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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.

The generation mix of Portugal now contains a significant amount of variable renewable energy sources (RES) and the amount of RES is expected to grow substantially. This has led to concerns being raised regarding the security of the supply of the Portuguese electric system as well as concerns relating to system inertia. Deploying and efficiently using various flexibility options is proposed as a solution to these concerns. Among these flexibility options proposed is the use of battery energy storage systems (BESSs) as well as relaxing system inertia constraints such as the system nonsynchronous penetration (SNSP). This article proposes a stochastic mixed-integer linear programming problem formulation, which examines the effects of deploying BESS in a power system. The model is deployed on a real-world test case and results show that the optimal use of BESS can reduce system costs by as much as 10% relative to a baseline scenario and the costs are reduced further when the SNSP constraint is relaxed. The amount of RES curtailment is also reduced with the increased flexibility of the power system through the use of BESS. Thus, the efficiency of the Portuguese transmission system is greatly increased by the use of flexibility measures, primarily the use of BESS.

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.

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.

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 paper explores this potential by presenting an innovative multi-level 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

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).

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.

There is an ongoing paradigm shift occurring in the electricity sector. In particular, previously passive consumers are now becoming active prosumers and they can now offer important and cost-effective new forms of flexibility and demand response potential to the electricity sector and this can translate into system-wide operational and economic benefits. This work focuses on developing a model where prosumers participate in demand response programs through varying tariff schemes, and the model also quantifies the benefits of this flexibility and cost-reductions. This work includes transactive energy trading between various prosumers, the grid and the neighborhood. A stochastic tool is developed for this analysis, which also allows the quantification of the collective behavior so that the periods with the greatest demand response potential can be identified. Numerical results indicate that the optimization of energy transactions amongst the prosumers, and including the grid, leads to considerable cost reductions as well as introducing additional flexibility in the presence of demand response mechanisms.

This paper analyses how people's attitudes towards onshore wind power and overhead transmission lines affect the costoptimal development of electricity generation mixes, under a high renewable energy policy. For that purpose, we use a power systems generation and transmission expansion planning model, combined with information on public attitudes towards energy infrastructure on the island of Ireland. Overall, households have a positive attitude towards onshore wind power but their willingness to accept wind farms near their homes tends to be low. Opposition to overhead transmission lines is even greater. This can lead to a substantial increase in the costs of expanding the power system. In the Irish case, costs escalate by more than 4.3% when public opposition is factored into the constrained optimisation of power generation and grid expansion planning across the island. This is mainly driven by the compounded effects of higher capacity investments in more expensive technologies such as offshore wind and solar photovoltaic to compensate for lower levels of onshore wind generation and grid reinforcements. The results also reveal the effect of public opposition on the value of onshore wind, via shadow prices. The higher the level of public opposition, the higher the shadow value of onshore wind. And, this starkly differs across regions: regions with more wind resource or closest to major demand centres have the highest shadow prices. The shadow costs can guide policy makers when designing incentive mechanisms to garner public support for onshore wind installations.

Electrifying energy sectors using renewable rich electricity is one of the many decarbonization pathways being adopted to curb greenhouse gas (GHG) emissions. Among these, the heating and cooling sector, both energy and carbon intensive, is attracting a lot of attention. Power-to-heat technology (PtH) along with thermal energy storage systems is widely adopted to decarbonise this sector. However, increased penetration of PtH may cause congestion in existing electrical grid infrastructures, and hence needs for network upgrades. In this context, our paper presents a quantitative analysis on the impact of electrifying domestic dwellings (existing and new) in Ireland. The analysis encompasses costs, benefits, renewable power curtailment and regional distribution of optimal electrification of the housing stock. Analysis reveal significant grid expansion needs with increasing levels of PtH. This impact is pronounced without appropriate thermal storage. On the flip side, it leads to a more efficient utilisation of renewable energy by reducing curtailment.

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.