مجله کیفیت و بهره وری صنعت برق ایران
سال یازدهم شماره 4 (پیاپی 29، زمستان 1401)

At present, projectification has become a common approach in management science and many organizations in the electric industry are project-based. However, there is no comprehensive model to evaluate and improve project management performance in project-based organizations in the electric industry. Therefore, the purpose of this research is to identify the knowledge areas of project management in project-based organizations in the electric industry and determine the causal relationships among them with a model using the DEMATEL technique. The present research was an explorative-descriptive study whose statistical population consisted of managers and officers with bachelor’s degrees and more than five years of experience that worked in project-based organizations in the electric industry, including syndicates, manufacturers, suppliers, and consultant and contractor companies. The sampling method of the research was the snowballing and purposive techniques, and the sample size was equal to 20 experts. The data collection instrument was a questionnaire whose validity and reliability were confirmed using the content validity index and threshold value mechanism, respectively. To analyze data, the managers and officers were initially asked to determine the importance of project management knowledge areas using a 3-point Lawshe scale. Then, the content validity ratio of project management knowledge areas was calculated and with respect to the number of experts, i.e., 20 people, those project management knowledge areas with a content validity ratio of more than 0.42 were selected as the final knowledge areas of project management. Thereafter, the DEMATEL technique was used to design the Network Relation Map (NRM) of project management knowledge areas. Based on the results, 13 knowledge areas were identified for project management in project-based organizations in the electric industry. On the other hand, the results indicated that the areas of cost management, strategic management, human resource management, time management, and quality management had the most interaction with other areas. Furthermore, the results revealed that the areas of governance and leadership management, strategic management, human resource management, communication and stakeholder management, procurement and outsourcing management, inventories and technologies management, and information protection, safety, and health management were casual areas and integration management, scope management, time management, cost management, quality management, and risk management were effect areas.  The findings provide a clear road map for managers to focus their efforts on the most important knowledge areas of project management in order to enhance the performance of project-based organizations in the electric industry effectively and efficiently.

Energy supply is the most important need of human societies because life is impossible without energy. Therefore, the operation of energy resources is a substantial subject in the management of these resources. On the other hand, energy resources are often interdependent, which can help their management. In other words, the integrated operation of energy resources can be useful in energy management. In this regard, “the energy hub” has been introduced as a new concept for the integrated operation of energy resources. Using the concept of an energy hub, this paper tries to reduce the energy supply costs of consumers and manage these resources by integrated and simultaneous operation of electricity, natural gas, and water. In modeling, various pieces of equipment, such as energy storage devices, combined heat and power systems, and renewable sources, are used. Power-to-gas technology is also used to produce hydrogen and natural gas from water and electricity to supply hydrogen loads and inject the natural gas produced into the gas network. Power-to-gas technology uses excess electricity produced by renewable sources to produce hydrogen, which is obtained from the breakdown of water molecules. Given that the technology requires carbon dioxide to produce natural gas, this can reduce air pollution. In addition, a demand response program is implemented to shift a part of the electricity and heat consumption from peak hours to off-peak hours in order to reduce operating costs. Load transfer can be done with different methods, such as incentive plans or load management on the demand side. This modeling is a mixed integer linear programming. As mentioned, the model presented in this article is linear, so it is necessary to linearize the nonlinear equations. In this modeling, a method called "the Cartesian" method is used for linearization. After linearization of the nonlinear equations, this problem has been solved by GAMS software using the CPLEX solver. The results show that the proposed model has a significant impact on reducing operating costs and air pollution. In other words, devices such as electric heaters, combined heat and power, and power-to-gas units could reduce operating costs by 22% by converting energy carriers into different forms of energy, thereby significantly reducing pollutant emissions into the air so that, in the presence of the power-to-gas unit, the amount of pollution in the air can be decreased by 26%.

