With the increase in Internet usage over the last half century, technology has also benefited from the growth and development of the Internet. It is no wonder that smart grids are emerging as one of the most popular technologies of today. As smart grids are connected to the internet, they are constantly vulnerable to cyberattacks. It is always possible for attackers to gain access to metering system infrastructure which is a major and critical part of smart grids. When this system is not working properly because of some security threats, irreparable damage is in the place for a country and its industries. This article suggests utilizing dynamic Applications that Participate in Their Own Defense defenses to secure such systems and infrastructures.
The concept of smart micro grids has been shaped to locally provide electric power by distributed energy sources at the level of the low voltage system. Distributed power plants are located at close proximity to consumers and are employed for several reasons such as environmental problems of large power plants and their low efficiency, high costs of production and transmission and distribution, etc. The energy management system plays a vital role in coordinating the various devices in smart distribution micro grids. A precise and targeted work plan must be considered to accurately use the equipment and resources in distribution networks for increasing efficiency. Furthermore, the optimal operation of power systems requires proper planning. In this study, the planning and implementation of location and the determination of the optimal capacity of distributed generation plants are discussed to reduce the losses, costs, and environmental pollution's for exploiting smart micro grids in the network. Considering supply constraints and distributed generation sources constraints, the optimal operation of production is examined based on the genetic algorithm for different objectives such as the lowest pollution, losses and the cost of installation and operation. Based on the proposed model, the cost, lose, and emission functions are simultaneously considered for the target function. Finally, the presented results illustrate the high accuracy of the utilized approach for managing the energy resources.
In this study, a structure is presented to manage the energy of the electric vehicle (EV) charger, which will be able to handle the energy of the charger at the same time, predict the production of solar generators located in the charging stations, and manage the EV charger in the workplace busbar. Uncertainty is often considered in the battery capacity of EVs. The objective function is to increase the penetration coefficient of EVs and minimize the deviation of the voltage from the desired value. The studied network is the IEEE 33-busbar test network. This research uses Digsilent software for simulation, uncertainty analysis, and optimization. The algorithm used for optimization is the Cuckoo search algorithm (CSA). The results indicate that the presented structure can significantly increase the percentage of penetration of EVs into the network, minimize the voltage deviation and reduce the cost of purchasing power from the upstream network.
Recently, smart grids have emerged as a new solution for the next generation of power systems. Cyber-attacks can take place against these. System performance may be reduced as a result of this sabotage. The purpose of this research is to the impact of a vandalism model with possible cyber-attacks on the electricity market, which is to make the generation unit or transmission line unavailable. The operator intends to have the minimum cost before and after the system attack from an economic viewpoint. For this purpose, several scenarios have been implemented in the system. The CPLEX solver is used to solve the proposed Mixed Integer Linear Programming (MILP) model. The obtained results show that the costs increased after the attack due to considering combined attacks on the network. Also, the worst-case scenario that has led to the change of output power for peak hours is identified.
This paper presents a novel detection and clarifying algorithm to enhance the resiliency of islanded microgrids against cyber-attacks. The attack type is false data injection (FDI), attacking the decision-making unit of the controlling system. To control these inverter-based resources (IBRs) we use hybrid control, including a proportional-integral (PI) Controller in the primary and model predictive control (MPC) in the secondary control unit of these IBRs. To detect and clarify the cyber-attack, firstly we calculate the residual value. In order to do that we use the difference between the predicted value of frequency and voltage, which was predicted by the MPC interior model in the previous step, and the measured value in the next step. a non-zero residual value reveals an unpredicted change in microgrids such as cyber-attacks. In the following, two additional parts of the algorithm are proposed to identify the frequency, voltage, and active or reactive false data. In the first part of the algorithm, we compare measured and calculated active and reactive power in receiver units. In the following part of the algorithm, we use the power change synchronization between IBRs, which is dictated by droop characterization.
Conventional battery packs, especially lithiumion type, usually consist of series-connected cells and modules, needing a separate equalizer beside the charger. This paper proposes a distributed battery management system that performs as a charger/discharger combined with an active cell balancer. This idea does not need any difficulties for estimating the battery SOCs, and it will be proved that each cell's voltage is the only variable needed to equalize the battery pack. Actually, in this system, there is no need for a separate circuit for equalizing the cells. The charger which consists of several power electronic converters with a reduced power rate will balance the whole pack dynamically, leading to improvement in cells consistency as the number of charge/discharge cycles and operating duration increase. An unbalanced scenario in each mode, charge and discharge, has been simulated, proving that charge or discharge and equalization can be performed simultaneously only by using cell voltages.
