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Inverter air-conditioning (IAC) units have been proved to be effective in frequency regulation by providing flexible capacities. This paper proposes a brain emotional learning (BEL)-based controller to provide the IACs with control signals to be efficiently involved in the frequency regulation in power systems. The BEL-based controller can learn quick-auto, making it appropriate in systems facing uncertainty. To assess the BEL controller performance in realistic conditions, the uncertainties as a consequence of variations in system parameters and load level are considered. The goal is to use the BEL controller to increase the IAC units' ability to track regulation signals accurately in uncertain circumstances. The controller is compared to a fuzzy-PI control, a proportional control scheme, a model predictive control and a linear quadratic regulator control. A delay-dependent stability criterion is used to calculate the highest time delay in the IACs response under which the system maintains stability. In addition, this paper presents an BEL-based coordinator to coordinate the IACs and traditional generation units for compensating considerable frequency variations caused by the time delays. Case studies are accomplished on a multi-area power system in MATLAB/Simulink environment. Eventually, real-time verifications by OPAL-RT real-time digital simulator on the simulated power system are executed to assess the control method.

The role of electric vehicles (EVs) in energy systems will be crucial over the upcoming years due to their environmental-friendly nature and ability to mitigate/absorb excess power from renewable energy sources. Currently, a significant focus is given to EV smart charging (EVSC) solutions by researchers and industries around the globe to suitably meet the EVs' charging demand while overcoming their negative impacts on the power grid. Therefore, effective EVSC strategies and technologies are required to address such challenges. This review paper outlines the benefits and challenges of the EVSC procedure from different points of view. The role of EV aggregator in EVSC, charging methods and objectives, and required infrastructure for implementing EVSC are discussed. The study also deals with ancillary services provided by EVSC and EVs' load forecasting approaches. Moreover, the EVSC integrated energy systems, including homes, buildings, integrated energy systems, etc., are reviewed, followed by the smart green charging solutions to enhance the environmental benefit of EVs. The literature review shows the efficiency of EVSC in reducing charging costs by 30 %, grid operational costs by 10 %, and renewable curtailment by 40 %. The study gives key findings and recommendations which can be helpful for researchers and policymakers.

Power systems are exposed to various physical threats due to extreme events, technical failures, human errors, and deliberate damage. Physical threats are among the most destructive factors to endanger the power systems security by intelligently targeting power systems components, such as Transmission Lines (TLs), to damage/destroy the facilities or disrupt the power systems operation. The aim of physical attacks in disrupting power systems can be power systems instability, load interruptions, unserved energy costs, repair/displacement costs, and even cascading failures and blackouts. Due to dispersing in large geographical areas, power transmission systems are more exposed to physical threats. Power systems operators, as the system defenders, protect power systems in different stages of a physical attack by minimizing the impacts of such destructive attacks. In this regard, many studies have been conducted in the literature. In this paper, an overview of the previous research studies related to power systems protection against physical attacks is conducted. This paper also outlines the main characteristics, such as physical attack adverse impacts, defending actions, optimization methods, understudied systems, uncertainty considerations, expansion planning, and cascading failures. Furthermore, this paper gives some key findings and recommendations to identify the research gap in the literature.

In recent years, the vector coupling of energy systems is in progress by integrating different energy vectors such as power, heat, and gas and also integration of different sectors such as residential, transportation, commercial, and industry parts for efficient utilization of energy sources such as natural gas, coal, diesel, and renewable energy sources. The energy crisis, air pollution issues, increasing trend of energy consumption, reliability issues, and especially increasing the share of variable renewable energy sources are the major drivers of this transition. Interaction among energy systems is achieved through a real or physical node or even a virtual node so-called the energy hub or multi-energy node. Enabling technologies for integrating energy systems are energy conversion systems (such as cogeneration and trigeneration systems, heat pumps, diesel generator, and boilers), energy storage systems (such as battery, thermal, cold, and hydrogen storage), information and communication technologies, and particularly decarbonizing components. Demand-side management is also essential for energy integration since it contributes to energy conservation and flexibility of energy demands. In this chapter, the concept and definition of vector-coupling concept in the whole energy systems is discussed. In addition, enabling technologies and challenges associated with integrating energy vectors are discussed.

