In the future smart grids, Plug-in Hybrid Electric Vehicles (PHEVs) are seen as an important means of transportation to reduce greenhouse gas emissions. One of the main issues regarding to this sort ...of vehicles is managing their charging time to prevent high peak loads over time. Deploying advanced metering and automatic chargers can be a practical way not only for the vehicle owners to manage their energy consumption, but also for the utilities to manage the electricity load during the day by shifting the charging loads to the off-peak periods. Additionally, an efficient charging schedule can reduce the users' electricity bill cost. In this paper we propose a new practical demand response (DR) program for PHEVs charging scheduling based on game theoretic approach, aiming at optimizing customers charging cost. In the proposed method, a stochastic model is given for starting time of charging, which makes the method a practical tool for simulating the vehicle owners charging behavior effectively.
In smart grid, demand response (DR) programs can be deployed to encourage electricity consumers towards scheduling their controllable demands to off-peak periods. Motivating the consumers to ...participate in a DR program is a challenging task, as they experience a confidential discomfort cost by modifying their load demand from the desirable pattern to the scheduled pattern. Meanwhile, to balance the load and generation, the independent system operator (ISO) requires to motivate the suppliers towards modifying their generation profiles to follow the changes in the load demands. Additionally, to protect the entities' privacy, the ISO needs to apply an effective well-designed pricing scheme. In this paper, we focus on proposing a decentralized DR framework considering the operating constraints of the grid. In our proposed framework, each individual entity responds to the control signals called conjectured prices from the ISO to modify its demand or generation profile with the locally-available information. We formulate the centralized problem of the ISO that jointly minimizes the suppliers' generation cost and the consumers' discomfort cost. We also discuss how the ISO determines the conjectured prices to motivate the entities toward an operating point that coincides with the solution to the centralized problem. The performance of the proposed algorithm is evaluated on a modified IEEE 14-bus in reducing the suppliers' and consumers' cost, as well as the transmission lines congestion.
Distributed generations (DGs) have significant benefits in the electric power industry, such as a reduction in CO2 and NOX emissions in electricity generation, improvement of voltage profile in ...distribution feeders, amending voltage stability in heavy load levels, enhancement of reliability and power quality, as well as securing the power market. Despite the numerous advantages of DG technologies, weak capability in dispatching and management of DGs is a major challenge for distribution system operators. Hence, during recent years, several studies about various aspects of control, operation, placement, and sizing of DGs have been conducted. This paper presents a novel application of multiobjective particle swarm optimization with the aim of determining the optimal DGs places, sizes, and their generated power contract price. In the proposed multiobjective optimization, not only are the operational aspects, such as improving voltage profile and stability, power-loss reduction, and reliability enhancement taken into account, but also an economic analysis is performed based on the distribution company's and DG owner's viewpoints. The simulation study is performed on the IEEE 33-bus distribution test system and the consequent discussions prove the effectiveness of the proposed approach.
Load aggregators can use demand response programs to motivate residential users toward reducing electricity demand during peak time periods. This article proposes a demand response algorithm for ...residential users, while accounting for uncertainties in the load demand and electricity price, users' privacy concerns, and power flow constraints imposed by the distribution network. To address the uncertainty issues, we develop a deep reinforcement learning (DRL) algorithm using an actor-critic method. We apply federated learning to enable users to determine the neural network parameters in a decentralized fashion without sharing private information (e.g., load demand, users' potential discomfort due to load scheduling). To tackle the nonconvex power flow constraints, we apply convex relaxation and transform the problem of updating the neural network parameters into a sequence of semidefinite programs (SDPs). Simulations on an IEEE 33-bus test feeder with 32 households show that the proposed demand response algorithm can reduce the peak load by 33% and the expected cost of each user by 13%. Also, we demonstrate the scalability of the proposed algorithm in 330-bus and 1650-bus feeders with real-time pricing scheme.