This paper proposes a PSO optimization algorithm for specifying the most appropriate photovoltaic-based distributed generation size and location in power grids. The issue has been investigated in four seasons of the year and two operation modes (PV and PQ) on IEEE 96-bus distribution systems. Given the network's requirement for active and reactive power compensators, renewable sources can improve power quality on the grid through active and reactive power control. Results indicate that the proposed method improves the voltage profile and reduces losses in the electrical distribution system. The voltage profile improvement in the PQ operation mode is more significant than in the PV operation mode; furthermore, the proposed algorithm in the PQ operation mode has significantly reduced both active and reactive losses in all four seasons compared to the PV operation mode since, in the PQ operation, the reactive power required is also injected into the network in addition to the active power. Finally, despite the amount of consumer PV and PQ demand being different in the year's four seasons, the proposed PSO algorithm can choose the most suitable location and sizing of Photovoltaic-based DGs in Electric power distribution.
In this paper, calculation of electrical energy losses in practical distribution systems is investigated. A feedforward based neural network (NNET) is proposed in which available data such as feeders and customers' specifications are used as inputs of the model and electrical energy losses is considered as the output of the NNET. Realistic data from 39 DISCOs are used to train the network and examine its performance. The data are gathered from annual statistical reports for 8 years. Optimal number of hidden layers is obtained by trial and error method. Simulation results demonstrate that the obtained network have high performance and can solve the challenges related to electrical energy loss calculation in practical systems.
Simultaneously with the increasing growth of information technology in the information/digital era and the development of intelligent systems, the subject of smart grids in the electrical engineering field and smart management of power grids is also taken into consideration. One of the critical issues which should be noticed is the threats that might target active companies in this field. In this article, while referring to a novel lightweight, and quick risk management framework based on IT assets for these companies, we present a new approach to business continuity management. In this framework, we presented a flow for periodical quick risk management with the explanation details that can be a good document for a better understanding of the subject.
Nowadays, the possibility of cyber attacks is increased due to the vulnerability of two-way communication pathways and the low security of smart meters. Hence, designing proper defense strategies is crucial for having a secure power grid. In this paper, a bi-Ievel optimization formulation is presented for analyzing the interactions between the attacker and defender. At the upper level, the attacker finds the most influential consumers affecting the load variations considering the limited budget. Then, through the False Data Injection Attack (FDIA), the real-time price signals are manipulated to increase the total consumption of the grid during peak periods. At the lower level, two defensive methods are utilized after detecting the FDIA to alleviate the financial losses caused by outages. These two methods are decreasing the peak consumption using an incentive-based Demand Response Program (DRP) (the main contribution) and reducing the network losses using a network reconfiguration. The simulations are implemented on the modified IEEE 94-bus system, and the results demonstrate the correctness and effectiveness of the proposed model and formulation.
Three main effective factors on the efficiency of photovoltaic panels include kind of cell, radiation intensity, and cell temperature. In tropical regions, a considerable part of decreasing efficiency of photovoltaic cells is related to destructive effects resulted from increasing temperature. The average rate of radiation in many regions of Iran is more than the world average rate needed for installing solar stations. However, some regions are in tropical conditions and the high temperature of the environment influences the efficiency of the solar plants. For this purpose, this paper tries to do an economic evaluation of a fan active cooling system for a set of photovoltaic modules based on meteorological information of a tropical town (Jiroft in the south of Kerman City). In order to evaluate the cooling system on producing power of the photovoltaic panel, a photovoltaic heat-temperature model with twelve modules is simulated in MATLAB Software environment, and the effect of connecting a 25-watt fan is studied. Results show that applying a fan cooling system increases producing power of a photovoltaic panel inserted in Jiroft by about 12 percent. Therefore, considering power consumption, photovoltaic panel efficiency increases 7%.
Due to the increasing amount of industrial data worldwide, deep learning solutions have become extensively popular for preventive maintenance programs. Preventive maintenance reduces the costs of equipment failures, thus crafting detailed preventive maintenance programs are very effective. Neural networks are intelligent computing techniques inspired by biological neurons. Neural networks are one of the most common and practical machine learning algorithms that are utilized in many industrial applications. In this research, a fully connected neural network is developed to predict the systems reliability indices, i.e. the number of failures occurring in feeders and the amount of energy not served (ENS) in electricity distribution feeders. As a result, a data-driven improvement is provided for the preventive maintenance program.
Since photovoltaic (PV) power plants are becoming more prevalent on the grid, their participation in increasing grid stability seems necessary. Dynamic voltage support during fault times is a requirement of modern grid codes for integrating PV power plants into the grid. However, the effects of these requirements on other grid characteristics should be investigated. This article conducts comprehensive analyses of the impact of the reactive power injection rate of PV power plants to support the grid voltage on transient stability. Simulation is performed on an IEEE 9-bus system containing conventional synchronous generator (SG)-based power plants and PV plants using DIgSILENT to analyze the performance.
In recent years, the grid penetration of renewable energy resources and electric vehicles has increased rapidly. However, their environmental benefits will lead to uncertainty in their profile due to their stochastic behavior. In this article, a linearized energy management model is proposed to improve the operating cost of the microgrid, including renewable energy resources, energy storage systems, distributed generation, combined heat and power, and electric vehicle parking. Uncertainty in stochastic parameters of the system, including load demand, electricity price, and profile of renewable energy resources, are managed using a machine learning approach and the availability of electric vehicles in the parking lot using the point estimate method. Furthermore, the effect of an electricity price increase on the total operating cost has been evaluated, and the results show that despite a 40% increase in the price of electricity, the total cost increases by 5.35%.
