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End-users are more active because of demand response programs and the penetration of distributed energy resources in the bottom-layer of the power systems. This paper presents a virtual organization of agents of the power distribution grid for local energy trade. An iterative algorithm is proposed; it enables interaction between end-users and the Distribution Company (DisCo). Then, the performance of the proposed algorithm is evaluated in a 33-bus distribution network; its effectiveness is measured in terms of its impact on the energy trading scenarios and, thus, of its contribution to the energy management problem. According to the simulation results, although aggregators do not play the role of decision makers in the proposed model, our iterative algorithm is profitable for them.

This paper analyses customers’ demand flexibility in a local power trading scenario through an Ising spin-based model. We look at quantitative information on the two-way relationships between power exchanges and spin dynamics. To this end, a modified version of the Metropolis-Hastings algorithm was implemented, including a gradient descent through the constraint surface. This allowed us to analyse the system on a large scale (considering the cumulated benefit of all the actors involved) and also from the perspective of total aggregation. In a maximum flexibility scenario, the total aggregation profit increases with the number of aggregators. We also investigate numerically the effect of aggregator boundaries on the spin dynamics.

This paper proposes a predictive dispatch model to manage energy flexibility in the domestic energy system. Electric Vehicles (EV), batteries and shiftable loads are devices that provide energy flexibility in the proposed system. The proposed energy management problem consists of two stages: day-ahead and real time. A hybrid method is defined for the first time in this paper to model the uncertainty of the PV power generation based on its power prediction. In the day-ahead stage, the uncertainty is modeled by interval bands. On the other hand, the uncertainty of PV power generation is modeled through a stochastic scenario-based method in the real-time stage. The performance of the proposed hybrid Interval-Stochastic (InterStoch) method is compared with the Modified Stochastic Predicted Band (MSPB) method. Moreover, the impacts of energy flexibility and the demand response program on the expected profit and transacted electrical energy of the system are assessed in the case study presented in this paper.

The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.

In recent years, local energy markets (LEMs) have been introduced to empower end-customers within energy communities at the distribution level of the power system, in order to be able to trade their energy locally in a competitive and fair environment. However, there is still some challenge with regard to the most efficient approach in organising the LEMs for the electricity exchange between consumers and prosumers while ensuring that they are responsible for their electricity-related choices, and concerning which LEM model is suitable for which prosumer or consumer type. This paper presents a hierarchical model for the organisation of agent-based local energy markets. According to the proposed model, prosumers and consumers are enabled to transact electricity within the local energy community and with the grid in a coordinated manner to ensure technical and economic benefits for the LEM’s agents. The model is implemented in a software tool called Grid Singularity Exchange (GSyE), and it is verified in a real German energy community case study. The simulation results demonstrate that trading electricity within the LEM offers economic and technical benefits compared to transacting with the up-stream grid. This can further lead to the decarbonization of the power system sector. Furthermore, we propose two models for LEMs consisting of multi-layer and single-layer hierarchical agent-based structures. According to our study, the multi-layer hierarchical model is more profitable for household prosumers as compared to trading within the single-layer hierarchical LEM. However, the single-layer LEM is more be beneficial for industrial prosumers.

It is crucial for maintaining the security of supply that transmission networks continue to operate even if a single line fails. Modeling \\(\\mathcal{N} - 1\\) security in power system capacity expansion problems introduces many extra constraints if all possible outages are accounted for, which leads to a high computational burden. Typical approaches to avoid this burden consider only a subset of possible outages relevant to a given dispatch situation. However, this relies on knowing the dispatch situation beforehand, and it is not suitable for investment optimization problems where the generation fleet is not known in advance. In this paper, we introduce a heuristic approach to model the fully secured \\(\\mathcal{N}-1\\) feasible space using a smaller number of constraints in a way that only depends on the topology of transmission networks. In our proposed approach, the network's security is modelled by comparing the polytope of the feasible space of nodal net power obtained from the security-constrained linearized AC optimal power flow problem. To approximate this polytope, a buffer capacity factor is defined for transmission lines in the \\(\\mathcal{N}-0\\) secure case, thereby avoiding the introduction of many additional constraints. In this way, three approaches are introduced for obtaining a buffer capacity factor consisting of approximate, robust and line-specific approaches. Finally, the performance of our proposed approaches is assessed in different scales of transmission networks for determining the proposed buffer capacity factors, contingency analysis and economic evaluation. Moreover, we find that our proposed heuristics provide excellent approximations of the fully secured \\(\\mathcal{N}-1\\) solutions with a much lower computational burden.

The preventative strategies for \\(N-1\\) network security dominant in European networks mean that network capacity is kept free in case a line fails. If instead fast corrective actions are used to overcome network overloading when single lines fail, this has the potential to free up network capacity that is otherwise underused in preventive \\(N-1\\) security strategies. In this paper, we investigate the impact on renewable integration of a corrective network security strategy, whereby storage or other flexibility assets are used to correct overloading shortly after line outages. In this way, we find significant cost savings for the integration of renewable energy of up to 2.4 billion euros per year in an aggregated 50-bus model of the German power system utilizing these flexibility assets, so-called network boosters (NB). This offers a role for storage beyond energy arbitrage or ancillary services like frequency control. While previous literature has focused on the potential savings of NB in the short-term operation, we focus on the long-term benefits in systems with high shares of renewable energy sources, where the capacities and dispatch of generation and NB are optimised. We demonstrate the benefits of NB for various shares of renewable energy, NB and flexibility costs, as well as different allowed levels of temporary overloading the lines in both (i) a sequential model, where long-run generation investments are optimised separately from the NB capacities, and (ii) a simultaneous model, where generation is co-optimised with NB investment so that mixed preventive-corrective approaches are possible.