• 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.

In this paper, an operational framework is presented to improve electrical distribution network resilience based on the Mobile Energy Hubs (MEHs) concept. In fact, critical loads should be immediately islanded in a post-flood state and then recovered. Accordingly, this paper focuses on providing an effective management solution to enhance the functioning of electricity distribution systems with the objective of maximizing restoration of critical loads and minimizing their restoration time span based on MEH. To this end, MEHs are installed on trucks to deliver the required power for supplying the islanded critical loads in zones affected by a flood. Besides, in order to demonstrate a practical resilient structure, possible damage inflicted on other critical infrastructures is considered. Moreover, obstacles resulting from the destruction of the transportation infrastructure caused by a flood are overcome by using the shortest path algorithm (SPA). In this case, the optimization algorithm determines the shortest possible path for transporting the MEHs to supply critical loads in the least time aiming to improve the network resilience indicators. Finally, the proposed framework is studied in a standard test electricity distribution network. Simulations are carried out to evaluate the network resilience indicators of the proposed framework in obtaining a resilient distribution network during natural disasters. ; © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; fi=vertaisarvioitu|en=peerReviewed|

With the negative climate impact of fossil fuel power generation and the requirement of global policy to shift towards a green mix of energy production, the investment in renewable energy is an opportunity in developing countries. However, poor economy associated with limited income, funds availability, and regulations governing project funding and development are key factors that challenge investors in the energy sector. Given the various power generation resources, including renewables, it is necessary to evaluate the possible power generation investment options from an economic perspective. To realize this objective, solar PV, wind and diesel power generations are economically compared, considering the incremental rate of return and incremental benefit to cost ratio techniques. The alternative investment options of distributed generation technologies are evaluated for Maharashtra, India under different depreciation methods, and the effect of the latter on selecting the best investment candidate is investigated. The paper also conducts sensitivity analysis to examine the impact of capital cost, operation and maintenance cost, and fuel cost variations on the selection decision considering a comparison of the different general projects’ cash flow structures discussed in the literature. The economic aspects of selecting a project among possible alternatives for an investment in the power sector are analyzed, and the presented review provides comprehensive comparisons with respect to the literature approaches. The results reveal that, in the benchmark case study, the PV project is rejected and disregarded from further comparisons with other candidate projects since its equity internal rate of return (10.25%) is less than the minimum accepted rate of return, leaving the selection between wind and diesel energy projects. The study reveals that the incremental rates of return under such a comparison are 37.88%, 45.94% and 37.50% when MACRS, declining balance and straight line depreciations techniques are applied, respectively. Thus, the wind energy project is the favored option in this case. For the economic assessment of other case studies, the application of both sensitivity analysis on the capital cost and operation and maintenance cost and literature approaches to structure the projects reveal that wind energy for Maharashtra, India is a more attractive and feasible option compared to other distribution generation projects, while diesel is only considered to be a good option when its fuel cost is reduced by 5%. Finally, the paper highlights policy implications that can influence the decision to move towards investment in distributed generation technologies as a future research direction.

This paper proposes a univariate prognostic approach based on wavelet transform and support vector regression (SVR) to predict the tidal current speed and direction with high accuracy. The proposed model decomposes the tidal current data into some subharmonic components. The details and approximation components are later fed to several SVR models to attend the prediction process. In order to increase the robustness of the model, the idea of combined prediction is used to model each subharmonic signal by several SVRs. The median operator is further used to determine the aggregated forecast tidal current data. Due to the high reliance of SVR model on the kernel function and hyperplane parameters, a new optimization method based on the bat algorithm is used to train the SVR model. The final forecast tidal current data are constructed using an aggregation operator in the output of the SVRs. The accuracy and satisfying performance of the proposed model are examined on the practical tidal data collected from the Bay of Fundy, NS, Canada. The experimental results reveal the high capability and robustness of the proposed hybrid model for the tidal current prediction.

This paper presents a multi-objective algorithm to solve stochastic distribution feeder reconfiguration (SDFR) problem for systems with distributed wind power generation (WPG) and fuel cells (FC). The four objective functions investigated are 1) the total electrical energy losses, 2) the cost of electrical energy generated, 3) the total emissions produced, and 4) the bus voltage deviation. A probabilistic power flow based on the point estimate method (PEM) is employed to include uncertainty in the WPG output and load demand, concurrently. Different wind penetration strategies are examined to capture all economical, operational and environmental aspects of the problem. An interactive fuzzy satisfying optimization algorithm based on adaptive particle swarm optimization (APSO) is employed to determine the optimal plan under different conditions. The proposed method is applied to Taiwan Power system and the results are validated in terms of efficiency and accuracy.

Summary This paper suggests a new self-adaptive modification method using firefly algorithm (FA) to investigate the multi-objective probabilistic distribution feeder reconfiguration problem. In this regard, the idea of phase angle vector is employed to replace the traditional Cartesian framework in the FA and thus called θ-FA. Also, a new modification method based on an adaptive mechanism is suggested that will allow each firefly to choose the appropriate modification technique during the optimization suitably. As regards the objective functions, the main focus of this paper is to assess the effect of the reconfiguration on the reliability indices including active power losses, voltage deviation, and system average interruption frequency index. In order to handle the uncertainty effects, a sufficient framework based on 2m + 1 point estimate method is proposed too. The satisfying performance of the proposed method is checked using IEEE 32-bus radial distribution system. Copyright © 2014 John Wiley & Sons, Ltd.

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 proposes a reliability-oriented stochastic aggregated integer linear framework for full observability of the automated distributed systems based on the μ-synchrophasor units. The μ-synchrophasor unit as a newly introduced high-tech device makes it possible for an accurate and highspeed measurement of the voltage and current waveforms in the distribution systems. This paper proposes a multi-stage strategy for the μ-synchrophasor unit placement together with the communication system requirements in the reconfigurable distribution systems, considering the zero-injection constraints in the model. To determine the optimal topology at the end of each phase, a reliability-based cost function is developed to optimize the customer interruption costs and power losses simultaneously. In order to model the uncertainties of forecast error in the active and reactive load demands as well as the failure rate and repair rate parameters, a stochastic framework based on the fuzzy cloud theory is employed. The proposed bi-level mixed integer linear programing approach is used to co-optimize the network switching scheme as well as the optimal μ-synchrophasor positions and communication infrastructure costs in the same framework. The simulation results on a practical test system verify the observability of the automated reconfigurable distribution system during the reconfiguration process.

This paper proposes a framework and its mathematical model for optimal routing and charging of an electric vehicle fleet for high-efficiency dynamic transit systems, while taking into account energy efficiency and charging price. Based on an extended pickup and delivery problem, an optimization model is formulated from the transit service providers’ perspective and is applied to an electric vehicle (EV) fleet with economically efficient but small batteries in very urbanized areas. It aims to determine the best route from the origin to the final destination for each EV to satisfy the welfare of all passengers (e.g., travel time and passengers’ travel distance), while maximizing the energy efficiency (e.g., by reducing fuel and charging cost), subject to local/global constraints (e.g., EV charging station availability and battery state-of-charge dynamics). This optimization model is solved as a mixed-integer quadratically constrained programming problem. This paper also explores the potential impact of EV fleet of dynamic commuter transit services on electric distribution systems, such as increased average load.