• Energy Research

The concept of smart cities has emerged as an ongoing research in recent years. In this case, there is a proven association between the smart cities and the smart devices, which have caused the power systems to become more flexible, controllable and detectable. Along with these promising results, many disputes have been generated over the cyber-attacks as unpredictable destructive threats, if not properly repelled, which could seriously endanger the power system. With this in mind, this paper explores a novel stochastic virtual assignment (SVA) method based on a directed acyclic graph (DAG) approach, where the essential data of the system sections are broadcasted decentralized through the data blocks, as a worthwhile step to deal with the cyber attacks' risk. To do so, an additional security layer is added to the data blocks aiming to enhance the security of the data against the long lasting data sampling by virtually assigning the hash addresses (HAs) to the data blocks, which are randomly changed based on a stochastic process. The basic network architecture is based on a Provchain structure as a new framework to constantly monitor data operation. Two pivotal strategies also represented to deal with the energy and time needed for the HAs generation process, which have improved the proposed method. In this paper, the proposed security framework is implemented in a smart city environment to provide a secure energy transaction platform. Results show the authenticity of this model and demonstrate the effectiveness of the SVA method in decreasing the successful probability of cyber threat, increasing the time needed for the cyber attacker to decrypt and manipulate the data block.

Flexibility is one of the most important solutions for facilitating the variability of renewable energy sources (RESs) in a distribution network. It is predicted that electric vehicles (EVs) can play an effective role in improving it in the distribution networks. So, this paper presents multiobjective scheduling of batteries of EVs in parking lots (EVPLs) to improve the storage-based flexibility of smart distribution networks (SDNs). The proposed formulation minimizes the energy cost and the voltage deviation function and maximizes the system flexibility (SF) as multiobjective functions that will be optimized subject to the AC load flow, RES and EV constraints, and the allowable limits of the flexibility and operation indices. The resulting model is in the form of a nonlinear programming (NLP) model. Therefore, an equivalent linear programming (LP) formulation is obtained for the original problem to achieve the global optimum result. The stochastic programming approach is used to model uncertainties of the load, active power generation of RESs, price of energy, and EV parameters. The flexible power management is formulated as one of the objective functions of the proposed multiobjective framework, which is solved by using the ε-constraint method, reaching the best possible compromise solution by a fuzzy decision-maker. The proposed framework is tested by using a 33-bus radial test distribution network in the GAMS software environment to evaluate the EVs capability in improving the flexibility indices. Based on the numerical results, it is observed that the proposed scheme with optimal energy management of EVs is able to obtain a high flexibility for SDN. It can also reduce energy losses in terms of network operation and provide a rather smooth voltage profile.

Industrial and commercial electricity customers have significant potential in providing flexibility for power systems through diverse demand response (DR) programs. However, the industrial and commercial potential of DR is not yet completely understood, especially regarding the emerging and advanced technologies associated with the smart grid. Advances in smart meter technology that allow monitoring and controlling responsive loads in real time will also be key enablers of DR potential. It can be more complex to implement DR for industrial loads if compared to residential loads mainly due to the reliability management that is more vital for industrial plants. Hence, this paper aims at providing a comprehensive review of the most recent advances on industrial and commercial DR. On this basis, this survey first presents the potential and technologies of DR in industrial and commercial sectors. Then, the existing models of DR in the mentioned sectors are presented. The presence of industrial and commercial DR in electricity markets is also investigated. Finally, the main positive and beneficial aspects, as well as challenges and barriers of industrial and commercial DR, are investigated.

Increasing demand of electrical energy has leaded to utilization of more and more Distributed generation (DG) sources in distribution systems. Since the locations and capacities of the DG sources connected to the distribution system profoundly impact on reducing system loss and improving system reliability, so placement and sizing indication of DGs is the most substantial process in distribution systems. By adding the reliability objective to this problem, it becomes more complicated than before and it needs to be solved with an accurate algorithm. To this reason, to solve the proposed problem a new approach based on the mixture of two algorithms named as Particle Swarm Optimization (PSO) and the Shuffle Frog Leaping algorithm (SFLA) is applied in this paper. A meticulous performance analysis is fulfilled on a 33-bus system in order to demonstrate the effectiveness of the presented methodology.

