Optimal planning of power distribution systems with local resources is crucial to meet energy demand and avoid disruptions in energy supply for consumers. This requires the system operators to manage available resources and utilize suitable risk management tools to control and study uncertainties and their potential consequences. This paper proposes an uncertainty-based optimization framework based on the robust optimization and scenario methodology for optimal sizing and siting of electrical vehicle charging stations (EVCSs). The proposed model seeks to take advantage of the flexibility introduced by EVCSs and gain financial profit for the operator of the power distribution system through reducing power losses and offering services to electricity markets. To handle the uncertainties posed by different resources, two risk measures are employed simultaneously. The uncertainty originating from the state of charge (SOC) of electric vehicles (EVs) is addressed through stochastic programming, while the robust optimization method (ROM) enables the operator of the power distribution system to be informed of the consequences of uncertainty in electricity load. Therefore, appropriate strategies can be taken to tackle the uncertainties while keeping the system operation stable and gaining financial profit. Thus, three strategies are studied in the proposed model as follows: risk-neutral, risk-averse, and risk-taker. In addition, the non-linear terms in power flow modeling were linearized through a set of linear functions which transforms the proposed model to a MILP problem. The IEEE 33-bus test system under different levels of load uncertainty and considering the uncertainty in SOC of EVs is utilized to ensure the effectiveness of the proposed model. The results highlight the efficiency of the proposed model in considering uncertainties and taking advantage of the consideration of different risk attitudes by the decision-maker that ROM provides for the optimal operation of the power distribution system.

As power systems are gradually evolving into more efficient and intelligent cyber–physical energy systems with the large-scale penetration of renewable energies and information technology, they become increasingly reliant upon more accurate and complex forecasting. The accuracy and generalizability of the forecasting rest, to a great extent, upon the data quality, which is very susceptible to cyberattacks. False data injection (FDI) attacks constitute a class of cyberattacks that could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby deteriorating the forecasting performance causing catastrophic consequences in the power system. This article proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resiliency of energy forecasting systems. The proposed mechanism is based on cross-validation, least-squares, andz-score metric providing accurate detections with low computational cost and high scalability without utilizing either system’s models or parameters. The effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models. Experiments indicate that corrupted data injected into input, output, and input–output data is properly located and removed, whereby the accuracy and generalizability of the final forecasts are recovered.

Frequency response analysis (FRA) suffers from the interpretation of results despite its potential ability to detect faults related to the power transformer windings. This article presents a technique for interpreting frequency responses, which is based on image processing and a deep learning method called graph convolutional neural network (CNN). The proposed procedure transfers frequency responses into 2-D images through a visualization technique. The resulting images are aggregated into a dataset to be used as the CNN input. The proposed technique is applied on frequency responses of two different winding models with short circuit (SC) faults. The SC faults with different intensities are applied on different sections of a simulated ladder model winding and a 20 kV winding of a 1.6 MVA distribution transformer. After determining the frequency response for each faulty case and applying the visualization technique, the precise locating of the SC faults is performed by the CNN. Then, the results are analyzed by performance evaluation metrics. At this stage, the high performance of the CNN in the use of 2-D images instead of the conventional method is observed. Finally, by testing the high impedance SC faults in different sections of the simulated winding model and applying the suggested method step by step, early detection of the SC fault is also performed in this article. It should be noted that the suggested technique, in addition to its accuracy and high detection speed, can be considered as an important step in automatic interpretation of frequency responses for online monitoring of transformers.