The concept of demand response (DR) continues to evolve, and its various capabilities are being investigated to enhance the efficiency of nowadays electric power industries. To this end, the barriers that limit DR capabilities should be resolved. This paper provides a new efficient decision model for energy service providers in smart distribution networks to make the maximum use of DR potential as the most cost-effective solution. The correct and proper application of the DR problem provides special capabilities for these entities and can lead to more profit. On the other hand, participating in the upstream market and demand allocation in the downstream network are two main tasks of energy providers. These two tasks affect each other, and simultaneous attention to them is needed for more efficiency. Generally, conservative participation in the upstream market is the main problem of these entities due to the uncertainty of load forecasting, especially considering that the DR problem will aggravate this uncertainty. In these conditions, the interactions between the load curve and price changes should also be considered. To better understand, suppose that an energy provider wants to reduce its energy purchase cost by applying DR. This entity initially forecasts its load consumption and participates in the electricity market. After market clearing, the values of locational marginal price (LMP) are determined for the next 24 hours. Now, applying DR and moving the load consumption to the less expensive hours will reduce the final purchase cost. However, moving the load consumption leads to changes in the LMP values in the substation bus of the distribution network. It is due to the dependencies between the load consumption and the prices. Disregarding these dependencies will limit DR capabilities. Therefore, a new two-step sequential framework is proposed in this paper to enhance the performance of the energy providers in the smart distribution network. The main problem is the optimization of the power purchase cost for the downstream network using DR. The subsidiary problem includes electricity market modeling. The load curve is determined in the main problem, and the amounts of the energy price under different conditions are determined in the subsidiary problem recursively. This framework guides the energy provider to analyze how market clearing affects DR and vice versa. To model load flexibility, a residential distribution network with different types of responsive appliances is utilized, and the model is studied using two case studies. The results demonstrate that applying the proposed framework leads to more reliable and optimal results and has significant benefits for the strategic performance of energy service providers.

Electric power and power distribution are prominent infrastructures for economic development in any developing country like Iran. Also, the power distribution network is a very important supply chain that combines a variety of processes. Smart electrical energy distribution networks are one of the latest technologies in the world. The main goal of these networks is to provide reliable electricity, increase the reliability factor and network stability, and respond to the growing needs of customers with minimal damage to the environment, profit, and high efficiency. In the last three decades, the rapid evolution and prevalent adoption of information systems, distribution analysis tools, computational models, and more recently, the emergence of smart grid technologies have given utilities access to the data and tools required for improving these analyses and the possibility of increasing the efficiency of power distribution systems (by, for example, reducing losses and optimizing voltage profiles). Forecasting the future state of the network with the least error brings us closer to the smart network. Because electricity is a mortal product, a comprehensive approach to unplanned power extinction (outage) time is very valuable in preventing any power distribution losses. Various accidents disrupt (cause breakdowns in) the power distribution network, which can be repaired and restored without a hotline. One of the main reasons for customers' power outages is the blackouts in the distribution field, which are affected by technical and non-technical events in the electricity distribution networks. Forecasting these events and managing them can be effective in reducing unplanned power extinction (outage) time. The purpose of this article is to present a model for predicting the duration of unplanned power extinction (outage) and unsold energy based on the data recorded from 121 systems (controllers), the urban network of the three power distribution companies in Yazd province. The final result shows that the ARIMAX model(s) shows less error than the ARIMA model(s) and presents better prediction. Therefore, using exogenous variables in predictions and not being satisfied with the fluctuations of a variable can improve predictions. The model proposed for predicting unsold energy is ARIMAX(1,0,1)(0,0,0) considering the number of incidents and the time of unplanned outages as exogenous variables. The model also shows that in July 2022, the unplanned power extinction (outage) time of this network will be approximately ten hours and also the unsold power will be approximately 6 MWH. On the other hand, the community is without electricity and dissatisfaction has arisen, which lies in economic and social losses. Therefore, with this warning, managers should re-examine the factors of disruption and lack of electricity supply and think of measures to reduce these blackouts when planning for this month of the year