In this paper, trajectory tracking control of an underwater vehicle in three-dimensional (3D) space has been addressed. The assumed underwater vehicle has 6 degrees of freedom and the aim is to ...control all system rotations and displacements. In this paper, a finite-time sliding mode controller as a robust control method is proposed for an underwater vehicle with 6 degrees of freedom in 3D space using the method without simplifications or decouplings. Therefore, both system positions and orientations are controlled in the presence of disturbances and uncertainties. In previous research works, control of two-dimensional underwater vehicles is commonly studied. In this paper, a novel stable control algorithm is proposed for an underwater vehicle with 6 degrees of freedom. The stability of the closed-loop system is analyzed using the Lyapunov theory. The designed algorithm can cover 3D complicated tasks. Also, the designed algorithm as a robust control approach can attenuate external disturbances. The performance and stability of this approach are compared with the sliding mode controller. The numerical comparison results show that the proposed approach is effective and applicable in practice.
•A decentralized energy trading algorithm is proposed considering the integration of renewable energy resources.•Our method optimizes the cost of load aggregators and profit of the generators.•The ...proposed optimization problem minimizes as the risk of shortage in the renewable generation.•A risk measure called the conditional-value-at-risk (CVaR) is used to model uncertainty of renewables.•The simulation results validate the effectiveness of the proposed decentralized algorithm.
The uncertainties in renewable power generators and the proliferation of price-responsive load aggregators make it a challenge for independent system operators (ISOs) to manage the energy trading in the power markets. Hence, a centralized framework for the energy trading market may not be remained practical for the ISOs mainly due to violating the privacy of different entities, i.e., load aggregators and generators. It can also suffer from the high computational burden in a market with a large number of entities. Instead, in this paper, we focus on proposing a decentralized energy trading framework enabling the ISO to incentivize the entities toward an operating point that jointly optimize the cost of load aggregators and profit of the generators, as well as the risk of shortage in the renewable generation. To address the uncertainties in the renewable resources, we apply a risk measure called the conditional value-at-risk (CVaR) with the goal of limiting the likelihood of high renewable generation shortage with a certain confidence level. Then by considering the risk attitude of the ISO and the generators, we develop a decentralized energy trading algorithm with some control signals that properly coordinate the entities toward the market operating point of the ISO’s centralized approach. Simulation results on the IEEE 30-bus test system show that the proposed decentralized algorithm converges to the solution of the ISO’s centralized problem in a timely fashion. Furthermore, the load aggregators can help their consumers reduce their electricity cost by 18% on average through managing their loads using locally available information. Meanwhile, the generators can benefit from 17.1% increase in their total profit through decreasing their generation cost.
The proliferation of technologies such as combine heat and power systems has accelerated the integration of energy resources in energy hubs. Besides, the advances in smart grid technologies motivate ...the electricity utility companies toward developing demand response (DR) programs to influence the electricity usage behavior of the customers. In this paper, we modify the conventional DR programs in smart grid to develop an integrated DR (IDR) program for multiple energy carriers fed into an energy hub in smart grid, namely a smart energy (S. E.) hub. In our model, the IDR program is formulated for the electricity and natural gas networks. The interaction among the S. E. hubs is modeled as an ordinal potential game with unique Nash equilibrium. Besides, a distributed algorithm is developed to determine the equilibrium. Simulation results show that in addition to load shifting, the customers in the S. E. hubs can participate in the IDR program by switching the energy resources (e.g., from the electricity to the natural gas) during the peak hours. Moreover, the IDR program can increase the S. E. hubs' daily payoff and the utility companies' daily profit.
El propósito de esta investigación fue compilar un código de ética del arbitraje deportivo en Irán. El método de la presente investigación fue mixto (cualitativo y cuantitativo). Se entrevistó a un ...total de 15 expertos en el campo de la ética del arbitraje deportivo mediante el método de muestreo de bola de nieve. Los resultados obtenidos en la entrevista dieron lugar a un cuestionario válido y fiable que se distribuyó aleatoriamente entre los árbitros y árbitros asistentes de las principales ligas iraníes de deportes de equipo (fútbol, voleibol, balonmano y baloncesto) y deportes individuales (taekwondo, kárate, lucha libre y natación), con una muestra total de 224 personas. El análisis y la codificación se realizaron utilizando los softwares Max Kyoda, SPSS y Smart PLS. Los hallazgos de la investigación mostraron que en este campo se extrajeron 8 temas y 61 subtemas, que incluyen los componentes de comportamiento, corrupción, comunicación, aspecto sociocultural, familia, respeto, legalidad y justicia, en este orden de importancia. En general, prestar atención a los aspectos de comportamiento y corrupción juega un papel importante en la mejora del estatus ético de los árbitros deportivos en Irán.
