rahmat allah hooshmand

By integrating the power and natural gas systems (IPGS), these two systems can synergistically provide electric and gas power through mutual energy sharing. However, disruptions occurring in one system can have detrimental effects on the optimal performance of the other system. Hence, the assessment of IPGS reliability becomes imperative. In this article, a reliability evaluation model is proposed that utilizes machine learning algorithms to tackle uncertainties associated with the failure rate of IPGS components in the presence of gas storage resources. To calculate system reliability indices, we employ Sequential Monte Carlo Simulation (SMCS) and an optimal load-shedding program. Moreover, a random forest (RF) algorithm is adopted to classify elements based on their importance in upholding the overall system's reliability. The proposed model is implemented using MATLAB, GAMS, and Python software, with the IEEE 14-bus system and the 10-node natural gas system as case studies. The simulation results illustrate that by considering the gas storage resources, the reliability of the power system and natural gas can be improved by 2.12 and 8.3%, respectively, and the overall reliability of IPGS is improved by 5.25%. Also, the prioritization of these resources is determined in IPGS.

The fixed capacitor-magnetically controlled reactor (FC-MCR) is a type of static var compensator (SVC) that can greatly contribute to the availability and stability of power systems. This paper proposes a comprehensive reliability model for the FC-MCR using the Markov process approach. The modeling process adheres to actual operational principles and divides the MCR structure into two sections: electro-magnetic section and core magnetization section. Subsequently, the Markov models proposed for these sections are integrated with the Markov model of the fixed capacitor bank to derive the FC-MCR reliability model. Recognizing the impact of environmental conditions on electrical equipment failure rates, the proposed reliability model takes into account temperature variations and assesses their influence on the probabilities of the FC-MCR operating state. By examining the simulation results and conducting sensitivity analysis, it was found that the availability of the FC-MCR is influenced by various components and environmental conditions, which necessitates different reliability enhancement measures. Moreover, a comparison between the reliability indices of the FC-MCR and its counterpart (FC-TCR) in diverse environmental conditions revealed that the FC-MCR is less affected by temperature variations compared to the FC-TCR.

Nowadays, using telecommunication systems and advanced measuring devices underlies cyberattacks on electrical grids. Bad data injection and failure to detect it on time, cause drastic damage to the network. This paper presents a new method for bad data detection (BDD) in state estimating when a cyber attacker manipulates the important measurements. Therefore, the new attack index is defined by simultaneously manipulating the network parameters and injecting incorrect data into the measured values. For this purpose, considering the masking and swamping effect, the diagnostic robust generalized potential (DRGP) algorithm detected and isolated high-leverage measurements installed in important locations from low-leverage measurements. Then, the state estimation process performs using low-leverage measurements. The Generalized Studentized Residual (GSR) algorithm detects bad data. With simultaneous manipulation of network parameters and measurement values, conventional BDD methods are unable to detect an attack. To evaluate the performance of the proposed method, they were implemented on the IEEE standard 14-bus network using MATLAB and Rstudio software. The simulation results show the ability of the proposed algorithm to detect a bad data attack.

Lack of coordination between the operation of capacitors and OLTC to voltage control is one of the basic problems in distribution networks, which can cause problems in the control function. In this paper, a new control method is proposed to create coordination between the operation of capacitors and OLTC. The criterion of this coordination is based on fuzzy logic to determine the status of transformer and capacitor function, which reduces unnecessary switching. In this method, the control function is considered with more accuracy such that the quantity of voltage and power factor are set within the range of 0.98 to 1.02 p.u and between 0.95 lag and lead, respectively. Another advantage of the proposed method is that it is automatic and eliminates human resources to adjust the position of the transformer and capacitors.  Simulation results are done on the 14-bus network by using two software MATLAB and DIGSILENT, that these results show the accuracy of the proposed method in the desired performance in distribution networks.