In recent years, energy saving has attracted the attention of researchers due to environment, energy, and reliability issues. Energy saving due to these advantages is one of the major steps toward sustainable cities and society. In this regard, the low-voltage section of the distribution system, including buildings and public lighting systems (PLSs), has great energy-saving potential. Accordingly, the present work reviews the potential of different energy-saving options and their environmental impact on buildings of different sectors and PLSs. In addition to direct energy-saving options such as using renewable energy sources and energy-efficient luminaries, available indirect options such as transactive energy, using energy storage systems and demand response programs are reviewed. For both the building and PLS sectors, available control strategies and technologies and related energy and emission saving potential are discussed. The detailed highlights of the previous works associated with the location of each research or experimental study are given in this review study. Finally, the key findings regarding the gap in the literature of the energy saving topic are discussed. This study is influential for policy-makers to take effective actions for energy saving through existing approaches and technologies, and is beneficial for researchers of the energy saving topic.

Optimal placement and sizing of shunt capacitors are important issues to improve voltage profile and reduce power losses in distribution networks. In this regard, dealing with stochastic nature of wind farms (WFs), the issue becomes more complex in wind-integrated radial distribution networks. This study aims at optimal siting and sizing of shunt capacitors considering wind uncertainty. Application of probabilistic approach causes to access more real and extensive information about the network. To do so, Monte Carlo simulation (MCS) is employed to assess stochastic variation of wind farm output. As the salient feature of this research, K-means-based data clustering approach is utilised, in which all data-points of the output power of WFs are bunched into desired clusters to ease the solving of simple MCS. The voltage profile and power losses are analysed to do the optimal siting and sizing of capacitors. In order to assess the effectiveness of the proposed method, results are compared with the simple MCS that proves the efficiency of the proposed method from the viewpoint of computation time.

This article proposes a method based on data clustering for the optimal placement and sizing of wind farms (WFs) in radial distribution systems considering the wind uncertainty and the wake effect. The stochastic output of wind turbines makes the optimization problem more challenging. Using the probabilistic methods for such analyses proves efficient to yield more reliable results. The data clustering-based Monte Carlo simulation is used to bunch the output powers of wind farm into clusters. The network loss and voltage profile are then evaluated to achieve the optimal candidate bus for the placement of WF with the optimal number of turbines. To accelerate the procedure, a technical constraint is used so as to eliminate the nonimportant samples in WF size evaluation, which results in a reduction in computational burden. In addition, two methods for load flow calculations are investigated, namely the direct load flow and indirect backward/forward sweep load flow methods to evaluate the time burden of load flow method on the proposed problem. The effectiveness of the proposed methodology is illustated by conducting case studies.

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. The Brushless Doubly-Fed Reluctance Machine (BDFRM) has been widely investigated in numerous research studies since it is brushless and cageless and there is no winding on the rotor of this emerging machine. This feature leads to several advantages for this machine in comparison with its induction counterpart, i.e., Brushless Doubly-Fed Induction Machine (BDFIM). Less maintenance, less power losses, and also more reliability are the major advantages of BDFRM compared to BDFIM. The design complexity of its reluctance rotor, as well as flux patterns for indirect connection between the two windings mounted on the stator including power winding and control winding, have restricted the development of this machine technology. In the literature, there is not a comprehensive review of the research studies related to BDFRM. In this paper, the previous research studies are reviewed from different points of view, such as operation, design, control, transient model, dynamic model, power factor, Maximum Power Point Tracking (MPPT), and losses. It is revealed that the BDFRM is still evolving since the theoretical results have shown that this machine operates efficiently if it is well-designed.

The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM.