Recently, the decarbonization of the electric power system has led to substantial efforts for designing a pathway toward 100% renewable energy resources (RERs). In this article, we propose a novel operational model for the effective participation of the interconnected microgrids with 100% RERs in the transactive energy market. The novelty of the proposed model is mostly related to the use of transactive energy technology for developing the free energy trading environment for the microgrids with 100% RERs as the local energy-trading market to establish a dynamic energy balance in the system. To capture the intermittencies in the system, a hybrid version of the stochastic programming and information gap decision theory (IGDT) method with the risk-averse and risk-seeker strategies is proposed in the deregulated environment. The proposed model is validated by selecting the modified IEEE 14-bus test system. The results indicate the effectiveness of the proposed model in providing the same percentage of cost-saving for microgrids when they simultaneously participate in the transaction energy market. The cooperative energy interactions of the microgrids in the transactive energy market based on the proposed model lead to 18.34% cost-saving for them in comparison with the base model.

Power transformers usually confront various mechanical and electromagnetic stresses during an operation that may lead to defects in their windings. The short circuit in the windings is one of those severe defects. Early detection of short-circuits is necessary as extra heating in the shorted location can lead to progressive damage in windings insulation. Frequency response analysis (FRA) is a well-known method to diagnose short-circuits in transformers. Despite the accuracy of FRA, the interpretation of the obtained frequency response traces (FRTs) is still an intricate task. Due to the unknown impact of faults on FRTs, extracting efficient features from such traces is necessary for the interpretation of transformer's frequency response. In this article, an isometric feature mapping (Isomap) is used as a nonlinear dimensionality reduction technique to locate interturn faults in transformer windings due to its capability of capturing the nonlinear phenomena in FRT of power transformers. It is revealed that, after constructing the isometric mapping for a transformer, there is no need for any expertise to detect fault location even in nondirect (high impedance) short-circuits. In other words, it can be the first step for the automated interpretation of FRA of power transformers.

At present, the power system is developing toward a fully renewable energy resources (RERs) equipped system due to significant challenges with the conventional units. This trend has led to remarkable new challenges for power system planners considering the stochastic nature of RERs, application of new emerging technologies, etc. In this article, a two-stage robust stochastic programming model for the optimal scheduling of commercial microgrids equipped with 100% RERs to handle the existing uncertainties is presented. In the day-ahead electricity market, microgrids maximize their expected profits by optimizing their bidding strategy, while minimizing the imbalance cost is targeted for microgrids by adjusting the distributed energy resources in the real-time balancing market. Transactive energy technology is effectively applied to manage the energy trading between microgrids with each other in the local energy transaction market and with the power grid. For demand-side management, the demand response program is posed considering the shiftable and interruptible features of the load. Simulation results on the IEEE 33-bus standard system integrated with microgrids verify that the proposed model could provide satisfactory profits for microgrids participated in the energy exchanging process based on the transactive energy architecture.

Reactive power planning problem is the key to secure and economic operation of power systems. Optimal management of existing reactive sources leads to loss minimization and economic performance of the system. Because of the nonlinear inter-relation between the physical parameters of the electric grid, this problem is a highly nonlinear and nonconvex constrained optimization problem. The application of semidefinite programming (SDP) to power system problems has recently gained considerable research attention. A recent SDP formulation uses a convex relaxation to the nonconvex optimal power flow problem under some technical conditions. This paper proposes a novel equivalent convex optimization formulation for the optimal reactive power dispatch (ORPD) problem and presents a new framework for finding the global optimum. Numerical results for the IEEE 30-bus and 118-bus test systems show that the proposed scheme obtains the optimum operation point and outperforms various state-of-the-art methods significantly.

Information about power consumption patterns of devices, helps the residential consumers manage their energy usage. Nonintrusive load monitoring is an effective tool to extract the consumption patterns from the measured aggregated data at the meter. In this paper, an optimization-based method is proposed to disaggregate the total load, using low frequency data. The proposed algorithm is enhanced by enforcing the power profiles of appliances to be piecewise constant over specific time durations. Moreover, the state transitions of the appliances are determined and then employed as the optimization constraints to improve the estimation results. The proposed method is evaluated using almanac of minutely power data set (AMPds) and reference energy disaggregation data set (REDD) datasets by several performance metrics. Results indicate that the designed algorithm is able to recognize the frequently varying appliances in spite of piecewise constancy presumption. Furthermore, breaking down the optimization problem to the smaller parts enhances the ability of the algorithm to be operated in real time.