Resilience characteristics in power systems refer to the system's ability to withstand against severe disturbances with a low probability of occurrence. As extreme dust storms in the south and southwest in recent years have caused heavy damage to the IRAN's electricity industry, in this paper, a bi-level planning model is proposed to simultaneous hardening of distribution lines and substations against this phenomenon. In the first and second levels of the proposed model, the investment costs of distribution system hardening and total expected operating costs are minimized subject to financial and operational constraints, respectively. The planning results in different case studies have shown that the simultaneous hardening planning of substations and distribution lines can, in addition to reducing operating costs in the emergency conditions, play a significant role in reducing investment costs. The proposed model is implemented on a large-scale power distribution system in Khuzestan province, and the simulation results confirm the efficiency of the proposed scheme at different budget levels.

Nowadays, determining the contribution of the individual consumers to the harmonic contamination is required for improving power quality. Due to changes occurred in lines, transformers, loads and generators over time, network operating conditions and impedance of different sectors are frequently varied resulting in the change in the contribution of harmonic sources. In this paper, an intelligent-based three-step method is developed to continuously determine the contribution of each harmonic source to the harmonic voltage of different buses without any access to the voltage phase data at different buses in the network. In this algorithm, firstly, the K-means clustering method is used for pre-processing of the measured data in order to reduce the background harmonic destructive effect on accuracy of the harmonic contribution determination. After calculating the harmonic contribution, the K-nearest neighbor method is used to generalize the results, and subsequently to create a continuous harmonic contribution matrix (HCM). Finally, the method is applied to a standard power network-based calculation example. The results demonstrate the capability of the proposed algorithm to evaluate the effects of harmonic sources in power networks.

Installing new energy sources as redundant black-start (BS) units is an efficient way to enhance the speed of power system restoration, especially when there is a high risk that the available power plants considered as BS units fail to operate. In this regard, this paper provides a new optimal design for the placement of the Gas Turbine (GT) as the redundant energy source to improve the power system performance during both restoration and normal conditions. In doing so, there will be contradictory objective functions to be minimized. Therefore, a multi-objective problem (MOP), as a mixed integer linear programming (MILP), is defined. The Pareto optimal solutions of the MOP are obtained by using a new population-based meta-heuristic technique, called Crow Search Algorithm (CSA). Two power systems are used for the validation of the proposed method. The simulation results show that the system can benefit from this method not only to increase the capability of black-start generation, but also to improve the power system performance in normal conditions. During the restoration process, it also provides the optimal start-up sequences of non-black-start (NBS) units with the optimal transmission paths.

In a competitive electricity market, an accurate short term price forecasting is essential for all the participants in market as a risk management technique.  For both spot markets and long-term contracts, price forecast is necessary to develop bidding strategies or negotiation skills in order to maximize benefit.  This paper proposes an efficient tool for short-term electricity price forecasting with a simple model and acceptable computation time by combining several intelligent methods.  Using inference, Adaptive Network-based Fuzzy Inference System (ANFIS) is used to determine the nonlinear relation between large quantities of input variables and forecasted price (output variable).  To decrease the complexity and improve the accuracy, mutual information (MI) technique is used to efficiently select the best set of input variables which have important information concerning forecasted price.  Moreover, Particle Swarm Optimization (PSO) algorithm with new strategy in choosing the particles is adopted to tune ANFIS parameters more precisely.  To evaluate the accuracy and performance, the proposed hybrid Mutual Information-ANFIS-PSO (MIAP) methodology is implemented on the real world case study of Spanish electricity market.  The results show the great potential of this proposed method in fast and accurate short-term price forecasting in comparison with some of the previous price forecasting techniques.

Incremental increasing use of the electrical energy and restructuring the power networks and the problems that they have been brought with them, cause increased the power operator’s interest to use of WAMS instead of SCADA. One of the main issues in the WAMS is the cost reduction of the optimal placement of PMU and correlated communication infrastructure. In this paper, a new method is presented for WAMS designing, with the aim of cost reduction. For this purpose, the greedy algorithm is used for optimal placement of PMUs and simultaneously, with the use of the Bellman-Ford algorithm, minimum communication infrastructure is designed so that the total cost of WAMS designing is reduced. Also the optimum location of the PDC is determined with the Bellman-Ford algorithm. This method is proposed in the normal operation condition and also in N-1 contingency conditions ( e.g. a single PMU loss and a single line outage). Simulations are conducted on the IEEE 30- and 118-bus power networks and the 230kv and 400kv of the Isfahan power network. Simulation results show good performance of the proposed method.