The periodic outage planning of generation units for maintenance services is critical for their economic operation. Ignoring it can lead to premature degradation, high repair costs, and system reliability deterioration due to unexpected outages. Accordingly, generation maintenance scheduling (GMS) can optimally handle this challenge. In a power system with combined heat and power (CHP), although CHP units can efficiently supply power and heat demand, continuous operation of such units can cause an increase in unexpected failure rate and repair costs. Accordingly, this research accomplishes GMS coordination in CHP-integrated energy systems to optimally determine the maintenance plan of units. Firstly, the yearly GMS is planned and then the short-term generation scheduling is optimized. The proposed GMS problem is modeled as a mixed-integer nonlinear programming problem while considering the technical constraints, CHP feasible operation region, and power and heat balance. Moreover, the uncertainty in the market price is considered and the CVaR measure is employed to deal with the risks in the GMS. Furthermore, demand response is also assumed in the short-term generation scheduling while evaluating the impact of the yearly GMS. Numerical simulations with scenario-based optimization are used to evaluate the proposed CHP GMS problem with uncertainty in market price.

Generation maintenance scheduling (GMS) is one of the most important scheduling problems in the restructured power systems. The maintenance time interval of generation units is the crucial factor of GMS for an operation lifespan of generation units, particularly within the smart grid which needs high reliability. Accordingly, this study proposes a multi-objective-GMS (MO-GMS) optimisation model for maintenance scheduling of generation units based on the global criterion approach, adopting a suitable compromise function. The proposed MO-GMS model determines the maintenance intervals, aims to maximise both the generation company's (GenCo's) financial returns from selling electricity and the system reserve at every time interval from the independent system operator (ISO) standpoint. This method searches the optimal maintenance weeks for each generation unit, considering the objectives of both GenCo and ISO, simultaneously. The proposed MO-GMS model is formulated as a mixed-integer non-linear programming problem and examined on the IEEE 24-bus and IEEE 118-bus test systems. The success of the proposed multi-objective model is validated by comparing the obtained results with intelligent optimisation algorithms.

This paper aims to develop a decision-making procedure for efficient placement and sizing of energy storage system (ESS) within virtual power plants (VPPs) premises under the uncertainty of market price. The main aim is to minimize the overall cost of VPP within a fixed time horizon planning. The understudy VPP consists of wind turbine, photovoltaic system, curtailable loads, ESS, and diesel generators in which the VPP trades electricity with the upstream grid. The proposed framework investigates both optimal power and optimal energy of ESS based on the available budget for investment. To hedge against the uncertainty of investment, an efficient risk management manner based on the concept of conditional value at risk is applied. Moreover, from the reliability point of view, two reliability indices: the loss of load expectation and energy expected not served, are evaluated under different levels of investment to assess the impact of ESS on VPP reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved by the well-known General Algebraic Modeling System commercial software package. The effectiveness of the proposed risk-based optimization approach is demonstrated through vigorous case studies and comprehensive cost-benefit analysis.

Yearly generation maintenance scheduling (GMS) of generation units is important in each system such as combined heat and power (CHP)-based systems to decrease sudden failures and premature degradation of units. Imposing repair costs and reliability deterioration of system are the consequences of ignoring the GMS program. In this regard, this research accomplishes GMS inside CHP-based systems in order to determine the optimal intervals for predetermined maintenance required duration of CHPs and other units. In this paper, cost minimization is targeted, and violation of units’ technical constraints like feasible operation region of CHPs and power/heat demand balances are avoided by considering related constraints. Demand-response-based short-term generation scheduling is accomplished in this paper considering the maintenance intervals obtained in the long-term plan. Numerical simulation is performed and discussed in detail to evaluate the application of the suggested mixed-integer quadratic programming model that implemented in the General Algebraic Modeling System software package for optimization. Numerical simulation is performed to justify the model effectiveness. The results reveal that long-term maintenance scheduling considerably impacts short-term generation scheduling and total operation cost. Additionally, it is found that the demand response is effective from the cost perspective and changes the generation schedule.

