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Electric power systems are in state of transition as they attempt to evolve to meet new challenges provided by growing environmental concerns, increases in the penetration of distributed renewable energy sources (DRES) as well as the challenges associated with integrating new technologies to enable smart grids. New techniques to improve the electrical power system, including the distribution system, are thus needed. One such technique is dynamic distribution system reconfiguration (DNSR), which involves altering the network topology during operation, providing significant benefits regarding the increased integration of DRES. This paper lays out an improved model which aimed to optimize the system operation in a coordinated way, where DRES, energy storage systems (ESS) and DNSR are considered as well as the uncertainty of these resources. The objective function was modeled to incentivize the uptake of DRES by considering the cost of emissions to incentivize the decarbonization of the power system. Also, the switching costs were modeled to consider not only the switching, but also the cost of degradation of these mechanisms in the system operation. Two systems are used to validate the model, the IEEE 119-bus system, and a real system in São Miguel Island. The results of this paper show that using DNSR, DRES, and ESS can lead to a significant 59% reduction in energy demand through a 24-hour period. In addition, using these technologies results in a healthier, more efficient, and higher quality system. This shows the benefits of using a variety of smart grid technologies in a coordinated manner. [ABSTRACT FROM AUTHOR]

Emerging technologies are helping to accelerate the ongoing energy transition. At the forefront of these new technologies is blockchain, which has the potential to disrupt energy trading markets. This article explores this potential by presenting an innovative multilevel transactive energy (TE) optimization model for the scheduling of distributed energy resources (DERs) within connected virtual power plants (VPPs). The model allows for energy transactions within a given VPP as well as between connected VPPs. A blockchain-based smart contract layer is applied on top of the TE optimization model to automate and record energy transactions. The model is formulated to adhere to the new regulations for the self-generation and self-consumption of energy in Portugal. This new set of regulations can ease barriers to entry for consumers and increase their active participation in energy markets. Results show a decrease in energy costs for consumers and increased generation of locally produced electricity. This model shows that blockchain-based smart contracts can be successfully integrated into a hierarchical energy trading model, which respects the novel energy regulation. This combination of technologies can be used to increase consumer participation, lower energy bills, and increase the penetration of locally generated electricity from renewable energy sources. [ABSTRACT FROM AUTHOR]

Conventional electrical networks are slowly changing. A strong sense of policy urges as well as commitments have recently been surfacing in many countries to integrate more environmentally friendly energy sources into electrical systems. In particular, stern efforts have been made to integrate more and more solar and wind energy sources. One of the major setbacks of such resources arises as a result of their intermittent nature, creating several problems in the electrical systems from a technical, market, operation, and planning perspectives. This work focuses on the operation of an electrical system with large-scale integration of solar and wind power. In order to cope with the intermittency inherent to such power sources, it is necessary to introduce more flexibility into the system. In this context, demand response, energy storage systems, and dynamic reconfiguration of the system are introduced, and the operational performance of the resulting system is thoroughly analyzed. To carry out the required analysis, a stochastic mixed-integer linear programming operational model is developed, whose efficacy is tested on an IEEE 119-bus standard network system. Numerical results indicate that the joint deployment and management of various flexibility mechanisms into the system can support a seamless integration of large-scale intermittent renewable energies. [ABSTRACT FROM AUTHOR]

Economic development and changing lifestyles are leading to the extensive use of energy-intensive technologies by consumers. As a result, this has led to a dramatically increased demand for electricity. In addition, the consumers’ increasing demand for a more reliable and uninterrupted energy supply is posing enormous challenge for service providers. This necessitates the development of novel solutions that should be at the system operators’ disposal, particularly at distribution levels. One way to partly address this concern is by automating distribution systems and equipping them with intelligent technologies–a transformation to smart distribution systems. Such a transformation should improve system reliability and operational efficiency because such systems will be capable of operating and immediately restoring discontinued service to consumers. To facilitate this, it is necessary to replace manual switches by remotely controlled ones, improving the system restoration capability, which is one of the key features of smart grids. This paper presents a new framework to determine the minimal set of switches that have to be replaced or optimally allocated in order to automate the system. This is supported by a sensitivity analysis. Different topologies are also assessed taking into account various reliability indices and power losses in system operation following the system's automation. Such an optimization work is done under a massive integration of renewable energy sources and energy storage systems. All this simultaneously addresses the economic and functional requirements of the automated system, ultimately improving system's reliability. The standard IEEE 119-bus standard system is used as a case study, where different types of loads are considered (residential, commercial, and industrial). [ABSTRACT FROM AUTHOR]

