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Community opposition has caused delays in renewable energy investments. The literature suggests that increasing setback distances, decreasing scale and increasing self-sufficiency among others may increase community acceptance of energy infrastructures. We perform an analysis of the evolution of the Irish power system under these policies by constraining the maximum onshore renewable generation installation at each transmission node, accounting for network and spatial effects. We also consider the impacts of uncertainty in storage costs. An unconstrained renewable development portfolio is marginally cheaper than a constrained one. However, there are substantial differences in the final generation expansion portfolios and spatial allocations. The network reinforcement requirements are also greater under the unconstrained approach. Lower storage costs only slightly mitigate the costs of capacity constraints but significantly alter the spatial distribution of generation investments. The differential in costs between the unconstrained and constrained cases increases non-linearly with renewable generation targets. Future work will consider transmission as well as generation investments. • Power system impacts of constrained vs unconstrained renewable developments analysed. • Constrained case: higher setback distance and self-sufficiency, and decreased scale. • An unconstrained renewable portfolio is marginally cheaper than a constrained one. • Lower storage costs slightly mitigate cost of capacity constraints. • Cost differential between portfolios increases non-linearly with RES targets. [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]

Abstract As a result of the increased awareness of the dangers posed by global climate changes (mainly caused by growing global energy consumption needs), the quest for clean and sustainable energy future is becoming of paramount importance. This can be largely realized via a large-scale integration of variable renewable energy sources (RESs) such as wind and solar, which have relatively low carbon footprints. In many power systems, the level of integration of such resources is dramatically increasing. However, their intermittent nature poses significant challenges in the predominantly conventional power systems that currently exist. Among others, frequency and voltage regulation issues can, for example, arise because of improperly balanced and largely uncoordinated RES supply and demand. Generally, the higher the integration level of intermittent power sources is, the higher the flexibility needs are in the system under consideration. Flexibility, in a power systems context, refers to the ability of such a system to effectively cope with unforeseen changes in operational situations, which are mainly induced by the inherent uncertainty and variability arising from the supply side, demand side or any other external factors. In the absence of appropriate flexibility mechanisms, it is increasingly difficult to manage the imbalances between generation and demand as a result of their natural variations in real-time. This paper presents an extensive and critical review of the main existing and emerging flexibility options that can be deployed in power systems to support the integration of "carbon-free" and variable power production technologies. Starting from a broader definition of flexibility, we highlight the growing importance of such flexibility in renewable-rich energy systems, and provide insights into the challenges and opportunities associated with various flexibility options provided by different technologies. Highlights • This paper presents an extensive and critical review of the main existing and emerging flexibility options. • Starting from a broader definition of flexibility, we highlight the growing importance of such flexibility. • Insights into the challenges and opportunities associated with various flexibility options are provided. • Certain barriers that may hinder their developments in the short to medium terms are also provided. [ABSTRACT FROM AUTHOR]

Nowadays, there is a wide consensus about integrating more renewable energy sources-RESs to solve a multitude of global concerns such as meeting an increasing demand for electricity, reducing energy security and heavy dependence on fossil fuels for energy production, and reducing the overall carbon footprint of power production. Framed in this context, the coordination of RES integration with energy storage systems (ESSs), along with the network’s switching capability and/or reinforcement, is expected to significantly improve system flexibility, thereby increasing the capability of the system in accommodating large-scale RES power. Hence, this paper presents a novel mechanism to quantify the impacts of network switching and/or reinforcement as well as deployment of ESSs on the level of renewable power integrated in the system. To carry out this analysis, a dynamic and multi-objective stochastic mixed integer linear programming (S-MILP) model is developed, which jointly takes the optimal deployment of RES-based DGs and ESSs into account in coordination with distribution network reinforcement and/or reconfiguration. The IEEE 119-bus test system is used as a case study. Numerical results clearly show the capability of ESS deployment in dramatically increasing the level of renewable DGs integrated in the system. Although case-dependent, the impact of network reconfiguration on RES power integration is not significant. [ABSTRACT FROM AUTHOR]

The global drive for integration of RESs (renewable energy sources) means that TEP (transmission expansion planning) has to be carried out over geographically wide and large-scale networks under high levels of uncertainty. This leads to complex combinatorial TEP optimization problems, requiring a huge amount of OPF (optimal power flow) computations. The algorithm employed to calculate the OPF must be reasonably accurate but computationally very efficient, because it has to be run for a lot of operational conditions. Overly simplified OPF formulations are not adequate, since they neglect aspects which play a relevant role in TEP. Specifically, network losses significantly influence TEP solutions, but they are often disregarded due to their computational burden. Given their potential impact on the optimal TEP solution, the main focus of this paper is to find an appropriate losses model in the context of medium to long-term TEP for large-scale power systems. Keeping the balance between accuracy and computation time is essential in such problems. The paper presents two alternative linear losses models, as well as two variants of existing ones. These models are compared and tested using case studies, including small, medium and large-scale networks. Practical conclusions and recommendations are drawn from numerical results. [ABSTRACT FROM AUTHOR]

