based on reliability, which is a significant problem for efficient transmission cost allotment. In this paper, a hybrid technique is suggested to allocate and minimize the embedded cost of transmission based on the reliability margin by permitting its users for the transactions under the Deregulated power system market. The hybrid technique combines the Elephant Herd Optimization (EHO) algorithm and the Fuzzy Logic Controller (FLC). The Elephant Heard Optimization algorithmic program is impressed by the swarming activities of elephant grouping. The FLC is utilized to boost the performance of the Elephant Heard Optimization Method. Initially, the Optimal Power flow is analyzed using the Newton‐Raphson (N‐R) approach. The objective function is to define and analyze the power loss, capacity cost, Expected Transmission Margin Unit (ETMU), reliability cost, and embedded cost for the base case. After that, the transmission system's contingency and analysis of the embedded cost and power loss are analyzed. The outcomes of the suggested method are analyzed with cases such as contingency, transactions. The suggested technique is tested with the IEEE 5 and IEEE 24 Reliability Test System bus system and analyzed the corresponding capacity cost, reliability cost, ETMU, Embedded cost, and Power loss of the system. The suggested method is implemented in MATLAB software. Results are validated with the traditional methods, namely Bat Algorithm (Bat) and Firefly Algorithm (FA). [ABSTRACT FROM AUTHOR]

Summary: The electrical power generation from conventional thermal power plants needs to be interconnected with natural resources like solar, wind, hydro units with all‐day planning, and operation strategies to save mother nature and meet the current electricity demand. The complexity and size of the power network are increasing rapidly day by day. The enhanced power transfer from one section to another section in the existing grid system is the subject of available transfer capability (ATC), which is the modern power system's critical factor. In this paper, the minimisation of power generation cost of the thermal power units is achieved by incorporating renewable sources, says hydro, winds, and solar plants for 24 hours scheduled, and ATC calculation is the prime objective. In recent literature, the Mayfly algorithm (MA) optimisation approach, which combines the advantages of evolutionary algorithms and swarms intelligence to attend better results, is successfully implemented. In this article, optimum power flow (OPF) based ATC is enforced under various conditions with hydro‐thermal‐solar‐wind scheduling concept on the IEEE 9, IEEE 39, and IEEE 118 test bus systems to check the performance of the proposed chaotic MA. The chaotic MA is a hybridised format of the MA and chaotic map (C‐MAP) method with opposition based learning. It is noted from the simulation study that the suggested hybrid C‐MAP approach has a dominant nature over other well‐established optimisation algorithms. In case of single objective function, the optimum value of the cost function is better than 13% to its nearest competitor approach. For multi‐objective, it is improved by more than 19% and ATC value is enhanced by near about 56% and more. [ABSTRACT FROM AUTHOR]