Abstract This paper addresses the stochastic security constrained unit commitment (SSCUC) problem with flexibility resources for managing the uncertainty of wind power generation (WPG). Departing from the traditional flexibility resources such as the thermal units with fast up/down spinning reserves and transmission switching (TS), this paper explores also the use of demand response (DR) and energy storage (ES) systems in an innovative integrated scheme. The proposed scheme utilizes a stochastic optimization framework to coordinate the flexibility resources dealing with the uncertainty of WPGs and equipment failures. The stochastic optimization model is formulated as a mixed-integer linear programming (MIP), and this problem is large and computationally complex even for medium sized systems. Accordingly, we present a novel accelerating decomposition technique aimed at solving this problem and reducing the number of iterations and CPU time. Numerical simulation results on the modified 6-bus system and on large-scale power systems, i.e. IEEE 118 and 300-bus systems, clearly demonstrate the benefits of applying flexibility resources for uncertainty management and the efficacy of the proposed solution strategy for large-scale systems.

Today's power systems are subject to various challenges arising from the large-scale integration of renewable energy sources (RES), especially wind energy production. System flexibility, or the capability of a system to address deviations in variable RES production, is becoming more and more relevant. This paper aims to provide a systematic approach to evaluate the level of flexibility of a power system by unequivocally considering fast-ramping units (FRU), hourly demand response (DR) and energy storage (ES). In addition, to research the flexibility role in power system operation, an “online” index is considered to evaluate the technical aptitude of the FRU, hourly DR and ES system to deliver the required flexibility. The mathematical representation of day-ahead scheduling, with the added modeling of an online flexibility index, is a mixed-integer nonlinear program (MINLP). This paper presents a method to convert this MINLP into a mixed-integer linear program without loss of accuracy. The adapted 6-bus and IEEE 118-bus systems are employed to assess the suggested models and flexibility metric, demonstrating the proficiency of the online flexibility index.

Substantial benefits can be achieved from electricity trade through the interconnection of electricity networks of neighboring countries. In this study, we introduce a regional electricity exchange model to obtain an optimal power trade level between electrically-interconnected countries. In addition, producing clean electricity is one of the primary goals of many countries. Moreover, electricity production can be optimized in some areas using renewable sources and exported to regions with high demand. Thus, the power exchange using the proposed method and framework is investigated. Additionally, a decision model for planning an optimal configuration of hybrid wind/solar power systems and optimal electricity trade is presented. We analyze the economic, engineering, management, and policy issues to facilitate optimization of power generation and trade at the country level and quantify the gains from the increased trade. The proposed decision model is employed to plan electricity trade considering hybrid wind and solar energy resources. Furthermore, the effectiveness of reducing the variability of hourly power supply is evaluated by integrating the wind and solar power generation technologies. The main decision variables are the power trade value and number of installed wind turbines and solar systems. In case studies, the computational results using the proposed model yielded an optimal power trade between Iran and its neighboring countries, including Turkey, Turkmenistan, Afghanistan, Pakistan, Azerbaijan, Armenia, and Iraq. Consequently, Iran can export a maximum power capacity of 5,300 MW to its neighbors, where Iraq receives the highest share of 2,000 MW. When the electricity demand in Iran is high, Turkey, Turkmenistan, Azerbaijan, and Armenia can export 1,200, 1,000, 80, and 100 MW of electricity to Iran, respectively. Finally, various configurations of a hybrid wind/solar power generation system considering various cost and power supply variations are presented to partly supply the load requirements of the traded electricity. Consequently, Iran can supply 20% of the total demand of the electricity trade by wind and solar system installed capacities of 316.5 and 267 MW, respectively.

Abstract This study relies on a dynamic reliability-based model for distributed energy resources (DER) planning in electric energy distribution networks (EEDN) with the aim of maximizing the profit of EEDN companies by increasing income and reducing costs. Load uncertainty is considered in the proposed planning model and the robust optimization (RO) approach is employed to cope with the uncertainty. The developed methodology is illustrated using real-world voltage-dependent load models, including residential, commercial and industrial types. These load models are used in evaluating the reliability cost and energy selling for customers. The reliability cost is calculated based on the total unsupplied load after an outage. Furthermore, a new modified harmony search algorithm is proposed to solve the formulated robust dynamic DER planning problem. The solution of the proposed optimization model provides the size, location, and power factor of DER. Furthermore, the need for transformers or lines upgrades and the best year for DER installation are other decision variables determined by the model. The effectiveness and capability of the developed model have been demonstrated with the aid of a case study based on a typical EEDN. The obtained results indicate that installing DER in EEDNs can relieve congestion on feeders; therefore, it can mitigate or defer upgrade investment. Moreover, if carefully planned, other benefits of DER integration such as reliability improvement and energy loss reduction can be achieved.