The global drive for integration of RESs (renewable energy sources) means they will have an increasing role in power systems. It is inevitable that such resources introduce more operational variability and uncertainty to system functioning because of their intermittent nature. As a result, uncertainty management becomes a critical issue in long-term TEP (Transmission Expansion Planning) in power systems which feature a significant share of renewable power generation, especially in terms of computational requirements. A significant part of this uncertainty is often handled by a set of operational states, here referred to as “snapshots”. Snapshots are generation—demand patterns that lead to OPF (optimal power flow) patterns in the network. A set of snapshots, each one with an estimated probability, is then used in network expansion optimization. In long-term TEP of large networks, the amount of operational states must be reduced to make the problem computationally tractable. This paper shows how representative snapshots can be selected by means of clustering, without relevant loss of accuracy in a TEP context, when appropriate classification variables are used for the clustering process. The approach relies on two ideas. First, snapshots are characterized by their OPF patterns instead of generation—demand patterns. This is simply because network expansion is the target problem, and losses and congestions are the drivers of network investments. Second, OPF patterns are classified using a “moments” technique, a well-known approach to address Optical Pattern Recognition problems. Numerical examples are presented to illustrate the benefits of the proposed clustering methodology. The method seems to be very promising in terms of clustering efficiency and accuracy of the TEP solutions. [ABSTRACT FROM AUTHOR]

Electricity generation capacity expansion is driven by both economic and socio-political realities. Policy makers determine public infrastructural decisions, such as climate and renewable targets, and transmission infrastructure, and the optimal generation capacity expansion follows. Policy makers therefore require planning models that can determine the optimal generation capacity mix in the long run under various scenarios, including policy choices. This work presents a planning model based on linearised alternating current optimal power flow which determines optimal generation capacity expansion and operation, in a least-cost manner, given global and local technical constraints, as well as policy decisions. We apply the model to a test case of the island of Ireland, which has two weakly interconnected systems, high renewable generation targets and low storage and interconnection. We determine the optimal generation expansion and operation out to 2030 considering the effects of increased multi-area interconnection, existing fossil fuel generation phase-out and increased renewable generation targets and carbon prices. Our results find that costs and emissions are driven primarily by the decommissioning of old inefficient generation units. High renewable targets, on the other hand, render increased carbon prices relatively ineffective in reducing system emissions. Furthermore, high renewable generation targets crowd out low-carbon power generation options such as carbon capture and storage (CCS). The strategic north-south interconnection has little effect on renewable energy source installations required to achieve renewable power generation targets but does impact on security of supply and the congestion level across the island. • A decision support model for medium to long-term generation expansion planning. • Insights from an Irish study on optimal generation expansion mix under various policy scenarios. • Effects of multi-area interconnection, fossil fuel phase-out, RES-E targets and carbon prices. • Costs and emissions driven primarily by decommissioning of old inefficient generation units. • High renewable targets render increased carbon prices ineffective in reducing emissions. [ABSTRACT FROM AUTHOR]

Electrical distribution system operators (DSOs) are facing an increasing number of challenges, largely as a result of the growing integration of distributed energy resources (DERs), such as photovoltaic (PV) and wind power. Amid global climate change and other energy-related concerns, the transformation of electrical distribution systems (EDSs) will most likely go ahead by modernizing distribution grids so that more DERs can be accommodated. Therefore, new operational strategies that aim to increase the flexibility of EDSs must be thought of and developed. This action is indispensable so that EDSs can seamlessly accommodate large amounts of intermittent renewable power. One plausible strategy that is worth considering is operating distribution systems in a meshed topology. The aim of this work is, therefore, related to the prospects of gradually adopting such a strategy. The analysis includes the additional level of flexibility that can be provided by operating distribution grids in a meshed manner, and the utilization level of variable renewable power. The distribution operational problem is formulated as a mixed integer linear programming approach in a stochastic framework. Numerical results reveal the multi-faceted benefits of operating distribution grids in a meshed manner. Such an operation scheme adds considerable flexibility to the system and leads to a more efficient utilization of variable renewable energy source (RES)-based distributed generation. [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]