The most challenging issue in high voltage direct current (HVDC) systems in terms of protection is the DC-side fault, which should be interrupted quickly. In addition to fault current clearance, safe reclosing of the HVDC system is essential. Otherwise, a second strike can endanger the system components as semiconductor devices in modular multilevel converters and DC circuit breakers (DCCBs) are vulnerable to another high current. In this paper, a new method for DC fault identification and localization in auto-reclosing of the HVDC system is proposed, in which an energy absorption module added to a hybrid DC circuit breaker is used to reach these objectives. In the proposed approach, a mechanical switch in the energy absorption module provides a path for discharge of its capacitor into the line, which helps to get valuable information about the type of the fault and its location by the characteristics of the discharge waveform. The effectiveness of the proposed method is validated by a point-to-point HVDC test system based on modular multilevel converters built in PSCAD/EMTDC platform, and curve fitting toolbox in MATLAB software. The simulation results confirm that the proposed method has no identification dead zone and is robust to fault resistance. Moreover, it does not affect other parts of the system.
Electromagnetic interference (EMI) analysis for the grid and the connected equipment is essential to ensure that the defined EMI standards are not violated. In this paper, the common mode (CM) noise sources of low power boost power factor correction (PFC) converters operating in transition mode (TM) are investigated. The main portions of the propagated CM noise are due to the bridge rectifier diodes and the transformed differential mode (DM) noise to CM noise. Identifying these noise sources reveals noise reduction solutions. By applying these considerations, the maximum amount of noise at the critical frequency is determined. Passive filters tend to be bulky and their volume reaches up to 30% of the entire system volume. Thus, using an optimized approach, the filter with the minimum volume is dimensioned. A single-phase 80W TM-PFC with an exact model of operated controller IC is also simulated in LTspice to show the validity of the presented investigations.
In this paper, a game-theoretic strategy is used to investigate residential units' cooperation on excess demand. Since household consumption during the weekdays is approximately consistent, retailers should buy energy from substations to supply the consumer demand and announce day-ahead market prices to them. conversely, if a unit needs excess demand, it must purchase it on the spot market at a higher price. Using social-technical cooperation of nearby neighbors, this study examines the allocated excess budget costs due to unscheduled excess demand. In this complete information game-theoretic social approach, every player has their own economic characteristics based on their level of cooperation and decision making. The proposed method is applied on residential test microgrid utilizing GAMS employing CPLEX solver. Based on simulation results, neighbors are able to cooperate and supply excess demand among themselves. Furthermore, the given approach reduces total cost by up to 25%.
Maintaining customer satisfaction and improving the reliability of energy distribution systems are essential, due to changes in customer's higher expectations and modern life style. Due to the penetration of renewable energy sources on the customer premises and the ability of customers to improve the operations of the distribution systems, these sources and the customers can play an important role in reducing the costs of the distribution network operations and improving its reliability. The original idea of the paper is reinforced restoration of the smart distribution systems by utilizing the transactive flexibility gained from the prosumer's solar distributed energy resources, which is its main contribution. The problem is modelled as a MILP optimization problem, and the network technical constraints, including feeder loadings, and power flow formulations are considered in the model. Subsequently, the problem model is applied to two feeders of the Roy Bilinton test system, and solved by SBB (Simple Branch & Bound) solver in the GAMS. The results reveal the effectiveness of transactive flexibility of the prosumers in improving both reliability and operation costs.
In recent years, the increase in the penetration of prosumers in distribution networks has led to more flexibility in the structure of energy markets and the emergence of peer-to-peer energy markets. This market allows prosumers to share energy for benefits, which are usually economic. The prosumer's economic benefit depends on the existence of an optimal physical path for energy exchange, which is affected by factors such as network configuration and electrical distance between prosumers. A distribution system operator (DSO) could change the network's topology or reconfigure it for goals like improving reliability, decreasing loss, and balancing loads. It is clear that in the reconfigured network, the electrical distance between prosumers may also change, which affects their decision to share energy. This study presents a topology-based model for managing P2P energy sharing, which can use for a prosumer-based distribution network. A central manager manages energy sharing in the proposed model to minimize the overall cost. In the end, the results of its implementation on a 33-bus distribution network are argued to investigate the model's performance.
The application supercapacitors (SCs) alongside batteries is very useful to decrease battery life degradation in the form of hybrid Energy Storage Systems (ESSs). In this regard, the battery responds to low-frequency currents while the SC deals with high-frequency peak currents. Considering that adding SC and batter life enhancement respectively increases and decreases overall storage cost, his paper attempts to optimally size the SC size to increase battery health and hence, its life cycle. The proposed methodology is tested on a Hybrid Battery-SC ESS used for energy management of an islanded microgrid. The results show that by implementing the SC, the battery tends to age about 34% which also decrease the overall ESS cost by 2145$.