Following deregulation in the electricity grids, power systems has faced new challenges in terms of diversification of generation units and demands types, which requires a more comprehensive management framework. For this purpose, several new players were introduced to resolve the challenges between generation and demand side. Among others, retailers are the one that play a crucial role by creating a link between electricity market operators and the consumers, seeking maximize profits and reduction of the costs of their customers. Electric vehicles (EVs), meanwhile, are among the bilateral consumers which retailers are able to both provide energy for as well as see as an energy sources for sales in Day-Ahead (DA) energy and reserve markets. Nevertheless, Retailers face several uncertainties regarding the physical characteristics of electric vehicles, the behavior of their owners, in addition to the uncertainties inherent in energy and reserve markets faced by any player. In order to optimal participation of retailers in those markets as well as to meeting the needs of electric vehicles, a two-stage optimization framework is presented in this paper. Vehicle clustering is also utilized to model all uncertainties simultaneously.
Thus the main contributions of this paper can be summarized as follows:
A new method for classifying electric vehicles based on battery characteristics (such as battery capacity, charge and discharge rate, etc.) and owners' behaviors (availability at parking stations, arrival and departure times, initial charge state, etc.) is proposed. This clustering helps reduce the computational load by avoiding duplicate calculations.
A novel model is presented for retailers to the participate in the reserve market using the capabilities of electric vehicles. Therefore, in this paper, retailers participate in the energy and reserve markets simultaneously using the potential of electric vehicles.
A two-stage stochastic linear model has been introduced to consider most of the uncertainties with respect to the aggregation of the potential of EVs by the retailer to plan their participation in different electricity markets.
Using the proposed optimization framework and vehicle classification, all uncertainties related to the initial charge of the EVs’ batteries, type and capacity of batteries, the expected final state of charge, the times of arrivals and departures of vehicles to / from parking lots, EVs’ battery charging and discharging rates, EVs’ battery efficiency, reserve market call status, as well as uncertainties related to DA energy and spinning reserve prices, and the number of EVs in parking lots are modeled simultaneously.
Finally, the model has been implemented in GAMS considering the option of the retailer participation as a seller in the energy and spinning reserve markets. It has been shown that if the retailer has the mentioned choice, (s)he can benefit from selling in both markets even if sell energy at a low price to electric vehicles in parking lots.

This paper presents a new formulation and algorithm for the problem of unit commitment with security constraints, which can obtain the worst case of transmission network line outage and solve the problem under such conditions. Optimal charging and discharging of electric vehicles, optimal charging and discharging of energy storage systems, and flexible loads along with renewable energy resources are considered in the problem of unit commitment. Uncertainty of energy resources is modeled as a scenario-based method. In this paper, a multi-objective function that includes reduction of operating cost, no-load and unit start-up/shutdown, load shedding costs, load shifting, unit pollution, optimal charging and discharging of storages, and the power cut of renewable energy resources is considered. The proposed formulation is a mixed integer linear programming (MILP) model whose absolute optimal solution is guaranteed by powerful Gurobi solvers. To validate the proposed formulation, several study cases and test 6- and 24-bus networks are analyzed. The simulation results show that the proposed algorithm is effective in identifying the worst possible line exit of the transmission network so that the objective function of the problem increases by about 8% after the worst-case line exit from the network.

Multi-carrier energy systems play an important role in reconfigured power networks. According to the recent researches, only the energy carriers modeling and system optimization are taken into account, however the sensitivity analysis of output parameters due to input variables variations considering price risk, are being studied in this paper. The sensitivity analysis make the independent system operator (ISO) very precisionist to the carriers-based decisions. In other word, the ISO and the whole control system have to be able to determine each share of all energy carriers considering emergency conditions or passive defense, low consumption and high efficiency. This goal will be achieved through energy carriers sharing variations to compensate the lack of the other resources. All of thermal and electrical demand are procured and the energy not supplies will be calculated in each scenario. The risk modeling used in this study is formulated based on conditional value at risk (CVaR) in four weeks time horizon.