This paper proposes a methodology for optimal allocation of multi-period-based switchable capacitor in radial distribution systems to minimize the energy loss and improve the voltage profile. In the proposed methodology, each monthly typical day is divided to numerous time segments under a so-called number of segments (NOS) factor. Several possible values of NOS are evaluated to seek its impact on system's yearly loss. In the optimization process, by considering a common value of NOS, an optimal candidate bus for locating capacitor is obtained while the capacitor step is optimized in each segment. To speed up the solving process, a technical constraint is offered to ignore the non-important states for capacitor size evaluation. Moreover, two methods for load flow calculations including the direct load flow and backward/forward sweep methods are investigated. The proposed methodology is applied to IEEE 10-bus, 33-bus, 69-bus, and 85-bus systems, and thereby relevant conclusions are drawn.

Data clustering incorporated in Monte Carlo Simulation (MCS) proves efficient in Probabilistic Load Flow (PLF) of the power grids under uncertainty of renewable energy resources. Fixed cluster agents are assumed for the clusters in the investigations reported in literature. This assumption ignores the changeable characteristics of Normal Data Clustering (NDC). This implies that the agents may change in another execution of the NDC. Under such circumstances, providing precise results for the PLF during frequent executions is not practical and there is high error in the executions. This paper presents a robust data clustering (RDC) scheme to overcome the problem arising from varying results of the NDC, and provides closer solutions to MCS. The proposed RDC method obtains the average of solutions by performing numerous NDCs under a so-called normal to robust (N2R) factor so as to solve the PLF problem in wind-integrated radial distribution systems. The proposed method is applied to various IEEE test systems, and the results are discussed. The results demonstrate the efficacy of the proposed RDC method for probabilistic load flow.

The ongoing study aims to establish a direct probabilistic load flow (PLF) for the analysis of wind integrated radial distribution systems. Because of the stochastic output power of wind farms, it is very important to find a method which can reduce the calculation burden significantly, without having compromising the accuracy of results. In the proposed approach, a K-means based data clustering algorithm is employed, in which all data points are bunched into desired clusters. In this regard, probable agents are selected to run the PLF algorithm. The clustered data are used to employ the Monte Carlo simulation (MCS) method. In this paper, the analysis is performed in terms of simulation run-time. Also, this research follows a two-fold aim. In the first stage, the superiority of data clustering-based MCS over the unsorted data MCS is demonstrated properly. Moreover, the impact of data clustering-based MCS and unsorted data-based MCS is investigated using an indirect probabilistic forward/backward sweep (PFBS) method. Thus, in the second stage, the simulation run-time comparison is carried out rigorously between the proposed direct PLF and the indirect PFBS method to examine the computational burden effects. Simulation results are exhibited on the IEEE 33-bus and 69-bus radial distribution systems.

Maintenance of the generation units is critically important due to reliability of power grid. However, the operation of the grid is directly impacted by the generation maintenance scheduling (GMS). In order to avoid a decrease in the required reserve power, it is necessary to coordinate the planned outage of power plants by the independent system operator (ISO) in a restructured environment. In this study, the risk-constrained short-term GMS problem in an oligopolistic electricity market is accomplished to decrease the risk of reserve power shortage. Decision-making under uncertainty of price and behavior of rivals in an electricity market are investigated. Moreover, the possibility of offering some proposals for maintenance intervals by the generation companies (GenCos) is also considered. Although all the proposals are considered by the ISO, there is no guarantee for their approval, due to the security constraints. In the case that the priorities for a typical GenCo are unacceptable, the offers for the unaccepted intervals are recognized and minimum reserve level is decreased by the ISO. Moreover, the long-term maintenance scheduling is established for provision of the rivals’ behavior of individual GenCos. IEEE reliability test system is employed to assess the effectiveness of the proposed GMS.