With the rapid growth of electric vehicles (EVs) in distribution systems, a new player, called EV parking lot operator (EV PLO), is emerging around the world. Furthermore, the integration of distributed generation in the distribution level, in particular, renewable energy sources, is leading to the establishment of various markets in distribution systems. On one hand, such PLOs aim at managing their EVs within their parking lots to participate in the distribution markets and to maximize their profits. On the other hand, a distribution system operator seeks to minimize the system-wide cost while minimizing renewable power spillage and the side-effects of its intermittency. This interaction inspires the innovative two-level model proposed in this paper. In the first level, a new model is proposed for EV PLOs which models the EVs’ characteristics, including EV owners’ uncertainties, in a reasonably accurate manner. These PLOs are allowed to participate in energy, reserve and regulation distribution markets by optimally managing their EVs. In the second level, a new model is developed to ensure that the technical constraints in the distribution networks are met while minimizing the overall system cost. In addition, this paper evaluates the effects of the penetration level and the placement of wind and solar PV on the offering strategies of EV parking lots, as well as on the overall performance of the distribution systems. [ABSTRACT FROM PUBLISHER]

This paper presents a comprehensive sensitivity analysis to identify the uncertain parameters which significantly influence the decision-making process in distributed generation (DG) investments and quantify their degree of influence. To perform the analysis, a DG investment planning model is formulated as a novel multistage and multiscenario optimization problem. Moreover, to ensure tractability and make use of exact solution methods, the entire problem is kept as a mixed-integer linear programming optimization. A real-world distribution network system is used to carry out the analysis. The results of the analysis generally show that uncertainty as well as operational variability of the considered parameters have meaningful impacts on investment decisions of DG. The degree of influence varies from one parameter to another. But, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. Hence, the analysis made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices. [ABSTRACT FROM PUBLISHER]

This two-part work presents a new multistage and stochastic mathematical model, developed to support the decision-making process of planning distribution network systems (DNS) for integrating large-scale “clean” energy sources. Part I is devoted to the theoretical aspects and mathematical formulations in a comprehensive manner. The proposed model, formulated from the system operator's viewpoint, determines the optimal sizing, timing, and placement of distributed energy technologies (particularly, renewables) in coordination with energy storage systems and reactive power sources. The ultimate goal of this optimization work is to maximize the size of renewable power absorbed by the system, while maintaining the required/standard levels of power quality and system stability at a minimum possible cost. From the methodological perspective, the entire problem is formulated as a mixed integer linear programming optimization, allowing one to obtain an exact solution within a finite simulation time. Moreover, it employs a linearized ac network model which captures the inherent characteristics of electric networks and balances well accuracy with computational burden. The IEEE 41-bus radial DNS is used to test validity and efficiency of the proposed model, and carry out the required analysis from the standpoint of the objectives set. Numerical results are presented and discussed in Part II of this paper to unequivocally demonstrate the merits of the model. [ABSTRACT FROM PUBLISHER]

A new multistage and stochastic mathematical model of an integrated distribution system planning problem is described in Part I. The efficiency and validity of this model are tested by carrying out a case study on a standard IEEE 41-bus radial distribution system. The numerical results show that the simultaneous integration of energy storage systems (ESSs) and reactive power sources largely enables a substantially increased penetration of variable generation (wind and solar) in the system, and consequently, reduces overall system costs and network losses. For the system, a combined wind and solar PV power of up to nearly three times the base-case peak load is installed over a three-year planning horizon. In addition, the proposed planning approach also considerably defers network expansion and/or reinforcement needs. Generally, it is clearly demonstrated in an innovative way that the joint planning of distributed generation, reactive power sources, and ESSs, brings significant improvements to the system such as reduction of losses, electricity cost, and emissions as a result of increased renewable energy sources (RESs) penetration. Besides, the proposed modeling framework considerably improves the voltage profile in the system, which is crucial for a normal operation of the system as a whole. Finally, the novel planning model proposed can be considered as a major leap forward toward developing controllable grids, which support large-scale integration of RESs [ABSTRACT FROM PUBLISHER]