• Energy Research

This paper proposes an optimization framework to deal with the uncertainty in a day-ahead scheduling of smart active distribution networks (ADNs). The optimal scheduling for a power grid is obtained such that the operation costs of distributed generations (DGs) and the main grid are minimized. Unpredictable demand and photovoltaics (PVs) impose some challenges such as uncertainty. So, the uncertainty of demand and PVs forecasting errors are modeled using a hybrid stochastic/robust (HSR) optimization method. The proposed model is used for the optimal day-ahead scheduling of ADNs in a way to benefit from the advantages of both methods. Also, in this paper, the ac load flow constraints are linearized to moderate the complexity of the formulation. Accordingly, a mixed-integer linear programming (MILP) formulation is presented to solve the proposed day-ahead scheduling problem of ADNs. To evaluate the performance of the proposed linearized HSR (LHSR) method, the IEEE 33-bus distribution test system is used as a case study.

AbstractThe study aims to predict solar energy generation to ensure the successful operation of solar power plants. This objective is crucial in light of the increasing energy demand, global warming concerns, and greenhouse gas emissions. To achieve this, the study employs multiple linear regression and feature selection techniques to calculate energy generation. Additionally, long short‐term memory (LSTM) is used to predict energy generation levels based on climate conditions. Furthermore, the spatial distribution of energy generation is analyzed using inverse distance weighting. The results of the study reveal that temperature, solar radiation, relative humidity, wind speed, wind direction, and vapor pressure deficit are the most significant parameters for predicting energy generation. The LSTM method proves to be highly accurate in predicting fluctuating energy generation patterns. Notably, the southern regions of the study area exhibit a greater potential for energy generation compared to the northern regions. Approximately 30% of the region generates over 1400 kWh, with the southern areas, characterized by hot and dry climates, producing around 1500 kWh, while the northern regions, with cold and humid climates, generate approximately 1100 kWh.

This paper proposes a coordinated strategy of a hybrid power plant (HPP), which includes a wind power aggregator and a commercial compressed air energy storage (CAES) aggregator to participate in three electricity markets (day-ahead, intraday, and balancing markets). The CAES aggregator has an extra ability which is called a simple-cycle mode operation that makes it works like a gas turbine when needed, which helps the HPP to economically handle the miscalculations of the wind power and electricity price predictions. The coordinated strategy of the HPP is formulated as a three-stage stochastic optimization problem. To control the financial risks, the conditional value-at-risk model is added to the optimization problem. Moreover, the proposed offering method is capable of submitting both bidding quantity and curves to the day-ahead market. A mixed integer linear programming formulation is written for the problem that can be easily solved by commercially available software such as GAMS. The results that were tested on a realistic-based case study located in Spain show the applicability of the suggested method to increase the joint operation profit and decrease the financial risks.

Unlike the traditional policy, Generation Expansion Planning (GEP) problem in competitive framework is complicated. In the new policy, each Generation Company (GENCO) decides to invest in such a way that obtains as much profit as possible. This paper presents a new hybrid algorithm to determine GEP in a Pool market. The proposed algorithm is divided in two programming levels: master and slave. In the master level a Modified Game Theory (MGT) is proposed to evaluate the contrast of GENCOs by the Independent System Operator (ISO). In the slave level, an Improved Genetic Algorithm (IGA) method is used to find the best solution of each GENCO for decision-making of investment. The validity of the proposed method is examined in the case study including three GENCOs with multi-type of power plants. The results show that the presented method is both satisfactory and consistent with expectation.

Nowadays, Electric Vehicles (EVs) are the new technology to reduce the usage of fossil fuels and to prevent the environmental issues. But, increasing the number of EVs and mismanagement of their energy in distribution networks would cause higher operational costs and lower network security. This paper evaluates the voltage security of distribution networks in the presence of EVs. Accordingly, the maximization of voltage security margin (VSM) and the minimization of operational cost are considered as the main objective functions in the optimization problem of active and reactive power management. The constraints of the proposed optimization problem include power flow equations, system operating limits and EVs constraints. It is supposed that the EVs are equipped with bidirectional chargers to control active and reactive power, simultaneously. The proposed model is implemented on the 33-bus distribution network to evaluate the performance of the proposed optimization scheme for distribution network management in the presence of EVs.

Abstract The hybrid AC/DC microgrids have become considerably popular as they are reliable, accessible and robust. They are utilized for solving environmental, economic, operational and power-related political issues. Having this increased necessity taken into consideration, this paper performs a comprehensive review of the fundamentals of hybrid AC/DC microgrids and describes their components. Mathematical models and valid comparisons among different renewable energy sources’ generations are discussed. Subsequently, various operational zones, control and optimization methods, power flow calculations in the presence of uncertainties related to renewable energy sources are reviewed.

This study presents multiperiod multiobjective generation expansion planning (MMGEP) model of power electric system including renewable energy sources (RES). The model optimises simultaneously multiple objectives (i.e. minimisation of total costs, emissions, energy consumption and portfolio investment risk as well as maximisation of system reliability). The mixed-integer linear programming (MILP) is used for the proposed optimisation and an efficient linearisation technique is proposed to convert the non-linear reliability metrics into a set of linear expressions. The proposed solution for multiobjective mathematical programming (MMP) framework includes a hybrid augmented-weighted epsilon constraint and lexicographic optimisation approach to obtain the Pareto optimal or efficient solutions for the MMGEP problem. Finally, fuzzy decision making is implemented to select the most preferred solution among Pareto solutions based on the goals of decision makers (DMs). A synthetic test system including seven types of candidate units is considered here for GEP in a 6-year planning horizon. The effectiveness of the proposed modifications is illustrated in detail.

Abstract Today's, policy makers, governments, and academic experts in flourishing societies are interested in employing power systems considering high reliability, quality, and efficiency factors. Moreover, climatic concerns force power system appliers to utilize these systems more environmental friendly. To obtain the mentioned aims, MGs (microgrids) act as key solutions. MGs are invented not only to operate power systems more reliable and efficient but also to penetrate CHP (combined heat and power)-based DG (distributed generation) into power systems with an optimal control on their generation. This paper presents a new optimal operation of a CHP-based MG comprising ESS (energy storage system), three types of thermal power generation units, and DRPs (demand response programs). In this paper, DRPs are treated as virtual generation units along with all of realization constraints. In a multi-objective self-scheduling optimization problem of a MG, the first objective deals with minimizing total operational cost of the CHP-MG in an OPF-based formulation and the second refers to the emission minimization of DGs. The proposed model implements a simple MIP (mixed-integer programming) that can be easily integrated in the MGCC (MG central controller). The effectiveness of the proposed methodology has been investigated on a typical 24-bus MG.

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|