The planning stage of any project, could it be for an industry, a commercial or energy supply system, has crucial significance and involves judicious contribution from field experts to decision makers (DM). The objective of this paper is presenting a model for planning of energy sources for microgrid using multi-criteria decision making (MCDM) based on analytic hierarchic process (AHP) approach. For developing a model, an educational institution's electrical energy load demand has been considered as reference. In this assessment, the main-utility grid as the primary source of electricity, alongside conventional sources like diesel generator (DG), gas-based combined heat-and-power (CHP) with absorption chiller to meet cooling demand of facility is taken into account. Moreover, proven and comparatively most environmentally friendly renewable energy sources, such as solar photovoltaic (PV) together with battery energy storage system (BESS) have been taken into account. Moreover, the assessment and evaluation for prioritization of energy sources based on critical criteria or attributes and their associated sub-criteria have been judged to make decision. In this model, most of the critically influencing criteria, such as economic, technical, structural, operational and maintenance, environmental and societal aspects are being focused on. In total, nine alternatives-combinations of grid and other energy source(s)-are identified to form the microgrid. The weight score for each combination of sources is computed for each of the 22 criteria and could be presented DMs to enlist priority of alternatives to choose from.

Recent advancements in energy systems have led to a series of new challenges in the decision-making process. Harmony search (HS) algorithm, which is a music-inspired optimisation technique, has been applied to some of these decision-making processes to obtain optimal set points within these energy systems. HS is based on the music improvisation process where musicians try to find better harmonies. Some of the advantages of HS method are that it is relatively simple to implement and require less algorithmic parameters. This paper aims to provide a comprehensive review on the applications of HS method to energy systems, that concentrate on two main objectives. First, the improved versions of HS introduced in recent studies will be reported. Second, contributed researches in energy systems by using HS will be analysed.

Uncertainty management is a challenging task in decision making of the operators of the power systems. Prediction of the system state is vital for the operation of a system with stochastic behavior especially in a power system with a significant amount of renewable energies such as wind power. Predictable power systems are in more interest of operators, of course. This paper proposes a multiobjective framework for optimal placement and parameters setting of a unified power flow controller (UPFC) considering system predictability. The well-known multiobjective nondominated sorting genetic algorithm is implemented to handle various objective functions such as active power losses and predictability of system in the presence of operational constraints and uncertainties. The point estimate method is used for modeling probabilistic nature of the wind power. Using the proposed method, statistical information of voltage magnitude and apparent power of converters of UPFCs can be obtained, which are very useful in making decision on the sizing of UPFCs. Comprehensive discussions are provided using the simulations on the IEEE 57-bus test system. Also, in order to validate the obtained results, a multiobjective particle swarm optimization algorithm is implemented and the results of two algorithms are compared with each other.

Optimal generation companies' participation in different electricity markets can be modeled as a competitive game. Each company tries to behave optimally considering other players' actions, in the game. Generally, the player's profit is formulated based on expected utility method with objective behavior completely. However, the subjectivity of players causes the differences between expected and real actions. This paper proposes a game of generation companies' participation in adjusting the optimal bidding strategies for the day ahead energy market. Moreover, the subjectivity of players and how it affects their strategy determination have been modeled by applying the prospect theory on the game. In addition, this paper presents the full formulation of mixed strategy noncooperative game of GENCOs in both conditions of objective and subjective behavior of players. Finally, simulation results have been provided to show how subjectivity causes players' avoidance from the strategies expected to have higher losses than others.

