• Energy Research

This paper introduces a modified consensus-based real-time optimization framework for utility-connected and islanded microgrids scheduling in normal conditions and under cyberattacks. The exchange of power with the utility is modeled, and the operation of the microgrid energy resources is optimized to minimize the total energy cost. This framework tracks both generation and load variations to decide optimal power generations and the exchange of power with the utility. A linear cost function is defined for the utility where the rates are updated at every time interval. In addition, a realistic approach is taken to limit the power generation from renewable energy sources, including photovoltaics (PVs), wind turbines (WTs), and dispatchable distributed generators (DDGs). The maximum output power of DDGs is limited to their ramp rates. Besides this, a specific cloud-fog architecture is suggested to make the real-time operation and monitoring of the proposed method feasible for utility-connected and islanded microgrids. The cloud-fog-based framework is flexible in applying demand response (DR) programs for more efficiency of the power operation. The algorithm’s performance is examined on the 14 bus IEEE network and is compared with optimal results. Three operating scenarios are considered to model the load as light and heavy, and after denial of service (DoS) attack to indicate the algorithm’s feasibility, robustness, and proficiency. In addition, the uncertainty of the system is analyzed using the unscented transformation (UT) method. The simulation results demonstrate a robust, rapid converging rate and the capability to track the load variations due to the probable responsive loads (considering DR programs) or natural alters of load demand.

This paper proposes a distributed multi-agent based framework organized on three-layer fog computing architecture for effective optimal economic dispatch in the microgrids. This framework tracks load changes at any time of the day considering sudden entries and exits of the units. To this end, the attendance of the various renewable energy sources including photovoltaics (PVs), wind turbines (WTs), micro turbines (MT) and fuel cells (FCs) is taken into account. The optimization algorithm used in this model is a fast consensus- based algorithm modified by a fuzzy adaptive leader method applicable by taking advantage of fog computing. Lastly, the performance of the framework is examined on a six-bus microgrid. The simulation results show the fast convergence rate and capability of the method to track the load changes with real- time interactions.

This paper tries to develop a multiagent consensus-based algorithm to handle the energy management problem (EMP) in the smart grids in the presence of dispatchable loads and distributed generators. To this end, the neighbor virtualization concept is employed to improve the system performance by introducing a novel communication structure for distributed algorithms. Accuracy, quickness, and scalability are the most important benefits of this algorithm that make it applicable in real power systems. The increasing penetration of wind turbines and the stochastic nature of load demand can inject much uncertainty in our problem. In order to deal with this issue, unscented transform as a powerful tool is used to model the problem uncertainties. The proposed problem is formulated in the form of a single-objective stochastic optimization framework maximizing the generators and consumers welfare. The feasibility and high performance of the proposed framework are examined on two test systems, including the IEEE 39-bus test system and a large grid with 1305 agents in MATLAB simulation. The simulation results advocate the high capability and effectiveness of the proposed framework for EMP of smart grids.

Abstract This paper presents a multi-agent-based algorithm for optimal power scheduling of multi-agent-based medium voltage direct current shipboard power systems of all-electric ships. shipboard power system is the crucial part of every all-electric ship due to its vital duty in generating electrical energy for various parts of the ship, particularly in long-distance travels. Moreover, the shipboard power system should be efficiently operated to reduce operating costs and energy to serve heavy loads for long periods of time. Thus, a system with resilient, secure, and efficient performance is needed to operate this essential power system. Traditionally, shipboard power systems were operated using centralized systems that were threatened by single-point failures, security issues, and central communication and computation burden. Considering the existing concerns about operating centralized systems, a multi-agent-based operation system could be best fitted for this goal. To this aim, this paper introduces a new multi-agent-based operation system based on the primal-dual method of multipliers, in which the generator stations are operated by the communication of agents with each other and without any central commander. The presence of energy storage systems is considered to enhance the reliability of the proposed power system. Furthermore, single- and twin-shaft turbogenerators are assumed to construct the main power generators of the shipboard power system as reliable, efficient, and maneuverable types of generators. The local speed and efficiency of the individual turbogenerators are also calculated using the proposed multi-agent-based method. The proposed method is simulated on a five-zone shipboard power test system in the presence of an energy storage systems and five turbogenerators, and is compared with a precise centralized scheme and a well-known multi-agent-based method during a 24-h time period. The simulation results demonstrate the accuracy of the proposed method with the total 24-h scheduling cost error of 0.2% in comparison with the optimal results gained by the centralized method. Besides, the proposed method decreases the error in calculating the total 24-h scheduling cost by almost 71% in fewer required iterations compared to the other well-known multi-agent-based scheme.

In this paper, a distributed consensus-based algorithm is introduced for social welfare maximization problems in the presence of responsive loads and Wind Turbines (WTs) in smart grids. This distributed algorithm eliminates the need for a central controller in the system. Consequently, it prevents the curse of dimensionality, the computational burden and the communication congestion which are some drawbacks of using centralized algorithms for controlling smart grids. The effect of uncertainty of output power of WTs generation is another discussed issue. Modeling this uncertainty is investigated using Unscented Transformation (UT) method. In MATLAB simulation, the performance of this distributed algorithm is illustrated in the presence of demand response in some multi-agent systems. Additionally, the impact of WTs uncertainty is inspected in a 29-node system.

This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called "preparation range" to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.