The purpose of this research was to compile a sports refereeing ethics code in Iran. The method of the present research was mixed (qualitative and quantitative). A total of 15 experts in the field of refereeing ethics in sports were interviewed by the method of snowball sampling. The results obtained from the interview led to a valid and reliable questionnaire that was randomly distributed among the referees and assistant referees of the Iranian premier leagues of team sports (football, volleyball, handball and basketball) and individual sports (taekwondo, karate, wrestling and swimming), with a total sample of 224 people. Analysis and coding were performed using the softwares Max Kyoda, SPSS and Smart PLS. Research findings showed that 8 themes and 61 sub-themes were extracted in this field, which include the components of behavior, corruption, communication, sociocultural aspect, family, respect, legality and justice, in this order of importance. In general, paying attention to the behavioral and corruption aspects plays an important role in improving the ethical status of sports referees in Iran.
The development of technologies such as micro turbines and gas furnaces has been the major driving force towards integration of electricity and natural gas networks in the EH (energy hubs). Besides, ...the existing power grids are getting smarter by implementing the new generation of information technologies and communication systems. In smart grid, the electricity suppliers can modify the customers' electrical load consumption by employing appropriate scheduling schemes such as DR (demand response) programs. In this paper, we consider the S. E. Hubs (smart energy hubs) framework, in which the customers can use EMS (energy management system) to access to the electricity and natural gas prices data and wisely manage their daily energy consumption. We extend the existing DR programs to the IDR (integrated demand response) programs with the aim of modifying both electricity and natural gas consumption on the customer side. The interaction between S.E. hubs in the IDR program is formulated as a non-cooperative game. The goal of the IDR game is to maximize the natural gas and electricity utility companies' profit and to minimize the customers' consumption cost. It is shown that the proposed game model is an ordinal potential game with unique Nash equilibrium. Simulations are performed on an energy system with 6 S. E. Hubs, one electricity utility, and one natural gas utility companies. The results confirm that the IDR program can benefit both the customer side, by reducing the electricity and gas consumption cost, and the supplier side, by reducing the peak load demand in the electricity and natural gas load profiles.
•The integration of the electricity and natural gas networks is considered in smart grids.•An integrated demand response (IDR) is proposed in multi energy carrier systems.•Benefits of utility companies are considered in pricing electricity and gas.•IDR is modeled as an ordinal potential game with strictly concave potential function.•A distributed algorithm is developed to determine the NE of the IDR game.
Smart grid infrastructures enable consumers with technologies, such as electric vehicles and energy storages, to participate in electric regulation services. Usually with such technologies, the ...implementation of large-scale regulation services confronts high interruption cost, uncertainties in availability, and batteries' degradation cost. This motivates us to explore an alternative solution by participating energy hubs with energy conversion technologies to adjust the conversion of natural gas into electricity if the electric grid calls for demand shaping and regulation services. To exploit the potential of energy hubs, we propose an auction for their participation in regulation services. The energy hubs' interaction in the auction is modeled as a non-cooperative game with coupling constraints. To study the existence and uniqueness of the generalized Nash equilibrium (GNE) we show that the underlying game admits a best response potential function, whose global minimum corresponds to the GNE. We also design a distributed algorithm to achieve that equilibrium. Simulations are performed to illustrate the convergence properties and scalability of the proposed algorithm. Results show that if a participant becomes an energy hub, its profit increases by 60% on average. The electric system operator also benefits from 31% payment reduction to the participants.
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