Optimal operation and bidding strategy of renewable units are two important problems of the restructured power market. In this paper، a new method for the joint operation of wind، photovoltaic and pump-storage units in day ahead power market is studied to increase the profit of joint units. In this study، artificial neural network is used to predict the wind power generation of wind farms. Since، there are uncertainties in energy and reserve prices، wind and photovoltaic power generation، the optimal operation of joint units can be modeled as a stochastic optimization problem. For this purpose، uncertainties of parameters are modeled by scenario tree method. The performance of the proposed method is evaluated on the renewable energy resources (wind farms، photovoltaic and pump-storage units) of the modified IEEE 118 bus test system. Results of the proposed joint operation of renewable resources confirm that the value of expected profit increases in comparison with uncoordinated operation (UO) of units.

Today modern power systems are operated in lower security level due to power system deregulation and increasing the power transfer capacity. Extensive power systems blackouts in recent years show the remarkable increase of power system vulnerability in contingency situations. Load shedding is one of the last corrective actions for keeping power system stability. In this paper a real time optimal under frequency load shedding by using artificial neural network is presented. This structure contains two offline and online studies. In offline studies according to the values of vulnerability and security margin indices of total power system، minimum frequency، reduction rate of equivalent inertial center frequency (dfc/dt) for N-K contingency scenarios، the power system security is determined and the ANN inputs data base will be established. In each scenario، the necessary active and reactive load shedding value for preserving power system stability is determined by solving an offline optimization problem by using intelligent hybrid CPCE algorithm. The values of the active and reactive load shedding in each load shedding step in each contingency scenario are considered as the ANN outputs. Genetic algorithm is employed for optimizing the ANN training process. The trained ANN will be used for online application in power system by using real time operation information that is collected by wide area monitoring system (WAMS) and phasor measurement units (PMU). Simulation results for IEEE 118-bus test system shows the effectiveness of the proposed method.

In this paper, the multi-machine power system is simulated and in addition, VURPSO optimization algorithm is used to optimize the parameters of simultaneous controllers of Static Var Compensator (SVC) and power system stabilizer (PSS) in order that the power system stability increases. By SVC, the transferrable power in steady state can be increased and the voltage profile can be controlled along the line. The main role of PSS is damping the generator oscillations by controlling its stimulation by use of auxiliary supplementary signals. If it is assumed in this paper that a three phase short circuit fault with the ground has occurred in a four-machine power system, SVC and PSS simultaneous controllers are used to damp the oscillations and stabilize the system.

Nowadays, the optimal application of phasor measurement units (PMUs) in power systems is increasing because of their advantages such as the capability for dynamical state estimation and improvements in the speed of control and protection systems. In this paper, a new method is presented using binary integer linear programming for the optimal placement of PMUs to guarantee full observability of a power system. Moreover, the problem of the optimal placement of these units in the case of a single line outage is investigated. In all of the investigations, the effect of zero-injection buses in the power system was considered. The ability of the proposed method in solving any of these problems was demonstrated by applying to the 14-, 30-, 39-, 57- and 118-bus IEEE standard systems, 42-bus transmission network of Esfahan Regional Electric and also two very large-scale systems with 2383 and 2746 buses. The simulation results verified the effective and appropriate performance of the proposed method in comparison with other methods.

In this paper, a new bidding strategy in generation scheduling and commitment is proposed for uncoordinated and joint scheduling of wind farms and pump-storage power plants in the day-ahead electricity markets and ancillary services. Also, the value at risk (VaR) of joint and uncoordinated operations (JO and UO) for these two plants is calculated and evaluated. Uncertainties of wind power generation (WPG) and the market prices convert this scheduling problem to a stochastic scheduling problem. On the other hand, the pump-storage plants have the ability to manage of positive and negative energy imbalances over time. Therefore, with joint scheduling of pump-storage plants with wind units, the benefit of these plants is increased compared with UO of these plants. Uncertainty of market price and amount of WPG are modeled using scenario tree. The proposed method is tested on pump-storage and wind farm of IEEE 118-bus standard system. Also, by evaluating the scheduling operation risk, the VaR level has determined. The results of the benefit and risk for JO and UO of pump storage and wind farm verifies that these joint operations improve the benefit and risk values of the system.