This paper presents a risk-based stochastic model to sizing a photovoltaic (PV) /diesel/storage hybrid power system of a merchant marine vessel. Integrating renewable, in particular PV, energy resources offer significant advantages to bulk transportation by reducing the greenhouse gas emissions, improving the energy efficiency, and increasing the energy security. To this end, the sizing problem of PV/diesel/storage merchant marine vessel hybrid power system would optimally determine appropriate configuration of the PV system and energy storage system to design hybrid power system of a merchant marine vessel in an optimal and reliable manner. The uncertainty related to the hourly global solar radiation and its effect on the output power of the PV system is taken into account and modeled using proper scenario generation methods. Additionally, the scenario reduction technique is applied for the domination of dimensionality. Furthermore, an appropriate risk measurement, the conditional value-at-risk methodology, is incorporated with the proposed stochastic model to quantify the potential risk of sizing the problem. Finally, the proposed model is applied to a comprehensive test case to illustrate the efficiency and the applicability of the proposed approach.

Solution of optimization problems is inseparable part of science and engineering. The close dependence of industry applications on science and engineering clarifies need to optimization algorithms for modern industries. In this paper, the proposition of an evolutionary optimization algorithm is presented. The proposed algorithm is inspired from wild goats' climbing. The living in the groups and cooperation between members of groups are main ideas which have been inspired. Along the procedure of the algorithm, leaders of groups attract group's other members and eventually the leader of the biggest group reaches the highest point of mountain. Besides examining with a number of benchmark functions, the performance of the algorithm is gone through by one of the energy systems' important problems, which is known as combined heat and power economic dispatch (CHPED) problem. The aim of the CHPED problem is supplying power and heat demand in an economical manner by conventional thermal units, CHP units, and heat-only units. The effect of valve-point and transmission losses is taken into account in order to consider practical CHPED model. The algorithm is tested on three test systems and the results show the ability of the algorithm to converge the optimum values.

This paper investigates the robust multiloop control for power flow controller in power transmission grids. A hybrid power flow controller (HPFC) is proposed, which comprises two shunt modular-multilevel-converter (MMC) based voltage source converters and one series capacitor. A nonlinear multiloop controller is designed via control Lyapunov function to achieve fast tracking performance, and robustness against system uncertainties and disturbances. Stability of the closed-loop nonlinear HPFC system is proved using Lyapunov stability theorem. The proposed finite-time controller (FTC) is decentralized using adaptive observer to estimate the nonlocal system parameters, in case of communication failure. Simulation and experimental studies are used to validate the proposed FTC with detailed model of MMCs, and the interarea oscillation damping with Phasor model of HPFC. Comparisons of the proposed FTC and the conventional PI controller show that the FTC has fast control response, small transient overshoot, and improved robustness.

This study presents a stochastic approach to design a wind integrated energy hub with multiple energy systems. Energy hub system offers significant advantages to energy services by providing the flexibility to cope with the challenging effects of intermittent renewable energy sources penetration. To this end, the wind integrated energy hub design problem would optimally determine appropriate number and size of system components that satisfy electricity and thermal demand and system constraints. To secure operation, the reliability indices such as the loss-of-load expectation and the expected energy not supplied are considered. The wind power generation and load forecasting uncertainties as well as the random outages of components are modeled as a scenario using proper scenario generation methods. The scenario reduction technique is also introduced to reduce the computational burden of the scenario-based design model. Finally, the proposed model is applied to a test case to illustrate effectiveness of the proposed approach.

Microgrids (MGs) are considered as a key solution for integrating renewable and distributed energy resources, combined heat and power (CHP) systems, as well as distributed energy-storage systems. This paper presents a stochastic programming framework for conducting optimal 24-h scheduling of CHP-based MGs consisting of wind turbine, fuel cell, boiler, a typical power-only unit, and energy storage devices. The objective of scheduling is to find the optimal set points of energy resources for profit maximization considering demand response programs and uncertainties. The impact of the wind speed, market, and MG load uncertainties on the MG scheduling problem is characterized through a stochastic programming formulation. This paper studies three cases to confirm the performance of the proposed model. The effect of CHP-based MG scheduling in the islanded and grid-connected modes, as well as the effectiveness of applying the proposed DR program is investigated in the case studies.